diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 0000000000000000000000000000000000000000..89946d7f7e033833c5d21c904be967dfb5d1d6f4 --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,30 @@ +# Data Changelog + +## cleaned-20261107 + +All 779 questions, 292 dataset folders, and 214 `test_current.csv` oracles are retained. Question IDs and the inference/evaluation JSON structure are unchanged. + +- Corrected reviewed qualitative/categorical descriptions, codebook interpretations, feature aliases, outcome proxies, and ranking direction metadata. +- Applied strict visible-input group holdout to seven hotel batches: removed 387 overlapping history rows, without changing current candidates or outcome labels. +- Restored 541 hidden health target cells and 2,877,541 CSV target cells from exact original 2020 records using the documented `GenHealth` / `GeneralHealth` crosswalk. CSV counts include repeated views, not unique people. Removed the redundant input alias. +- Replaced three genuinely unlabelled health candidates with observed 2022 records, preserving IDs and filter eligibility; removed their six matching history records. Rebuilt complete classification oracles from observed labels. +- Made the placed-only salary cohort explicit in seven campus recruitment questions and filters. Non-placed salaries remain missing and outside the scoring cohort, not imputed as zero. +- Removed one observed health-grade statement from a query that asks for that grade; clarified one wine target class and one mall spending objective. These edits do not change outcome labels. +- Corrected all eight forest-fire questions to monthly rather than daily counts and explained the original January-1 year marker. Restored corrupted month/state characters against the original Latin-1 Kaggle archive, with full-record matching and numeric values preserved. +- Restored the corrupted chocolate maker name against the original rating publisher's matching records; rating values are unchanged. +- Clarified seven gun-incident comparisons so they do not claim that distinct source records have identical report contents or identify a causal effect. Their provided numerical conditions, observed outcomes, and gold trends are unchanged. Auxiliary source-record differences remain a documented limitation. + +### Compatibility And Limits + +The original used-car archive contains 104 released history descriptions with one unrecoverable replacement character each. Exact descriptions were verified against the original archive and retained verbatim, not guessed or silently dropped. These source-text artifacts are separate from the repaired structured-category encoding errors. + +Historical model outputs and summary scores are not results for this cleaned version. Re-run inference and evaluation, preserving previous outputs separately. `test_current.csv` and JSON gold must remain evaluator-only. + +Deterministic validation and full-dataset automatic semantic screening, followed by a second pass over flagged cases, were supplemented by source-based adjudication. This is not exhaustive human annotation or a guarantee that every qualitative phrase is error-free. Qualitative language has no published one-to-one quantile mapping. Treatment Effect Analysis is predictive scenario comparison rather than causal identification. + +The previous public dataset remains available at revision [`643090b74d9beb0da9f2da3ff650af70c12511f1`](https://huggingface.co/datasets/LAMDA-Tabular/TopBench/tree/643090b74d9beb0da9f2da3ff650af70c12511f1). + +### Character-Restoration Sources + +- [Forest Fires in Brazil: original Kaggle data](https://www.kaggle.com/datasets/gustavomodelli/forest-fires-in-brazil) (Latin-1 archive; not the already-lossy local UTF-8 copy). +- [Chocolate Bar Ratings: Kaggle data](https://www.kaggle.com/datasets/rtatman/chocolate-bar-ratings) and [the original Flavors of Cacao database](https://flavorsofcacao.com/database_w_REF.html). 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predictive reasoning over tabular data. Each example asks a model to infer an unobserved outcome, decision, treatment effect, or ranked/filtering result from historical tables and a natural-language query. +**A Benchmark for Implicit Predictive Reasoning in Tabular Question Answering** -## Layout +[![Paper](https://img.shields.io/badge/arXiv-2604.28076-b31b1b.svg)](https://arxiv.org/abs/2604.28076) +[![Code](https://img.shields.io/badge/GitHub-TopBench-181717?logo=github)](https://github.com/LAMDA-Tabular/TopBench) -```text -single_point_prediction/ -decision_making/ -treatment_effect_analysis/ -ranking_and_filtering/ +TopBench evaluates implicit predictive reasoning from natural-language questions and historical tables across daily, financial, and medical domains. + +| Task | Directory | Questions | +| --- | --- | ---: | +| Single-Point Prediction | `single_point_prediction` | 274 | +| Decision Making | `decision_making` | 186 | +| Treatment Effect Analysis | `treatment_effect_analysis` | 105 | +| Ranking and Filtering | `ranking_and_filtering` | 214 | +| **Total** | | **779** | + +## Download + +```bash +pip install -U huggingface_hub +hf download LAMDA-Tabular/TopBench --repo-type dataset \ + --revision cleaned-20261107 --local-dir data ``` -Each task directory contains dataset folders with `history.csv`, query JSON files, and metadata. The `ranking_and_filtering` task also includes `current.csv` and expects structured CSV outputs. +See the [code README](https://github.com/LAMDA-Tabular/TopBench#readme) for environment setup, sandbox configuration, inference, and evaluation. Use the downloaded `data` directory as `--data-root`. + +## Files + +Task folders contain `history.csv`, question JSON files, and `info.json` / `info_mod.json` metadata. Ranking and Filtering additionally provides a target-free `current.csv` and an oracle `test_current.csv`. + +- Model inputs are the question, history, and, where applicable, target-free current candidates. JSON `ground_truth` is evaluator-only. +- `test_current.csv` contains labeled oracle selections. Keep it for evaluation, but never expose it to the model as an input or training table. +- The original B1-B4 identifiers are retained inside files for compatibility; the directory names above are the public task names. + +## Data Version -Use the code release at https://github.com/LAMDA-Tabular/TopBench for validation, inference, evaluation, and the predict-only baseline. +`cleaned-20261107` is an audited data update, **not the original paper-score dataset**. It corrects documented query mappings, target aliases, ranking scope and completeness, corrupted category text, and hotel visible-input group overlap. All 779 question slots and 214 oracle files are retained. + +For the previous public data, pin revision [`643090b74d9beb0da9f2da3ff650af70c12511f1`](https://huggingface.co/datasets/LAMDA-Tabular/TopBench/tree/643090b74d9beb0da9f2da3ff650af70c12511f1). Re-run inference and evaluation on this version in a fresh output directory; previous model outputs and scores are not interchangeable with the cleaned data. See [CHANGELOG.md](CHANGELOG.md) and [data_version.json](data_version.json). + +Checks cover file integrity, loaders, ranking oracles, observed targets, source-supported repairs, and full-dataset automatic semantic screening with a second pass over flagged cases and evidence-based adjudication. They are not exhaustive independent human annotation. Qualitative descriptions remain approximate rather than invertible numeric encodings. Treatment Effect Analysis uses predictive comparisons of observed scenarios, not identified causal effects; auxiliary fields in source records may differ beyond the attributes stated in a question. + +Original tables retain their source-specific terms; source URLs are provided in dataset metadata. + +The original used-car archive contains 104 history descriptions with an unrecoverable replacement character. These free-text strings are retained verbatim, rather than guessed; structured labels are not changed. + +## Citation + +```bibtex +@misc{ji2026topbenchbenchmarkimplicitpredictive, + title={TopBench: A Benchmark for Implicit Predictive Reasoning in Tabular Question Answering}, + author={An-Yang Ji and Jun-Peng Jiang and De-Chuan Zhan and Han-Jia Ye}, + year={2026}, + eprint={2604.28076}, + archivePrefix={arXiv}, + primaryClass={cs.CL}, + url={https://arxiv.org/abs/2604.28076}, +} +``` diff --git a/data_version.json b/data_version.json new file mode 100644 index 0000000000000000000000000000000000000000..dbface9ed44d79f4eeeac9c4f8722b3bacd3a788 --- /dev/null +++ b/data_version.json @@ -0,0 +1,19 @@ +{ + "version": "cleaned-20261107", + "release_date": "2026-11-07", + "repo": "LAMDA-Tabular/TopBench", + "previous_public_revision": "643090b74d9beb0da9f2da3ff650af70c12511f1", + "total_questions": 779, + "question_counts": { + "decision_making": 186, + "ranking_and_filtering": 214, + "single_point_prediction": 274, + "treatment_effect_analysis": 105 + }, + "dataset_folders": 292, + "ranking_oracle_files": 214, + "score_compatibility": "Not interchangeable with historical model outputs or paper scores; re-evaluate separately", + "changelog": "CHANGELOG.md", + "manifest": "DATA_MANIFEST.json", + "validation_scope": "Deterministic integrity and source checks; automatic semantic review plus evidence-based adjudication, not exhaustive human annotation" +} diff --git a/decision_making/daily/classification/Apple_Quality_B2/history.csv b/decision_making/daily/classification/Apple_Quality_B2/history.csv index 9059fce619fa458c3ba281f380a96284400d77e1..71acb918d781178cd823eabe2ebb57df8ac2de9d 100644 --- a/decision_making/daily/classification/Apple_Quality_B2/history.csv +++ b/decision_making/daily/classification/Apple_Quality_B2/history.csv @@ -3485,7 +3485,6 @@ A_id,Size,Weight,Sweetness,Crunchiness,Juiciness,Ripeness,Acidity,Quality 3997.0,-2.634515299,-2.13824672,-2.440461285,0.657222891,2.19970859,4.763859177,-1.334611391,bad 3998.0,-4.008003744,-1.779337107,2.366396966,-0.200329367,2.161435121,0.214488384,-2.229719806,good 3999.0,0.27853965,-1.715505028,0.121217251,-1.154074758,1.2666774,-0.77657147,1.599796456,good -,,,,,,,Created_by_Nidula_Elgiriyewithana, 3985.0,-0.230550165,-0.669955966,-1.896049211,0.657545411,1.843633558,0.473194498,1.461085428,bad 2038.0,3.947883038,-1.641135894,-4.28281972,-0.298038556,6.32830431,-0.195911529,1.885183745,bad 3104.0,-1.704699162,-1.73050323,-0.830481119,-0.315856763,0.719482353,4.35595832,-0.64743073,bad diff --git a/decision_making/daily/classification/Apple_Quality_B2/info.json b/decision_making/daily/classification/Apple_Quality_B2/info.json index 061594cd032cd189afdf78ebe43a94ad53166b97..8de202273508b7431d0bbe83b679ee5373c9efe5 100644 --- a/decision_making/daily/classification/Apple_Quality_B2/info.json +++ b/decision_making/daily/classification/Apple_Quality_B2/info.json @@ -1,66 +1,69 @@ -{ - "name": "Apple Quality", - "source": "https://www.kaggle.com/datasets/nelgiriyewithana/apple-quality/data", - "data_intro": "This dataset contains information about various attributes of a set of fruits, providing insights into their characteristics. The dataset includes details such as fruit ID, size, weight, sweetness, crunchiness, juiciness, ripeness, acidity, and quality.", - "is_splited": false, - "overall_size": 4000, - "train_size": 0, - "test_size": 0, - "c_classes": 2, - "n_classes": 7, - "task_type": "classification", - "target": { - "Quality": "Build a model to predict the quality rating of fruits using various attributes." - }, - "cat_feature_intro": { - "Acidity": "- Acidity: Acidity level of the fruit", - "Quality": "- Quality: Overall quality of the fruit." - }, - "num_feature_intro": { - "A_id": "- A_id: Unique identifier for each fruit", - "Size": "- Size: Size of the fruit", - "Weight": "- Weight: Weight of the fruit", - "Sweetness": "- Sweetness: Degree of sweetness of the fruit", - "Crunchiness": "- Crunchiness: Texture indicating the crunchiness of the fruit", - "Juiciness": "- Juiciness: Level of juiciness of the fruit", - "Ripeness": "- Ripeness: Stage of ripeness of the fruit" - }, - "evaluation_metric": null, - "num_feature_value": { - "A_id": [ - 0.0, - 3999.0 - ], - "Crunchiness": [ - -6.055057805, - 7.619851801 - ], - "Juiciness": [ - -5.961897048, - 7.364402864 - ], - "Ripeness": [ - -5.864598918, - 7.237836684 - ], - "Size": [ - -7.151703059, - 6.406366899 - ], - "Sweetness": [ - -6.894485494, - 6.374915513 - ], - "Weight": [ - -7.149847675, - 5.79071359 - ] - }, - "cat_feature_value": { - "Acidity": "string", - "Quality": [ - "bad", - "good" - ] - } -} \ No newline at end of file +{ + "name": "Apple Quality", + "source": "https://www.kaggle.com/datasets/nelgiriyewithana/apple-quality/data", + "data_intro": "This dataset contains information about various attributes of a set of fruits, providing insights into their characteristics. The dataset includes details such as fruit ID, size, weight, sweetness, crunchiness, juiciness, ripeness, acidity, and quality.", + "is_splited": false, + "overall_size": 4000, + "train_size": 0, + "test_size": 0, + "c_classes": 1, + "n_classes": 8, + "task_type": "classification", + "target": { + "Quality": "Build a model to predict the quality rating of fruits using various attributes." + }, + "cat_feature_intro": { + "Quality": "- Quality: Overall quality of the fruit." + }, + "num_feature_intro": { + "A_id": "- A_id: Unique identifier for each fruit", + "Size": "- Size: Size of the fruit", + "Weight": "- Weight: Weight of the fruit", + "Sweetness": "- Sweetness: Degree of sweetness of the fruit", + "Crunchiness": "- Crunchiness: Texture indicating the crunchiness of the fruit", + "Juiciness": "- Juiciness: Level of juiciness of the fruit", + "Ripeness": "- Ripeness: Stage of ripeness of the fruit", + "Acidity": "- Acidity: Acidity level of the fruit" + }, + "evaluation_metric": null, + "num_feature_value": { + "A_id": [ + 0.0, + 3999.0 + ], + "Crunchiness": [ + -6.055057805, + 7.619851801 + ], + "Juiciness": [ + -5.961897048, + 7.364402864 + ], + "Ripeness": [ + -5.864598918, + 7.237836684 + ], + "Size": [ + -7.151703059, + 6.406366899 + ], + "Sweetness": [ + -6.894485494, + 6.374915513 + ], + "Weight": [ + -7.149847675, + 5.79071359 + ], + "Acidity": [ + -7.010538475, + 7.404736238 + ] + }, + "cat_feature_value": { + "Quality": [ + "bad", + "good" + ] + } +} diff --git a/decision_making/daily/classification/Apple_Quality_B2/info_mod.json b/decision_making/daily/classification/Apple_Quality_B2/info_mod.json index ed172aa47e101eb167b3b86984fd1b74f7e122f3..f7777abe9021042f7030f4c3e3823bc7b06e30be 100644 --- a/decision_making/daily/classification/Apple_Quality_B2/info_mod.json +++ b/decision_making/daily/classification/Apple_Quality_B2/info_mod.json @@ -52,8 +52,8 @@ 7.237836684 ], "Acidity": [ - -6.46098926, - 6.70139469 + -7.010538475, + 7.404736238 ] }, "cat_feature_value": {}, @@ -91,4 +91,4 @@ "open_text_feature_intro": {}, "open_text_features": [], "missing_from_original_info": [] -} \ No newline at end of file +} diff --git a/decision_making/daily/classification/Hotel_booking_demand_B2/Hotel_booking_demand_B2_001.json b/decision_making/daily/classification/Hotel_booking_demand_B2/Hotel_booking_demand_B2_001.json index b674d3dc08103c749ca65a2491b72ae135b3c904..535c550a7520dd588044b938db5ff98605458462 100644 --- a/decision_making/daily/classification/Hotel_booking_demand_B2/Hotel_booking_demand_B2_001.json +++ b/decision_making/daily/classification/Hotel_booking_demand_B2/Hotel_booking_demand_B2_001.json @@ -5,7 +5,7 @@ "perspective": "user", "dataset_name": "Hotel_booking_demand", "table_path": "kaggle/Hotel_booking_demand", - "query": "It’s tricky comparing these two cases side by side. The February booking, which was canceled, had those specific details: arrival year 2016, month February, week number 7, day of month 7, one weekend night stay, two adults, zero children and zero babies, a BB meal, country Thailand, market segment Online TA, distribution channel TA/TO, not a repeated guest, zero previous bookings not canceled, reserved room A and assigned room A, zero booking changes, zero days on a waiting list, customer type Transient, an average daily rate of 93.0, and zero required parking spaces. Then there’s the March booking with a 43-day lead time, arrival year 2016, month March, zero weekend nights but one weekday night, two adults, zero babies, BB meal, country Italy, market segment Online TA, not a repeated guest, zero previous cancellations, zero previous bookings not canceled, reserved and assigned room F, zero booking changes, deposit type No Deposit, agent 9.0, zero waiting list days, customer type Transient, an ADR of 153.9, zero parking spaces, and zero special requests. Flipping between them, the differences in rate, lead time, and the fact one is already canceled make it hard to judge. Given what we know about no-shows, does one of these stand out as more likely to have been a complete no-show?", + "query": "It’s tricky comparing these two cases side by side. The February booking had these details: arrival year 2016, month February, week number 7, day of month 7, one weekend night stay, two adults, zero children and zero babies, a BB meal, country Thailand, market segment Online TA, distribution channel TA/TO, not a repeated guest, zero previous bookings not canceled, reserved room A and assigned room A, zero booking changes, zero days on a waiting list, customer type Transient, an average daily rate of 93.0, and zero required parking spaces. Then there’s the March booking with a 43-day lead time, arrival year 2016, month March, zero weekend nights but one weekday night, two adults, zero babies, BB meal, country Italy, market segment Online TA, not a repeated guest, zero previous cancellations, zero previous bookings not canceled, reserved and assigned room F, zero booking changes, deposit type No Deposit, agent 9.0, zero waiting list days, customer type Transient, an ADR of 153.9, zero parking spaces, and zero special requests. Flipping between them, the rates and the available booking details make it hard to judge. Given what we know about no-shows, does one of these stand out as more likely to have been a complete no-show?", "meta_info": { "domain": "daily" }, @@ -15,7 +15,6 @@ "scenario_id": "001", "features": { "hotel": null, - "is_canceled": "1", "lead_time": null, "arrival_date_year": "2016", "arrival_date_month": "February", @@ -44,15 +43,13 @@ "adr": "93.0", "required_car_parking_spaces": "0", "total_of_special_requests": null, - "reservation_status": "No-Show", - "reservation_status_date": null + "reservation_status": "No-Show" } }, { "scenario_id": "002", "features": { "hotel": null, - "is_canceled": null, "lead_time": "43", "arrival_date_year": "2016", "arrival_date_month": "March", @@ -81,8 +78,7 @@ "adr": "153.9", "required_car_parking_spaces": "0", "total_of_special_requests": "0", - "reservation_status": "Canceled", - "reservation_status_date": null + "reservation_status": "Canceled" } } ], @@ -96,4 +92,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/decision_making/daily/classification/Hotel_booking_demand_B2/Hotel_booking_demand_B2_002.json b/decision_making/daily/classification/Hotel_booking_demand_B2/Hotel_booking_demand_B2_002.json index 7a76469d644042f0b19db1b3285b60df53d5b980..e911f8b30ce34bec565e89e8a4c01966535a152c 100644 --- a/decision_making/daily/classification/Hotel_booking_demand_B2/Hotel_booking_demand_B2_002.json +++ b/decision_making/daily/classification/Hotel_booking_demand_B2/Hotel_booking_demand_B2_002.json @@ -5,7 +5,7 @@ "perspective": "user", "dataset_name": "Hotel_booking_demand", "table_path": "kaggle/Hotel_booking_demand", - "query": "Take a look: the first is a City Hotel reservation with a 9-day lead, for 2016, week 2, the 8th, planning 2 weekend and 3 weeknight stays. Just one adult, no children or babies, a BB meal, booked through Offline TA/TO. The guest isn't a repeat, has no history of cancellations or prior kept bookings, reserved and got room A, made no changes, paid no deposit, wasn't on a waiting list, is a Transient, and had no special requests. The second entry, for the City Hotel, is flagged canceled, booked 104 days ahead for June 9, 2016, week 24, with 0 weekend nights and 3 weeknights. Two adults, no babies, BB meal, guest from Brazil, booked via Online TA on the TA/TO channel, not a repeat guest, with zero previous bookings not canceled, reserved and assigned room A, no deposit, agent ID 9.0, average rate 126.9, and no parking needed. Third, another City Hotel cancellation, lead time 5 days for 2017 week 19, includes 2 weekend nights, two adults, no babies, from Brazil, Online TA/TA/TO booking. Not a repeat guest, zero previous cancellations, zero previous uncanceled bookings, room A reserved and assigned, zero changes, agent 9.0, zero waiting list days, Transient type, rate 150.0, and one special request. With all this info, can you point to the booking that successfully reached check-out?", + "query": "Take a look: the first is a City Hotel reservation with a 9-day lead, for 2016, week 2, the 8th, planning 2 weekend and 3 weeknight stays. Just one adult, no children or babies, a BB meal, booked through Offline TA/TO. The guest isn't a repeat, has no history of cancellations or prior kept bookings, reserved and got room A, made no changes, paid no deposit, wasn't on a waiting list, is a Transient, and had no special requests. The second entry is a City Hotel booking made 104 days ahead for June 9, 2016, week 24, with 0 weekend nights and 3 weeknights. Two adults, no babies, BB meal, guest from Brazil, booked via Online TA on the TA/TO channel, not a repeat guest, with zero previous bookings not canceled, reserved and assigned room A, no deposit, agent ID 9.0, average rate 126.9, and no parking needed. The third is another City Hotel booking, lead time 5 days for 2017 week 19, includes 2 weekend nights, two adults, no babies, from Brazil, Online TA/TA/TO booking. Not a repeat guest, zero previous cancellations, zero previous uncanceled bookings, room A reserved and assigned, zero changes, agent 9.0, zero waiting list days, Transient type, rate 150.0, and one special request. With all this info, can you point to the booking most likely to complete its stay and check out?", "meta_info": { "domain": "daily" }, @@ -15,7 +15,6 @@ "scenario_id": "001", "features": { "hotel": "City Hotel", - "is_canceled": null, "lead_time": "9", "arrival_date_year": "2016", "arrival_date_month": null, @@ -44,15 +43,13 @@ "adr": null, "required_car_parking_spaces": null, "total_of_special_requests": "0", - "reservation_status": "Check-Out", - "reservation_status_date": null + "reservation_status": "Check-Out" } }, { "scenario_id": "002", "features": { "hotel": "City Hotel", - "is_canceled": "1", "lead_time": "104", "arrival_date_year": "2016", "arrival_date_month": "June", @@ -81,15 +78,13 @@ "adr": "126.9", "required_car_parking_spaces": "0", "total_of_special_requests": null, - "reservation_status": "Canceled", - "reservation_status_date": null + "reservation_status": "Canceled" } }, { "scenario_id": "003", "features": { "hotel": "City Hotel", - "is_canceled": "1", "lead_time": "5", "arrival_date_year": "2017", "arrival_date_month": null, @@ -118,8 +113,7 @@ "adr": "150.0", "required_car_parking_spaces": null, "total_of_special_requests": "1", - "reservation_status": "No-Show", - "reservation_status_date": null + "reservation_status": "No-Show" } } ], @@ -133,4 +127,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/decision_making/daily/classification/Hotel_booking_demand_B2/Hotel_booking_demand_B2_003.json b/decision_making/daily/classification/Hotel_booking_demand_B2/Hotel_booking_demand_B2_003.json index c14719302cb1588349434f860c1d25fcefe97648..c1ca387e378744c990ae58565a01b4a59b248a7b 100644 --- a/decision_making/daily/classification/Hotel_booking_demand_B2/Hotel_booking_demand_B2_003.json +++ b/decision_making/daily/classification/Hotel_booking_demand_B2/Hotel_booking_demand_B2_003.json @@ -5,7 +5,7 @@ "perspective": "user", "dataset_name": "Hotel_booking_demand", "table_path": "kaggle/Hotel_booking_demand", - "query": "I’ve got two booking profiles here and I’m trying to spot the one that was canceled, but every detail seems to tell a different story. First, a Resort Hotel booking, canceled, with a lead time of 48 days for an arrival in July 2017, specifically the 11th, which is in week 28. The stay is three weeknights with zero weekend nights, for two adults, zero children, zero babies. The meal is Half Board, the country is Portugal, and it was booked in the Online TA market segment via the TA/TO distribution channel. The guest is not a repeated guest, has zero previous cancellations and zero previous bookings not canceled, reserved room type A and was assigned the same, made zero booking changes, selected No Deposit, and was handled by agent 240.0. They spent zero days on a waiting list, are a Transient customer, have an average daily rate of 180.67, required zero car parking spaces, and made zero special requests. The second is a City Hotel booking, not canceled, booked 263 days ahead for arrival on June 23rd, 2017, in week 25, with two weekend nights and two weeknights, also two adults, zero children, zero babies, but with a Bed & Breakfast meal. Also from Portugal, but this was a Groups market segment booking via the TA/TO channel. Again, not a repeated guest, with zero previous cancellations and zero previous successful bookings, reserved and assigned room A, zero changes, No Deposit, agent 37.0, zero waiting list days, but the customer type is Transient-Party, the rate is 105.0, zero parking needed, and they made one special request. Juggling all these numbers and categories—from the agent IDs 240.0 and 37.0 to the rates of 180.67 and 105.0—leaves me uncertain. Between these two, which reservation actually ended up being canceled?", + "query": "I've got two booking profiles to compare against our historical booking records. First, a Resort Hotel booking with a lead time of 48 days for an arrival in July 2017, specifically the 11th, which is in week 28. The stay is three weeknights with zero weekend nights, for two adults, zero children, zero babies. The meal is Half Board, the country is Portugal, and it was booked in the Online TA market segment via the TA/TO distribution channel. The guest is not a repeated guest, has zero previous cancellations and zero previous bookings not canceled, reserved room type A and was assigned the same, made zero booking changes, selected No Deposit, and was handled by agent 240.0. They spent zero days on a waiting list, are a Transient customer, have an average daily rate of 180.67, required zero car parking spaces, and made zero special requests. The second is a City Hotel booking made 263 days ahead for arrival on June 23rd, 2017, in week 25, with two weekend nights and two weeknights, also two adults, zero children, zero babies, but with a Bed & Breakfast meal. Also from Portugal, but this was a Groups market segment booking via the TA/TO channel. Again, not a repeated guest, with zero previous cancellations and zero previous successful bookings, reserved and assigned room A, zero changes, No Deposit, agent 37.0, zero waiting list days, but the customer type is Transient-Party, the rate is 105.0, zero parking needed, and they made one special request. Juggling all these numbers and categories—from the agent IDs 240.0 and 37.0 to the rates of 180.67 and 105.0—leaves me uncertain. Between these two, which reservation looks more likely to be canceled?", "meta_info": { "domain": "daily" }, @@ -15,7 +15,6 @@ "scenario_id": "001", "features": { "hotel": "Resort Hotel", - "is_canceled": "1", "lead_time": "48", "arrival_date_year": "2017", "arrival_date_month": "July", @@ -44,15 +43,13 @@ "adr": "180.67", "required_car_parking_spaces": "0", "total_of_special_requests": "0", - "reservation_status": "Canceled", - "reservation_status_date": null + "reservation_status": "Canceled" } }, { "scenario_id": "002", "features": { "hotel": "City Hotel", - "is_canceled": "0", "lead_time": "263", "arrival_date_year": "2017", "arrival_date_month": "June", @@ -81,8 +78,7 @@ "adr": "105.0", "required_car_parking_spaces": "0", "total_of_special_requests": "1", - "reservation_status": "Check-Out", - "reservation_status_date": null + "reservation_status": "Check-Out" } } ], @@ -96,4 +92,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/decision_making/daily/classification/Hotel_booking_demand_B2/Hotel_booking_demand_B2_004.json b/decision_making/daily/classification/Hotel_booking_demand_B2/Hotel_booking_demand_B2_004.json index 88b34b9790eedd9fffa80ea579b749ce09ec91f0..dcbd6ae3ded28a825852e7d4a9771287a3e23ed4 100644 --- a/decision_making/daily/classification/Hotel_booking_demand_B2/Hotel_booking_demand_B2_004.json +++ b/decision_making/daily/classification/Hotel_booking_demand_B2/Hotel_booking_demand_B2_004.json @@ -5,7 +5,7 @@ "perspective": "data_holder", "dataset_name": "Hotel_booking_demand", "table_path": "kaggle/Hotel_booking_demand", - "query": "Having to decide which upcoming booking is most likely to be a \"Canceled\" reservation is giving me a headache. I’ve got two different ones here. The first is for a City Hotel, it’s marked as canceled already with a lead time of 20 days, set to arrive in May on the 3rd in 2017. That guest is staying for zero weekend nights and two week nights, just one adult with no children or babies from Portugal, booked through the Online TA market segment via the TA/TO channel. They’re not a repeated guest, have no previous cancellations or non-canceled bookings, reserved room type A, made zero booking changes, had no deposit, zero days on a waiting list, are a Transient customer type, and required zero car parking spaces. The second booking is for a Resort Hotel, currently not canceled, with an 18-day lead time, arriving in the 12th week of 2016. That stay involves zero weekend nights and one week night, for two adults with no children or babies, on a BB meal plan, also from Portugal via the Online TA segment. They also have zero previous cancellations and non-canceled bookings, reserved room type A, made zero changes, had no deposit, were booked by agent 240.0, spent zero days on a waiting list, are a Transient customer, and needed zero parking spaces. I’ve shared our historical booking archives with you. Considering all these details from my records, is the Resort Hotel booking actually the one more prone to end up canceled?", + "query": "Having to decide which upcoming booking is most likely to be a \"Canceled\" reservation is giving me a headache. I’ve got two different ones here. The first is for a City Hotel with a lead time of 20 days, set to arrive in May on the 3rd in 2017. That guest is staying for zero weekend nights and two week nights, just one adult with no children or babies from Portugal, booked through the Online TA market segment via the TA/TO channel. They’re not a repeated guest, have no previous cancellations or non-canceled bookings, reserved room type A, made zero booking changes, had no deposit, zero days on a waiting list, are a Transient customer type, and required zero car parking spaces. The second booking is for a Resort Hotel with an 18-day lead time, arriving in the 12th week of 2016. That stay involves zero weekend nights and one week night, for two adults with no children or babies, on a BB meal plan, also from Portugal via the Online TA segment. They also have zero previous cancellations and non-canceled bookings, reserved room type A, made zero changes, had no deposit, were booked by agent 240.0, spent zero days on a waiting list, are a Transient customer, and needed zero parking spaces. I’ve shared our historical booking archives with you. Considering all these details from my records, which of these bookings looks more likely to end up canceled?", "meta_info": { "domain": "daily" }, @@ -15,7 +15,6 @@ "scenario_id": "001", "features": { "hotel": "City Hotel", - "is_canceled": "1", "lead_time": "20", "arrival_date_year": "2017", "arrival_date_month": "May", @@ -44,15 +43,13 @@ "adr": null, "required_car_parking_spaces": "0", "total_of_special_requests": null, - "reservation_status": "Canceled", - "reservation_status_date": null + "reservation_status": "Canceled" } }, { "scenario_id": "002", "features": { "hotel": "Resort Hotel", - "is_canceled": "0", "lead_time": "18", "arrival_date_year": "2016", "arrival_date_month": null, @@ -81,8 +78,7 @@ "adr": null, "required_car_parking_spaces": "0", "total_of_special_requests": null, - "reservation_status": "Check-Out", - "reservation_status_date": null + "reservation_status": "Check-Out" } } ], @@ -96,4 +92,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/decision_making/daily/classification/Hotel_booking_demand_B2/Hotel_booking_demand_B2_005.json b/decision_making/daily/classification/Hotel_booking_demand_B2/Hotel_booking_demand_B2_005.json index dcc7a4229f65adeda7f62d2dbaf08c6ce8f450c0..9564047f0c9dfe9b0f571532377f0f572bb3c290 100644 --- a/decision_making/daily/classification/Hotel_booking_demand_B2/Hotel_booking_demand_B2_005.json +++ b/decision_making/daily/classification/Hotel_booking_demand_B2/Hotel_booking_demand_B2_005.json @@ -5,7 +5,7 @@ "perspective": "data_holder", "dataset_name": "Hotel_booking_demand", "table_path": "kaggle/Hotel_booking_demand", - "query": "Flipping through these old booking records, I'm stuck between two specific cases. The first guest booked a City Hotel stay a whopping 293 days in advance for August 6, 2015, taking 2 weeknights and 0 weekend nights for 2 adults (no children or babies) from Portugal. It was distributed via TA/TO, they weren't a repeat guest but had canceled once before with zero completed bookings, reserved and got room A, agent 1 handled it, no waitlist days, they were a contract customer, needed no parking, and made no special requests—canceled. The second guest also booked a City Hotel, but just 19 days ahead for a February 1st arrival in week 6, wanting 1 weekend night and 5 weeknights for 2 adults and 2 children from China. It was an Online TA booking through TA/TO, not a repeat guest, with no prior cancellations or non-canceled bookings, reserved room B, no deposit, agent 9, transient-party type, average daily rate of 79.88, no parking, no special requests—canceled as well. Given all this detail from our past logs, please help me decide: between these two, who do you think was the actual no-show?", + "query": "I'm comparing two booking profiles against our historical archive. The first guest booked a City Hotel stay a whopping 293 days in advance for August 6, 2015, taking 2 weeknights and 0 weekend nights for 2 adults (no children or babies) from Portugal. It was distributed via TA/TO, they weren't a repeat guest but had canceled once before with zero completed bookings, reserved and got room A, agent 1 handled it, no waitlist days, they were a contract customer, needed no parking, and made no special requests. The second guest also booked a City Hotel, but just 19 days ahead for a February 1st arrival in week 6, wanting 1 weekend night and 5 weeknights for 2 adults and 2 children from China. It was an Online TA booking through TA/TO, not a repeat guest, with no prior cancellations or non-canceled bookings, reserved room B, no deposit, agent 9, transient-party type, average daily rate of 79.88, no parking, no special requests. Given all this detail from our past logs, please help me decide: between these two, who do you think was the actual no-show?", "meta_info": { "domain": "daily" }, @@ -15,7 +15,6 @@ "scenario_id": "001", "features": { "hotel": "City Hotel", - "is_canceled": "1", "lead_time": "293", "arrival_date_year": "2015", "arrival_date_month": "August", @@ -44,15 +43,13 @@ "adr": null, "required_car_parking_spaces": "0", "total_of_special_requests": "0", - "reservation_status": "Canceled", - "reservation_status_date": null + "reservation_status": "Canceled" } }, { "scenario_id": "002", "features": { "hotel": "City Hotel", - "is_canceled": "1", "lead_time": "19", "arrival_date_year": null, "arrival_date_month": "February", @@ -81,8 +78,7 @@ "adr": "79.88", "required_car_parking_spaces": "0", "total_of_special_requests": "0", - "reservation_status": "No-Show", - "reservation_status_date": null + "reservation_status": "No-Show" } } ], @@ -96,4 +92,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/decision_making/daily/classification/Hotel_booking_demand_B2/Hotel_booking_demand_B2_006.json b/decision_making/daily/classification/Hotel_booking_demand_B2/Hotel_booking_demand_B2_006.json index 4c87feae5ea622503abe7c7d186c93d74408e177..d3a621d475c5ee5b4103714c6b55433068c7a4ed 100644 --- a/decision_making/daily/classification/Hotel_booking_demand_B2/Hotel_booking_demand_B2_006.json +++ b/decision_making/daily/classification/Hotel_booking_demand_B2/Hotel_booking_demand_B2_006.json @@ -5,7 +5,7 @@ "perspective": "data_holder", "dataset_name": "Hotel_booking_demand", "table_path": "kaggle/Hotel_booking_demand", - "query": "This City Hotel booking for December 2016 was made way back, 195 days before arrival, for a two-night stay during the week with two adults on a BB plan, booked online by a guest from France. They’re not a repeat customer, have no history of cancellations, were assigned the B room they reserved, changed the booking once, didn’t pay a deposit, worked with agent 9.0, are considered a transient-party, and required no parking or special extras. Meanwhile, another City Hotel booking for August 14th was canceled—it was booked with no advance notice, for two weeknights, no children, on an HB meal plan, arranged through an offline travel agent. The guest isn’t a repeat visitor either, has no prior cancellations, got the A room they wanted, paid no deposit, used agent 6.0, waited zero days, was charged 109.0 per night, and made no special requests. Then there’s a third reservation for December 6th, 2016, in week 50, not canceled, booked 27 days out for a four-weeknight stay with two adults on an SC meal, from France via a travel agent channel. This guest also isn’t a repeat visitor, has no cancellation history, received their reserved A room, made no changes, gave no deposit, used agent 9.0, had zero waiting time, is a transient customer, paid 74.8 nightly, and made one special request. I’ve sent you our archive of past bookings—from your analysis of the trends, do you think the first, second, or third one will be checked out?", + "query": "This City Hotel booking for December 2016 was made way back, 195 days before arrival, for a two-night stay during the week with two adults on a BB plan, booked online by a guest from France. They’re not a repeat customer, have no history of cancellations, were assigned the B room they reserved, changed the booking once, didn’t pay a deposit, worked with agent 9.0, are considered a transient-party, and required no parking or special extras. Meanwhile, another City Hotel booking for August 14th was made with no advance notice, for two weeknights, no children, on an HB meal plan, arranged through an offline travel agent. The guest isn’t a repeat visitor either, has no prior cancellations, got the A room they wanted, paid no deposit, used agent 6.0, waited zero days, was charged 109.0 per night, and made no special requests. Then there’s a third reservation for December 6th, 2016, in week 50, booked 27 days out for a four-weeknight stay with two adults on an SC meal, from France via a travel agent channel. This guest also isn’t a repeat visitor, has no cancellation history, received their reserved A room, made no changes, gave no deposit, used agent 9.0, had zero waiting time, is a transient customer, paid 74.8 nightly, and made one special request. I’ve sent you our archive of past bookings—from your analysis of the trends, do you think the first, second, or third one will be checked out?", "meta_info": { "domain": "daily" }, @@ -15,7 +15,6 @@ "scenario_id": "001", "features": { "hotel": "City Hotel", - "is_canceled": null, "lead_time": "195", "arrival_date_year": "2016", "arrival_date_month": "December", @@ -44,15 +43,13 @@ "adr": null, "required_car_parking_spaces": "0", "total_of_special_requests": "0", - "reservation_status": "Canceled", - "reservation_status_date": null + "reservation_status": "Canceled" } }, { "scenario_id": "002", "features": { "hotel": "City Hotel", - "is_canceled": "1", "lead_time": "0", "arrival_date_year": null, "arrival_date_month": "August", @@ -81,15 +78,13 @@ "adr": "109.0", "required_car_parking_spaces": null, "total_of_special_requests": "0", - "reservation_status": "Canceled", - "reservation_status_date": null + "reservation_status": "Canceled" } }, { "scenario_id": "003", "features": { "hotel": null, - "is_canceled": "0", "lead_time": "27", "arrival_date_year": "2016", "arrival_date_month": "December", @@ -118,8 +113,7 @@ "adr": "74.8", "required_car_parking_spaces": null, "total_of_special_requests": "1", - "reservation_status": "Check-Out", - "reservation_status_date": null + "reservation_status": "Check-Out" } } ], @@ -133,4 +127,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/decision_making/daily/classification/Hotel_booking_demand_B2/current.csv b/decision_making/daily/classification/Hotel_booking_demand_B2/current.csv index 04772d0af9becfa40de73ec65650341cee2f0a9d..64728dbb09a6c6b413ebe828ca46aece9b8785d9 100644 --- a/decision_making/daily/classification/Hotel_booking_demand_B2/current.csv +++ b/decision_making/daily/classification/Hotel_booking_demand_B2/current.csv @@ -1,15 +1,15 @@ -hotel,is_canceled,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests,reservation_status,reservation_status_date -City Hotel,1,12,2016,February,7,7,1,0,2,0.0,0,BB,THA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,93.0,0,2,No-Show,2016-02-07 -City Hotel,1,43,2016,March,10,1,0,1,2,2.0,0,BB,ITA,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,9.0,,0,Transient,153.9,0,0,Canceled,2016-02-26 -City Hotel,0,9,2016,January,2,8,2,3,1,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,27.0,,0,Transient,44.8,0,0,Check-Out,2016-01-13 -City Hotel,1,104,2016,June,24,9,0,3,2,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,126.9,0,0,Canceled,2016-03-01 -City Hotel,1,5,2017,May,19,11,2,3,2,0.0,0,SC,BRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,150.0,0,1,No-Show,2017-05-11 -Resort Hotel,1,48,2017,July,28,11,0,3,2,0.0,0,HB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,180.67,0,0,Canceled,2017-05-25 -City Hotel,0,263,2017,June,25,23,2,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,37.0,,0,Transient-Party,105.0,0,1,Check-Out,2017-06-27 -City Hotel,1,20,2017,May,18,3,0,2,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Transient,130.0,0,1,Canceled,2017-04-28 -Resort Hotel,0,18,2016,March,12,19,0,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,60.0,0,0,Check-Out,2016-03-20 -City Hotel,1,293,2015,August,32,6,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Contract,62.0,0,0,Canceled,2015-01-01 -City Hotel,1,19,2016,February,6,1,1,5,2,2.0,0,BB,CHN,Online TA,TA/TO,0,0,0,B,B,0,No Deposit,9.0,,0,Transient-Party,79.88,0,0,No-Show,2016-02-01 -City Hotel,1,195,2016,December,49,2,0,2,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,B,B,1,No Deposit,9.0,,0,Transient-Party,71.22,0,0,Canceled,2016-06-23 -City Hotel,1,0,2015,August,33,14,0,2,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,109.0,0,0,Canceled,2015-08-14 -City Hotel,0,27,2016,December,50,6,0,4,2,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,74.8,0,1,Check-Out,2016-12-10 +hotel,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests,reservation_status +City Hotel,12,2016,February,7,7,1,0,2,0.0,0,BB,THA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,93.0,0,2,No-Show +City Hotel,43,2016,March,10,1,0,1,2,2.0,0,BB,ITA,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,9.0,,0,Transient,153.9,0,0,Canceled +City Hotel,9,2016,January,2,8,2,3,1,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,27.0,,0,Transient,44.8,0,0,Check-Out +City Hotel,104,2016,June,24,9,0,3,2,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,126.9,0,0,Canceled +City Hotel,5,2017,May,19,11,2,3,2,0.0,0,SC,BRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,150.0,0,1,No-Show +Resort Hotel,48,2017,July,28,11,0,3,2,0.0,0,HB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,180.67,0,0,Canceled +City Hotel,263,2017,June,25,23,2,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,37.0,,0,Transient-Party,105.0,0,1,Check-Out +City Hotel,20,2017,May,18,3,0,2,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Transient,130.0,0,1,Canceled +Resort Hotel,18,2016,March,12,19,0,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,60.0,0,0,Check-Out +City Hotel,293,2015,August,32,6,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Contract,62.0,0,0,Canceled +City Hotel,19,2016,February,6,1,1,5,2,2.0,0,BB,CHN,Online TA,TA/TO,0,0,0,B,B,0,No Deposit,9.0,,0,Transient-Party,79.88,0,0,No-Show +City Hotel,195,2016,December,49,2,0,2,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,B,B,1,No Deposit,9.0,,0,Transient-Party,71.22,0,0,Canceled +City Hotel,0,2015,August,33,14,0,2,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,109.0,0,0,Canceled +City Hotel,27,2016,December,50,6,0,4,2,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,74.8,0,1,Check-Out diff --git a/decision_making/daily/classification/Hotel_booking_demand_B2/history.csv b/decision_making/daily/classification/Hotel_booking_demand_B2/history.csv index c656628a71b9381b38626d918c1b12ce30ff312e..59fc0782f057cf726124a012c7aa52beea5acc03 100644 --- a/decision_making/daily/classification/Hotel_booking_demand_B2/history.csv +++ b/decision_making/daily/classification/Hotel_booking_demand_B2/history.csv @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:fb9bc72b00c27e4643682b52db6e438244e0777f2ee8368321efc1817203e0e9 -size 16925331 +oid sha256:9597cd1eb3e4d4d54830cc53b7c348a44a5a7763ec004db6aa1293272ccf09ad +size 15374213 diff --git a/decision_making/daily/classification/Hotel_booking_demand_B2/info.json b/decision_making/daily/classification/Hotel_booking_demand_B2/info.json index 4816c05993309461836de8622438e342f2e503ff..20956c93d07801fe3772275e08832605a78593ed 100644 --- a/decision_making/daily/classification/Hotel_booking_demand_B2/info.json +++ b/decision_making/daily/classification/Hotel_booking_demand_B2/info.json @@ -1,398 +1,391 @@ -{ - "name": "Hotel Booking Demand", - "source": "https://www.kaggle.com/datasets/jessemostipak/hotel-booking-demand/data", - "data_intro": "This dataset contains hotel booking data for a city hotel and a resort hotel, providing details on booking dates, stay duration, guest count, cancellation trends, and pricing. It is useful for demand forecasting, revenue management, and customer behavior analysis.", - "is_splited": false, - "overall_size": 119390, - "train_size": 0, - "test_size": 0, - "c_classes": 12, - "n_classes": 20, - "cat_feature_intro": { - "hotel": "Type of hotel (City Hotel or Resort Hotel).", - "arrival_date_month": "Month of arrival for the booking.", - "meal": "Type of meal plan booked (e.g., Breakfast included, Half board, Full board).", - "country": "Country of origin of the guest.", - "market_segment": "Booking distribution channel (e.g., direct, corporate, travel agent).", - "distribution_channel": "Channel through which the booking was made (e.g., online travel agents, direct).", - "reserved_room_type": "Type of room reserved by the guest before any changes.", - "assigned_room_type": "Final room type assigned to the guest.", - "deposit_type": "Indicates if a deposit was made and its type (e.g., No Deposit, Refundable, Non-Refundable).", - "customer_type": "Type of customer (e.g., transient, group, contract).", - "reservation_status": "Final status of the booking (e.g., Canceled, Checked-out, No-show).", - "reservation_status_date": "Date when the reservation status was last updated." - }, - "num_feature_intro": { - "is_canceled": "Indicates whether the booking was canceled (1) or not (0).", - "lead_time": "Number of days between booking and arrival.", - "arrival_date_year": "Year of arrival for the booking.", - "arrival_date_week_number": "Week number of the arrival date.", - "arrival_date_day_of_month": "Day of the month of arrival.", - "stays_in_weekend_nights": "Number of weekend nights (Saturday or Sunday) the guest stayed.", - "stays_in_week_nights": "Number of weekday nights (Monday to Friday) the guest stayed.", - "adults": "Number of adults included in the booking.", - "children": "Number of children included in the booking.", - "babies": "Number of babies included in the booking.", - "is_repeated_guest": "Indicates whether the guest is a returning customer (1) or not (0).", - "previous_cancellations": "Number of times the guest has canceled previous bookings.", - "previous_bookings_not_canceled": "Number of previous bookings not canceled by the guest.", - "booking_changes": "Number of changes made to the booking.", - "agent": "ID of the booking agent (if applicable).", - "company": "ID of the company making the booking (if applicable).", - "days_in_waiting_list": "Number of days the booking was on the waiting list before confirmation.", - "adr": "Average daily rate (price per night per room).", - "required_car_parking_spaces": "Number of car parking spaces required by the guest.", - "total_of_special_requests": "Number of special requests made by the guest (e.g., high floor, twin beds)." - }, - "evaluation_metric": null, - "task_type": "classification", - "target": "reservation_status", - "num_feature_value": { - "adr": [ - -6.38, - 5400.0 - ], - "adults": [ - 0.0, - 55.0 - ], - "agent": [ - 1.0, - 535.0 - ], - "arrival_date_day_of_month": [ - 1.0, - 31.0 - ], - "arrival_date_week_number": [ - 1.0, - 53.0 - ], - "arrival_date_year": [ - 2015.0, - 2017.0 - ], - "babies": [ - 0.0, - 10.0 - ], - "booking_changes": [ - 0.0, - 21.0 - ], - "children": [ - 0.0, - 10.0 - ], - "company": [ - 6.0, - 543.0 - ], - "days_in_waiting_list": [ - 0.0, - 391.0 - ], - "is_canceled": [ - 0.0, - 1.0 - ], - "is_repeated_guest": [ - 0.0, - 1.0 - ], - "lead_time": [ - 0.0, - 737.0 - ], - "previous_bookings_not_canceled": [ - 0.0, - 72.0 - ], - "previous_cancellations": [ - 0.0, - 26.0 - ], - "required_car_parking_spaces": [ - 0.0, - 8.0 - ], - "stays_in_week_nights": [ - 0.0, - 50.0 - ], - "stays_in_weekend_nights": [ - 0.0, - 19.0 - ], - "total_of_special_requests": [ - 0.0, - 5.0 - ] - }, - "cat_feature_value": { - "arrival_date_month": [ - "April", - "August", - "December", - "February", - "January", - "July", - "June", - "March", - "May", - "November", - "October", - "September" - ], - "assigned_room_type": [ - "A", - "B", - "C", - "D", - "E", - "F", - "G", - "H", - "I", - "K", - "L", - "P" - ], - "country": [ - "ABW", - "AGO", - "AIA", - "ALB", - "AND", - "ARE", - "ARG", - "ARM", - "ASM", - "ATA", - "ATF", - "AUS", - "AUT", - "AZE", - "BDI", - "BEL", - "BEN", - "BFA", - "BGD", - "BGR", - "BHR", - "BHS", - "BIH", - "BLR", - "BOL", - "BRA", - "BRB", - "BWA", - "CAF", - "CHE", - "CHL", - "CHN", - "CIV", - "CMR", - "CN", - "COL", - "COM", - "CPV", - "CRI", - "CUB", - "CYM", - "CYP", - "CZE", - "DEU", - "DJI", - "DMA", - "DNK", - "DOM", - "DZA", - "ECU", - "EGY", - "ESP", - "EST", - "ETH", - "FIN", - "FJI", - "FRA", - "FRO", - "GAB", - "GBR", - "GEO", - "GGY", - "GHA", - "GIB", - "GLP", - "GNB", - "GRC", - "GTM", - "GUY", - "HKG", - "HND", - "HRV", - "HUN", - "IDN", - "IMN", - "IND", - "IRL", - "IRN", - "IRQ", - "ISL", - "ISR", - "ITA", - "JAM", - "JEY", - "JOR", - "JPN", - "KAZ", - "KEN", - "KHM", - "KIR", - "KNA", - "KOR", - "KWT", - "LAO", - "LBN", - "LBY", - "LCA", - "LIE", - "LKA", - "LTU", - "LUX", - "LVA", - "MAC", - "MAR", - "MCO", - "MDG", - "MDV", - "MEX", - "MKD", - "MLI", - "MLT", - "MMR", - "MNE", - "MOZ", - "MRT", - "MUS", - "MWI", - "MYS", - "MYT", - "NAM", - "NCL", - "NGA", - "NIC", - "NLD", - "NOR", - "NPL", - "NZL", - "OMN", - "PAK", - "PAN", - "PER", - "PHL", - "PLW", - "POL", - "PRI", - "PRT", - "PRY", - "PYF", - "QAT", - "ROU", - "RUS", - "RWA", - "SAU", - "SDN", - "SEN", - "SGP", - "SLE", - "SLV", - "SMR", - "SRB", - "STP", - "SUR", - "SVK", - "SVN", - "SWE", - "SYC", - "SYR", - "TGO", - "THA", - "TJK", - "TMP", - "TUN", - "TUR", - "TWN", - "TZA", - "UGA", - "UKR", - "UMI", - "URY", - "USA", - "UZB", - "VEN", - "VGB", - "VNM", - "ZAF", - "ZMB", - "ZWE" - ], - "customer_type": [ - "Contract", - "Group", - "Transient", - "Transient-Party" - ], - "deposit_type": [ - "No Deposit", - "Non Refund", - "Refundable" - ], - "distribution_channel": [ - "Corporate", - "Direct", - "GDS", - "TA/TO", - "Undefined" - ], - "hotel": [ - "City Hotel", - "Resort Hotel" - ], - "market_segment": [ - "Aviation", - "Complementary", - "Corporate", - "Direct", - "Groups", - "Offline TA/TO", - "Online TA", - "Undefined" - ], - "meal": [ - "BB", - "FB", - "HB", - "SC", - "Undefined" - ], - "reservation_status": [ - "Canceled", - "Check-Out", - "No-Show" - ], - "reservation_status_date": "string", - "reserved_room_type": [ - "A", - "B", - "C", - "D", - "E", - "F", - "G", - "H", - "L", - "P" - ] - } -} \ No newline at end of file +{ + "name": "Hotel Booking Demand", + "source": "https://www.kaggle.com/datasets/jessemostipak/hotel-booking-demand/data", + "data_intro": "This dataset contains hotel booking data for a city hotel and a resort hotel, providing details on booking dates, stay duration, guest count, cancellation trends, and pricing. It is useful for demand forecasting, revenue management, and customer behavior analysis.", + "is_splited": false, + "overall_size": 119390, + "train_size": 0, + "test_size": 0, + "c_classes": 11, + "n_classes": 19, + "cat_feature_intro": { + "hotel": "Type of hotel (City Hotel or Resort Hotel).", + "arrival_date_month": "Month of arrival for the booking.", + "meal": "Type of meal plan booked (e.g., Breakfast included, Half board, Full board).", + "country": "Country of origin of the guest.", + "market_segment": "Booking distribution channel (e.g., direct, corporate, travel agent).", + "distribution_channel": "Channel through which the booking was made (e.g., online travel agents, direct).", + "reserved_room_type": "Type of room reserved by the guest before any changes.", + "assigned_room_type": "Final room type assigned to the guest.", + "deposit_type": "Indicates if a deposit was made and its type (e.g., No Deposit, Refundable, Non-Refundable).", + "customer_type": "Type of customer (e.g., transient, group, contract).", + "reservation_status": "Final status of the booking (e.g., Canceled, Checked-out, No-show)." + }, + "num_feature_intro": { + "lead_time": "Number of days between booking and arrival.", + "arrival_date_year": "Year of arrival for the booking.", + "arrival_date_week_number": "Week number of the arrival date.", + "arrival_date_day_of_month": "Day of the month of arrival.", + "stays_in_weekend_nights": "Number of weekend nights (Saturday or Sunday) the guest stayed.", + "stays_in_week_nights": "Number of weekday nights (Monday to Friday) the guest stayed.", + "adults": "Number of adults included in the booking.", + "children": "Number of children included in the booking.", + "babies": "Number of babies included in the booking.", + "is_repeated_guest": "Indicates whether the guest is a returning customer (1) or not (0).", + "previous_cancellations": "Number of times the guest has canceled previous bookings.", + "previous_bookings_not_canceled": "Number of previous bookings not canceled by the guest.", + "booking_changes": "Number of changes made to the booking.", + "agent": "ID of the booking agent (if applicable).", + "company": "ID of the company making the booking (if applicable).", + "days_in_waiting_list": "Number of days the booking was on the waiting list before confirmation.", + "adr": "Average daily rate (price per night per room).", + "required_car_parking_spaces": "Number of car parking spaces required by the guest.", + "total_of_special_requests": "Number of special requests made by the guest (e.g., high floor, twin beds)." + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "reservation_status", + "num_feature_value": { + "adr": [ + -6.38, + 5400.0 + ], + "adults": [ + 0.0, + 55.0 + ], + "agent": [ + 1.0, + 535.0 + ], + "arrival_date_day_of_month": [ + 1.0, + 31.0 + ], + "arrival_date_week_number": [ + 1.0, + 53.0 + ], + "arrival_date_year": [ + 2015.0, + 2017.0 + ], + "babies": [ + 0.0, + 10.0 + ], + "booking_changes": [ + 0.0, + 21.0 + ], + "children": [ + 0.0, + 10.0 + ], + "company": [ + 6.0, + 543.0 + ], + "days_in_waiting_list": [ + 0.0, + 391.0 + ], + "is_repeated_guest": [ + 0.0, + 1.0 + ], + "lead_time": [ + 0.0, + 737.0 + ], + "previous_bookings_not_canceled": [ + 0.0, + 72.0 + ], + "previous_cancellations": [ + 0.0, + 26.0 + ], + "required_car_parking_spaces": [ + 0.0, + 8.0 + ], + "stays_in_week_nights": [ + 0.0, + 50.0 + ], + "stays_in_weekend_nights": [ + 0.0, + 19.0 + ], + "total_of_special_requests": [ + 0.0, + 5.0 + ] + }, + "cat_feature_value": { + "arrival_date_month": [ + "April", + "August", + "December", + "February", + "January", + "July", + "June", + "March", + "May", + "November", + "October", + "September" + ], + "assigned_room_type": [ + "A", + "B", + "C", + "D", + "E", + "F", + "G", + "H", + "I", + "K", + "L", + "P" + ], + "country": [ + "ABW", + "AGO", + "AIA", + "ALB", + "AND", + "ARE", + "ARG", + "ARM", + "ASM", + "ATA", + "ATF", + "AUS", + "AUT", + "AZE", + "BDI", + "BEL", + "BEN", + "BFA", + "BGD", + "BGR", + "BHR", + "BHS", + "BIH", + "BLR", + "BOL", + "BRA", + "BRB", + "BWA", + "CAF", + "CHE", + "CHL", + "CHN", + "CIV", + "CMR", + "CN", + "COL", + "COM", + "CPV", + "CRI", + "CUB", + "CYM", + "CYP", + "CZE", + "DEU", + "DJI", + "DMA", + "DNK", + "DOM", + "DZA", + "ECU", + "EGY", + "ESP", + "EST", + "ETH", + "FIN", + "FJI", + "FRA", + "FRO", + "GAB", + "GBR", + "GEO", + "GGY", + "GHA", + "GIB", + "GLP", + "GNB", + "GRC", + "GTM", + "GUY", + "HKG", + "HND", + "HRV", + "HUN", + "IDN", + "IMN", + "IND", + "IRL", + "IRN", + "IRQ", + "ISL", + "ISR", + "ITA", + "JAM", + "JEY", + "JOR", + "JPN", + "KAZ", + "KEN", + "KHM", + "KIR", + "KNA", + "KOR", + "KWT", + "LAO", + "LBN", + "LBY", + "LCA", + "LIE", + "LKA", + "LTU", + "LUX", + "LVA", + "MAC", + "MAR", + "MCO", + "MDG", + "MDV", + "MEX", + "MKD", + "MLI", + "MLT", + "MMR", + "MNE", + "MOZ", + "MRT", + "MUS", + "MWI", + "MYS", + "MYT", + "NAM", + "NCL", + "NGA", + "NIC", + "NLD", + "NOR", + "NPL", + "NZL", + "OMN", + "PAK", + "PAN", + "PER", + "PHL", + "PLW", + "POL", + "PRI", + "PRT", + "PRY", + "PYF", + "QAT", + "ROU", + "RUS", + "RWA", + "SAU", + "SDN", + "SEN", + "SGP", + "SLE", + "SLV", + "SMR", + "SRB", + "STP", + "SUR", + "SVK", + "SVN", + "SWE", + "SYC", + "SYR", + "TGO", + "THA", + "TJK", + "TMP", + "TUN", + "TUR", + "TWN", + "TZA", + "UGA", + "UKR", + "UMI", + "URY", + "USA", + "UZB", + "VEN", + "VGB", + "VNM", + "ZAF", + "ZMB", + "ZWE" + ], + "customer_type": [ + "Contract", + "Group", + "Transient", + "Transient-Party" + ], + "deposit_type": [ + "No Deposit", + "Non Refund", + "Refundable" + ], + "distribution_channel": [ + "Corporate", + "Direct", + "GDS", + "TA/TO", + "Undefined" + ], + "hotel": [ + "City Hotel", + "Resort Hotel" + ], + "market_segment": [ + "Aviation", + "Complementary", + "Corporate", + "Direct", + "Groups", + "Offline TA/TO", + "Online TA", + "Undefined" + ], + "meal": [ + "BB", + "FB", + "HB", + "SC", + "Undefined" + ], + "reservation_status": [ + "Canceled", + "Check-Out", + "No-Show" + ], + "reserved_room_type": [ + "A", + "B", + "C", + "D", + "E", + "F", + "G", + "H", + "L", + "P" + ] + } +} diff --git a/decision_making/daily/classification/Hotel_booking_demand_B2/info_mod.json b/decision_making/daily/classification/Hotel_booking_demand_B2/info_mod.json index 9609e813606fc8bf0b15338ae4f5d5704da5f923..b10f27e7f41b7df00c38ade9c7caa9e2e7c7dc59 100644 --- a/decision_making/daily/classification/Hotel_booking_demand_B2/info_mod.json +++ b/decision_making/daily/classification/Hotel_booking_demand_B2/info_mod.json @@ -7,7 +7,7 @@ "train_size": 0, "test_size": 0, "c_classes": 12, - "n_classes": 20, + "n_classes": 19, "cat_feature_intro": { "hotel": "Type of hotel (City Hotel or Resort Hotel).", "arrival_date_month": "Month of arrival for the booking.", @@ -21,7 +21,6 @@ "customer_type": "Type of customer (e.g., transient, group, contract)." }, "num_feature_intro": { - "is_canceled": "Indicates whether the booking was canceled (1) or not (0).", "lead_time": "Number of days between booking and arrival.", "arrival_date_year": "Year of arrival for the booking.", "arrival_date_week_number": "Week number of the arrival date.", @@ -46,10 +45,6 @@ "task_type": "classification", "target": "reservation_status", "num_feature_value": { - "is_canceled": [ - 0.0, - 1.0 - ], "lead_time": [ 0.0, 737.0 @@ -315,4 +310,4 @@ "reservation_status_date" ], "missing_from_original_info": [] -} \ No newline at end of file +} diff --git a/decision_making/daily/classification/Human_Resources_Data_Set_B2/Human_Resources_Data_Set_B2_001.json b/decision_making/daily/classification/Human_Resources_Data_Set_B2/Human_Resources_Data_Set_B2_001.json index 1d2fbfddd326aea19f0927a86e4ac9d9264cdefb..7116116b45fba3e1d294b0a1cf673b08b6cc4b5f 100644 --- a/decision_making/daily/classification/Human_Resources_Data_Set_B2/Human_Resources_Data_Set_B2_001.json +++ b/decision_making/daily/classification/Human_Resources_Data_Set_B2/Human_Resources_Data_Set_B2_001.json @@ -5,7 +5,7 @@ "perspective": "user", "dataset_name": "Human_Resources_Data_Set", "table_path": "kaggle/Human_Resources_Data_Set", - "query": "I'm reviewing these two employee files side-by-side because we need to verify the flagged termination records. The first file, for employee ID 10171, shows a woman who was single and a US Citizen, identifying as White and not Hispanic/Latino. She worked as a Production Technician I in Massachusetts, zip code 2176, under manager Amy Dunn (Manager ID 11) in the Production department, which is Department ID 5. Her Position ID was 19. She was hired back on 5/16/2011 and was recruited through LinkedIn. Her performance was rated as \"Fully Meets\" (Performance Score ID 3), she scored a 3.45 on her engagement survey, and had an employee satisfaction score of 4. Her last review was on 5/13/2014. She wasn't involved in any special projects, was never late in the last 30 days, but had 5 absences. Her salary was 45998. Now, her employment status is listed as Voluntarily Terminated, with the termination reason being medical issues. Her status ID is 5, she wasn't from a diversity job fair, and the codes show she wasn't married (Married ID 0, Marital Status ID 0), her Gender ID is 0, and EmpStatusID is 5. The second record, for ID 10287, is also a single woman who is a US Citizen, White, and not Hispanic/Latino. She holds the same position, Production Technician I (Position ID 19), also in Massachusetts but in zip code 2021, and also in the Production department (Dept ID 5), though she reports to a different manager, David Stanley (Manager ID 14). She was hired more recently, on 2/17/2014, and was also sourced from LinkedIn. Her performance, however, \"Needs Improvement\" (PerfScoreID 2). Her engagement survey score is lower at 2.44, but her self-reported satisfaction is a 5. She hasn't done any special projects either. Her last performance review was quite recent, on 2/11/2019. In the last 30 days, she was late 4 times and has a total of 18 absences. She earns a salary of 63025. Crucially, her current status is Active, with no termination reason listed (it says N/A-StillEmployed). Her EmpStatusID is 1, and similar to the first, her MarriedID is 0, MaritalStatusID is 0, GenderID is 0, and she wasn't from a diversity job fair. Given that I'm looking for the one who was actually let go, which of these two employee records should I be focusing on?", + "query": "I'm reviewing these two employee files side-by-side against our historical staffing records. The first file, for employee ID 10171, shows a woman who was single and a US Citizen, identifying as White and not Hispanic/Latino. She worked as a Production Technician I in Massachusetts, zip code 2176, under manager Amy Dunn (Manager ID 11) in the Production department, which is Department ID 5. Her Position ID was 19. She was hired back on 5/16/2011 and was recruited through LinkedIn. Her performance was rated as \"Fully Meets\" (Performance Score ID 3), she scored a 3.45 on her engagement survey, and had an employee satisfaction score of 4. Her last review was on 5/13/2014. She wasn't involved in any special projects, was never late in the last 30 days, but had 5 absences. Her salary was 45998. She wasn't from a diversity job fair; the codes show she wasn't married (Married ID 0, Marital Status ID 0), and her Gender ID is 0. The second record, for ID 10287, is also a single woman who is a US Citizen, White, and not Hispanic/Latino. She holds the same position, Production Technician I (Position ID 19), also in Massachusetts but in zip code 2021, and also in the Production department (Dept ID 5), though she reports to a different manager, David Stanley (Manager ID 14). She was hired more recently, on 2/17/2014, and was also sourced from LinkedIn. Her performance, however, \"Needs Improvement\" (PerfScoreID 2). Her engagement survey score is lower at 2.44, but her self-reported satisfaction is a 5. She hasn't done any special projects either. Her last performance review was quite recent, on 2/11/2019. In the last 30 days, she was late 4 times and has a total of 18 absences. She earns a salary of 63025. Like the first person, her MarriedID is 0, MaritalStatusID is 0, GenderID is 0, and she wasn't from a diversity job fair. Looking at those profiles and our past staffing patterns, which person seems more likely to have left the company?", "meta_info": { "domain": "daily" }, @@ -19,7 +19,6 @@ "MarriedID": "0", "MaritalStatusID": "0", "GenderID": "0", - "EmpStatusID": "5", "DeptID": "5", "PerfScoreID": "3", "FromDiversityJobFairID": "0", @@ -36,9 +35,6 @@ "HispanicLatino": "No", "RaceDesc": "White", "DateofHire": "5/16/2011", - "DateofTermination": null, - "TermReason": "medical issues", - "EmploymentStatus": "Voluntarily Terminated", "Department": "Production ", "ManagerName": "Amy Dunn", "ManagerID": "11.0", @@ -60,7 +56,6 @@ "MarriedID": "0", "MaritalStatusID": "0", "GenderID": "0", - "EmpStatusID": "1", "DeptID": "5", "PerfScoreID": "2", "FromDiversityJobFairID": "0", @@ -77,9 +72,6 @@ "HispanicLatino": "No", "RaceDesc": "White", "DateofHire": "2/17/2014", - "DateofTermination": null, - "TermReason": "N/A-StillEmployed", - "EmploymentStatus": "Active", "Department": "Production ", "ManagerName": "David Stanley", "ManagerID": "14.0", @@ -104,4 +96,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/decision_making/daily/classification/Human_Resources_Data_Set_B2/Human_Resources_Data_Set_B2_002.json b/decision_making/daily/classification/Human_Resources_Data_Set_B2/Human_Resources_Data_Set_B2_002.json index baa0c56b9e2c43b115321b62515dafb7c76f8373..4fbd65e742cee6621ce90cb69fc0fe421cd21570 100644 --- a/decision_making/daily/classification/Human_Resources_Data_Set_B2/Human_Resources_Data_Set_B2_002.json +++ b/decision_making/daily/classification/Human_Resources_Data_Set_B2/Human_Resources_Data_Set_B2_002.json @@ -5,7 +5,7 @@ "perspective": "user", "dataset_name": "Human_Resources_Data_Set", "table_path": "kaggle/Human_Resources_Data_Set", - "query": "I'm going through these employee records to clear up some confusion about who was let go. Starting with the first file, this woman has employee ID 10296. She is single, a US citizen, not Hispanic or Latino, and her race is White. She was hired on 2/17/2014 for the role of Production Technician I in Massachusetts, ZIP code 2458, and she reported to manager Elijiah Gray, who has manager ID 16.0. Her department is Production with department ID 5, and her position ID is 19. With married ID 0, marital status ID 0, and gender ID 0, she wasn't married. Her employment status ID is 4, and she was terminated for cause due to no-call, no-show. Her performance score needs improvement, with performance score ID 2, and she wasn't from a diversity job fair, indicated by ID 0. Her salary was 59124, and she had an engagement survey score of 2.3, an employee satisfaction score of 3, and no special projects. Her last performance review was on 1/15/2017. In the last 30 days, she was late 5 times and had 19 absences. She was recruited via Google Search. Moving to the second record, employee ID 10104 is also a woman who is single, a US citizen, not Hispanic or Latino, and White. She was hired on 11/10/2014 as a Production Technician II in Massachusetts, ZIP code 1040, under the same manager Elijiah Gray with manager ID 16.0. Her department ID is 5, and position ID is 20. She has married ID 0, marital status ID 0, and gender ID 0, so she isn't married. Her employment status ID is 1, and she is still employed, with termination reason N/A. Her performance fully meets expectations, with performance score ID 3, and she wasn't from a diversity job fair. Her salary is 66738, engagement survey score 4.53, satisfaction 5, and no special projects. Her last review was on 1/16/2019, with no late days and 5 absences. She was recruited from Indeed. Finally, the third file is for employee ID 10060, a woman who is separated, a US citizen, not Hispanic or Latino, and White. She was hired on 1/6/2014 as a Production Technician I in Massachusetts, ZIP code 2109, under manager Kelley Spirea with manager ID 18.0. Department ID is 5, and position ID is 19. She has married ID 0, marital status ID 3, and gender ID 0. Her employment status ID is 1, so she is active. Her performance fully meets, with score ID 3, and diversity job fair ID 0. Salary is 60436, engagement survey 5.0, satisfaction 5, and no special projects. Last review on 1/21/2019, no late days, and 9 absences. She was recruited from LinkedIn. With all this info, can you point out the employee who was terminated?", + "query": "I'm comparing these employee profiles with our historical staffing records. Starting with the first file, this woman has employee ID 10296. She is single, a US citizen, not Hispanic or Latino, and her race is White. She was hired on 2/17/2014 for the role of Production Technician I in Massachusetts, ZIP code 2458, and she reported to manager Elijiah Gray, who has manager ID 16.0. Her department is Production with department ID 5, and her position ID is 19. With married ID 0, marital status ID 0, and gender ID 0, she wasn't married. Her performance score needs improvement, with performance score ID 2, and she wasn't from a diversity job fair, indicated by ID 0. Her salary was 59124, and she had an engagement survey score of 2.3, an employee satisfaction score of 3, and no special projects. Her last performance review was on 1/15/2017. In the last 30 days, she was late 5 times and had 19 absences. She was recruited via Google Search. Moving to the second record, employee ID 10104 is also a woman who is single, a US citizen, not Hispanic or Latino, and White. She was hired on 11/10/2014 as a Production Technician II in Massachusetts, ZIP code 1040, under the same manager Elijiah Gray with manager ID 16.0. Her department ID is 5, and position ID is 20. She has married ID 0, marital status ID 0, and gender ID 0, so she isn't married. Her performance fully meets expectations, with performance score ID 3, and she wasn't from a diversity job fair. Her salary is 66738, engagement survey score 4.53, satisfaction 5, and no special projects. Her last review was on 1/16/2019, with no late days and 5 absences. She was recruited from Indeed. Finally, the third file is for employee ID 10060, a woman who is separated, a US citizen, not Hispanic or Latino, and White. She was hired on 1/6/2014 as a Production Technician I in Massachusetts, ZIP code 2109, under manager Kelley Spirea with manager ID 18.0. Department ID is 5, and position ID is 19. She has married ID 0, marital status ID 3, and gender ID 0. Her performance fully meets, with score ID 3, and diversity job fair ID 0. Salary is 60436, engagement survey 5.0, satisfaction 5, and no special projects. Last review on 1/21/2019, no late days, and 9 absences. She was recruited from LinkedIn. Given these profiles and the patterns in our past records, which person seems more likely to have left the company?", "meta_info": { "domain": "daily" }, @@ -19,7 +19,6 @@ "MarriedID": "0", "MaritalStatusID": "0", "GenderID": "0", - "EmpStatusID": "4", "DeptID": "5", "PerfScoreID": "2", "FromDiversityJobFairID": "0", @@ -36,9 +35,6 @@ "HispanicLatino": "No", "RaceDesc": "White", "DateofHire": "2/17/2014", - "DateofTermination": null, - "TermReason": "no-call, no-show", - "EmploymentStatus": "Terminated for Cause", "Department": "Production ", "ManagerName": "Elijiah Gray", "ManagerID": "16.0", @@ -60,7 +56,6 @@ "MarriedID": "0", "MaritalStatusID": "0", "GenderID": "0", - "EmpStatusID": "1", "DeptID": "5", "PerfScoreID": "3", "FromDiversityJobFairID": "0", @@ -77,9 +72,6 @@ "HispanicLatino": "No", "RaceDesc": "White", "DateofHire": "11/10/2014", - "DateofTermination": null, - "TermReason": "N/A-StillEmployed", - "EmploymentStatus": "Active", "Department": "Production ", "ManagerName": "Elijiah Gray", "ManagerID": "16.0", @@ -101,7 +93,6 @@ "MarriedID": "0", "MaritalStatusID": "3", "GenderID": "0", - "EmpStatusID": "1", "DeptID": "5", "PerfScoreID": "3", "FromDiversityJobFairID": "0", @@ -118,9 +109,6 @@ "HispanicLatino": "No", "RaceDesc": "White", "DateofHire": "1/6/2014", - "DateofTermination": null, - "TermReason": "N/A-StillEmployed", - "EmploymentStatus": "Active", "Department": "Production ", "ManagerName": "Kelley Spirea", "ManagerID": "18.0", @@ -145,4 +133,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/decision_making/daily/classification/Human_Resources_Data_Set_B2/Human_Resources_Data_Set_B2_003.json b/decision_making/daily/classification/Human_Resources_Data_Set_B2/Human_Resources_Data_Set_B2_003.json index b42a906c4fb6af9528c2c2b0f87671abac36b090..7c7bdb8792e81b68991445a98033654165f328b7 100644 --- a/decision_making/daily/classification/Human_Resources_Data_Set_B2/Human_Resources_Data_Set_B2_003.json +++ b/decision_making/daily/classification/Human_Resources_Data_Set_B2/Human_Resources_Data_Set_B2_003.json @@ -5,7 +5,7 @@ "perspective": "user", "dataset_name": "Human_Resources_Data_Set", "table_path": "kaggle/Human_Resources_Data_Set", - "query": "The first employee record shows a woman with ID 10196 who is married, as per married ID 1 and marital status ID 1. Her gender ID is 0, so she's female. She has employment status ID 5 and works in department ID 5 as a Production Technician II, position ID 20. Located in MA, ZIP 1810, her marital description is Married, and she's a US Citizen, not Hispanic or Latino, White. Hired on 7/5/2011, but terminated voluntarily due to hours, with employment status Voluntarily Terminated. In the Production department, managed by Kissy Sullivan, manager ID 20.0. Recruited from LinkedIn, performance score Fully Meets with ID 3. Engagement survey 3.02, satisfaction 3, no special projects. Last performance review on 5/15/2012. No late days in last 30, but 3 absences. Not from diversity job fair, salary 64955. Shifting to the second file, this employee has ID 10082, not married with married ID 0 and marital status ID 0. Gender ID 0 for female. Employment status ID 2, department ID 3 as Sr. DBA, position ID 27. In MA, ZIP 1886. Single, US Citizen, not Hispanic or Latino, Black or African American. Hired 6/30/2016, termination reason N/A-StillEmployed, status Active. IT/IS department, manager Simon Roup with ID 4.0. Recruited from LinkedIn, performance Fully Meets, ID 3. Engagement 5.0, satisfaction 5, 6 special projects. Last review 2/18/2019. No late days, 7 absences. Not from diversity job fair, salary 100031. On the other hand, the third record is for ID 10232, also not married with IDs 0. Gender ID 0 female. Employment status ID 1, department ID 3 as Senior BI Developer, position ID 22. MA, ZIP 1886. Single, US Citizen, not Hispanic or Latino, Asian. Hired 10/2/2016, termination reason N/A-StillEmployed, status Active. IT/IS, manager Brian Champaigne, ID 13.0. Recruited from Indeed, performance Fully Meets, ID 3. Engagement 4.1, satisfaction 5, 7 special projects. Last review 1/8/2019. No late days, 2 absences. Not from diversity job fair, salary 81584. From this, can you identify who left the company?", + "query": "The first employee record shows a woman with ID 10196 who is married, as per married ID 1 and marital status ID 1. Her gender ID is 0, so she's female. Her profile lists department ID 5 as a Production Technician II, position ID 20. Located in MA, ZIP 1810, her marital description is Married, and she's a US Citizen, not Hispanic or Latino, White. Her hire date was 7/5/2011. In the Production department, managed by Kissy Sullivan, manager ID 20.0. Recruited from LinkedIn, performance score Fully Meets with ID 3. Engagement survey 3.02, satisfaction 3, no special projects. Last performance review on 5/15/2012. No late days in last 30, but 3 absences. Not from diversity job fair, salary 64955. Shifting to the second file, this employee has ID 10082, not married with married ID 0 and marital status ID 0. Gender ID 0 for female. Department ID 3 as Sr. DBA, position ID 27. In MA, ZIP 1886. Single, US Citizen, not Hispanic or Latino, Black or African American. Hired 6/30/2016. IT/IS department, manager Simon Roup with ID 4.0. Recruited from LinkedIn, performance Fully Meets, ID 3. Engagement 5.0, satisfaction 5, 6 special projects. Last review 2/18/2019. No late days, 7 absences. Not from diversity job fair, salary 100031. On the other hand, the third record is for ID 10232, also not married with IDs 0. Gender ID 0 female. Department ID 3 as Senior BI Developer, position ID 22. MA, ZIP 1886. Single, US Citizen, not Hispanic or Latino, Asian. Hired 10/2/2016. IT/IS, manager Brian Champaigne, ID 13.0. Recruited from Indeed, performance Fully Meets, ID 3. Engagement 4.1, satisfaction 5, 7 special projects. Last review 1/8/2019. No late days, 2 absences. Not from diversity job fair, salary 81584. From these profiles and our past staffing records, who seems more likely to have left the company?", "meta_info": { "domain": "daily" }, @@ -19,7 +19,6 @@ "MarriedID": "1", "MaritalStatusID": "1", "GenderID": "0", - "EmpStatusID": "5", "DeptID": "5", "PerfScoreID": "3", "FromDiversityJobFairID": "0", @@ -36,9 +35,6 @@ "HispanicLatino": "No", "RaceDesc": "White", "DateofHire": "7/5/2011", - "DateofTermination": null, - "TermReason": "hours", - "EmploymentStatus": "Voluntarily Terminated", "Department": "Production ", "ManagerName": "Kissy Sullivan", "ManagerID": "20.0", @@ -60,7 +56,6 @@ "MarriedID": "0", "MaritalStatusID": "0", "GenderID": "0", - "EmpStatusID": "2", "DeptID": "3", "PerfScoreID": "3", "FromDiversityJobFairID": "0", @@ -77,9 +72,6 @@ "HispanicLatino": "No", "RaceDesc": "Black or African American", "DateofHire": "6/30/2016", - "DateofTermination": null, - "TermReason": "N/A-StillEmployed", - "EmploymentStatus": "Active", "Department": "IT/IS", "ManagerName": "Simon Roup", "ManagerID": "4.0", @@ -101,7 +93,6 @@ "MarriedID": "0", "MaritalStatusID": "0", "GenderID": "0", - "EmpStatusID": "1", "DeptID": "3", "PerfScoreID": "3", "FromDiversityJobFairID": "0", @@ -118,9 +109,6 @@ "HispanicLatino": "No", "RaceDesc": "Asian", "DateofHire": "10/2/2016", - "DateofTermination": null, - "TermReason": "N/A-StillEmployed", - "EmploymentStatus": "Active", "Department": "IT/IS", "ManagerName": "Brian Champaigne", "ManagerID": "13.0", @@ -145,4 +133,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/decision_making/daily/classification/Human_Resources_Data_Set_B2/Human_Resources_Data_Set_B2_004.json b/decision_making/daily/classification/Human_Resources_Data_Set_B2/Human_Resources_Data_Set_B2_004.json index 9b27ed47d34d20cf92ff0de97c12fc03b39f1f90..cdb61b492360a297841660497da2dfe3470e1a9e 100644 --- a/decision_making/daily/classification/Human_Resources_Data_Set_B2/Human_Resources_Data_Set_B2_004.json +++ b/decision_making/daily/classification/Human_Resources_Data_Set_B2/Human_Resources_Data_Set_B2_004.json @@ -5,7 +5,7 @@ "perspective": "data_holder", "dataset_name": "Human_Resources_Data_Set", "table_path": "kaggle/Human_Resources_Data_Set", - "query": "This morning, the senior team is reviewing personnel files because we need to identify stable employees for a new long-term mentorship program—finding someone unlikely to leave is absolutely critical, and I'm stuck. I have three profiles in front of me. For the first, employee ID 10095, their marital IDs are zero and zero, gender ID is zero, employment status ID is five, and department ID is five. Their performance score ID is three, and their from diversity job fair ID is zero, with a salary of 63878 as a Production Technician II in Massachusetts, zip code 1851. They're listed as Single, a US Citizen, not Hispanic or Latino, and White. Hired on 10/26/2009, their termination reason was maternity leave - did not return, so their employment status is Voluntarily Terminated in the Production department under manager Michael Albert, recruited from CareerBuilder with a Fully Meets performance score. Their engagement survey was four point six eight, special projects count is zero, and their last performance review was on 4/2/2015. The second file is for employee ID 10235, with marital status ID one, gender ID one, employment status ID one, and department ID five. Their from diversity job fair ID is zero, salary 54285, and position ID nineteen for Production Technician I, also in Massachusetts, zip 2045, male, a US Citizen, and not Hispanic or Latino. Hired on 3/31/2014, termination reason is N/A-StillEmployed, so they're Active, managed by Kelley Spirea with manager ID eighteen point zero, recruited via Employee Referral, performance score Fully Meets, last review 1/11/2019, days late in last thirty is zero, and absences are three. The third is employee ID 10196, married ID one, marital status ID one, employment status ID five, department ID five, performance score ID three, from diversity job fair ID zero, salary 64955, position ID twenty for Production Technician II, zip 1810, female, marital description Married, not Hispanic or Latino, race White, hired 7/5/2011, employment status Voluntarily Terminated in Production under Kissy Sullivan, manager ID twenty point zero, performance score Fully Meets, engagement survey three point zero two, satisfaction three, and special projects count zero. I've uploaded our past records for you to compare all these specific IDs and details. Given the patterns in the file I sent, who should we pick for the stable, long-term role?", + "query": "This morning, the senior team is reviewing personnel files because we need to identify stable employees for a new long-term mentorship program—finding someone unlikely to leave is absolutely critical, and I'm stuck. I have three profiles in front of me. For the first, employee ID 10095, their marital IDs are zero and zero, gender ID is zero, and department ID is five. Their performance score ID is three, and their from diversity job fair ID is zero, with a salary of 63878 as a Production Technician II in Massachusetts, zip code 1851. They're listed as Single, a US Citizen, not Hispanic or Latino, and White. Their hire date was 10/26/2009; the profile lists the Production department, manager Michael Albert, CareerBuilder recruitment, and a Fully Meets performance score. Their engagement survey was four point six eight, special projects count is zero, and their last performance review was on 4/2/2015. The second file is for employee ID 10235, with marital status ID one, gender ID one and department ID five. Their from diversity job fair ID is zero, salary 54285, and position ID nineteen for Production Technician I, also in Massachusetts, zip 2045, male, a US Citizen, and not Hispanic or Latino. Hired on 3/31/2014, their profile lists manager Kelley Spirea with manager ID eighteen point zero, recruited via Employee Referral, performance score Fully Meets, last review 1/11/2019, days late in last thirty is zero, and absences are three. The third is employee ID 10196, married ID one, marital status ID one, department ID five, performance score ID three, from diversity job fair ID zero, salary 64955, position ID twenty for Production Technician II, zip 1810, female, marital description Married, not Hispanic or Latino, race White, hired 7/5/2011, with a Production profile under Kissy Sullivan, manager ID twenty point zero, performance score Fully Meets, engagement survey three point zero two, satisfaction three, and special projects count zero. I've uploaded our past records for you to compare all these specific IDs and details. Given the patterns in the file I sent, who seems most likely to still be with the company and fit the stable, long-term role?", "meta_info": { "domain": "daily" }, @@ -19,7 +19,6 @@ "MarriedID": "0", "MaritalStatusID": "0", "GenderID": "0", - "EmpStatusID": "5", "DeptID": "5", "PerfScoreID": "3", "FromDiversityJobFairID": "0", @@ -36,9 +35,6 @@ "HispanicLatino": "No", "RaceDesc": "White", "DateofHire": "10/26/2009", - "DateofTermination": null, - "TermReason": "maternity leave - did not return", - "EmploymentStatus": "Voluntarily Terminated", "Department": "Production ", "ManagerName": "Michael Albert", "ManagerID": null, @@ -60,7 +56,6 @@ "MarriedID": null, "MaritalStatusID": "1", "GenderID": "1", - "EmpStatusID": "1", "DeptID": "5", "PerfScoreID": null, "FromDiversityJobFairID": "0", @@ -77,9 +72,6 @@ "HispanicLatino": "No", "RaceDesc": null, "DateofHire": "3/31/2014", - "DateofTermination": null, - "TermReason": "N/A-StillEmployed", - "EmploymentStatus": "Active", "Department": null, "ManagerName": "Kelley Spirea", "ManagerID": "18.0", @@ -101,7 +93,6 @@ "MarriedID": "1", "MaritalStatusID": "1", "GenderID": null, - "EmpStatusID": "5", "DeptID": "5", "PerfScoreID": "3", "FromDiversityJobFairID": "0", @@ -118,9 +109,6 @@ "HispanicLatino": "No", "RaceDesc": "White", "DateofHire": "7/5/2011", - "DateofTermination": null, - "TermReason": null, - "EmploymentStatus": "Voluntarily Terminated", "Department": "Production ", "ManagerName": "Kissy Sullivan", "ManagerID": "20.0", @@ -145,4 +133,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/decision_making/daily/classification/Human_Resources_Data_Set_B2/Human_Resources_Data_Set_B2_005.json b/decision_making/daily/classification/Human_Resources_Data_Set_B2/Human_Resources_Data_Set_B2_005.json index e97ff1f5600ba39790840af7b665494ede967fd1..67b8b58cc344fdfc826d93487827f616c1b1fb2a 100644 --- a/decision_making/daily/classification/Human_Resources_Data_Set_B2/Human_Resources_Data_Set_B2_005.json +++ b/decision_making/daily/classification/Human_Resources_Data_Set_B2/Human_Resources_Data_Set_B2_005.json @@ -5,7 +5,7 @@ "perspective": "data_holder", "dataset_name": "Human_Resources_Data_Set", "table_path": "kaggle/Human_Resources_Data_Set", - "query": "Reviewing these three employee snapshots against the company’s past termination data is crucial for a report, and every single ID code needs to be accounted for. Starting with the first: a status showing MarriedID zero, MaritalStatusID zero, GenderID zero, working in DeptID three as a Database Administrator in MA, zip twenty-four ninety-three, Sex F, a US Citizen, not HispanicLatino, RaceDesc White, with a TermReason of N/A-StillEmployed and EmploymentStatus Active in the IT/IS Department under ManagerID four point zero, recruited via Indeed, PerformanceScore Fully Meets with a PerfScoreID of three, EngagementSurvey four point four eight, EmpSatisfaction five, involved in six SpecialProjectsCount, reviewed on one slash three slash twenty-nineteen, zero DaysLateLast30, and four Absences. The second snapshot is for EmpID ten thousand two hundred sixty-two: MarriedID zero, MaritalStatusID two, EmpStatusID five, DeptID five, PerfScoreID three, FromDiversityJobFairID zero, Salary forty-six thousand four hundred thirty, PositionID nineteen for Production Technician I in MA zip twenty-four seventy-four, Sex F, MaritalDesc Divorced, US Citizen, not HispanicLatino, hired nine slash twenty-four slash twenty-twelve, TermReason unhappy, EmploymentStatus Voluntarily Terminated, managed by Kissy Sullivan, EngagementSurvey four point five, EmpSatisfaction five, zero SpecialProjectsCount, last reviewed four slash two slash twenty-thirteen, and zero days late. The third is EmpID ten thousand two hundred sixty-seven: MarriedID zero, MaritalStatusID zero, GenderID zero, EmpStatusID five, DeptID five, PerfScoreID three, Salary fifty-seven thousand eight hundred fifteen, PositionID twenty, located in MA zip twenty-two ten, Sex F, MaritalDesc Single, a US Citizen, RaceDesc White, hired one slash ten slash twenty-eleven, left due to a career change with EmploymentStatus Voluntarily Terminated, manager Elijiah Gray with ManagerID sixteen point zero, PerformanceScore Fully Meets, EngagementSurvey four point eight, EmpSatisfaction five, zero SpecialProjectsCount, and five Absences. Looking at the trends in the historical file I sent, can you help me pick out the one who is definitely still employed?", + "query": "I'm comparing these three employee profiles with our company's past staffing records. Starting with the first: a profile showing MarriedID zero, MaritalStatusID zero, GenderID zero, working in DeptID three as a Database Administrator in MA, zip twenty-four ninety-three, Sex F, a US Citizen, not HispanicLatino, RaceDesc White, in the IT/IS Department under ManagerID four point zero, recruited via Indeed, PerformanceScore Fully Meets with a PerfScoreID of three, EngagementSurvey four point four eight, EmpSatisfaction five, involved in six SpecialProjectsCount, reviewed on one slash three slash twenty-nineteen, zero DaysLateLast30, and four Absences. The second snapshot is for EmpID ten thousand two hundred sixty-two: MarriedID zero, MaritalStatusID two, DeptID five, PerfScoreID three, FromDiversityJobFairID zero, Salary forty-six thousand four hundred thirty, PositionID nineteen for Production Technician I in MA zip twenty-four seventy-four, Sex F, MaritalDesc Divorced, US Citizen, not HispanicLatino, hired nine slash twenty-four slash twenty-twelve, managed by Kissy Sullivan, EngagementSurvey four point five, EmpSatisfaction five, zero SpecialProjectsCount, last reviewed four slash two slash twenty-thirteen, and zero days late. The third is EmpID ten thousand two hundred sixty-seven: MarriedID zero, MaritalStatusID zero, GenderID zero, DeptID five, PerfScoreID three, Salary fifty-seven thousand eight hundred fifteen, PositionID twenty, located in MA zip twenty-two ten, Sex F, MaritalDesc Single, a US Citizen, RaceDesc White, hired one slash ten slash twenty-eleven, manager Elijiah Gray with ManagerID sixteen point zero, PerformanceScore Fully Meets, EngagementSurvey four point eight, EmpSatisfaction five, zero SpecialProjectsCount, and five Absences. Looking at the trends in the historical file I sent, can you help me pick out the person most likely to still be with the company?", "meta_info": { "domain": "daily" }, @@ -19,7 +19,6 @@ "MarriedID": "0", "MaritalStatusID": "0", "GenderID": "0", - "EmpStatusID": null, "DeptID": "3", "PerfScoreID": "3", "FromDiversityJobFairID": null, @@ -36,9 +35,6 @@ "HispanicLatino": "No", "RaceDesc": "White", "DateofHire": null, - "DateofTermination": null, - "TermReason": "N/A-StillEmployed", - "EmploymentStatus": "Active", "Department": "IT/IS", "ManagerName": null, "ManagerID": "4.0", @@ -60,7 +56,6 @@ "MarriedID": "0", "MaritalStatusID": "2", "GenderID": null, - "EmpStatusID": "5", "DeptID": "5", "PerfScoreID": "3", "FromDiversityJobFairID": "0", @@ -77,9 +72,6 @@ "HispanicLatino": "No", "RaceDesc": null, "DateofHire": "9/24/2012", - "DateofTermination": null, - "TermReason": "unhappy", - "EmploymentStatus": "Voluntarily Terminated", "Department": null, "ManagerName": "Kissy Sullivan", "ManagerID": null, @@ -101,7 +93,6 @@ "MarriedID": "0", "MaritalStatusID": "0", "GenderID": "0", - "EmpStatusID": "5", "DeptID": "5", "PerfScoreID": "3", "FromDiversityJobFairID": null, @@ -118,9 +109,6 @@ "HispanicLatino": null, "RaceDesc": "White", "DateofHire": "1/10/2011", - "DateofTermination": null, - "TermReason": "career change", - "EmploymentStatus": "Voluntarily Terminated", "Department": null, "ManagerName": "Elijiah Gray", "ManagerID": "16.0", @@ -145,4 +133,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/decision_making/daily/classification/Human_Resources_Data_Set_B2/Human_Resources_Data_Set_B2_006.json b/decision_making/daily/classification/Human_Resources_Data_Set_B2/Human_Resources_Data_Set_B2_006.json index f225812fce4dbabf4222980612c3da40d2ad529f..7e3a6d1748ca2041abc96651570c6c1273a4e805 100644 --- a/decision_making/daily/classification/Human_Resources_Data_Set_B2/Human_Resources_Data_Set_B2_006.json +++ b/decision_making/daily/classification/Human_Resources_Data_Set_B2/Human_Resources_Data_Set_B2_006.json @@ -5,7 +5,7 @@ "perspective": "data_holder", "dataset_name": "Human_Resources_Data_Set", "table_path": "kaggle/Human_Resources_Data_Set", - "query": "I'm reviewing some employee profiles from our historical archives and need a second opinion. We've got three individuals here. First up is employee 10155. She's a single female, a US Citizen who is not Hispanic or Latino, and her race is Black or African American. Hired on 1/9/2012, she's an active Software Engineer in Massachusetts, ZIP 2176, under manager Alex Sweetwater (ID 10), and was recruited from CareerBuilder. Her performance is Fully Meets, she scored 3.79 on the engagement survey, and has an employee satisfaction of 5. She's involved in 5 special projects, had her last review on 1/25/2019, has 0 days late in the last 30 days, and 8 absences. Her salary is 101199. The internal IDs are: MarriedID 0, MaritalStatusID 0, GenderID 0, EmpStatusID 1, DeptID 4, PerfScoreID 3, FromDiversityJobFairID 0, PositionID 24. Next is employee 10100. She is separated, female, a US Citizen, not Hispanic or Latino, and also Black or African American. Hired 4/4/2011, she voluntarily terminated for \"more money.\" She was a Production Technician II in Massachusetts (ZIP 2343) in the Production department, managed by Kelley Spirea (ID 18), and found via Google Search. Her performance was Fully Meets, with an engagement score of 4.62 and satisfaction of 5. She worked on 0 special projects, her last review was 5/6/2015, she had 0 days late and 1 absence. Salary was 58275. Her IDs: MarriedID 0, MaritalStatusID 3, GenderID 0, EmpStatusID 5, DeptID 5, PerfScoreID 3, FromDiversityJobFairID 0, PositionID 20. The third is employee 10065. He is a single male, US Citizen, not Hispanic or Latino, and White. Also hired 4/4/2011, he voluntarily left for \"Another position.\" He was a Production Technician I in Massachusetts (ZIP 2155) in Production, managed by Kissy Sullivan (ID 20), and also recruited via Google Search. Performance was Fully Meets, engagement 5.0, satisfaction 5. Special projects: 0, last review 7/2/2018, 0 days late, 4 absences. Salary 53180. IDs: MarriedID 0, MaritalStatusID 0, GenderID 1, EmpStatusID 5, DeptID 5, PerfScoreID 3, FromDiversityJobFairID 0, PositionID 19. Looking at the trends in the file I sent, can you tell me which one best fits the profile of a current, retained employee?", + "query": "I'm reviewing some employee profiles from our historical archives and need a second opinion. We've got three individuals here. First up is employee 10155. She's a single female, a US Citizen who is not Hispanic or Latino, and her race is Black or African American. Her hire date was 1/9/2012, and her profile lists Software Engineer in Massachusetts, ZIP 2176, under manager Alex Sweetwater (ID 10), and was recruited from CareerBuilder. Her performance is Fully Meets, she scored 3.79 on the engagement survey, and has an employee satisfaction of 5. She's involved in 5 special projects, had her last review on 1/25/2019, has 0 days late in the last 30 days, and 8 absences. Her salary is 101199. The internal IDs are: MarriedID 0, MaritalStatusID 0, GenderID 0, DeptID 4, PerfScoreID 3, FromDiversityJobFairID 0, PositionID 24. Next is employee 10100. She is separated, female, a US Citizen, not Hispanic or Latino, and also Black or African American. Her hire date was 4/4/2011, and her profile lists Production Technician II in Massachusetts (ZIP 2343) in the Production department, managed by Kelley Spirea (ID 18), and found via Google Search. Her performance was Fully Meets, with an engagement score of 4.62 and satisfaction of 5. She worked on 0 special projects, her last review was 5/6/2015, she had 0 days late and 1 absence. Salary was 58275. Her IDs: MarriedID 0, MaritalStatusID 3, GenderID 0, DeptID 5, PerfScoreID 3, FromDiversityJobFairID 0, PositionID 20. The third is employee 10065. He is a single male, US Citizen, not Hispanic or Latino, and White. His hire date was also 4/4/2011, and his profile lists Production Technician I in Massachusetts (ZIP 2155) in Production, managed by Kissy Sullivan (ID 20), and also recruited via Google Search. Performance was Fully Meets, engagement 5.0, satisfaction 5. Special projects: 0, last review 7/2/2018, 0 days late, 4 absences. Salary 53180. IDs: MarriedID 0, MaritalStatusID 0, GenderID 1, DeptID 5, PerfScoreID 3, FromDiversityJobFairID 0, PositionID 19. Looking at the trends in the file I sent, can you tell me which person seems most likely to still be with the company?", "meta_info": { "domain": "daily" }, @@ -19,7 +19,6 @@ "MarriedID": "0", "MaritalStatusID": "0", "GenderID": "0", - "EmpStatusID": "1", "DeptID": "4", "PerfScoreID": "3", "FromDiversityJobFairID": "0", @@ -36,9 +35,6 @@ "HispanicLatino": "No", "RaceDesc": "Black or African American", "DateofHire": "1/9/2012", - "DateofTermination": null, - "TermReason": "N/A-StillEmployed", - "EmploymentStatus": "Active", "Department": "Software Engineering", "ManagerName": "Alex Sweetwater", "ManagerID": "10.0", @@ -60,7 +56,6 @@ "MarriedID": "0", "MaritalStatusID": "3", "GenderID": "0", - "EmpStatusID": "5", "DeptID": "5", "PerfScoreID": "3", "FromDiversityJobFairID": "0", @@ -77,9 +72,6 @@ "HispanicLatino": "No", "RaceDesc": "Black or African American", "DateofHire": "4/4/2011", - "DateofTermination": null, - "TermReason": "more money", - "EmploymentStatus": "Voluntarily Terminated", "Department": "Production ", "ManagerName": "Kelley Spirea", "ManagerID": "18.0", @@ -101,7 +93,6 @@ "MarriedID": "0", "MaritalStatusID": "0", "GenderID": "1", - "EmpStatusID": "5", "DeptID": "5", "PerfScoreID": "3", "FromDiversityJobFairID": "0", @@ -118,9 +109,6 @@ "HispanicLatino": "No", "RaceDesc": "White", "DateofHire": "4/4/2011", - "DateofTermination": null, - "TermReason": "Another position", - "EmploymentStatus": "Voluntarily Terminated", "Department": "Production ", "ManagerName": "Kissy Sullivan", "ManagerID": "20.0", @@ -145,4 +133,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/decision_making/daily/classification/Human_Resources_Data_Set_B2/current.csv b/decision_making/daily/classification/Human_Resources_Data_Set_B2/current.csv index a425bd4b3381fa7b96a0b4e2813664b6673e9fcf..47216971b0e1f6a5143d751a70dcba823d2e0ea3 100644 --- a/decision_making/daily/classification/Human_Resources_Data_Set_B2/current.csv +++ b/decision_making/daily/classification/Human_Resources_Data_Set_B2/current.csv @@ -1,18 +1,18 @@ -Employee_Name,EmpID,MarriedID,MaritalStatusID,GenderID,EmpStatusID,DeptID,PerfScoreID,FromDiversityJobFairID,Salary,Termd,PositionID,Position,State,Zip,DOB,Sex,MaritalDesc,CitizenDesc,HispanicLatino,RaceDesc,DateofHire,DateofTermination,TermReason,EmploymentStatus,Department,ManagerName,ManagerID,RecruitmentSource,PerformanceScore,EngagementSurvey,EmpSatisfaction,SpecialProjectsCount,LastPerformanceReview_Date,DaysLateLast30,Absences -"Perry, Shakira",10171,0,0,0,5,5,3,0,45998,1,19,Production Technician I,MA,2176,07/20/86,F,Single,US Citizen,No,White,5/16/2011,10/25/2015,medical issues,Voluntarily Terminated,Production ,Amy Dunn,11.0,LinkedIn,Fully Meets,3.45,4,0,5/13/2014,0,5 -"Trang, Mei",10287,0,0,0,1,5,2,0,63025,0,19,Production Technician I,MA,2021,05/16/83,F,Single,US Citizen,No,White,2/17/2014,,N/A-StillEmployed,Active,Production ,David Stanley,14.0,LinkedIn,Needs Improvement,2.44,5,0,2/11/2019,4,18 -"Evensen, April",10296,0,0,0,4,5,2,0,59124,1,19,Production Technician I,MA,2458,05/06/89,F,Single,US Citizen,No,White,2/17/2014,2/25/2018,"no-call, no-show",Terminated for Cause,Production ,Elijiah Gray,16.0,Google Search,Needs Improvement,2.3,3,0,1/15/2017,5,19 -"Nowlan, Kristie",10104,0,0,0,1,5,3,0,66738,0,20,Production Technician II,MA,1040,11/23/85,F,Single,US Citizen,No,White,11/10/2014,,N/A-StillEmployed,Active,Production ,Elijiah Gray,16.0,Indeed,Fully Meets,4.53,5,0,1/16/2019,0,5 -"Jhaveri, Sneha ",10060,0,3,0,1,5,3,0,60436,0,19,Production Technician I,MA,2109,04/13/64,F,Separated,US Citizen,No,White,1/6/2014,,N/A-StillEmployed,Active,Production ,Kelley Spirea,18.0,LinkedIn,Fully Meets,5.0,5,0,1/21/2019,0,9 -"Akinkuolie, Sarah",10196,1,1,0,5,5,3,0,64955,1,20,Production Technician II,MA,1810,09/19/88,F,Married,US Citizen,No,White,7/5/2011,9/24/2012,hours,Voluntarily Terminated,Production ,Kissy Sullivan,20.0,LinkedIn,Fully Meets,3.02,3,0,5/15/2012,0,3 -"Carr, Claudia N",10082,0,0,0,2,3,3,0,100031,0,27,Sr. DBA,MA,1886,06/06/86,F,Single,US Citizen,No,Black or African American,6/30/2016,,N/A-StillEmployed,Active,IT/IS,Simon Roup,4.0,LinkedIn,Fully Meets,5.0,5,6,2/18/2019,0,7 -"Le, Binh",10232,0,0,0,1,3,3,0,81584,0,22,Senior BI Developer,MA,1886,06/14/87,F,Single,US Citizen,No,Asian,10/2/2016,,N/A-StillEmployed,Active,IT/IS,Brian Champaigne,13.0,Indeed,Fully Meets,4.1,5,7,1/8/2019,0,2 -"Sloan, Constance",10095,0,0,0,5,5,3,0,63878,1,20,Production Technician II,MA,1851,11/25/87,F,Single,US Citizen,No,White,10/26/2009,4/8/2015,maternity leave - did not return,Voluntarily Terminated,Production ,Michael Albert,22.0,CareerBuilder,Fully Meets,4.68,4,0,4/2/2015,0,20 -"England, Rex",10235,1,1,1,1,5,3,0,54285,0,19,Production Technician I,MA,2045,08/25/78,M ,Married,US Citizen,No,White,3/31/2014,,N/A-StillEmployed,Active,Production ,Kelley Spirea,18.0,Employee Referral,Fully Meets,4.2,3,0,1/11/2019,0,3 -"Akinkuolie, Sarah",10196,1,1,0,5,5,3,0,64955,1,20,Production Technician II,MA,1810,09/19/88,F,Married,US Citizen,No,White,7/5/2011,9/24/2012,hours,Voluntarily Terminated,Production ,Kissy Sullivan,20.0,LinkedIn,Fully Meets,3.02,3,0,5/15/2012,0,3 -"Horton, Jayne",10112,0,0,0,1,3,3,0,97999,0,8,Database Administrator,MA,2493,02/21/84,F,Single,US Citizen,No,White,3/30/2015,,N/A-StillEmployed,Active,IT/IS,Simon Roup,4.0,Indeed,Fully Meets,4.48,5,6,1/3/2019,0,4 -"Purinton, Janine",10262,0,2,0,5,5,3,0,46430,1,19,Production Technician I,MA,2474,09/22/70,F,Divorced,US Citizen,No,White,9/24/2012,6/18/2013,unhappy,Voluntarily Terminated,Production ,Kissy Sullivan,20.0,Indeed,Fully Meets,4.5,5,0,4/2/2013,0,16 -"Bondwell, Betsy",10267,0,0,0,5,5,3,0,57815,1,20,Production Technician II,MA,2210,01/16/67,F,Single,US Citizen,No,White,1/10/2011,4/4/2014,career change,Voluntarily Terminated,Production ,Elijiah Gray,16.0,Google Search,Fully Meets,4.8,5,0,3/4/2014,0,5 -"Del Bosque, Keyla",10155,0,0,0,1,4,3,0,101199,0,24,Software Engineer,MA,2176,07/05/79,F,Single,US Citizen,No,Black or African American,1/9/2012,,N/A-StillEmployed,Active,Software Engineering,Alex Sweetwater,10.0,CareerBuilder,Fully Meets,3.79,5,5,1/25/2019,0,8 -"Demita, Carla",10100,0,3,0,5,5,3,0,58275,1,20,Production Technician II,MA,2343,02/25/51,F,Separated,US Citizen,No,Black or African American,4/4/2011,11/4/2015,more money,Voluntarily Terminated,Production ,Kelley Spirea,18.0,Google Search,Fully Meets,4.62,5,0,5/6/2015,0,1 -"Rossetti, Bruno",10065,0,0,1,5,5,3,0,53180,1,19,Production Technician I,MA,2155,03/18/87,M ,Single,US Citizen,No,White,4/4/2011,8/13/2018,Another position,Voluntarily Terminated,Production ,Kissy Sullivan,20.0,Google Search,Fully Meets,5.0,5,0,7/2/2018,0,4 +Employee_Name,EmpID,MarriedID,MaritalStatusID,GenderID,DeptID,PerfScoreID,FromDiversityJobFairID,Salary,Termd,PositionID,Position,State,Zip,DOB,Sex,MaritalDesc,CitizenDesc,HispanicLatino,RaceDesc,DateofHire,Department,ManagerName,ManagerID,RecruitmentSource,PerformanceScore,EngagementSurvey,EmpSatisfaction,SpecialProjectsCount,LastPerformanceReview_Date,DaysLateLast30,Absences +"Perry, Shakira",10171,0,0,0,5,3,0,45998,1,19,Production Technician I,MA,2176,07/20/86,F,Single,US Citizen,No,White,5/16/2011,Production ,Amy Dunn,11.0,LinkedIn,Fully Meets,3.45,4,0,5/13/2014,0,5 +"Trang, Mei",10287,0,0,0,5,2,0,63025,0,19,Production Technician I,MA,2021,05/16/83,F,Single,US Citizen,No,White,2/17/2014,Production ,David Stanley,14.0,LinkedIn,Needs Improvement,2.44,5,0,2/11/2019,4,18 +"Evensen, April",10296,0,0,0,5,2,0,59124,1,19,Production Technician I,MA,2458,05/06/89,F,Single,US Citizen,No,White,2/17/2014,Production ,Elijiah Gray,16.0,Google Search,Needs Improvement,2.3,3,0,1/15/2017,5,19 +"Nowlan, Kristie",10104,0,0,0,5,3,0,66738,0,20,Production Technician II,MA,1040,11/23/85,F,Single,US Citizen,No,White,11/10/2014,Production ,Elijiah Gray,16.0,Indeed,Fully Meets,4.53,5,0,1/16/2019,0,5 +"Jhaveri, Sneha ",10060,0,3,0,5,3,0,60436,0,19,Production Technician I,MA,2109,04/13/64,F,Separated,US Citizen,No,White,1/6/2014,Production ,Kelley Spirea,18.0,LinkedIn,Fully Meets,5.0,5,0,1/21/2019,0,9 +"Akinkuolie, Sarah",10196,1,1,0,5,3,0,64955,1,20,Production Technician II,MA,1810,09/19/88,F,Married,US Citizen,No,White,7/5/2011,Production ,Kissy Sullivan,20.0,LinkedIn,Fully Meets,3.02,3,0,5/15/2012,0,3 +"Carr, Claudia N",10082,0,0,0,3,3,0,100031,0,27,Sr. DBA,MA,1886,06/06/86,F,Single,US Citizen,No,Black or African American,6/30/2016,IT/IS,Simon Roup,4.0,LinkedIn,Fully Meets,5.0,5,6,2/18/2019,0,7 +"Le, Binh",10232,0,0,0,3,3,0,81584,0,22,Senior BI Developer,MA,1886,06/14/87,F,Single,US Citizen,No,Asian,10/2/2016,IT/IS,Brian Champaigne,13.0,Indeed,Fully Meets,4.1,5,7,1/8/2019,0,2 +"Sloan, Constance",10095,0,0,0,5,3,0,63878,1,20,Production Technician II,MA,1851,11/25/87,F,Single,US Citizen,No,White,10/26/2009,Production ,Michael Albert,22.0,CareerBuilder,Fully Meets,4.68,4,0,4/2/2015,0,20 +"England, Rex",10235,1,1,1,5,3,0,54285,0,19,Production Technician I,MA,2045,08/25/78,M ,Married,US Citizen,No,White,3/31/2014,Production ,Kelley Spirea,18.0,Employee Referral,Fully Meets,4.2,3,0,1/11/2019,0,3 +"Akinkuolie, Sarah",10196,1,1,0,5,3,0,64955,1,20,Production Technician II,MA,1810,09/19/88,F,Married,US Citizen,No,White,7/5/2011,Production ,Kissy Sullivan,20.0,LinkedIn,Fully Meets,3.02,3,0,5/15/2012,0,3 +"Horton, Jayne",10112,0,0,0,3,3,0,97999,0,8,Database Administrator,MA,2493,02/21/84,F,Single,US Citizen,No,White,3/30/2015,IT/IS,Simon Roup,4.0,Indeed,Fully Meets,4.48,5,6,1/3/2019,0,4 +"Purinton, Janine",10262,0,2,0,5,3,0,46430,1,19,Production Technician I,MA,2474,09/22/70,F,Divorced,US Citizen,No,White,9/24/2012,Production ,Kissy Sullivan,20.0,Indeed,Fully Meets,4.5,5,0,4/2/2013,0,16 +"Bondwell, Betsy",10267,0,0,0,5,3,0,57815,1,20,Production Technician II,MA,2210,01/16/67,F,Single,US Citizen,No,White,1/10/2011,Production ,Elijiah Gray,16.0,Google Search,Fully Meets,4.8,5,0,3/4/2014,0,5 +"Del Bosque, Keyla",10155,0,0,0,4,3,0,101199,0,24,Software Engineer,MA,2176,07/05/79,F,Single,US Citizen,No,Black or African American,1/9/2012,Software Engineering,Alex Sweetwater,10.0,CareerBuilder,Fully Meets,3.79,5,5,1/25/2019,0,8 +"Demita, Carla",10100,0,3,0,5,3,0,58275,1,20,Production Technician II,MA,2343,02/25/51,F,Separated,US Citizen,No,Black or African American,4/4/2011,Production ,Kelley Spirea,18.0,Google Search,Fully Meets,4.62,5,0,5/6/2015,0,1 +"Rossetti, Bruno",10065,0,0,1,5,3,0,53180,1,19,Production Technician I,MA,2155,03/18/87,M ,Single,US Citizen,No,White,4/4/2011,Production ,Kissy Sullivan,20.0,Google Search,Fully Meets,5.0,5,0,7/2/2018,0,4 diff --git a/decision_making/daily/classification/Human_Resources_Data_Set_B2/history.csv b/decision_making/daily/classification/Human_Resources_Data_Set_B2/history.csv index 30b2bd65b72194538f746e7776b727dcec7b14f1..a543597886fff55580338ab59ea2673dfe6de0b7 100644 --- a/decision_making/daily/classification/Human_Resources_Data_Set_B2/history.csv +++ b/decision_making/daily/classification/Human_Resources_Data_Set_B2/history.csv @@ -1,296 +1,296 @@ -Employee_Name,EmpID,MarriedID,MaritalStatusID,GenderID,EmpStatusID,DeptID,PerfScoreID,FromDiversityJobFairID,Salary,Termd,PositionID,Position,State,Zip,DOB,Sex,MaritalDesc,CitizenDesc,HispanicLatino,RaceDesc,DateofHire,DateofTermination,TermReason,EmploymentStatus,Department,ManagerName,ManagerID,RecruitmentSource,PerformanceScore,EngagementSurvey,EmpSatisfaction,SpecialProjectsCount,LastPerformanceReview_Date,DaysLateLast30,Absences -"Smith, Sade",10157,0,0,0,1,5,3,0,58939,0,19,Production Technician I,MA,2130,02/02/65,F,Single,US Citizen,No,White,11/11/2013,,N/A-StillEmployed,Active,Production ,Ketsia Liebig,19.0,Employee Referral,Fully Meets,3.73,3,0,1/24/2019,0,16 -"Foster-Baker, Amy",10080,1,1,0,1,1,3,0,99351,0,26,Sr. Accountant,MA,2050,04/16/79,F,Married,US Citizen,no,White,1/5/2009,,N/A-StillEmployed,Active,Admin Offices,Board of Directors,9.0,Other,Fully Meets,5.0,3,2,2/8/2019,0,3 -"Langford, Lindsey",10106,0,2,0,5,5,3,0,66074,1,20,Production Technician II,MA,2090,07/25/79,F,Divorced,US Citizen,No,Asian,1/7/2013,3/31/2014,Another position,Voluntarily Terminated,Production ,David Stanley,14.0,Indeed,Fully Meets,4.52,3,0,2/20/2014,0,20 -"Newman, Richard ",10063,1,1,1,3,5,3,0,48495,0,19,Production Technician I,MA,2136,04/08/77,M ,Married,US Citizen,No,White,5/12/2014,,N/A-StillEmployed,Active,Production ,Ketsia Liebig,19.0,LinkedIn,Fully Meets,5.0,5,0,2/18/2019,0,11 -"Beak, Kimberly ",10023,1,1,0,2,5,4,0,70131,0,20,Production Technician II,MA,2145,04/17/66,F,Married,US Citizen,No,White,7/21/2016,,N/A-StillEmployed,Active,Production ,Kelley Spirea,18.0,Employee Referral,Exceeds,4.4,3,0,1/14/2019,0,16 -"Hutter, Rosalie",10214,0,3,0,2,5,3,0,64995,0,20,Production Technician II,MA,2351,05/07/92,F,Separated,US Citizen,No,White,6/5/2015,,N/A-StillEmployed,Active,Production ,Webster Butler,,Indeed,Fully Meets,4.5,3,0,2/14/2019,0,6 -"Latif, Mohammed",10249,1,1,1,5,5,3,0,61962,1,20,Production Technician II,MA,2126,05/09/84,M ,Married,US Citizen,No,White,4/2/2012,4/15/2013,more money,Voluntarily Terminated,Production ,Kissy Sullivan,20.0,Google Search,Fully Meets,4.9,3,0,2/20/2013,0,20 -"Nguyen, Lei-Ming",10206,0,0,0,1,5,3,0,62061,0,19,Production Technician I,MA,2132,07/07/84,F,Single,US Citizen,No,White,7/8/2013,,N/A-StillEmployed,Active,Production ,David Stanley,14.0,LinkedIn,Fully Meets,3.6,5,0,1/2/2019,0,4 -"Ren, Kylo",10140,1,1,1,1,6,3,0,61809,0,3,Area Sales Manager,ID,83706,10/12/54,M ,Married,US Citizen,No,White,5/12/2014,,N/A-StillEmployed,Active,Sales,John Smith,17.0,CareerBuilder,Fully Meets,3.98,3,0,1/28/2019,0,4 -"McKinzie, Jac",10202,1,1,1,2,6,3,0,63291,0,3,Area Sales Manager,TX,78789,07/01/84,M ,Married,US Citizen,No,Two or more races,7/6/2016,,N/A-StillEmployed,Active,Sales,Lynn Daneault,21.0,Website,Fully Meets,3.4,4,0,1/29/2019,0,7 -"Monkfish, Erasumus",10213,1,1,1,1,5,3,0,58207,0,20,Production Technician II,MA,1450,08/17/92,M ,Married,US Citizen,No,White,11/7/2011,,N/A-StillEmployed,Active,Production ,David Stanley,14.0,LinkedIn,Fully Meets,3.7,3,0,1/8/2019,0,14 -"King, Janet",10089,1,1,0,1,2,3,0,250000,0,16,President & CEO,MA,1902,09/21/54,F,Married,US Citizen,Yes,White,7/2/2012,,N/A-StillEmployed,Active,Executive Office,Board of Directors,9.0,Indeed,Fully Meets,4.83,3,0,1/17/2019,0,10 -"Dolan, Linda",10133,1,1,0,1,3,3,0,70621,0,14,IT Support,MA,2119,07/18/88,F,Married,US Citizen,No,White,1/5/2015,,N/A-StillEmployed,Active,IT/IS,Peter Monroe,7.0,Employee Referral,Fully Meets,4.11,4,6,2/25/2019,0,16 -"Kinsella, Kathleen ",10166,1,1,0,5,5,3,0,54005,1,19,Production Technician I,MA,2170,12/08/73,F,Married,US Citizen,No,White,9/26/2011,6/4/2015,more money,Voluntarily Terminated,Production ,Webster Butler,39.0,Google Search,Fully Meets,3.6,5,0,3/1/2015,0,16 -"Darson, Jene'ya ",10056,1,1,0,1,5,3,0,57583,0,19,Production Technician I,MA,2110,11/05/78,F,Married,US Citizen,No,White,7/2/2012,,N/A-StillEmployed,Active,Production ,Elijiah Gray,16.0,Indeed,Fully Meets,5.0,3,0,2/25/2019,0,1 -"Goyal, Roxana",10256,1,1,0,3,5,3,0,49256,0,19,Production Technician I,MA,1864,10/09/74,F,Married,US Citizen,No,Asian,8/19/2013,,N/A-StillEmployed,Active,Production ,Kissy Sullivan,20.0,LinkedIn,Fully Meets,4.1,5,0,2/15/2019,0,3 -"Khemmich, Bartholemew",10231,0,0,1,1,6,3,0,65310,0,3,Area Sales Manager,CO,80820,11/27/79,M ,Single,US Citizen,No,White,8/19/2013,,N/A-StillEmployed,Active,Sales,Lynn Daneault,21.0,Indeed,Fully Meets,4.3,5,0,1/22/2019,0,13 -"Power, Morissa",10187,0,2,0,5,5,3,0,46799,1,19,Production Technician I,MA,1742,10/15/84,F,Divorced,Eligible NonCitizen,No,Asian,5/16/2011,6/4/2018,Another position,Voluntarily Terminated,Production ,Kissy Sullivan,20.0,Google Search,Fully Meets,3.17,4,0,4/2/2018,0,14 -"Soze, Keyser",10180,1,1,1,2,3,3,0,87565,0,28,Sr. Network Engineer,MA,1545,02/09/83,M ,Married,US Citizen,No,Asian,6/30/2016,,N/A-StillEmployed,Active,IT/IS,Peter Monroe,7.0,LinkedIn,Fully Meets,3.27,4,5,1/14/2019,0,13 -"Walker, Roger",10190,0,0,1,1,5,3,0,66541,0,20,Production Technician II,MA,2459,02/10/76,M ,Single,US Citizen,No,Black or African American,8/18/2014,,N/A-StillEmployed,Active,Production ,Ketsia Liebig,19.0,Employee Referral,Fully Meets,3.11,5,0,2/12/2019,0,4 -"DiNocco, Lily ",10284,1,1,0,1,5,2,0,61584,0,19,Production Technician I,MA,2351,12/02/78,F,Married,US Citizen,No,Black or African American,1/7/2013,,N/A-StillEmployed,Active,Production ,Brannon Miller,12.0,Indeed,Needs Improvement,3.88,4,0,1/18/2019,0,6 -"Monterro, Luisa",10025,0,0,0,1,5,4,0,72460,0,20,Production Technician II,MA,2126,04/24/70,F,Single,US Citizen,No,Black or African American,5/13/2013,,N/A-StillEmployed,Active,Production ,Kissy Sullivan,20.0,Indeed,Exceeds,4.7,3,0,1/14/2019,0,1 -"Ruiz, Ricardo",10222,0,2,1,5,3,3,1,148999,1,13,IT Manager - DB,MA,1915,01/04/64,M ,Divorced,US Citizen,No,Black or African American,1/9/2012,11/4/2015,hours,Voluntarily Terminated,IT/IS,Jennifer Zamora,5.0,Diversity Job Fair,Fully Meets,4.3,4,6,1/4/2015,0,8 -"Crimmings, Jean",10132,0,0,0,2,5,3,0,56149,0,19,Production Technician I,MA,1821,04/10/87,F,Single,US Citizen,No,White,7/6/2016,,N/A-StillEmployed,Active,Production ,Michael Albert,22.0,LinkedIn,Fully Meets,4.12,5,0,1/28/2019,0,15 -"Pham, Hong",10050,1,1,1,5,5,3,0,64724,1,19,Production Technician I,MA,2451,03/06/88,M ,Married,US Citizen,No,Asian,7/5/2011,11/30/2012,more money,Voluntarily Terminated,Production ,Brannon Miller,12.0,Google Search,Fully Meets,5.0,3,0,2/20/2012,0,13 -"Handschiegl, Joanne",10125,1,1,0,1,5,3,0,54828,0,19,Production Technician I,MA,2127,03/23/77,F,Married,US Citizen,No,White,11/28/2011,,N/A-StillEmployed,Active,Production ,Michael Albert,22.0,Google Search,Fully Meets,4.2,4,0,2/22/2019,0,13 -"Steans, Tyrone ",10147,0,0,1,1,1,3,0,63003,0,1,Accountant I,MA,2703,09/01/86,M ,Single,US Citizen,No,White,9/29/2014,,N/A-StillEmployed,Active,Admin Offices,Brandon R. LeBlanc,1.0,Indeed,Fully Meets,3.9,5,5,1/18/2019,0,9 -"Mckenna, Sandy",10145,1,1,0,1,5,3,0,62810,0,19,Production Technician I,MA,2184,01/07/87,F,Married,US Citizen,No,Black or African American,1/7/2013,,N/A-StillEmployed,Active,Production ,Kissy Sullivan,20.0,CareerBuilder,Fully Meets,3.93,3,0,1/30/2019,0,20 -"Delarge, Alex",10306,0,0,1,1,6,1,0,61568,0,3,Area Sales Manager,AL,36006,11/02/75,M ,Single,US Citizen,No,Two or more races,9/29/2014,,N/A-StillEmployed,Active,Sales,John Smith,17.0,Indeed,PIP,1.93,3,0,1/30/2019,6,5 -"Roby, Lori ",10162,1,1,0,1,3,3,0,89883,0,9,Data Analyst,MA,1886,10/11/81,F,Married,US Citizen,No,White,2/16/2015,,N/A-StillEmployed,Active,IT/IS,Simon Roup,4.0,Employee Referral,Fully Meets,3.69,5,6,2/14/2019,0,15 -"Voldemort, Lord",10118,1,1,1,4,3,3,0,113999,1,8,Database Administrator,MA,1960,08/07/86,M ,Married,US Citizen,No,Black or African American,2/16/2015,2/22/2017,"no-call, no-show",Terminated for Cause,IT/IS,Simon Roup,4.0,Employee Referral,Fully Meets,4.33,3,7,2/15/2017,0,9 -"Good, Susan",10237,1,1,0,3,5,3,0,66825,0,20,Production Technician II,MA,1886,05/25/86,F,Married,US Citizen,No,White,5/12/2014,,N/A-StillEmployed,Active,Production ,David Stanley,14.0,LinkedIn,Fully Meets,4.6,3,0,2/7/2019,0,20 -"Smith, Martin",10286,0,0,1,5,5,2,0,53564,1,19,Production Technician I,MA,2458,03/17/88,M ,Single,US Citizen,No,Black or African American,1/10/2011,12/28/2017,career change,Voluntarily Terminated,Production ,Webster Butler,39.0,Google Search,Needs Improvement,3.54,5,0,4/6/2017,4,15 -"Burkett, Benjamin ",10181,1,1,1,1,5,3,0,62162,0,20,Production Technician II,MA,1890,08/19/77,M ,Married,US Citizen,No,White,4/4/2011,,N/A-StillEmployed,Active,Production ,Ketsia Liebig,19.0,Indeed,Fully Meets,3.25,5,0,1/14/2019,0,15 -"Veera, Abdellah ",10014,0,2,1,5,5,4,0,58523,1,19,Production Technician I,MA,2171,01/31/87,M ,Divorced,US Citizen,No,White,8/13/2012,2/5/2016,maternity leave - did not return,Voluntarily Terminated,Production ,Kissy Sullivan,20.0,LinkedIn,Exceeds,4.5,5,0,2/1/2016,0,15 -"Anderson, Linda ",10002,0,0,0,1,5,4,0,57568,0,19,Production Technician I,MA,1844,05/22/77,F,Single,US Citizen,No,White,1/9/2012,,N/A-StillEmployed,Active,Production ,Amy Dunn,11.0,LinkedIn,Exceeds,5.0,5,0,1/7/2019,0,15 -"Clayton, Rick",10220,0,0,1,1,3,3,0,68678,0,14,IT Support,MA,2170,09/05/85,M ,Single,US Citizen,No,White,9/5/2012,,N/A-StillEmployed,Active,IT/IS,Eric Dougall,6.0,Indeed,Fully Meets,4.7,3,6,2/27/2019,0,2 -"Jung, Judy ",10138,1,1,0,5,5,3,0,61154,1,19,Production Technician I,MA,2446,04/17/86,F,Married,US Citizen,No,Black or African American,1/10/2011,4/1/2016,unhappy,Voluntarily Terminated,Production ,Elijiah Gray,16.0,CareerBuilder,Fully Meets,4.0,4,0,2/3/2016,0,4 -"Alagbe,Trina",10088,1,1,0,1,5,3,0,64991,0,19,Production Technician I,MA,1886,09/27/88,F,Married,US Citizen,No,White,1/7/2008,,N/A-StillEmployed,Active,Production ,Elijiah Gray,16.0,Indeed,Fully Meets,4.84,5,0,1/3/2019,0,15 -"Baczenski, Rachael ",10252,1,1,0,5,5,3,1,54670,1,19,Production Technician I,MA,1902,01/12/74,F,Married,US Citizen,Yes,Black or African American,1/10/2011,1/12/2017,Another position,Voluntarily Terminated,Production ,David Stanley,14.0,Diversity Job Fair,Fully Meets,4.2,4,0,1/30/2016,0,12 -"Driver, Elle",10006,0,0,0,1,6,4,0,74241,0,3,Area Sales Manager,CA,90007,11/08/88,F,Single,US Citizen,No,White,1/10/2011,,N/A-StillEmployed,Active,Sales,Lynn Daneault,21.0,Indeed,Exceeds,4.77,5,0,1/27/2019,0,14 -"Champaigne, Brian",10108,1,1,1,1,3,3,0,110929,0,5,BI Director,MA,2045,02/09/72,M ,Married,US Citizen,No,White,9/6/2016,,N/A-StillEmployed,Active,IT/IS,Jennifer Zamora,5.0,Indeed,Fully Meets,4.5,5,7,1/15/2019,0,8 -"Goble, Taisha",10246,0,0,0,4,3,3,0,114800,1,8,Database Administrator,MA,2127,10/23/71,F,Single,US Citizen,No,White,2/16/2015,3/15/2015,"no-call, no-show",Terminated for Cause,IT/IS,Simon Roup,4.0,Indeed,Fully Meets,4.6,4,4,1/20/2015,0,10 -"Robinson, Cherly",10285,1,1,0,4,5,2,0,61422,1,19,Production Technician I,MA,1460,01/07/85,F,Married,US Citizen,No,White,1/10/2011,5/17/2016,attendance,Terminated for Cause,Production ,Ketsia Liebig,19.0,Indeed,Needs Improvement,3.6,3,0,4/5/2016,4,16 -"Gonzalez, Maria",10101,0,3,0,1,3,3,0,61242,0,14,IT Support,MA,2472,04/16/81,F,Separated,US Citizen,Yes,White,1/5/2015,,N/A-StillEmployed,Active,IT/IS,Peter Monroe,7.0,Employee Referral,Fully Meets,4.61,4,5,1/28/2019,0,11 -"Langton, Enrico",10052,1,1,1,1,5,3,0,46120,0,19,Production Technician I,MA,2048,12/09/86,M ,Married,US Citizen,No,White,7/9/2012,,N/A-StillEmployed,Active,Production ,David Stanley,14.0,LinkedIn,Fully Meets,5.0,5,0,2/4/2019,0,13 -"Daneault, Lynn",10099,0,0,0,1,6,3,0,65729,0,21,Sales Manager,VT,5473,04/19/90,F,Single,US Citizen,No,White,5/5/2014,,N/A-StillEmployed,Active,Sales,Debra Houlihan,15.0,Indeed,Fully Meets,4.62,4,0,1/24/2019,0,8 -"Givens, Myriam",10255,0,0,0,1,6,3,0,61555,0,3,Area Sales Manager,IN,46204,09/22/89,F,Single,US Citizen,No,White,2/16/2015,,N/A-StillEmployed,Active,Sales,Lynn Daneault,21.0,Indeed,Fully Meets,4.5,5,0,1/25/2019,0,20 -"Lindsay, Leonara ",10008,0,0,0,1,3,4,1,51777,0,14,IT Support,CT,6070,10/05/88,F,Single,US Citizen,Yes,Black or African American,1/21/2011,,N/A-StillEmployed,Active,IT/IS,Eric Dougall,6.0,Diversity Job Fair,Exceeds,4.64,4,5,1/25/2019,0,14 -"Squatrito, Kristen",10030,0,2,0,5,5,4,0,62425,1,19,Production Technician I,MA,2359,03/26/73,F,Divorced,US Citizen,No,White,5/13/2013,6/29/2015,unhappy,Voluntarily Terminated,Production ,David Stanley,14.0,LinkedIn,Exceeds,4.1,4,0,3/2/2015,0,16 -"Jeannite, Tayana",10009,0,2,0,1,5,4,0,60724,0,20,Production Technician II,MA,1821,11/06/86,F,Divorced,US Citizen,No,American Indian or Alaska Native,7/5/2011,,N/A-StillEmployed,Active,Production ,Ketsia Liebig,19.0,LinkedIn,Exceeds,4.6,4,0,2/25/2019,0,11 -"Fraval, Maruk ",10258,0,0,1,1,6,3,0,67251,0,3,Area Sales Manager,CT,6050,08/28/63,M ,Single,US Citizen,No,Black or African American,9/6/2011,,N/A-StillEmployed,Active,Sales,Lynn Daneault,21.0,CareerBuilder,Fully Meets,4.3,3,0,1/27/2019,2,7 -"Tippett, Jeanette",10123,0,2,0,1,5,3,0,56339,0,19,Production Technician I,MA,2093,06/05/67,F,Divorced,US Citizen,No,Black or African American,2/18/2013,,N/A-StillEmployed,Active,Production ,Brannon Miller,12.0,Indeed,Fully Meets,4.21,5,0,1/14/2019,0,4 -"Cockel, James",10261,0,0,1,1,5,3,0,63108,0,19,Production Technician I,MA,2452,09/08/77,M ,Single,US Citizen,No,White,7/8/2013,,N/A-StillEmployed,Active,Production ,David Stanley,14.0,Employee Referral,Fully Meets,4.4,5,0,1/14/2019,0,3 -"Howard, Estelle",10182,1,1,0,1,1,3,0,49920,1,2,Administrative Assistant,MA,2170,09/16/85,F,Married,US Citizen,No,Black or African American,2/16/2015,4/15/2015,"no-call, no-show",Terminated for Cause,Admin Offices,Brandon R. LeBlanc,1.0,Indeed,Fully Meets,3.24,3,4,4/15/2015,0,6 -"Fett, Boba",10309,0,0,1,1,3,1,0,53366,0,15,Network Engineer,MA,2138,06/18/87,M ,Single,US Citizen,No,White,3/30/2015,,N/A-StillEmployed,Active,IT/IS,Peter Monroe,7.0,LinkedIn,PIP,1.2,3,6,2/4/2019,3,2 -"Manchester, Robyn",10077,1,1,0,2,5,3,0,62385,0,20,Production Technician II,MA,2324,08/25/76,F,Married,US Citizen,No,White,5/11/2016,,N/A-StillEmployed,Active,Production ,Webster Butler,,LinkedIn,Fully Meets,5.0,3,0,1/21/2019,0,4 -"Saada, Adell",10126,1,1,0,1,4,3,0,86214,0,24,Software Engineer,MA,2132,07/24/86,F,Married,US Citizen,No,White,11/5/2012,,N/A-StillEmployed,Active,Software Engineering,Alex Sweetwater,10.0,Indeed,Fully Meets,4.2,3,6,2/13/2019,0,2 -"Ferguson, Susan",10189,1,1,0,5,5,3,0,57748,1,19,Production Technician I,MA,2176,04/14/55,F,Married,US Citizen,No,White,11/7/2011,5/17/2016,military,Voluntarily Terminated,Production ,Webster Butler,39.0,Google Search,Fully Meets,3.13,3,0,2/4/2016,0,16 -"Whittier, Scott",10072,0,0,1,5,5,3,0,58371,1,19,Production Technician I,MA,2030,05/24/87,M ,Single,US Citizen,Yes,White,1/10/2011,5/15/2014,hours,Voluntarily Terminated,Production ,Webster Butler,39.0,LinkedIn,Fully Meets,5.0,5,0,5/15/2014,0,11 -"Estremera, Miguel",10280,0,0,1,4,5,2,0,60340,1,19,Production Technician I,MA,2129,09/02/83,M ,Single,US Citizen,No,White,4/2/2012,9/27/2018,attendance,Terminated for Cause,Production ,Michael Albert,22.0,Google Search,Needs Improvement,5.0,4,0,4/12/2018,5,16 -"Barbara, Thomas",10242,1,1,1,5,5,3,1,47211,1,19,Production Technician I,MA,2062,02/21/74,M ,Married,US Citizen,Yes,Black or African American,4/2/2012,9/19/2016,unhappy,Voluntarily Terminated,Production ,Kissy Sullivan,20.0,Diversity Job Fair,Fully Meets,4.2,3,0,5/6/2016,0,15 -"Kirill, Alexandra ",10170,1,1,0,5,5,3,0,45433,1,19,Production Technician I,MA,2127,10/08/70,F,Married,US Citizen,No,White,9/26/2011,1/9/2014,more money,Voluntarily Terminated,Production ,Amy Dunn,11.0,Google Search,Fully Meets,3.49,4,0,1/30/2013,0,6 -"Valentin,Jackie",10205,1,1,0,1,6,3,0,57859,0,3,Area Sales Manager,AZ,85006,05/23/91,F,Married,US Citizen,No,Two or more races,7/5/2011,,N/A-StillEmployed,Active,Sales,John Smith,17.0,Indeed,Fully Meets,2.81,3,0,1/17/2019,0,16 -"Athwal, Sam",10062,0,4,1,1,5,3,0,59365,0,19,Production Technician I,MA,2199,02/18/83,M ,Widowed,US Citizen,No,White,9/30/2013,,N/A-StillEmployed,Active,Production ,Ketsia Liebig,19.0,Employee Referral,Fully Meets,5.0,4,0,2/25/2019,0,19 -"Hunts, Julissa",10201,0,0,0,2,5,3,0,69340,0,20,Production Technician II,MA,2021,03/11/84,F,Single,US Citizen,No,White,6/6/2016,,N/A-StillEmployed,Active,Production ,Elijiah Gray,16.0,LinkedIn,Fully Meets,3.0,5,0,1/18/2019,0,4 -"Rhoads, Thomas",10058,0,2,1,5,5,3,0,45115,1,19,Production Technician I,MA,2176,07/22/82,M ,Divorced,US Citizen,Yes,White,5/16/2011,1/15/2016,retiring,Voluntarily Terminated,Production ,Elijiah Gray,16.0,LinkedIn,Fully Meets,5.0,4,0,3/30/2015,0,11 -"Bugali, Josephine ",10203,0,3,0,3,5,3,1,64375,0,19,Production Technician I,MA,2043,10/30/69,F,Separated,US Citizen,No,Black or African American,11/11/2013,,N/A-StillEmployed,Active,Production ,Kissy Sullivan,20.0,Diversity Job Fair,Fully Meets,3.5,5,0,1/21/2019,0,17 -"Dunne, Amy",10211,1,1,0,1,5,3,0,62514,0,19,Production Technician I,MA,1749,09/23/73,F,Married,US Citizen,No,White,4/26/2010,,N/A-StillEmployed,Active,Production ,Ketsia Liebig,19.0,Google Search,Fully Meets,2.9,3,0,1/21/2019,0,6 -"Gonzalez, Juan",10300,1,1,1,5,5,1,1,68898,1,20,Production Technician II,MA,2128,10/12/64,M ,Married,US Citizen,No,Black or African American,4/26/2010,5/30/2011,career change,Voluntarily Terminated,Production ,Brannon Miller,12.0,Diversity Job Fair,PIP,3.0,3,0,3/6/2011,3,10 -"Carey, Michael ",10115,0,0,1,1,5,3,0,52846,0,19,Production Technician I,MA,1701,02/02/83,M ,Single,US Citizen,No,Black or African American,3/31/2014,,N/A-StillEmployed,Active,Production ,Kelley Spirea,18.0,LinkedIn,Fully Meets,4.43,3,0,2/1/2019,0,14 -"Anderson, Carol ",10069,0,2,0,5,5,3,0,50825,1,19,Production Technician I,MA,2169,09/08/89,F,Divorced,US Citizen,No,White,7/11/2011,9/6/2016,return to school,Voluntarily Terminated,Production ,Webster Butler,39.0,Google Search,Fully Meets,5.0,4,0,2/1/2016,0,2 -"Wolk, Hang T",10174,0,0,0,1,5,3,0,60446,0,20,Production Technician II,MA,2302,04/20/85,F,Single,US Citizen,No,White,9/29/2014,,N/A-StillEmployed,Active,Production ,David Stanley,14.0,LinkedIn,Fully Meets,3.4,4,0,2/21/2019,0,14 -"Fancett, Nicole",10136,0,0,0,1,5,3,0,65902,0,20,Production Technician II,MA,2324,09/27/87,F,Single,US Citizen,No,Black or African American,2/17/2014,,N/A-StillEmployed,Active,Production ,Webster Butler,,LinkedIn,Fully Meets,4.0,4,0,1/7/2019,0,7 -"Ybarra, Catherine ",10301,0,0,0,5,5,1,0,48513,1,19,Production Technician I,MA,2458,05/04/82,F,Single,US Citizen,No,Asian,9/2/2008,9/29/2015,Another position,Voluntarily Terminated,Production ,Brannon Miller,12.0,Google Search,PIP,3.2,2,0,9/2/2015,5,4 -"Fernandes, Nilson ",10308,1,1,1,1,5,1,0,64057,0,19,Production Technician I,MA,2132,10/18/89,M ,Married,US Citizen,No,White,5/11/2015,,N/A-StillEmployed,Active,Production ,Amy Dunn,11.0,Indeed,PIP,1.56,5,0,1/3/2019,6,15 -"Bachiochi, Linda",10114,0,0,0,3,5,3,1,47837,0,19,Production Technician I,MA,1902,02/11/70,F,Single,US Citizen,No,Black or African American,7/6/2009,,N/A-StillEmployed,Active,Production ,Brannon Miller,12.0,Diversity Job Fair,Fully Meets,4.46,3,0,1/25/2019,0,4 -"Roehrich, Bianca",10149,0,0,0,5,3,3,0,120000,1,29,Principal Data Architect,MA,2703,05/27/73,F,Single,US Citizen,Yes,White,1/5/2015,11/10/2018,Another position,Voluntarily Terminated,IT/IS,Simon Roup,4.0,LinkedIn,Fully Meets,3.88,3,7,2/13/2018,0,12 -"Chivukula, Enola",10240,0,0,0,5,5,3,0,64786,1,19,Production Technician I,MA,1775,08/27/83,F,Single,US Citizen,No,White,6/27/2011,11/15/2015,relocation out of area,Voluntarily Terminated,Production ,Amy Dunn,11.0,Indeed,Fully Meets,4.3,4,0,3/10/2015,0,3 -"Theamstern, Sophia",10141,0,0,0,5,5,3,0,48413,1,19,Production Technician I,MA,2066,05/09/65,F,Single,US Citizen,No,White,7/5/2011,9/5/2016,return to school,Voluntarily Terminated,Production ,Amy Dunn,11.0,Indeed,Fully Meets,3.98,4,0,3/2/2016,0,1 -"Osturnka, Adeel",10021,1,1,1,1,5,4,0,47414,0,19,Production Technician I,MA,2478,12/11/76,M ,Married,US Citizen,No,White,9/30/2013,,N/A-StillEmployed,Active,Production ,Kelley Spirea,18.0,LinkedIn,Exceeds,5.0,3,0,2/7/2019,0,13 -"Clukey, Elijian",10029,1,1,1,2,5,4,0,50373,0,19,Production Technician I,MA,2134,08/26/80,M ,Married,US Citizen,No,White,7/6/2016,,N/A-StillEmployed,Active,Production ,Brannon Miller,12.0,Employee Referral,Exceeds,4.1,4,0,2/28/2019,0,5 -"Wilber, Barry",10048,1,1,1,5,5,3,0,55140,1,19,Production Technician I,MA,2324,09/09/65,M ,Married,Eligible NonCitizen,No,White,5/16/2011,9/7/2015,unhappy,Voluntarily Terminated,Production ,Amy Dunn,11.0,Website,Fully Meets,5.0,3,0,2/15/2015,0,7 -"Roper, Katie",10086,0,0,0,1,3,3,0,150290,0,7,Data Architect,MA,2056,11/21/72,F,Single,US Citizen,No,Black or African American,1/7/2017,,N/A-StillEmployed,Active,IT/IS,Brian Champaigne,13.0,Indeed,Fully Meets,4.94,3,5,2/6/2019,0,17 -"Chigurh, Anton",10200,0,0,1,1,6,3,0,66808,0,3,Area Sales Manager,TX,78207,06/11/70,M ,Single,Eligible NonCitizen,No,Black or African American,5/14/2012,,N/A-StillEmployed,Active,Sales,Lynn Daneault,21.0,Employee Referral,Fully Meets,3.0,5,0,1/19/2019,0,17 -"Spirea, Kelley",10090,1,1,0,1,5,3,0,65714,0,18,Production Manager,MA,2451,09/30/75,F,Married,US Citizen,No,White,10/2/2012,,N/A-StillEmployed,Active,Production ,Janet King,2.0,LinkedIn,Fully Meets,4.83,5,0,2/14/2019,0,15 -"Zamora, Jennifer",10010,0,0,0,1,3,4,0,220450,0,6,CIO,MA,2067,08/30/79,F,Single,US Citizen,No,White,4/10/2010,,N/A-StillEmployed,Active,IT/IS,Janet King,2.0,Employee Referral,Exceeds,4.6,5,6,2/21/2019,0,16 -"Bozzi, Charles",10175,0,0,1,5,5,3,0,74312,1,18,Production Manager,MA,1901,03/10/70,M ,Single,US Citizen,No,Asian,9/30/2013,8/7/2014,retiring,Voluntarily Terminated,Production ,Janet King,2.0,Indeed,Fully Meets,3.39,3,0,2/20/2014,0,14 -"Von Massenbach, Anna",10183,0,0,0,2,5,3,0,62068,0,19,Production Technician I,MA,2124,04/06/85,F,Single,US Citizen,No,White,7/5/2015,,N/A-StillEmployed,Active,Production ,Michael Albert,22.0,LinkedIn,Fully Meets,3.21,3,0,1/29/2019,0,7 -"Becker, Renee",10245,0,0,0,4,3,3,0,110000,1,8,Database Administrator,MA,2026,04/04/86,F,Single,US Citizen,Yes,White,7/7/2014,9/12/2015,performance,Terminated for Cause,IT/IS,Simon Roup,4.0,Google Search,Fully Meets,4.5,4,5,1/15/2015,0,8 -"Huynh, Ming",10160,0,2,0,5,5,3,0,68182,1,20,Production Technician II,MA,1742,09/22/76,F,Divorced,US Citizen,No,White,2/21/2011,4/1/2013,unhappy,Voluntarily Terminated,Production ,Amy Dunn,11.0,Google Search,Fully Meets,3.72,3,0,2/1/2013,0,18 -"Corleone, Vito",10019,0,0,1,1,5,4,0,170500,0,10,Director of Operations,MA,2030,03/19/83,M ,Single,US Citizen,No,Black or African American,1/5/2009,,N/A-StillEmployed,Active,Production ,Janet King,2.0,Indeed,Exceeds,3.7,5,0,2/4/2019,0,15 -"Boutwell, Bonalyn",10081,1,1,0,1,1,3,1,106367,0,26,Sr. Accountant,MA,2468,04/04/87,F,Married,US Citizen,No,Black or African American,2/16/2015,,N/A-StillEmployed,Active,Admin Offices,Brandon R. LeBlanc,3.0,Diversity Job Fair,Fully Meets,5.0,4,3,2/18/2019,0,4 -"Barbossa, Hector",10012,0,2,1,1,3,4,1,92328,0,9,Data Analyst,TX,78230,07/04/88,M ,Divorced,US Citizen,No,Black or African American,11/10/2014,,N/A-StillEmployed,Active,IT/IS,Simon Roup,4.0,Diversity Job Fair,Exceeds,4.28,4,5,2/25/2019,0,9 -"Rachael, Maggie",10239,1,1,0,1,3,3,0,95920,0,4,BI Developer,MA,2110,05/12/80,F,Married,US Citizen,No,Black or African American,10/2/2016,,N/A-StillEmployed,Active,IT/IS,Brian Champaigne,13.0,Indeed,Fully Meets,4.4,4,6,2/6/2019,0,10 -"Davis, Daniel",10143,0,0,1,1,5,3,0,56294,0,20,Production Technician II,MA,2458,09/14/79,M ,Single,Eligible NonCitizen,No,Two or more races,11/7/2011,,N/A-StillEmployed,Active,Production ,Kissy Sullivan,20.0,LinkedIn,Fully Meets,3.96,4,0,2/27/2019,0,6 -"Dietrich, Jenna ",10304,0,0,0,1,6,1,0,59231,0,3,Area Sales Manager,WA,98052,05/14/87,F,Single,US Citizen,Yes,White,2/20/2012,,N/A-StillEmployed,Active,Sales,John Smith,17.0,Website,PIP,2.3,1,0,1/29/2019,2,17 -"Morway, Tanya",10151,1,1,0,1,3,3,0,52599,0,15,Network Engineer,MA,2048,04/04/79,F,Married,US Citizen,No,White,2/16/2015,,N/A-StillEmployed,Active,IT/IS,Peter Monroe,7.0,CareerBuilder,Fully Meets,3.81,3,6,2/11/2019,0,6 -"Villanueva, Noah",10253,0,0,1,1,6,3,0,55875,0,3,Area Sales Manager,ME,4063,07/11/89,M ,Single,US Citizen,No,Asian,3/5/2012,,N/A-StillEmployed,Active,Sales,John Smith,17.0,Website,Fully Meets,4.5,4,0,1/18/2019,0,11 -"Monroe, Peter",10288,1,1,1,1,3,2,1,157000,0,13,IT Manager - Infra,MA,2134,10/05/86,M ,Married,Eligible NonCitizen,Yes,Black or African American,2/15/2012,,N/A-StillEmployed,Active,IT/IS,Jennifer Zamora,5.0,Diversity Job Fair,Needs Improvement,2.39,3,6,2/22/2019,4,13 -"Landa, Hans",10092,1,1,1,4,5,3,0,82758,1,18,Production Manager,MA,1890,07/01/72,M ,Married,US Citizen,No,White,1/10/2011,12/12/2015,attendance,Terminated for Cause,Production ,Janet King,2.0,Employee Referral,Fully Meets,4.78,4,0,2/15/2015,0,9 -"Torrence, Jack",10013,0,3,1,1,6,4,0,64397,0,3,Area Sales Manager,ND,58782,01/15/68,M ,Separated,US Citizen,No,White,1/9/2006,,N/A-StillEmployed,Active,Sales,Lynn Daneault,21.0,Indeed,Exceeds,4.1,3,0,1/4/2019,0,6 -"Kampew, Donysha",10244,0,0,0,5,6,3,0,68999,1,21,Sales Manager,PA,19444,11/11/89,F,Single,US Citizen,No,White,11/7/2011,4/24/2014,maternity leave - did not return,Voluntarily Terminated,Sales,Debra Houlihan,15.0,Google Search,Fully Meets,4.5,5,0,3/30/2013,0,2 -"Tejeda, Lenora ",10146,1,1,0,5,5,3,0,72202,1,20,Production Technician II,MA,2129,05/24/53,F,Married,US Citizen,No,White,5/16/2011,7/8/2017,Another position,Voluntarily Terminated,Production ,Elijiah Gray,16.0,Google Search,Fully Meets,3.93,3,0,4/18/2017,0,3 -"Robinson, Elias",10020,0,4,1,1,5,4,0,63353,0,19,Production Technician I,MA,1730,01/28/85,M ,Widowed,US Citizen,No,White,7/8/2013,,N/A-StillEmployed,Active,Production ,Brannon Miller,12.0,Employee Referral,Exceeds,3.6,5,0,2/11/2019,0,4 -"Ndzi, Colombui",10215,0,0,1,5,5,3,1,50470,1,19,Production Technician I,MA,2110,05/02/89,M ,Single,US Citizen,No,Black or African American,9/26/2011,4/4/2014,return to school,Voluntarily Terminated,Production ,Webster Butler,39.0,Diversity Job Fair,Fully Meets,4.3,3,0,3/2/2013,0,19 -"Ndzi, Horia",10185,1,1,1,5,5,3,0,46664,1,19,Production Technician I,MA,2421,03/28/83,M ,Married,US Citizen,No,White,4/1/2013,5/25/2016,more money,Voluntarily Terminated,Production ,Amy Dunn,11.0,Employee Referral,Fully Meets,3.18,3,0,3/6/2016,0,10 -"Johnson, George",10034,1,1,1,5,5,4,0,46837,1,19,Production Technician I,MA,2445,08/19/59,M ,Married,US Citizen,No,White,11/7/2011,4/29/2018,more money,Voluntarily Terminated,Production ,Michael Albert,22.0,CareerBuilder,Exceeds,4.7,4,0,2/14/2018,0,9 -"Rivera, Haley ",10011,1,1,0,1,5,4,0,46738,0,19,Production Technician I,MA,2171,01/12/73,F,Married,US Citizen,No,Asian,11/28/2011,,N/A-StillEmployed,Active,Production ,Webster Butler,,Google Search,Exceeds,4.36,5,0,2/11/2019,0,16 -"Close, Phil",10269,1,1,1,5,5,3,0,59369,1,20,Production Technician II,MA,2169,11/25/78,M ,Married,US Citizen,No,White,8/30/2010,9/26/2011,career change,Voluntarily Terminated,Production ,David Stanley,14.0,Indeed,Fully Meets,4.2,4,0,5/4/2011,0,6 -"Dickinson, Geoff ",10276,0,0,1,1,5,3,0,51259,0,19,Production Technician I,MA,2180,11/15/82,M ,Single,US Citizen,No,White,5/12/2014,,N/A-StillEmployed,Active,Production ,Ketsia Liebig,19.0,Indeed,Fully Meets,4.3,4,0,2/19/2019,0,1 -"Soto, Julia ",10119,1,1,0,1,3,3,0,66593,0,14,IT Support,MA,2360,03/12/73,F,Married,US Citizen,No,Black or African American,6/10/2011,,N/A-StillEmployed,Active,IT/IS,Eric Dougall,6.0,LinkedIn,Fully Meets,4.3,3,5,2/8/2019,0,19 -"Liebig, Ketsia",10017,1,1,0,1,5,4,0,77915,0,18,Production Manager,MA,2110,10/26/81,F,Married,US Citizen,No,White,9/30/2013,,N/A-StillEmployed,Active,Production ,Janet King,2.0,Website,Exceeds,4.1,3,0,1/21/2019,0,11 -"Lynch, Lindsay",10004,0,0,0,5,5,4,1,47434,1,19,Production Technician I,MA,1844,02/14/73,F,Single,US Citizen,Yes,Black or African American,11/7/2011,11/14/2015,Another position,Voluntarily Terminated,Production ,Webster Butler,39.0,Diversity Job Fair,Exceeds,5.0,4,0,2/2/2015,0,17 -"Shepard, Anita ",10179,1,1,0,1,3,3,0,50750,0,15,Network Engineer,MA,1773,04/14/81,F,Married,US Citizen,No,White,9/30/2014,,N/A-StillEmployed,Active,IT/IS,Peter Monroe,7.0,LinkedIn,Fully Meets,3.31,3,6,1/7/2019,0,7 -"Punjabhi, Louis ",10225,0,0,1,1,5,3,0,59472,0,19,Production Technician I,MA,2109,06/19/61,M ,Single,US Citizen,No,White,1/6/2014,,N/A-StillEmployed,Active,Production ,Kelley Spirea,18.0,Employee Referral,Fully Meets,4.8,3,0,1/7/2019,0,14 -"Goeth, Amon",10228,1,1,1,1,3,3,0,74679,0,14,IT Support,MA,2135,11/24/89,M ,Married,US Citizen,Yes,White,3/30/2015,,N/A-StillEmployed,Active,IT/IS,Peter Monroe,7.0,LinkedIn,Fully Meets,4.3,5,7,1/10/2019,0,20 -"Barton, Nader",10066,0,2,1,5,5,3,0,52505,1,19,Production Technician I,MA,2747,07/15/77,M ,Divorced,US Citizen,No,White,9/24/2012,4/6/2017,Another position,Voluntarily Terminated,Production ,Michael Albert,22.0,On-line Web application,Fully Meets,5.0,5,0,3/2/2017,0,1 -"Linares, Marilyn ",10186,1,1,0,5,5,3,0,52624,1,19,Production Technician I,MA,1886,03/26/81,F,Married,US Citizen,No,White,7/5/2011,9/26/2018,unhappy,Voluntarily Terminated,Production ,Michael Albert,22.0,Indeed,Fully Meets,3.18,4,0,3/2/2018,0,16 -"Quinn, Sean",10131,1,1,1,5,1,3,1,83363,1,23,Software Engineer,MA,2045,11/06/84,M ,Married,Eligible NonCitizen,No,Black or African American,2/21/2011,8/15/2015,career change,Voluntarily Terminated,Software Engineering,Janet King,2.0,Diversity Job Fair,Fully Meets,4.15,4,0,4/19/2014,0,4 -"Martin, Sandra",10110,0,0,0,1,4,3,0,105688,0,24,Software Engineer,MA,2135,11/07/87,F,Single,US Citizen,No,Asian,11/11/2013,,N/A-StillEmployed,Active,Software Engineering,Alex Sweetwater,10.0,Google Search,Fully Meets,4.5,5,4,1/14/2019,0,14 -"Bates, Norman",10061,0,0,1,4,5,3,0,57834,1,19,Production Technician I,MA,2050,10/18/81,M ,Single,US Citizen,No,White,2/21/2011,8/4/2017,attendance,Terminated for Cause,Production ,Kelley Spirea,18.0,Google Search,Fully Meets,5.0,4,0,4/5/2017,0,20 -"Owad, Clinton",10281,0,0,1,1,5,2,0,53060,0,19,Production Technician I,MA,1760,11/24/79,M ,Single,US Citizen,No,Black or African American,2/17/2014,,N/A-StillEmployed,Active,Production ,Michael Albert,22.0,LinkedIn,Needs Improvement,4.25,3,0,2/4/2019,4,6 -"Sander, Kamrin",10169,1,1,0,1,5,3,0,56147,0,19,Production Technician I,MA,2154,07/10/88,F,Married,US Citizen,No,Black or African American,9/29/2014,,N/A-StillEmployed,Active,Production ,Elijiah Gray,16.0,LinkedIn,Fully Meets,3.51,3,0,2/18/2019,0,2 -"Bunbury, Jessica",10188,1,1,0,5,6,3,0,74326,1,3,Area Sales Manager,VA,21851,06/01/64,F,Married,Eligible NonCitizen,No,Black or African American,8/15/2011,8/2/2014,Another position,Voluntarily Terminated,Sales,John Smith,17.0,Google Search,Fully Meets,3.14,5,0,2/10/2013,1,19 -"Heitzman, Anthony",10129,0,0,1,1,5,3,0,46998,0,19,Production Technician I,MA,2149,01/04/84,M ,Single,US Citizen,No,White,8/13/2012,,N/A-StillEmployed,Active,Production ,Ketsia Liebig,19.0,Google Search,Fully Meets,4.17,4,0,2/11/2019,0,1 -"Tredinnick, Neville ",10044,1,1,1,5,3,3,0,75281,1,15,Network Engineer,MA,1420,05/05/88,M ,Married,US Citizen,No,White,1/5/2015,2/12/2016,medical issues,Voluntarily Terminated,IT/IS,Peter Monroe,7.0,CareerBuilder,Fully Meets,5.0,3,5,4/15/2015,0,11 -"Rarrick, Quinn",10152,0,2,1,5,5,3,0,61729,1,19,Production Technician I,MA,2478,12/31/84,M ,Divorced,US Citizen,No,White,9/26/2011,4/7/2018,more money,Voluntarily Terminated,Production ,Michael Albert,22.0,Indeed,Fully Meets,3.8,5,0,2/4/2018,0,19 -"Harrington, Christie ",10097,0,0,0,5,5,3,0,52177,1,19,Production Technician I,MA,2324,08/18/52,F,Single,US Citizen,No,White,1/9/2012,12/15/2015,retiring,Voluntarily Terminated,Production ,Webster Butler,39.0,CareerBuilder,Fully Meets,4.64,4,0,5/2/2015,0,8 -"Costello, Frank",10193,1,1,1,1,3,3,0,83552,0,9,Data Analyst,MA,1810,08/26/86,M ,Married,US Citizen,No,White,3/30/2015,,N/A-StillEmployed,Active,IT/IS,Simon Roup,4.0,Indeed,Fully Meets,3.04,3,6,1/22/2019,0,2 -"LaRotonda, William ",10038,0,2,1,1,1,3,0,64520,0,1,Accountant I,MA,1460,04/26/84,M ,Divorced,US Citizen,No,Black or African American,1/6/2014,,N/A-StillEmployed,Active,Admin Offices,Brandon R. LeBlanc,1.0,Website,Fully Meets,5.0,4,4,1/17/2019,0,3 -"Vega, Vincent",10144,0,2,1,1,5,3,0,88976,0,17,Production Manager,MA,2169,10/10/68,M ,Divorced,US Citizen,No,White,8/1/2011,,N/A-StillEmployed,Active,Production ,Janet King,2.0,Employee Referral,Fully Meets,3.93,3,0,2/27/2019,0,19 -"Wang, Charlie",10172,0,0,1,1,3,3,0,84903,0,22,Senior BI Developer,MA,1887,07/08/81,M ,Single,US Citizen,No,Asian,2/15/2017,,N/A-StillEmployed,Active,IT/IS,Brian Champaigne,13.0,Indeed,Fully Meets,3.42,4,7,1/4/2019,0,17 -"Robertson, Peter",10224,1,1,1,5,5,3,0,55578,1,20,Production Technician II,MA,2138,07/03/72,M ,Married,US Citizen,No,White,7/5/2011,2/8/2012,Another position,Voluntarily Terminated,Production ,Kissy Sullivan,20.0,Indeed,Fully Meets,4.2,5,0,1/6/2012,0,13 -"Shields, Seffi",10091,1,1,0,1,5,3,0,52087,0,19,Production Technician I,MA,2149,08/24/85,F,Married,US Citizen,No,White,8/19/2013,,N/A-StillEmployed,Active,Production ,Amy Dunn,11.0,LinkedIn,Fully Meets,4.81,4,0,2/15/2019,0,15 -"Roup,Simon",10198,0,0,1,1,3,3,0,140920,0,13,IT Manager - DB,MA,2481,04/05/73,M ,Single,US Citizen,No,White,1/20/2013,,N/A-StillEmployed,Active,IT/IS,Jennifer Zamora,5.0,Indeed,Fully Meets,3.6,5,7,2/18/2019,0,13 -"Stansfield, Norman",10307,1,1,1,1,6,1,0,58273,0,3,Area Sales Manager,NV,89139,05/09/74,M ,Married,US Citizen,No,White,5/12/2014,,N/A-StillEmployed,Active,Sales,Lynn Daneault,21.0,Website,PIP,1.81,2,0,1/17/2019,3,5 -"Potts, Xana",10124,1,1,0,1,6,3,0,61844,0,3,Area Sales Manager,KY,40220,08/29/88,F,Married,US Citizen,No,Black or African American,1/9/2012,,N/A-StillEmployed,Active,Sales,Lynn Daneault,21.0,Website,Fully Meets,4.2,5,0,2/1/2019,0,9 -"Zhou, Julia",10043,0,0,0,1,3,3,0,89292,0,9,Data Analyst,MA,2148,02/24/79,F,Single,US Citizen,No,White,3/30/2015,,N/A-StillEmployed,Active,IT/IS,Simon Roup,4.0,Employee Referral,Fully Meets,5.0,3,5,2/1/2019,0,11 -"Saar-Beckles, Melinda",10295,0,0,0,2,5,2,1,47750,0,19,Production Technician I,MA,1801,06/06/68,F,Single,US Citizen,No,Black or African American,7/4/2016,,N/A-StillEmployed,Active,Production ,Kelley Spirea,18.0,Diversity Job Fair,Needs Improvement,2.6,4,0,2/18/2019,5,4 -"Johnston, Yen",10036,0,0,0,1,5,4,0,63322,0,20,Production Technician II,MA,2128,09/08/69,F,Single,US Citizen,No,White,7/7/2014,,N/A-StillEmployed,Active,Production ,Brannon Miller,12.0,LinkedIn,Exceeds,4.3,3,0,1/11/2019,0,1 -"Gray, Elijiah ",10098,0,2,1,1,5,3,0,62957,0,18,Production Manager,MA,1752,07/11/81,M ,Divorced,US Citizen,No,White,6/2/2015,,N/A-StillEmployed,Active,Production ,Janet King,2.0,Employee Referral,Fully Meets,4.63,3,0,1/4/2019,0,2 -"Gilles, Alex",10283,1,1,1,5,5,2,1,54933,1,19,Production Technician I,MA,2062,08/09/74,M ,Married,US Citizen,No,Black or African American,4/2/2012,6/25/2015,military,Voluntarily Terminated,Production ,Webster Butler,39.0,Diversity Job Fair,Needs Improvement,3.97,4,0,1/20/2015,3,15 -"Carabbio, Judith",10085,0,0,0,1,4,3,0,93396,0,24,Software Engineer,MA,2132,04/05/87,F,Single,US Citizen,No,White,11/11/2013,,N/A-StillEmployed,Active,Software Engineering,Alex Sweetwater,10.0,Indeed,Fully Meets,4.96,4,6,1/30/2019,0,3 -"Johnson, Noelle ",10156,1,1,0,3,3,3,0,105700,0,8,Database Administrator,MA,2301,11/07/86,F,Married,US Citizen,No,Asian,1/5/2015,,N/A-StillEmployed,Active,IT/IS,Simon Roup,4.0,Indeed,Fully Meets,3.75,3,5,2/11/2019,0,2 -"Gosciminski, Phylicia ",10218,0,3,0,3,5,3,0,66149,0,20,Production Technician II,MA,1824,12/08/83,F,Separated,US Citizen,No,American Indian or Alaska Native,9/30/2013,,N/A-StillEmployed,Active,Production ,Kissy Sullivan,20.0,Google Search,Fully Meets,4.4,5,0,2/21/2019,0,1 -"Bacong, Alejandro ",10250,0,2,1,1,3,3,0,50178,0,14,IT Support,MA,1886,01/07/88,M ,Divorced,US Citizen,No,White,1/5/2015,,N/A-StillEmployed,Active,IT/IS,Peter Monroe,7.0,Indeed,Fully Meets,5.0,5,6,2/18/2019,0,16 -"Stanford,Barbara M",10278,0,2,0,1,5,3,0,47961,0,19,Production Technician I,MA,2050,08/25/82,F,Divorced,US Citizen,No,Two or more races,1/10/2011,,N/A-StillEmployed,Active,Production ,Kissy Sullivan,20.0,Google Search,Fully Meets,4.1,4,0,2/7/2019,0,9 -"Dee, Randy",10311,1,1,1,1,6,1,0,56991,0,19,Production Technician I,MA,2138,04/15/88,M ,Married,US Citizen,No,White,7/9/2018,,N/A-StillEmployed,Active,Production ,Brannon Miller,12.0,Indeed,Fully Meets,4.3,4,3,1/31/2019,2,2 -"Cole, Spencer",10292,0,0,1,4,5,2,0,59144,1,19,Production Technician I,MA,1880,08/12/79,M ,Single,US Citizen,No,Black or African American,7/11/2011,9/23/2016,performance,Terminated for Cause,Production ,Kissy Sullivan,20.0,LinkedIn,Needs Improvement,2.0,3,0,5/1/2016,5,16 -"Ivey, Rose ",10139,0,0,0,1,5,3,0,51908,0,19,Production Technician I,MA,1775,01/28/91,F,Single,US Citizen,No,White,8/19/2013,,N/A-StillEmployed,Active,Production ,Brannon Miller,12.0,Indeed,Fully Meets,3.99,3,0,1/14/2019,0,14 -"Daniele, Ann ",10212,1,1,0,3,3,3,0,85028,0,28,Sr. Network Engineer,CT,6033,01/18/52,F,Married,US Citizen,No,White,11/10/2014,,N/A-StillEmployed,Active,IT/IS,Peter Monroe,7.0,LinkedIn,Fully Meets,3.1,5,8,2/12/2019,0,19 -"Dobrin, Denisa S",10207,0,0,0,1,5,3,0,46335,0,19,Production Technician I,MA,2125,10/07/86,F,Single,US Citizen,Yes,White,4/2/2012,,N/A-StillEmployed,Active,Production ,David Stanley,14.0,CareerBuilder,Fully Meets,3.4,5,0,2/19/2019,0,15 -"Adinolfi, Wilson K",10026,0,0,1,1,5,4,0,62506,0,19,Production Technician I,MA,1960,07/10/83,M ,Single,US Citizen,No,White,7/5/2011,,N/A-StillEmployed,Active,Production ,Michael Albert,22.0,LinkedIn,Exceeds,4.6,5,0,1/17/2019,0,1 -"Kreuger, Freddy",10165,0,0,1,1,6,3,1,71339,0,3,Area Sales Manager,NY,10171,02/24/69,M ,Single,US Citizen,Yes,Black or African American,3/7/2011,,N/A-StillEmployed,Active,Sales,John Smith,17.0,Diversity Job Fair,Fully Meets,3.65,5,0,1/17/2019,0,20 -"Wilkes, Annie",10204,0,2,0,5,5,3,0,58062,1,19,Production Technician I,MA,1876,07/30/83,F,Divorced,US Citizen,No,White,1/10/2011,5/14/2012,Another position,Voluntarily Terminated,Production ,Ketsia Liebig,19.0,Google Search,Fully Meets,3.6,5,0,2/6/2011,0,9 -"Rose, Ashley ",10054,0,3,0,1,5,3,0,60627,0,19,Production Technician I,MA,1886,12/05/74,F,Separated,US Citizen,No,White,1/6/2014,,N/A-StillEmployed,Active,Production ,David Stanley,14.0,Website,Fully Meets,5.0,4,0,1/31/2019,0,8 -"Ait Sidi, Karthikeyan ",10084,1,1,1,5,3,3,0,104437,1,27,Sr. DBA,MA,2148,05/05/75,M ,Married,US Citizen,No,White,3/30/2015,6/16/2016,career change,Voluntarily Terminated,IT/IS,Simon Roup,4.0,Indeed,Fully Meets,4.96,3,6,2/24/2016,0,17 -"Foreman, Tanya",10163,1,1,0,5,5,3,0,55965,1,20,Production Technician II,MA,2170,11/08/83,F,Married,US Citizen,No,White,4/4/2011,1/9/2013,career change,Voluntarily Terminated,Production ,Ketsia Liebig,19.0,Google Search,Fully Meets,3.66,3,0,1/7/2012,0,6 -"Girifalco, Evelyn",10018,0,0,0,1,5,4,0,57815,0,19,Production Technician I,MA,2451,05/08/80,F,Single,US Citizen,Yes,Two or more races,9/29/2014,,N/A-StillEmployed,Active,Production ,Amy Dunn,11.0,Indeed,Exceeds,3.9,4,0,2/7/2019,0,3 -"Corleone, Michael",10282,0,2,1,1,5,2,0,68051,0,18,Production Manager,MA,1803,12/17/75,M ,Divorced,US Citizen,No,White,7/20/2010,,N/A-StillEmployed,Active,Production ,Janet King,2.0,CareerBuilder,Needs Improvement,4.13,2,0,1/14/2019,3,3 -"Gold, Shenice ",10243,0,0,0,1,5,3,0,53018,0,19,Production Technician I,MA,2451,06/18/92,F,Single,US Citizen,Yes,White,11/11/2013,,N/A-StillEmployed,Active,Production ,Ketsia Liebig,19.0,Indeed,Fully Meets,4.3,5,0,2/18/2019,0,7 -"Mancuso, Karen",10073,1,1,0,5,5,3,0,68407,1,20,Production Technician II,MA,2176,12/10/86,F,Married,US Citizen,No,Two or more races,7/5/2011,8/19/2012,Another position,Voluntarily Terminated,Production ,Amy Dunn,11.0,LinkedIn,Fully Meets,5.0,4,0,7/2/2012,0,16 -"Roberson, May",10230,0,2,0,5,5,3,0,64971,1,20,Production Technician II,MA,1902,09/05/81,F,Divorced,Eligible NonCitizen,No,Black or African American,9/26/2011,10/22/2011,return to school,Voluntarily Terminated,Production ,David Stanley,14.0,Google Search,Fully Meets,4.5,4,0,10/22/2011,0,10 -"Engdahl, Jean",10247,0,0,1,1,5,3,0,48888,0,19,Production Technician I,MA,2026,05/31/74,M ,Single,US Citizen,No,White,11/10/2014,,N/A-StillEmployed,Active,Production ,Kelley Spirea,18.0,LinkedIn,Fully Meets,4.7,5,0,2/13/2019,0,8 -"Brown, Mia",10238,1,1,0,1,1,3,1,63000,0,1,Accountant I,MA,1450,11/24/87,F,Married,US Citizen,No,Black or African American,10/27/2008,,N/A-StillEmployed,Active,Admin Offices,Brandon R. LeBlanc,1.0,Diversity Job Fair,Fully Meets,4.5,2,6,1/15/2019,0,14 -"Warfield, Sarah",10127,0,4,0,1,3,3,0,107226,0,28,Sr. Network Engineer,MA,2453,05/02/78,F,Widowed,US Citizen,No,Asian,3/30/2015,,N/A-StillEmployed,Active,IT/IS,Peter Monroe,7.0,Employee Referral,Fully Meets,4.2,4,8,2/5/2019,0,7 -"Moumanil, Maliki ",10120,0,3,1,1,5,3,0,74417,0,20,Production Technician II,MA,1460,12/01/74,M ,Separated,US Citizen,No,Black or African American,5/13/2013,,N/A-StillEmployed,Active,Production ,Michael Albert,22.0,LinkedIn,Fully Meets,4.29,5,0,1/28/2019,0,11 -"Jacobi, Hannah ",10236,0,2,0,1,5,3,0,45069,0,19,Production Technician I,MA,1778,03/22/66,F,Divorced,US Citizen,No,White,9/30/2013,,N/A-StillEmployed,Active,Production ,Kissy Sullivan,20.0,Employee Referral,Fully Meets,4.3,5,0,2/22/2019,0,7 -"Peterson, Ebonee ",10130,1,1,0,5,5,3,0,74669,1,18,Production Manager,MA,2030,05/09/77,F,Married,US Citizen,No,White,10/25/2010,5/18/2016,Another position,Voluntarily Terminated,Production ,Janet King,2.0,Indeed,Fully Meets,4.16,5,0,3/5/2015,0,6 -"Fitzpatrick, Michael J",10093,0,0,1,5,5,3,0,72609,1,20,Production Technician II,MA,2143,10/01/81,M ,Single,US Citizen,Yes,White,5/16/2011,6/24/2013,hours,Voluntarily Terminated,Production ,Amy Dunn,11.0,Google Search,Fully Meets,4.76,5,0,4/5/2013,0,20 -"Medeiros, Jennifer",10068,0,0,0,1,5,3,0,55688,0,19,Production Technician I,MA,2346,09/22/76,F,Single,US Citizen,No,White,3/30/2015,,N/A-StillEmployed,Active,Production ,Michael Albert,22.0,CareerBuilder,Fully Meets,5.0,4,0,1/21/2019,0,10 -"Linden, Mathew",10137,1,1,1,3,5,3,0,63450,0,20,Production Technician II,MA,1770,03/19/79,M ,Married,US Citizen,No,White,7/8/2013,,N/A-StillEmployed,Active,Production ,Kelley Spirea,18.0,LinkedIn,Fully Meets,4.0,3,0,2/18/2019,0,7 -"Wallace, Courtney E",10274,1,1,0,5,5,3,1,80512,1,18,Production Manager,MA,2478,11/14/55,F,Married,US Citizen,No,Black or African American,9/26/2011,1/2/2012,Another position,Voluntarily Terminated,Production ,Janet King,2.0,Diversity Job Fair,Fully Meets,4.5,3,0,1/2/2012,0,5 -"Galia, Lisa",10273,0,0,0,1,3,3,0,65707,0,14,IT Support,CT,6040,07/06/68,F,Single,US Citizen,No,White,5/1/2010,,N/A-StillEmployed,Active,IT/IS,Eric Dougall,6.0,LinkedIn,Fully Meets,4.7,4,5,2/1/2019,0,1 -"Pearson, Randall",10259,1,1,1,5,3,3,0,93093,1,9,Data Analyst,MA,2747,09/05/84,M ,Married,US Citizen,No,White,12/1/2014,5/1/2016,performance,Voluntarily Terminated,IT/IS,Simon Roup,4.0,Employee Referral,Fully Meets,4.7,4,5,1/16/2016,0,19 -"Chan, Lin",10210,0,0,0,1,5,3,0,54237,0,19,Production Technician I,MA,2170,02/12/79,F,Single,US Citizen,No,White,5/12/2014,,N/A-StillEmployed,Active,Production ,Elijiah Gray,16.0,Indeed,Fully Meets,3.3,4,0,2/19/2019,0,11 -"Gonzalez, Cayo",10031,0,2,1,1,5,4,1,59892,0,19,Production Technician I,MA,2108,09/29/69,M ,Divorced,US Citizen,No,Black or African American,7/11/2011,,N/A-StillEmployed,Active,Production ,Brannon Miller,12.0,Diversity Job Fair,Exceeds,4.5,4,0,2/18/2019,0,1 -"Becker, Scott",10277,0,0,1,3,5,3,0,53250,0,19,Production Technician I,MA,2452,04/06/79,M ,Single,US Citizen,No,Asian,7/8/2013,,N/A-StillEmployed,Active,Production ,Webster Butler,,LinkedIn,Fully Meets,4.2,4,0,1/11/2019,0,13 -"Jackson, Maryellen",10227,0,0,0,1,5,3,0,61242,0,19,Production Technician I,MA,2081,09/11/72,F,Single,US Citizen,No,Black or African American,11/5/2012,,N/A-StillEmployed,Active,Production ,David Stanley,14.0,LinkedIn,Fully Meets,4.1,3,0,1/17/2019,0,7 -"Navathe, Kurt",10079,0,0,1,1,3,3,0,87921,0,22,Senior BI Developer,MA,2056,04/25/70,M ,Single,US Citizen,No,Asian,2/10/2017,,N/A-StillEmployed,Active,IT/IS,Brian Champaigne,13.0,Indeed,Fully Meets,5.0,3,6,2/25/2019,0,17 -"Buccheri, Joseph ",10184,0,0,1,1,5,3,0,65288,0,20,Production Technician II,MA,1013,07/28/83,M ,Single,US Citizen,No,White,9/29/2014,,N/A-StillEmployed,Active,Production ,Webster Butler,,Google Search,Fully Meets,3.19,3,0,2/1/2019,0,9 -"Pelletier, Ermine",10297,1,1,0,5,5,2,0,60270,1,20,Production Technician II,MA,2472,07/18/89,F,Married,US Citizen,No,Asian,7/5/2011,9/15/2015,unhappy,Voluntarily Terminated,Production ,Amy Dunn,11.0,CareerBuilder,Needs Improvement,2.4,5,0,2/6/2015,5,2 -"Sullivan, Kissy ",10158,1,1,0,1,5,3,0,63682,0,18,Production Manager,MA,1776,03/28/78,F,Married,US Citizen,No,Black or African American,1/8/2009,,N/A-StillEmployed,Active,Production ,Janet King,2.0,Indeed,Fully Meets,3.73,4,0,1/24/2019,0,12 -"Billis, Helen",10003,1,1,0,1,5,4,0,62910,0,19,Production Technician I,MA,2031,09/01/89,F,Married,US Citizen,No,White,7/7/2014,,N/A-StillEmployed,Active,Production ,Brannon Miller,12.0,Indeed,Exceeds,5.0,3,0,2/27/2019,0,19 -"Cady, Max ",10150,0,0,1,1,4,3,0,77692,0,25,Software Engineering Manager,MA,2184,11/22/66,M ,Single,US Citizen,No,White,8/15/2011,,N/A-StillEmployed,Active,Software Engineering,Jennifer Zamora,5.0,Google Search,Fully Meets,3.84,3,5,1/21/2019,0,4 -"Maurice, Shana",10053,1,1,0,1,5,3,0,54132,0,19,Production Technician I,MA,2330,11/22/77,F,Married,US Citizen,No,White,5/31/2011,,N/A-StillEmployed,Active,Production ,David Stanley,14.0,Indeed,Fully Meets,5.0,4,0,1/10/2019,0,8 -"Forrest, Alex",10305,1,1,1,1,6,3,0,70187,1,3,Area Sales Manager,MA,2330,07/07/75,M ,Married,US Citizen,No,White,9/29/2014,8/19/2018,Fatal attraction,Terminated for Cause,Sales,Lynn Daneault,21.0,Employee Referral,PIP,2.0,5,0,1/28/2019,4,7 -"Eaton, Marianne",10064,1,1,0,5,5,3,0,60070,1,19,Production Technician I,MA,2343,09/05/91,F,Married,US Citizen,No,White,4/4/2011,6/6/2017,military,Voluntarily Terminated,Production ,Kissy Sullivan,20.0,Google Search,Fully Meets,5.0,3,0,4/9/2017,0,7 -"Tannen, Biff",10173,1,1,1,1,3,3,0,90100,0,4,BI Developer,MA,2134,10/24/87,M ,Married,US Citizen,No,White,4/20/2017,,N/A-StillEmployed,Active,IT/IS,Brian Champaigne,13.0,Indeed,Fully Meets,3.4,3,6,1/2/2019,0,14 -"Volk, Colleen",10022,1,1,0,4,5,4,0,49773,1,19,Production Technician I,MA,2747,06/03/86,F,Married,US Citizen,No,White,9/26/2011,2/8/2016,gross misconduct,Terminated for Cause,Production ,Kelley Spirea,18.0,Google Search,Exceeds,4.3,5,0,2/1/2015,0,18 -"Chace, Beatrice ",10067,0,0,0,1,5,3,0,61656,0,19,Production Technician I,MA,2763,01/02/51,F,Single,US Citizen,No,White,9/29/2014,,N/A-StillEmployed,Active,Production ,Michael Albert,22.0,Google Search,Fully Meets,5.0,4,0,2/12/2019,0,11 -"Lecter, Hannibal",10251,1,1,1,1,5,3,0,64738,0,19,Production Technician I,MA,1776,09/02/82,M ,Married,US Citizen,No,Asian,5/14/2012,,N/A-StillEmployed,Active,Production ,Elijiah Gray,16.0,Google Search,Fully Meets,4.1,3,0,2/22/2019,0,10 -"Strong, Caitrin",10241,1,1,0,1,6,3,0,60120,0,3,Area Sales Manager,MT,59102,05/12/89,F,Married,US Citizen,No,Black or African American,9/27/2010,,N/A-StillEmployed,Active,Sales,John Smith,17.0,Indeed,Fully Meets,4.1,4,0,1/31/2019,0,18 -"Dunn, Amy ",10105,0,0,0,1,5,3,0,75188,0,18,Production Manager,MA,1731,11/28/73,F,Single,US Citizen,No,White,9/18/2014,,N/A-StillEmployed,Active,Production ,Janet King,2.0,Google Search,Fully Meets,4.52,4,0,1/15/2019,0,4 -"Zima, Colleen",10271,0,4,0,1,5,3,0,45046,0,19,Production Technician I,MA,1730,08/17/78,F,Widowed,US Citizen,No,Asian,9/29/2014,,N/A-StillEmployed,Active,Production ,David Stanley,14.0,LinkedIn,Fully Meets,4.5,5,0,1/30/2019,0,2 -"Ozark, Travis",10041,0,0,1,1,6,3,0,68829,0,3,Area Sales Manager,NC,27229,05/19/82,M ,Single,US Citizen,No,White,1/5/2015,,N/A-StillEmployed,Active,Sales,John Smith,17.0,Website,Fully Meets,5.0,5,0,1/14/2019,0,18 -"Woodson, Jason",10135,0,0,1,1,5,3,0,65893,0,20,Production Technician II,MA,1810,05/11/85,M ,Single,US Citizen,No,White,7/7/2014,,N/A-StillEmployed,Active,Production ,Kissy Sullivan,20.0,LinkedIn,Fully Meets,4.07,4,0,2/28/2019,0,13 -"Cierpiszewski, Caroline ",10168,0,0,0,1,5,3,0,64816,0,19,Production Technician I,MA,2044,05/31/88,F,Single,Non-Citizen,No,Black or African American,10/3/2011,,N/A-StillEmployed,Active,Production ,Ketsia Liebig,19.0,Indeed,Fully Meets,3.58,5,0,1/30/2019,0,3 -"Simard, Kramer",10178,1,1,1,1,3,3,0,87826,0,9,Data Analyst,MA,2110,02/08/70,M ,Married,US Citizen,Yes,White,1/5/2015,,N/A-StillEmployed,Active,IT/IS,Simon Roup,4.0,Employee Referral,Fully Meets,3.32,3,7,1/14/2019,0,16 -"Meads, Elizabeth",10128,0,0,0,5,5,3,1,62659,1,19,Production Technician I,MA,1760,05/30/68,F,Single,US Citizen,No,Black or African American,4/2/2012,11/11/2016,Another position,Voluntarily Terminated,Production ,Kelley Spirea,18.0,Diversity Job Fair,Fully Meets,4.18,4,0,2/5/2016,0,17 -"Williams, Jacquelyn ",10264,0,0,0,5,5,3,1,59728,1,19,Production Technician I,MA,2109,10/02/69,F,Single,US Citizen,Yes,Black or African American,1/9/2012,6/27/2015,relocation out of area,Voluntarily Terminated,Production ,Ketsia Liebig,19.0,Diversity Job Fair,Fully Meets,4.3,4,0,6/2/2014,0,16 -"DeVito, Tommy",10197,0,0,1,1,3,3,0,96820,0,4,BI Developer,MA,2045,09/04/83,M ,Single,US Citizen,No,White,2/15/2017,,N/A-StillEmployed,Active,IT/IS,Brian Champaigne,13.0,Indeed,Fully Meets,3.01,5,7,1/23/2019,0,15 -"Miller, Brannon",10116,0,0,1,1,5,3,0,83667,0,18,Production Manager,MA,2045,08/10/81,M ,Single,US Citizen,yes,Hispanic,8/16/2012,,N/A-StillEmployed,Active,Production ,Janet King,2.0,Indeed,Fully Meets,4.37,3,0,1/14/2019,0,2 -"Petingill, Shana ",10217,1,1,0,1,5,3,0,74226,0,20,Production Technician II,MA,2050,03/10/79,F,Married,Eligible NonCitizen,No,Asian,4/2/2012,,N/A-StillEmployed,Active,Production ,Brannon Miller,12.0,LinkedIn,Fully Meets,4.3,3,0,1/14/2019,0,14 -"Dougall, Eric",10028,0,0,1,1,3,4,0,138888,0,13,IT Manager - Support,MA,1886,07/09/70,M ,Single,US Citizen,No,Black or African American,1/5/2014,,N/A-StillEmployed,Active,IT/IS,Jennifer Zamora,5.0,Indeed,Exceeds,4.3,5,5,1/4/2019,0,4 -"Hudson, Jane",10248,0,0,0,1,5,3,0,55425,0,19,Production Technician I,MA,2176,06/10/86,F,Single,US Citizen,No,White,2/20/2012,,N/A-StillEmployed,Active,Production ,Ketsia Liebig,19.0,LinkedIn,Fully Meets,4.8,4,0,1/7/2019,0,4 -"Gruber, Hans",10234,1,1,1,1,3,3,0,99020,0,4,BI Developer,MA,2134,06/30/89,M ,Married,US Citizen,No,Black or African American,4/20/2017,,N/A-StillEmployed,Active,IT/IS,Brian Champaigne,13.0,Indeed,Fully Meets,4.2,5,5,1/28/2019,0,8 -"Miller, Ned",10298,0,0,1,5,5,1,0,55800,1,20,Production Technician II,MA,2472,06/29/85,M ,Single,US Citizen,No,White,8/15/2011,9/4/2014,unhappy,Voluntarily Terminated,Production ,Brannon Miller,12.0,LinkedIn,PIP,3.0,2,0,1/14/2013,6,6 -"O'hare, Lynn",10303,0,0,0,4,5,1,0,52674,1,19,Production Technician I,MA,2152,09/30/80,F,Single,US Citizen,No,Two or more races,3/31/2014,5/1/2018,performance,Terminated for Cause,Production ,Kissy Sullivan,20.0,LinkedIn,PIP,2.33,2,0,3/9/2018,6,3 -"Carter, Michelle ",10040,0,0,0,1,6,3,0,71860,0,3,Area Sales Manager,VT,5664,05/15/63,F,Single,US Citizen,No,White,8/18/2014,,N/A-StillEmployed,Active,Sales,John Smith,17.0,Indeed,Fully Meets,5.0,5,0,1/21/2019,0,7 -"Brill, Donna",10177,1,1,0,5,5,3,0,53492,1,19,Production Technician I,MA,1701,08/24/90,F,Married,US Citizen,No,White,4/2/2012,6/15/2013,Another position,Voluntarily Terminated,Production ,David Stanley,14.0,Google Search,Fully Meets,3.35,4,0,3/4/2013,0,6 -"Carthy, B'rigit",10076,0,0,0,1,5,3,0,55315,0,20,Production Technician II,MA,2149,05/21/87,F,Single,US Citizen,No,Black or African American,3/30/2015,,N/A-StillEmployed,Active,Production ,Ketsia Liebig,19.0,LinkedIn,Fully Meets,5.0,5,0,2/7/2019,0,16 -"Homberger, Adrienne J",10195,1,1,0,5,5,3,0,63478,1,20,Production Technician II,MA,2445,02/16/84,F,Married,Non-Citizen,No,White,8/15/2011,4/7/2012,relocation out of area,Voluntarily Terminated,Production ,Michael Albert,30.0,Indeed,Fully Meets,3.03,5,0,3/5/2012,0,16 -"Sewkumar, Nori",10071,0,0,0,3,5,3,0,50923,0,19,Production Technician I,MA,2191,03/10/75,F,Single,US Citizen,No,Asian,9/30/2013,,N/A-StillEmployed,Active,Production ,Webster Butler,,Google Search,Fully Meets,5.0,5,0,2/6/2019,0,14 -"Gerke, Melisa",10122,0,2,0,5,5,3,1,51505,1,19,Production Technician I,MA,2330,05/15/70,F,Divorced,US Citizen,No,Black or African American,11/7/2011,11/15/2016,hours,Voluntarily Terminated,Production ,Elijiah Gray,16.0,Diversity Job Fair,Fully Meets,4.24,4,0,4/29/2016,0,2 -"Mahoney, Lauren ",10219,0,0,0,1,5,3,0,45395,0,19,Production Technician I,MA,2189,07/07/86,F,Single,US Citizen,No,White,1/6/2014,,N/A-StillEmployed,Active,Production ,Ketsia Liebig,19.0,LinkedIn,Fully Meets,4.6,4,0,2/26/2019,0,14 -"Hankard, Earnest",10074,0,0,1,1,5,3,0,64246,0,20,Production Technician II,MA,2155,08/10/88,M ,Single,US Citizen,Yes,White,11/11/2013,,N/A-StillEmployed,Active,Production ,Kelley Spirea,18.0,LinkedIn,Fully Meets,5.0,3,0,1/8/2019,0,20 -"Chang, Donovan E",10154,0,0,1,1,5,3,0,60380,0,19,Production Technician I,MA,1845,08/24/83,M ,Single,US Citizen,No,White,7/8/2013,,N/A-StillEmployed,Active,Production ,Webster Butler,,LinkedIn,Fully Meets,3.8,5,0,1/14/2019,0,4 -"Houlihan, Debra",10272,1,1,0,1,6,3,0,180000,0,11,Director of Sales,RI,2908,03/17/66,F,Married,US Citizen,No,White,5/5/2014,,N/A-StillEmployed,Active,Sales,Janet King,2.0,LinkedIn,Fully Meets,4.5,4,0,1/21/2019,0,19 -"Blount, Dianna",10294,0,0,0,1,5,2,0,66441,0,20,Production Technician II,MA,2171,09/21/90,F,Single,US Citizen,No,White,4/4/2011,,N/A-StillEmployed,Active,Production ,Michael Albert,22.0,CareerBuilder,Needs Improvement,2.0,3,0,2/27/2019,2,3 -"Knapp, Bradley J",10208,0,0,1,1,5,3,0,46654,0,19,Production Technician I,MA,1721,11/10/77,M ,Single,US Citizen,No,Black or African American,2/17/2014,,N/A-StillEmployed,Active,Production ,Ketsia Liebig,19.0,LinkedIn,Fully Meets,3.1,3,0,2/6/2019,0,3 -"Gentry, Mildred",10159,1,1,0,1,5,3,0,51337,0,19,Production Technician I,MA,2145,10/01/90,F,Married,US Citizen,No,Black or African American,3/30/2015,,N/A-StillEmployed,Active,Production ,Michael Albert,22.0,LinkedIn,Fully Meets,3.73,3,0,1/16/2019,0,19 -"MacLennan, Samuel",10191,0,4,1,5,5,3,0,52788,1,19,Production Technician I,MA,1938,11/09/72,M ,Widowed,US Citizen,No,White,9/24/2012,9/26/2017,hours,Voluntarily Terminated,Production ,Amy Dunn,11.0,Indeed,Fully Meets,3.08,4,0,4/1/2017,0,18 -"Kretschmer, John",10176,1,1,1,1,5,3,0,63973,0,19,Production Technician I,MA,1801,02/02/80,M ,Married,US Citizen,No,Asian,1/10/2011,,N/A-StillEmployed,Active,Production ,Brannon Miller,12.0,Indeed,Fully Meets,3.38,3,0,1/21/2019,0,17 -"Patronick, Lucas",10005,0,0,1,5,4,4,1,108987,1,24,Software Engineer,MA,1844,02/20/79,M ,Single,US Citizen,No,Black or African American,11/7/2011,9/7/2015,Another position,Voluntarily Terminated,Software Engineering,Alex Sweetwater,10.0,Diversity Job Fair,Exceeds,5.0,5,3,8/16/2015,0,13 -"Sutwell, Barbara",10209,0,0,0,1,5,3,0,59238,0,19,Production Technician I,MA,2718,08/15/68,F,Single,Eligible NonCitizen,No,Asian,5/14/2012,,N/A-StillEmployed,Active,Production ,Elijiah Gray,16.0,Indeed,Fully Meets,3.4,5,0,1/31/2019,0,13 -"Candie, Calvin",10001,0,0,1,1,5,4,0,72640,0,18,Production Manager,MA,2169,08/09/83,M ,Single,US Citizen,No,White,1/28/2016,,N/A-StillEmployed,Active,Production ,Janet King,2.0,Indeed,Exceeds,5.0,3,0,2/22/2019,0,14 -"Smith, Joe",10027,0,0,1,1,5,4,0,60656,0,20,Production Technician II,MA,2045,10/30/63,M ,Single,US Citizen,No,White,9/29/2014,,N/A-StillEmployed,Active,Production ,Elijiah Gray,16.0,Indeed,Exceeds,4.3,3,0,1/28/2019,0,4 -"Winthrop, Jordan ",10033,0,0,1,5,5,4,0,70507,1,20,Production Technician II,MA,2045,11/07/58,M ,Single,US Citizen,No,White,1/7/2013,2/21/2016,retiring,Voluntarily Terminated,Production ,Brannon Miller,12.0,LinkedIn,Exceeds,5.0,3,0,1/19/2016,0,7 -"Harrison, Kara",10007,1,1,0,1,5,4,0,62065,0,19,Production Technician I,MA,1886,05/02/74,F,Married,US Citizen,No,White,5/12/2014,,N/A-StillEmployed,Active,Production ,Amy Dunn,11.0,CareerBuilder,Exceeds,4.76,4,0,2/15/2019,0,5 -"Wallace, Theresa",10293,0,0,0,5,5,2,0,50274,1,19,Production Technician I,MA,1887,08/02/80,F,Single,US Citizen,No,White,8/13/2012,9/1/2015,career change,Voluntarily Terminated,Production ,Elijiah Gray,16.0,CareerBuilder,Needs Improvement,2.5,3,0,9/5/2014,6,13 -"Leruth, Giovanni",10103,0,3,1,1,6,3,0,70468,0,3,Area Sales Manager,UT,84111,12/27/88,M ,Separated,US Citizen,No,Black or African American,4/30/2012,,N/A-StillEmployed,Active,Sales,John Smith,17.0,Website,Fully Meets,4.53,3,0,1/29/2019,0,16 -"True, Edward",10102,0,0,1,5,4,3,1,100416,1,24,Software Engineer,MA,2451,06/14/83,M ,Single,Non-Citizen,No,Black or African American,2/18/2013,4/15/2018,medical issues,Voluntarily Terminated,Software Engineering,Alex Sweetwater,10.0,Diversity Job Fair,Fully Meets,4.6,3,4,2/12/2017,0,9 -"Burke, Joelle",10107,0,0,0,1,5,3,0,63763,0,20,Production Technician II,MA,2148,03/02/80,F,Single,US Citizen,No,Black or African American,3/5/2012,,N/A-StillEmployed,Active,Production ,Amy Dunn,11.0,Employee Referral,Fully Meets,4.51,4,0,2/21/2019,0,3 -"Lundy, Susan",10096,0,4,0,5,5,3,0,67237,1,20,Production Technician II,MA,2122,12/26/76,F,Widowed,US Citizen,No,White,7/8/2013,9/15/2016,more money,Voluntarily Terminated,Production ,Michael Albert,22.0,LinkedIn,Fully Meets,4.65,4,0,6/10/2016,0,15 -"Nguyen, Dheepa",10042,0,0,0,1,6,3,0,63695,0,3,Area Sales Manager,GA,30428,03/31/89,F,Single,US Citizen,No,Two or more races,7/8/2013,,N/A-StillEmployed,Active,Sales,Lynn Daneault,21.0,Indeed,Fully Meets,5.0,5,0,1/25/2019,0,2 -"Beatrice, Courtney ",10055,0,0,0,1,5,3,0,59026,0,19,Production Technician I,MA,1915,10/27/70,F,Single,Eligible NonCitizen,No,White,4/4/2011,,N/A-StillEmployed,Active,Production ,Elijiah Gray,16.0,Google Search,Fully Meets,5.0,5,0,1/14/2019,0,12 -"Trzeciak, Cybil",10270,0,0,0,5,5,3,0,74813,1,20,Production Technician II,MA,1778,03/15/85,F,Single,US Citizen,No,White,1/10/2011,7/2/2014,unhappy,Voluntarily Terminated,Production ,Amy Dunn,11.0,LinkedIn,Fully Meets,4.4,3,0,1/5/2014,0,5 -"Biden, Lowan M",10226,0,2,0,1,5,3,0,64919,0,19,Production Technician I,MA,2027,12/27/58,F,Divorced,US Citizen,No,Asian,8/19/2013,,N/A-StillEmployed,Active,Production ,Ketsia Liebig,19.0,Indeed,Fully Meets,4.2,3,0,1/10/2019,0,2 -"Terry, Sharlene ",10161,0,0,0,1,6,3,0,58370,0,3,Area Sales Manager,OR,97756,05/07/65,F,Single,US Citizen,No,Black or African American,9/29/2014,,N/A-StillEmployed,Active,Sales,Lynn Daneault,21.0,Indeed,Fully Meets,3.69,3,0,1/28/2019,0,18 -"Singh, Nan ",10039,0,0,0,1,1,3,0,51920,0,2,Administrative Assistant,MA,2330,05/19/88,F,Single,US Citizen,No,White,5/1/2015,,N/A-StillEmployed,Active,Admin Offices,Brandon R. LeBlanc,1.0,Website,Fully Meets,5.0,3,5,1/15/2019,0,2 -"Guilianno, Mike",10109,0,0,1,5,6,3,0,71707,1,3,Area Sales Manager,TN,37129,02/09/69,M ,Single,US Citizen,No,Two or more races,3/7/2012,10/31/2014,relocation out of area,Voluntarily Terminated,Sales,John Smith,17.0,LinkedIn,Fully Meets,4.5,5,0,2/1/2013,0,20 -"Leach, Dallas",10087,0,0,0,5,5,3,0,63676,1,19,Production Technician I,MA,1810,01/17/79,F,Single,US Citizen,No,Asian,9/26/2011,8/19/2018,return to school,Voluntarily Terminated,Production ,Kissy Sullivan,20.0,CareerBuilder,Fully Meets,4.88,3,0,7/2/2017,0,17 -"Motlagh, Dawn",10254,0,2,0,1,5,3,0,63430,0,19,Production Technician I,MA,2453,07/07/84,F,Divorced,US Citizen,No,White,4/1/2013,,N/A-StillEmployed,Active,Production ,Elijiah Gray,16.0,LinkedIn,Fully Meets,4.4,4,0,1/17/2019,0,18 -"Sadki, Nore ",10260,0,0,1,5,5,3,0,46428,1,19,Production Technician I,MA,2148,12/21/74,M ,Single,US Citizen,No,White,1/5/2009,7/30/2018,relocation out of area,Voluntarily Terminated,Production ,Michael Albert,22.0,Google Search,Fully Meets,4.6,5,0,2/5/2018,0,7 -"Immediato, Walter",10289,1,1,1,5,5,2,0,83082,1,18,Production Manager,MA,2128,11/15/76,M ,Married,US Citizen,No,Asian,2/21/2011,9/24/2012,unhappy,Voluntarily Terminated,Production ,Janet King,2.0,Indeed,Needs Improvement,2.34,2,0,4/12/2012,3,4 -"Panjwani, Nina",10148,1,1,0,5,5,3,0,63515,1,19,Production Technician I,MA,2351,05/01/79,F,Married,US Citizen,No,White,2/7/2011,1/12/2014,Another position,Voluntarily Terminated,Production ,Elijiah Gray,16.0,Google Search,Fully Meets,3.89,4,0,3/4/2013,0,7 -"Keatts, Kramer ",10192,0,0,1,1,5,3,0,50482,0,19,Production Technician I,MA,1887,01/19/76,M ,Single,US Citizen,No,White,9/30/2013,,N/A-StillEmployed,Active,Production ,Michael Albert,22.0,Indeed,Fully Meets,3.07,4,0,1/23/2019,0,10 -"Thibaud, Kenneth",10268,0,4,1,5,5,3,0,67176,1,20,Production Technician II,MA,2472,09/16/75,M ,Widowed,US Citizen,No,White,6/25/2007,8/30/2010,military,Voluntarily Terminated,Production ,Webster Butler,39.0,Other,Fully Meets,4.1,4,0,7/14/2010,0,15 -"Robinson, Alain ",10047,1,1,1,5,5,3,0,50428,1,19,Production Technician I,MA,1420,01/07/74,M ,Married,US Citizen,No,Black or African American,1/10/2011,1/26/2016,attendance,Voluntarily Terminated,Production ,Amy Dunn,11.0,Indeed,Fully Meets,5.0,3,0,1/10/2015,0,11 -"Lajiri, Jyoti",10113,1,1,1,3,3,3,0,93206,0,28,Sr. Network Engineer,MA,2169,04/23/86,M ,Married,US Citizen,No,White,11/10/2014,,N/A-StillEmployed,Active,IT/IS,Peter Monroe,7.0,Employee Referral,Fully Meets,4.46,5,6,1/7/2019,0,7 -"LeBlanc, Brandon R",10134,1,1,1,1,1,3,0,93046,0,23,Shared Services Manager,MA,1460,06/10/84,M ,Married,US Citizen,No,White,1/5/2016,,N/A-StillEmployed,Active,Admin Offices,Janet King,2.0,CareerBuilder,Fully Meets,4.1,4,0,1/28/2019,0,20 -"Petrowsky, Thelma",10016,1,1,0,1,3,4,0,93554,0,9,Data Analyst,MA,1886,09/16/84,F,Married,US Citizen,No,Black or African American,11/10/2014,,N/A-StillEmployed,Active,IT/IS,Simon Roup,4.0,Employee Referral,Exceeds,4.6,5,7,1/4/2019,0,16 -"Exantus, Susan",10290,1,1,0,4,4,2,0,99280,1,24,Software Engineer,MA,1749,05/15/87,F,Married,US Citizen,No,Black or African American,5/2/2011,6/5/2013,attendance,Terminated for Cause,Software Engineering,Alex Sweetwater,10.0,Indeed,Needs Improvement,2.1,5,4,8/10/2012,4,19 -"Oliver, Brooke ",10078,1,1,0,5,5,3,0,71966,1,20,Production Technician II,MA,2492,02/11/52,F,Married,US Citizen,No,Asian,5/14/2012,8/19/2013,unhappy,Voluntarily Terminated,Production ,Webster Butler,39.0,LinkedIn,Fully Meets,5.0,3,0,7/2/2013,0,17 -"Moran, Patrick",10223,0,0,1,3,5,3,1,72106,0,20,Production Technician II,MA,2127,12/03/76,M ,Single,US Citizen,No,Black or African American,1/9/2012,,N/A-StillEmployed,Active,Production ,Kelley Spirea,18.0,Diversity Job Fair,Fully Meets,4.1,4,0,1/31/2019,0,12 -"Smith, John",10291,0,2,1,1,6,2,1,72992,0,21,Sales Manager,MA,1886,08/16/84,M ,Divorced,US Citizen,No,Black or African American,5/18/2014,,N/A-StillEmployed,Active,Sales,Debra Houlihan,15.0,Diversity Job Fair,Needs Improvement,2.4,4,0,1/16/2019,2,16 -"Onque, Jasmine",10121,0,0,0,1,6,3,0,63051,0,3,Area Sales Manager,FL,33174,05/11/90,F,Single,US Citizen,Yes,White,9/30/2013,,N/A-StillEmployed,Active,Sales,Lynn Daneault,21.0,Indeed,Fully Meets,4.28,3,0,1/25/2019,0,1 -"Stoica, Rick",10266,1,1,1,1,5,3,0,61355,0,19,Production Technician I,MA,2301,03/14/85,M ,Married,US Citizen,No,Asian,2/17/2014,,N/A-StillEmployed,Active,Production ,Kelley Spirea,18.0,LinkedIn,Fully Meets,4.7,3,0,1/11/2019,0,4 -"Andreola, Colby",10194,0,0,0,1,4,3,0,95660,0,24,Software Engineer,MA,2110,05/24/79,F,Single,US Citizen,No,White,11/10/2014,,N/A-StillEmployed,Active,Software Engineering,Alex Sweetwater,10.0,LinkedIn,Fully Meets,3.04,3,4,1/2/2019,0,19 -"Gordon, David",10051,1,1,1,1,5,3,0,48285,0,19,Production Technician I,MA,2169,05/21/79,M ,Married,US Citizen,No,White,7/2/2012,,N/A-StillEmployed,Active,Production ,David Stanley,14.0,LinkedIn,Fully Meets,5.0,3,0,1/14/2019,0,2 -"Foss, Jason",10015,0,0,1,1,3,4,0,178000,0,12,IT Director,MA,1460,07/05/80,M ,Single,US Citizen,No,Black or African American,4/15/2011,,N/A-StillEmployed,Active,IT/IS,Jennifer Zamora,5.0,Indeed,Exceeds,5.0,5,5,1/7/2019,0,15 -"Sparks, Taylor ",10302,1,1,0,1,5,1,0,64021,0,19,Production Technician I,MA,2093,07/20/68,F,Married,US Citizen,No,White,2/20/2012,,N/A-StillEmployed,Active,Production ,Brannon Miller,12.0,Indeed,PIP,2.4,2,1,2/25/2019,6,20 -"Hitchcock, Alfred",10167,1,1,1,1,6,3,0,70545,0,3,Area Sales Manager,NH,3062,09/14/88,M ,Married,US Citizen,No,American Indian or Alaska Native,8/18/2014,,N/A-StillEmployed,Active,Sales,John Smith,17.0,Indeed,Fully Meets,3.6,5,0,1/30/2019,0,9 -"Mangal, Debbie",10279,1,1,0,1,5,3,0,61349,0,19,Production Technician I,MA,2451,11/07/74,F,Married,US Citizen,No,White,11/11/2013,,N/A-StillEmployed,Active,Production ,Brannon Miller,12.0,LinkedIn,Fully Meets,4.1,3,0,1/22/2019,0,11 -"Bernstein, Sean",10046,0,0,1,1,5,3,0,51044,0,19,Production Technician I,MA,2072,12/22/70,M ,Single,US Citizen,Yes,White,4/2/2012,,N/A-StillEmployed,Active,Production ,Amy Dunn,11.0,Google Search,Fully Meets,5.0,3,0,1/14/2019,0,13 -"Cornett, Lisa ",10094,1,1,0,1,5,3,0,63381,0,19,Production Technician I,MA,2189,03/31/77,F,Married,US Citizen,Yes,White,1/5/2015,,N/A-StillEmployed,Active,Production ,Kelley Spirea,18.0,Indeed,Fully Meets,4.73,5,0,2/14/2019,0,6 -"Cross, Noah",10083,0,0,1,1,3,3,0,92329,0,28,Sr. Network Engineer,CT,6278,09/09/65,M ,Single,US Citizen,No,White,11/10/2014,,N/A-StillEmployed,Active,IT/IS,Peter Monroe,7.0,Employee Referral,Fully Meets,5.0,3,4,1/2/2019,0,5 -"Booth, Frank",10199,0,0,1,4,3,3,0,103613,1,30,Enterprise Architect,CT,6033,07/30/64,M ,Single,US Citizen,No,Black or African American,2/17/2014,2/19/2016,Learned that he is a gangster,Terminated for Cause,IT/IS,Simon Roup,4.0,LinkedIn,Fully Meets,3.5,5,7,1/10/2016,0,2 -"Faller, Megan ",10263,1,1,0,1,5,3,0,71776,0,20,Production Technician II,MA,1824,09/22/78,F,Married,US Citizen,No,Black or African American,7/7/2014,,N/A-StillEmployed,Active,Production ,Elijiah Gray,16.0,LinkedIn,Fully Meets,4.4,5,0,2/22/2019,0,17 -"Salter, Jason",10229,0,2,1,5,3,3,0,88527,1,9,Data Analyst ,MA,2452,12/17/87,M ,Divorced,US Citizen,No,Black or African American,1/5/2015,10/31/2015,hours,Voluntarily Terminated,IT/IS,Simon Roup,4.0,LinkedIn,Fully Meets,4.2,3,5,4/20/2015,0,2 -"Smith, Leigh Ann",10153,1,1,0,5,1,3,1,55000,1,2,Administrative Assistant,MA,1844,06/14/87,F,Married,US Citizen,No,Black or African American,9/26/2011,9/25/2013,career change,Voluntarily Terminated,Admin Offices,Brandon R. LeBlanc,1.0,Diversity Job Fair,Fully Meets,3.8,4,4,8/15/2013,0,17 -"Lunquist, Lisa",10035,0,0,0,1,5,4,0,73330,0,20,Production Technician II,MA,2324,03/28/82,F,Single,US Citizen,No,Black or African American,8/19/2013,,N/A-StillEmployed,Active,Production ,Elijiah Gray,16.0,Indeed,Exceeds,4.2,4,0,2/12/2019,0,19 -"Erilus, Angela",10299,0,3,0,1,5,1,0,56847,0,20,Production Technician II,MA,2133,08/25/89,F,Separated,US Citizen,No,White,7/7/2014,,N/A-StillEmployed,Active,Production ,Michael Albert,22.0,Indeed,PIP,3.0,1,0,2/25/2019,2,5 -"DeGweck, James",10070,1,1,1,5,5,3,0,55722,1,19,Production Technician I,MA,1810,10/31/77,M ,Married,US Citizen,No,White,5/16/2011,6/8/2016,unhappy,Voluntarily Terminated,Production ,Webster Butler,39.0,Indeed,Fully Meets,5.0,4,0,4/2/2016,0,14 -"Gill, Whitney ",10142,0,4,0,4,6,3,0,59370,1,3,Area Sales Manager,OH,43050,07/10/71,F,Widowed,US Citizen,No,Black or African American,7/7/2014,9/5/2015,attendance,Terminated for Cause,Sales,John Smith,17.0,CareerBuilder,Fully Meets,3.97,4,0,1/15/2014,0,7 -"Myers, Michael",10216,0,0,1,1,5,3,0,57575,0,19,Production Technician I,MA,1550,04/18/80,M ,Single,US Citizen,No,Asian,7/8/2013,,N/A-StillEmployed,Active,Production ,Kissy Sullivan,20.0,LinkedIn,Fully Meets,4.1,4,0,1/22/2019,0,13 -"Barone, Francesco A",10265,0,0,1,1,5,3,0,58709,0,19,Production Technician I,MA,1810,07/20/83,M ,Single,US Citizen,No,Two or more races,2/20/2012,,N/A-StillEmployed,Active,Production ,Kelley Spirea,18.0,Google Search,Fully Meets,4.6,4,0,2/14/2019,0,7 -"Tavares, Desiree ",10221,1,1,0,5,5,3,1,60754,1,19,Production Technician I,MA,1801,04/03/75,F,Married,Non-Citizen,No,Black or African American,4/27/2009,4/1/2013,Another position,Voluntarily Terminated,Production ,Webster Butler,39.0,Diversity Job Fair,Fully Meets,4.5,5,0,2/15/2012,0,11 -"Gaul, Barbara",10257,0,0,0,1,5,3,0,53171,0,19,Production Technician I,MA,2121,12/02/83,F,Single,US Citizen,Yes,Black or African American,5/16/2011,,N/A-StillEmployed,Active,Production ,Kelley Spirea,18.0,LinkedIn,Fully Meets,4.2,4,0,2/26/2019,0,12 -"Turpin, Jumil",10045,1,1,1,1,3,3,0,76029,0,15,Network Engineer,MA,2343,03/31/69,M ,Married,Eligible NonCitizen,No,White,3/30/2015,,N/A-StillEmployed,Active,IT/IS,Peter Monroe,7.0,Employee Referral,Fully Meets,5.0,4,7,1/14/2019,0,8 -"Hendrickson, Trina",10075,0,0,0,5,5,3,0,68099,1,20,Production Technician II,MA,2021,08/27/72,F,Single,US Citizen,No,White,1/10/2011,6/18/2013,hours,Voluntarily Terminated,Production ,Kelley Spirea,18.0,CareerBuilder,Fully Meets,5.0,3,0,1/30/2013,0,15 -"Peters, Lauren",10032,1,1,0,5,5,4,0,57954,1,20,Production Technician II,MA,1886,08/17/86,F,Married,US Citizen,No,White,5/16/2011,2/4/2013,more money,Voluntarily Terminated,Production ,Ketsia Liebig,19.0,Indeed,Exceeds,4.2,5,0,1/10/2013,0,12 -"Cloninger, Jennifer",10275,1,1,0,5,5,3,0,64066,1,20,Production Technician II,MA,1752,08/31/81,F,Married,US Citizen,No,White,5/16/2011,1/7/2013,unhappy,Voluntarily Terminated,Production ,Brannon Miller,12.0,Google Search,Fully Meets,4.2,5,0,5/3/2012,0,9 -"Garcia, Raul",10111,0,0,1,1,5,3,0,52249,0,19,Production Technician I,MA,1905,09/15/85,M ,Single,US Citizen,Yes,White,3/30/2015,,N/A-StillEmployed,Active,Production ,David Stanley,14.0,Employee Referral,Fully Meets,4.5,3,0,2/18/2019,0,5 -"Sullivan, Timothy",10117,1,1,1,1,5,3,0,63025,0,19,Production Technician I,MA,2747,10/07/82,M ,Married,US Citizen,Yes,White,1/5/2015,,N/A-StillEmployed,Active,Production ,Michael Albert,22.0,Google Search,Fully Meets,4.36,5,0,1/24/2019,0,10 -"Desimone, Carl ",10310,1,1,1,1,5,1,0,53189,0,19,Production Technician I,MA,2061,04/19/67,M ,Married,US Citizen,No,White,7/7/2014,,N/A-StillEmployed,Active,Production ,Amy Dunn,11.0,Indeed,PIP,1.12,2,0,1/31/2019,4,9 -"Ngodup, Shari ",10037,0,3,0,1,5,4,1,52984,0,19,Production Technician I,MA,1810,06/03/67,F,Separated,US Citizen,No,Black or African American,4/1/2013,,N/A-StillEmployed,Active,Production ,Brannon Miller,12.0,Diversity Job Fair,Exceeds,4.0,3,0,2/13/2019,0,12 -"Sahoo, Adil",10233,1,1,1,1,5,3,0,57975,0,20,Production Technician II,MA,2062,04/26/86,M ,Married,US Citizen,No,White,8/30/2010,,N/A-StillEmployed,Active,Production ,Kelley Spirea,18.0,CareerBuilder,Fully Meets,4.1,3,0,1/10/2019,0,13 -"Pitt, Brad ",10164,0,0,1,1,5,3,0,47001,0,19,Production Technician I,MA,2451,11/23/81,M ,Single,US Citizen,No,White,11/5/2007,,N/A-StillEmployed,Active,Production ,David Stanley,14.0,Google Search,Fully Meets,3.66,3,0,2/25/2019,0,15 -"Fidelia, Libby",10049,1,1,0,1,5,3,0,58530,0,19,Production Technician I,MA,2155,03/16/81,F,Married,US Citizen,No,White,1/9/2012,,N/A-StillEmployed,Active,Production ,Brannon Miller,12.0,Google Search,Fully Meets,5.0,5,0,1/29/2019,0,19 -"Lydon, Allison",10057,1,1,0,3,5,3,0,52057,0,19,Production Technician I,MA,2122,10/22/75,F,Married,US Citizen,No,Black or African American,2/16/2015,,N/A-StillEmployed,Active,Production ,Elijiah Gray,16.0,Website,Fully Meets,5.0,3,0,1/23/2019,0,6 -"Gross, Paula",10059,0,2,0,5,5,3,0,63813,1,19,Production Technician I,MA,2176,05/21/83,F,Divorced,US Citizen,No,White,2/21/2011,1/11/2014,more money,Voluntarily Terminated,Production ,Kelley Spirea,18.0,CareerBuilder,Fully Meets,5.0,5,0,6/3/2013,0,17 -"Szabo, Andrew",10024,0,0,1,1,4,4,0,92989,0,24,Software Engineer,MA,2140,05/06/83,M ,Single,US Citizen,No,White,7/7/2014,,N/A-StillEmployed,Active,Software Engineering,Alex Sweetwater,10.0,LinkedIn,Exceeds,4.5,5,5,2/18/2019,0,1 +Employee_Name,EmpID,MarriedID,MaritalStatusID,GenderID,DeptID,PerfScoreID,FromDiversityJobFairID,Salary,Termd,PositionID,Position,State,Zip,DOB,Sex,MaritalDesc,CitizenDesc,HispanicLatino,RaceDesc,DateofHire,Department,ManagerName,ManagerID,RecruitmentSource,PerformanceScore,EngagementSurvey,EmpSatisfaction,SpecialProjectsCount,LastPerformanceReview_Date,DaysLateLast30,Absences +"Smith, Sade",10157,0,0,0,5,3,0,58939,0,19,Production Technician I,MA,2130,02/02/65,F,Single,US Citizen,No,White,11/11/2013,Production ,Ketsia Liebig,19.0,Employee Referral,Fully Meets,3.73,3,0,1/24/2019,0,16 +"Foster-Baker, Amy",10080,1,1,0,1,3,0,99351,0,26,Sr. Accountant,MA,2050,04/16/79,F,Married,US Citizen,no,White,1/5/2009,Admin Offices,Board of Directors,9.0,Other,Fully Meets,5.0,3,2,2/8/2019,0,3 +"Langford, Lindsey",10106,0,2,0,5,3,0,66074,1,20,Production Technician II,MA,2090,07/25/79,F,Divorced,US Citizen,No,Asian,1/7/2013,Production ,David Stanley,14.0,Indeed,Fully Meets,4.52,3,0,2/20/2014,0,20 +"Newman, Richard ",10063,1,1,1,5,3,0,48495,0,19,Production Technician I,MA,2136,04/08/77,M ,Married,US Citizen,No,White,5/12/2014,Production ,Ketsia Liebig,19.0,LinkedIn,Fully Meets,5.0,5,0,2/18/2019,0,11 +"Beak, Kimberly ",10023,1,1,0,5,4,0,70131,0,20,Production Technician II,MA,2145,04/17/66,F,Married,US Citizen,No,White,7/21/2016,Production ,Kelley Spirea,18.0,Employee Referral,Exceeds,4.4,3,0,1/14/2019,0,16 +"Hutter, Rosalie",10214,0,3,0,5,3,0,64995,0,20,Production Technician II,MA,2351,05/07/92,F,Separated,US Citizen,No,White,6/5/2015,Production ,Webster Butler,,Indeed,Fully Meets,4.5,3,0,2/14/2019,0,6 +"Latif, Mohammed",10249,1,1,1,5,3,0,61962,1,20,Production Technician II,MA,2126,05/09/84,M ,Married,US Citizen,No,White,4/2/2012,Production ,Kissy Sullivan,20.0,Google Search,Fully Meets,4.9,3,0,2/20/2013,0,20 +"Nguyen, Lei-Ming",10206,0,0,0,5,3,0,62061,0,19,Production Technician I,MA,2132,07/07/84,F,Single,US Citizen,No,White,7/8/2013,Production ,David Stanley,14.0,LinkedIn,Fully Meets,3.6,5,0,1/2/2019,0,4 +"Ren, Kylo",10140,1,1,1,6,3,0,61809,0,3,Area Sales Manager,ID,83706,10/12/54,M ,Married,US Citizen,No,White,5/12/2014,Sales,John Smith,17.0,CareerBuilder,Fully Meets,3.98,3,0,1/28/2019,0,4 +"McKinzie, Jac",10202,1,1,1,6,3,0,63291,0,3,Area Sales Manager,TX,78789,07/01/84,M ,Married,US Citizen,No,Two or more races,7/6/2016,Sales,Lynn Daneault,21.0,Website,Fully Meets,3.4,4,0,1/29/2019,0,7 +"Monkfish, Erasumus",10213,1,1,1,5,3,0,58207,0,20,Production Technician II,MA,1450,08/17/92,M ,Married,US Citizen,No,White,11/7/2011,Production ,David Stanley,14.0,LinkedIn,Fully Meets,3.7,3,0,1/8/2019,0,14 +"King, Janet",10089,1,1,0,2,3,0,250000,0,16,President & CEO,MA,1902,09/21/54,F,Married,US Citizen,Yes,White,7/2/2012,Executive Office,Board of Directors,9.0,Indeed,Fully Meets,4.83,3,0,1/17/2019,0,10 +"Dolan, Linda",10133,1,1,0,3,3,0,70621,0,14,IT Support,MA,2119,07/18/88,F,Married,US Citizen,No,White,1/5/2015,IT/IS,Peter Monroe,7.0,Employee Referral,Fully Meets,4.11,4,6,2/25/2019,0,16 +"Kinsella, Kathleen ",10166,1,1,0,5,3,0,54005,1,19,Production Technician I,MA,2170,12/08/73,F,Married,US Citizen,No,White,9/26/2011,Production ,Webster Butler,39.0,Google Search,Fully Meets,3.6,5,0,3/1/2015,0,16 +"Darson, Jene'ya ",10056,1,1,0,5,3,0,57583,0,19,Production Technician I,MA,2110,11/05/78,F,Married,US Citizen,No,White,7/2/2012,Production ,Elijiah Gray,16.0,Indeed,Fully Meets,5.0,3,0,2/25/2019,0,1 +"Goyal, Roxana",10256,1,1,0,5,3,0,49256,0,19,Production Technician I,MA,1864,10/09/74,F,Married,US Citizen,No,Asian,8/19/2013,Production ,Kissy Sullivan,20.0,LinkedIn,Fully Meets,4.1,5,0,2/15/2019,0,3 +"Khemmich, Bartholemew",10231,0,0,1,6,3,0,65310,0,3,Area Sales Manager,CO,80820,11/27/79,M ,Single,US Citizen,No,White,8/19/2013,Sales,Lynn Daneault,21.0,Indeed,Fully Meets,4.3,5,0,1/22/2019,0,13 +"Power, Morissa",10187,0,2,0,5,3,0,46799,1,19,Production Technician I,MA,1742,10/15/84,F,Divorced,Eligible NonCitizen,No,Asian,5/16/2011,Production ,Kissy Sullivan,20.0,Google Search,Fully Meets,3.17,4,0,4/2/2018,0,14 +"Soze, Keyser",10180,1,1,1,3,3,0,87565,0,28,Sr. Network Engineer,MA,1545,02/09/83,M ,Married,US Citizen,No,Asian,6/30/2016,IT/IS,Peter Monroe,7.0,LinkedIn,Fully Meets,3.27,4,5,1/14/2019,0,13 +"Walker, Roger",10190,0,0,1,5,3,0,66541,0,20,Production Technician II,MA,2459,02/10/76,M ,Single,US Citizen,No,Black or African American,8/18/2014,Production ,Ketsia Liebig,19.0,Employee Referral,Fully Meets,3.11,5,0,2/12/2019,0,4 +"DiNocco, Lily ",10284,1,1,0,5,2,0,61584,0,19,Production Technician I,MA,2351,12/02/78,F,Married,US Citizen,No,Black or African American,1/7/2013,Production ,Brannon Miller,12.0,Indeed,Needs Improvement,3.88,4,0,1/18/2019,0,6 +"Monterro, Luisa",10025,0,0,0,5,4,0,72460,0,20,Production Technician II,MA,2126,04/24/70,F,Single,US Citizen,No,Black or African American,5/13/2013,Production ,Kissy Sullivan,20.0,Indeed,Exceeds,4.7,3,0,1/14/2019,0,1 +"Ruiz, Ricardo",10222,0,2,1,3,3,1,148999,1,13,IT Manager - DB,MA,1915,01/04/64,M ,Divorced,US Citizen,No,Black or African American,1/9/2012,IT/IS,Jennifer Zamora,5.0,Diversity Job Fair,Fully Meets,4.3,4,6,1/4/2015,0,8 +"Crimmings, Jean",10132,0,0,0,5,3,0,56149,0,19,Production Technician I,MA,1821,04/10/87,F,Single,US Citizen,No,White,7/6/2016,Production ,Michael Albert,22.0,LinkedIn,Fully Meets,4.12,5,0,1/28/2019,0,15 +"Pham, Hong",10050,1,1,1,5,3,0,64724,1,19,Production Technician I,MA,2451,03/06/88,M ,Married,US Citizen,No,Asian,7/5/2011,Production ,Brannon Miller,12.0,Google Search,Fully Meets,5.0,3,0,2/20/2012,0,13 +"Handschiegl, Joanne",10125,1,1,0,5,3,0,54828,0,19,Production Technician I,MA,2127,03/23/77,F,Married,US Citizen,No,White,11/28/2011,Production ,Michael Albert,22.0,Google Search,Fully Meets,4.2,4,0,2/22/2019,0,13 +"Steans, Tyrone ",10147,0,0,1,1,3,0,63003,0,1,Accountant I,MA,2703,09/01/86,M ,Single,US Citizen,No,White,9/29/2014,Admin Offices,Brandon R. LeBlanc,1.0,Indeed,Fully Meets,3.9,5,5,1/18/2019,0,9 +"Mckenna, Sandy",10145,1,1,0,5,3,0,62810,0,19,Production Technician I,MA,2184,01/07/87,F,Married,US Citizen,No,Black or African American,1/7/2013,Production ,Kissy Sullivan,20.0,CareerBuilder,Fully Meets,3.93,3,0,1/30/2019,0,20 +"Delarge, Alex",10306,0,0,1,6,1,0,61568,0,3,Area Sales Manager,AL,36006,11/02/75,M ,Single,US Citizen,No,Two or more races,9/29/2014,Sales,John Smith,17.0,Indeed,PIP,1.93,3,0,1/30/2019,6,5 +"Roby, Lori ",10162,1,1,0,3,3,0,89883,0,9,Data Analyst,MA,1886,10/11/81,F,Married,US Citizen,No,White,2/16/2015,IT/IS,Simon Roup,4.0,Employee Referral,Fully Meets,3.69,5,6,2/14/2019,0,15 +"Voldemort, Lord",10118,1,1,1,3,3,0,113999,1,8,Database Administrator,MA,1960,08/07/86,M ,Married,US Citizen,No,Black or African American,2/16/2015,IT/IS,Simon Roup,4.0,Employee Referral,Fully Meets,4.33,3,7,2/15/2017,0,9 +"Good, Susan",10237,1,1,0,5,3,0,66825,0,20,Production Technician II,MA,1886,05/25/86,F,Married,US Citizen,No,White,5/12/2014,Production ,David Stanley,14.0,LinkedIn,Fully Meets,4.6,3,0,2/7/2019,0,20 +"Smith, Martin",10286,0,0,1,5,2,0,53564,1,19,Production Technician I,MA,2458,03/17/88,M ,Single,US Citizen,No,Black or African American,1/10/2011,Production ,Webster Butler,39.0,Google Search,Needs Improvement,3.54,5,0,4/6/2017,4,15 +"Burkett, Benjamin ",10181,1,1,1,5,3,0,62162,0,20,Production Technician II,MA,1890,08/19/77,M ,Married,US Citizen,No,White,4/4/2011,Production ,Ketsia Liebig,19.0,Indeed,Fully Meets,3.25,5,0,1/14/2019,0,15 +"Veera, Abdellah ",10014,0,2,1,5,4,0,58523,1,19,Production Technician I,MA,2171,01/31/87,M ,Divorced,US Citizen,No,White,8/13/2012,Production ,Kissy Sullivan,20.0,LinkedIn,Exceeds,4.5,5,0,2/1/2016,0,15 +"Anderson, Linda ",10002,0,0,0,5,4,0,57568,0,19,Production Technician I,MA,1844,05/22/77,F,Single,US Citizen,No,White,1/9/2012,Production ,Amy Dunn,11.0,LinkedIn,Exceeds,5.0,5,0,1/7/2019,0,15 +"Clayton, Rick",10220,0,0,1,3,3,0,68678,0,14,IT Support,MA,2170,09/05/85,M ,Single,US Citizen,No,White,9/5/2012,IT/IS,Eric Dougall,6.0,Indeed,Fully Meets,4.7,3,6,2/27/2019,0,2 +"Jung, Judy ",10138,1,1,0,5,3,0,61154,1,19,Production Technician I,MA,2446,04/17/86,F,Married,US Citizen,No,Black or African American,1/10/2011,Production ,Elijiah Gray,16.0,CareerBuilder,Fully Meets,4.0,4,0,2/3/2016,0,4 +"Alagbe,Trina",10088,1,1,0,5,3,0,64991,0,19,Production Technician I,MA,1886,09/27/88,F,Married,US Citizen,No,White,1/7/2008,Production ,Elijiah Gray,16.0,Indeed,Fully Meets,4.84,5,0,1/3/2019,0,15 +"Baczenski, Rachael ",10252,1,1,0,5,3,1,54670,1,19,Production Technician I,MA,1902,01/12/74,F,Married,US Citizen,Yes,Black or African American,1/10/2011,Production ,David Stanley,14.0,Diversity Job Fair,Fully Meets,4.2,4,0,1/30/2016,0,12 +"Driver, Elle",10006,0,0,0,6,4,0,74241,0,3,Area Sales Manager,CA,90007,11/08/88,F,Single,US Citizen,No,White,1/10/2011,Sales,Lynn Daneault,21.0,Indeed,Exceeds,4.77,5,0,1/27/2019,0,14 +"Champaigne, Brian",10108,1,1,1,3,3,0,110929,0,5,BI Director,MA,2045,02/09/72,M ,Married,US Citizen,No,White,9/6/2016,IT/IS,Jennifer Zamora,5.0,Indeed,Fully Meets,4.5,5,7,1/15/2019,0,8 +"Goble, Taisha",10246,0,0,0,3,3,0,114800,1,8,Database Administrator,MA,2127,10/23/71,F,Single,US Citizen,No,White,2/16/2015,IT/IS,Simon Roup,4.0,Indeed,Fully Meets,4.6,4,4,1/20/2015,0,10 +"Robinson, Cherly",10285,1,1,0,5,2,0,61422,1,19,Production Technician I,MA,1460,01/07/85,F,Married,US Citizen,No,White,1/10/2011,Production ,Ketsia Liebig,19.0,Indeed,Needs Improvement,3.6,3,0,4/5/2016,4,16 +"Gonzalez, Maria",10101,0,3,0,3,3,0,61242,0,14,IT Support,MA,2472,04/16/81,F,Separated,US Citizen,Yes,White,1/5/2015,IT/IS,Peter Monroe,7.0,Employee Referral,Fully Meets,4.61,4,5,1/28/2019,0,11 +"Langton, Enrico",10052,1,1,1,5,3,0,46120,0,19,Production Technician I,MA,2048,12/09/86,M ,Married,US Citizen,No,White,7/9/2012,Production ,David Stanley,14.0,LinkedIn,Fully Meets,5.0,5,0,2/4/2019,0,13 +"Daneault, Lynn",10099,0,0,0,6,3,0,65729,0,21,Sales Manager,VT,5473,04/19/90,F,Single,US Citizen,No,White,5/5/2014,Sales,Debra Houlihan,15.0,Indeed,Fully Meets,4.62,4,0,1/24/2019,0,8 +"Givens, Myriam",10255,0,0,0,6,3,0,61555,0,3,Area Sales Manager,IN,46204,09/22/89,F,Single,US Citizen,No,White,2/16/2015,Sales,Lynn Daneault,21.0,Indeed,Fully Meets,4.5,5,0,1/25/2019,0,20 +"Lindsay, Leonara ",10008,0,0,0,3,4,1,51777,0,14,IT Support,CT,6070,10/05/88,F,Single,US Citizen,Yes,Black or African American,1/21/2011,IT/IS,Eric Dougall,6.0,Diversity Job Fair,Exceeds,4.64,4,5,1/25/2019,0,14 +"Squatrito, Kristen",10030,0,2,0,5,4,0,62425,1,19,Production Technician I,MA,2359,03/26/73,F,Divorced,US Citizen,No,White,5/13/2013,Production ,David Stanley,14.0,LinkedIn,Exceeds,4.1,4,0,3/2/2015,0,16 +"Jeannite, Tayana",10009,0,2,0,5,4,0,60724,0,20,Production Technician II,MA,1821,11/06/86,F,Divorced,US Citizen,No,American Indian or Alaska Native,7/5/2011,Production ,Ketsia Liebig,19.0,LinkedIn,Exceeds,4.6,4,0,2/25/2019,0,11 +"Fraval, Maruk ",10258,0,0,1,6,3,0,67251,0,3,Area Sales Manager,CT,6050,08/28/63,M ,Single,US Citizen,No,Black or African American,9/6/2011,Sales,Lynn Daneault,21.0,CareerBuilder,Fully Meets,4.3,3,0,1/27/2019,2,7 +"Tippett, Jeanette",10123,0,2,0,5,3,0,56339,0,19,Production Technician I,MA,2093,06/05/67,F,Divorced,US Citizen,No,Black or African American,2/18/2013,Production ,Brannon Miller,12.0,Indeed,Fully Meets,4.21,5,0,1/14/2019,0,4 +"Cockel, James",10261,0,0,1,5,3,0,63108,0,19,Production Technician I,MA,2452,09/08/77,M ,Single,US Citizen,No,White,7/8/2013,Production ,David Stanley,14.0,Employee Referral,Fully Meets,4.4,5,0,1/14/2019,0,3 +"Howard, Estelle",10182,1,1,0,1,3,0,49920,1,2,Administrative Assistant,MA,2170,09/16/85,F,Married,US Citizen,No,Black or African American,2/16/2015,Admin Offices,Brandon R. LeBlanc,1.0,Indeed,Fully Meets,3.24,3,4,4/15/2015,0,6 +"Fett, Boba",10309,0,0,1,3,1,0,53366,0,15,Network Engineer,MA,2138,06/18/87,M ,Single,US Citizen,No,White,3/30/2015,IT/IS,Peter Monroe,7.0,LinkedIn,PIP,1.2,3,6,2/4/2019,3,2 +"Manchester, Robyn",10077,1,1,0,5,3,0,62385,0,20,Production Technician II,MA,2324,08/25/76,F,Married,US Citizen,No,White,5/11/2016,Production ,Webster Butler,,LinkedIn,Fully Meets,5.0,3,0,1/21/2019,0,4 +"Saada, Adell",10126,1,1,0,4,3,0,86214,0,24,Software Engineer,MA,2132,07/24/86,F,Married,US Citizen,No,White,11/5/2012,Software Engineering,Alex Sweetwater,10.0,Indeed,Fully Meets,4.2,3,6,2/13/2019,0,2 +"Ferguson, Susan",10189,1,1,0,5,3,0,57748,1,19,Production Technician I,MA,2176,04/14/55,F,Married,US Citizen,No,White,11/7/2011,Production ,Webster Butler,39.0,Google Search,Fully Meets,3.13,3,0,2/4/2016,0,16 +"Whittier, Scott",10072,0,0,1,5,3,0,58371,1,19,Production Technician I,MA,2030,05/24/87,M ,Single,US Citizen,Yes,White,1/10/2011,Production ,Webster Butler,39.0,LinkedIn,Fully Meets,5.0,5,0,5/15/2014,0,11 +"Estremera, Miguel",10280,0,0,1,5,2,0,60340,1,19,Production Technician I,MA,2129,09/02/83,M ,Single,US Citizen,No,White,4/2/2012,Production ,Michael Albert,22.0,Google Search,Needs Improvement,5.0,4,0,4/12/2018,5,16 +"Barbara, Thomas",10242,1,1,1,5,3,1,47211,1,19,Production Technician I,MA,2062,02/21/74,M ,Married,US Citizen,Yes,Black or African American,4/2/2012,Production ,Kissy Sullivan,20.0,Diversity Job Fair,Fully Meets,4.2,3,0,5/6/2016,0,15 +"Kirill, Alexandra ",10170,1,1,0,5,3,0,45433,1,19,Production Technician I,MA,2127,10/08/70,F,Married,US Citizen,No,White,9/26/2011,Production ,Amy Dunn,11.0,Google Search,Fully Meets,3.49,4,0,1/30/2013,0,6 +"Valentin,Jackie",10205,1,1,0,6,3,0,57859,0,3,Area Sales Manager,AZ,85006,05/23/91,F,Married,US Citizen,No,Two or more races,7/5/2011,Sales,John Smith,17.0,Indeed,Fully Meets,2.81,3,0,1/17/2019,0,16 +"Athwal, Sam",10062,0,4,1,5,3,0,59365,0,19,Production Technician I,MA,2199,02/18/83,M ,Widowed,US Citizen,No,White,9/30/2013,Production ,Ketsia Liebig,19.0,Employee Referral,Fully Meets,5.0,4,0,2/25/2019,0,19 +"Hunts, Julissa",10201,0,0,0,5,3,0,69340,0,20,Production Technician II,MA,2021,03/11/84,F,Single,US Citizen,No,White,6/6/2016,Production ,Elijiah Gray,16.0,LinkedIn,Fully Meets,3.0,5,0,1/18/2019,0,4 +"Rhoads, Thomas",10058,0,2,1,5,3,0,45115,1,19,Production Technician I,MA,2176,07/22/82,M ,Divorced,US Citizen,Yes,White,5/16/2011,Production ,Elijiah Gray,16.0,LinkedIn,Fully Meets,5.0,4,0,3/30/2015,0,11 +"Bugali, Josephine ",10203,0,3,0,5,3,1,64375,0,19,Production Technician I,MA,2043,10/30/69,F,Separated,US Citizen,No,Black or African American,11/11/2013,Production ,Kissy Sullivan,20.0,Diversity Job Fair,Fully Meets,3.5,5,0,1/21/2019,0,17 +"Dunne, Amy",10211,1,1,0,5,3,0,62514,0,19,Production Technician I,MA,1749,09/23/73,F,Married,US Citizen,No,White,4/26/2010,Production ,Ketsia Liebig,19.0,Google Search,Fully Meets,2.9,3,0,1/21/2019,0,6 +"Gonzalez, Juan",10300,1,1,1,5,1,1,68898,1,20,Production Technician II,MA,2128,10/12/64,M ,Married,US Citizen,No,Black or African American,4/26/2010,Production ,Brannon Miller,12.0,Diversity Job Fair,PIP,3.0,3,0,3/6/2011,3,10 +"Carey, Michael ",10115,0,0,1,5,3,0,52846,0,19,Production Technician I,MA,1701,02/02/83,M ,Single,US Citizen,No,Black or African American,3/31/2014,Production ,Kelley Spirea,18.0,LinkedIn,Fully Meets,4.43,3,0,2/1/2019,0,14 +"Anderson, Carol ",10069,0,2,0,5,3,0,50825,1,19,Production Technician I,MA,2169,09/08/89,F,Divorced,US Citizen,No,White,7/11/2011,Production ,Webster Butler,39.0,Google Search,Fully Meets,5.0,4,0,2/1/2016,0,2 +"Wolk, Hang T",10174,0,0,0,5,3,0,60446,0,20,Production Technician II,MA,2302,04/20/85,F,Single,US Citizen,No,White,9/29/2014,Production ,David Stanley,14.0,LinkedIn,Fully Meets,3.4,4,0,2/21/2019,0,14 +"Fancett, Nicole",10136,0,0,0,5,3,0,65902,0,20,Production Technician II,MA,2324,09/27/87,F,Single,US Citizen,No,Black or African American,2/17/2014,Production ,Webster Butler,,LinkedIn,Fully Meets,4.0,4,0,1/7/2019,0,7 +"Ybarra, Catherine ",10301,0,0,0,5,1,0,48513,1,19,Production Technician I,MA,2458,05/04/82,F,Single,US Citizen,No,Asian,9/2/2008,Production ,Brannon Miller,12.0,Google Search,PIP,3.2,2,0,9/2/2015,5,4 +"Fernandes, Nilson ",10308,1,1,1,5,1,0,64057,0,19,Production Technician I,MA,2132,10/18/89,M ,Married,US Citizen,No,White,5/11/2015,Production ,Amy Dunn,11.0,Indeed,PIP,1.56,5,0,1/3/2019,6,15 +"Bachiochi, Linda",10114,0,0,0,5,3,1,47837,0,19,Production Technician I,MA,1902,02/11/70,F,Single,US Citizen,No,Black or African American,7/6/2009,Production ,Brannon Miller,12.0,Diversity Job Fair,Fully Meets,4.46,3,0,1/25/2019,0,4 +"Roehrich, Bianca",10149,0,0,0,3,3,0,120000,1,29,Principal Data Architect,MA,2703,05/27/73,F,Single,US Citizen,Yes,White,1/5/2015,IT/IS,Simon Roup,4.0,LinkedIn,Fully Meets,3.88,3,7,2/13/2018,0,12 +"Chivukula, Enola",10240,0,0,0,5,3,0,64786,1,19,Production Technician I,MA,1775,08/27/83,F,Single,US Citizen,No,White,6/27/2011,Production ,Amy Dunn,11.0,Indeed,Fully Meets,4.3,4,0,3/10/2015,0,3 +"Theamstern, Sophia",10141,0,0,0,5,3,0,48413,1,19,Production Technician I,MA,2066,05/09/65,F,Single,US Citizen,No,White,7/5/2011,Production ,Amy Dunn,11.0,Indeed,Fully Meets,3.98,4,0,3/2/2016,0,1 +"Osturnka, Adeel",10021,1,1,1,5,4,0,47414,0,19,Production Technician I,MA,2478,12/11/76,M ,Married,US Citizen,No,White,9/30/2013,Production ,Kelley Spirea,18.0,LinkedIn,Exceeds,5.0,3,0,2/7/2019,0,13 +"Clukey, Elijian",10029,1,1,1,5,4,0,50373,0,19,Production Technician I,MA,2134,08/26/80,M ,Married,US Citizen,No,White,7/6/2016,Production ,Brannon Miller,12.0,Employee Referral,Exceeds,4.1,4,0,2/28/2019,0,5 +"Wilber, Barry",10048,1,1,1,5,3,0,55140,1,19,Production Technician I,MA,2324,09/09/65,M ,Married,Eligible NonCitizen,No,White,5/16/2011,Production ,Amy Dunn,11.0,Website,Fully Meets,5.0,3,0,2/15/2015,0,7 +"Roper, Katie",10086,0,0,0,3,3,0,150290,0,7,Data Architect,MA,2056,11/21/72,F,Single,US Citizen,No,Black or African American,1/7/2017,IT/IS,Brian Champaigne,13.0,Indeed,Fully Meets,4.94,3,5,2/6/2019,0,17 +"Chigurh, Anton",10200,0,0,1,6,3,0,66808,0,3,Area Sales Manager,TX,78207,06/11/70,M ,Single,Eligible NonCitizen,No,Black or African American,5/14/2012,Sales,Lynn Daneault,21.0,Employee Referral,Fully Meets,3.0,5,0,1/19/2019,0,17 +"Spirea, Kelley",10090,1,1,0,5,3,0,65714,0,18,Production Manager,MA,2451,09/30/75,F,Married,US Citizen,No,White,10/2/2012,Production ,Janet King,2.0,LinkedIn,Fully Meets,4.83,5,0,2/14/2019,0,15 +"Zamora, Jennifer",10010,0,0,0,3,4,0,220450,0,6,CIO,MA,2067,08/30/79,F,Single,US Citizen,No,White,4/10/2010,IT/IS,Janet King,2.0,Employee Referral,Exceeds,4.6,5,6,2/21/2019,0,16 +"Bozzi, Charles",10175,0,0,1,5,3,0,74312,1,18,Production Manager,MA,1901,03/10/70,M ,Single,US Citizen,No,Asian,9/30/2013,Production ,Janet King,2.0,Indeed,Fully Meets,3.39,3,0,2/20/2014,0,14 +"Von Massenbach, Anna",10183,0,0,0,5,3,0,62068,0,19,Production Technician I,MA,2124,04/06/85,F,Single,US Citizen,No,White,7/5/2015,Production ,Michael Albert,22.0,LinkedIn,Fully Meets,3.21,3,0,1/29/2019,0,7 +"Becker, Renee",10245,0,0,0,3,3,0,110000,1,8,Database Administrator,MA,2026,04/04/86,F,Single,US Citizen,Yes,White,7/7/2014,IT/IS,Simon Roup,4.0,Google Search,Fully Meets,4.5,4,5,1/15/2015,0,8 +"Huynh, Ming",10160,0,2,0,5,3,0,68182,1,20,Production Technician II,MA,1742,09/22/76,F,Divorced,US Citizen,No,White,2/21/2011,Production ,Amy Dunn,11.0,Google Search,Fully Meets,3.72,3,0,2/1/2013,0,18 +"Corleone, Vito",10019,0,0,1,5,4,0,170500,0,10,Director of Operations,MA,2030,03/19/83,M ,Single,US Citizen,No,Black or African American,1/5/2009,Production ,Janet King,2.0,Indeed,Exceeds,3.7,5,0,2/4/2019,0,15 +"Boutwell, Bonalyn",10081,1,1,0,1,3,1,106367,0,26,Sr. Accountant,MA,2468,04/04/87,F,Married,US Citizen,No,Black or African American,2/16/2015,Admin Offices,Brandon R. LeBlanc,3.0,Diversity Job Fair,Fully Meets,5.0,4,3,2/18/2019,0,4 +"Barbossa, Hector",10012,0,2,1,3,4,1,92328,0,9,Data Analyst,TX,78230,07/04/88,M ,Divorced,US Citizen,No,Black or African American,11/10/2014,IT/IS,Simon Roup,4.0,Diversity Job Fair,Exceeds,4.28,4,5,2/25/2019,0,9 +"Rachael, Maggie",10239,1,1,0,3,3,0,95920,0,4,BI Developer,MA,2110,05/12/80,F,Married,US Citizen,No,Black or African American,10/2/2016,IT/IS,Brian Champaigne,13.0,Indeed,Fully Meets,4.4,4,6,2/6/2019,0,10 +"Davis, Daniel",10143,0,0,1,5,3,0,56294,0,20,Production Technician II,MA,2458,09/14/79,M ,Single,Eligible NonCitizen,No,Two or more races,11/7/2011,Production ,Kissy Sullivan,20.0,LinkedIn,Fully Meets,3.96,4,0,2/27/2019,0,6 +"Dietrich, Jenna ",10304,0,0,0,6,1,0,59231,0,3,Area Sales Manager,WA,98052,05/14/87,F,Single,US Citizen,Yes,White,2/20/2012,Sales,John Smith,17.0,Website,PIP,2.3,1,0,1/29/2019,2,17 +"Morway, Tanya",10151,1,1,0,3,3,0,52599,0,15,Network Engineer,MA,2048,04/04/79,F,Married,US Citizen,No,White,2/16/2015,IT/IS,Peter Monroe,7.0,CareerBuilder,Fully Meets,3.81,3,6,2/11/2019,0,6 +"Villanueva, Noah",10253,0,0,1,6,3,0,55875,0,3,Area Sales Manager,ME,4063,07/11/89,M ,Single,US Citizen,No,Asian,3/5/2012,Sales,John Smith,17.0,Website,Fully Meets,4.5,4,0,1/18/2019,0,11 +"Monroe, Peter",10288,1,1,1,3,2,1,157000,0,13,IT Manager - Infra,MA,2134,10/05/86,M ,Married,Eligible NonCitizen,Yes,Black or African American,2/15/2012,IT/IS,Jennifer Zamora,5.0,Diversity Job Fair,Needs Improvement,2.39,3,6,2/22/2019,4,13 +"Landa, Hans",10092,1,1,1,5,3,0,82758,1,18,Production Manager,MA,1890,07/01/72,M ,Married,US Citizen,No,White,1/10/2011,Production ,Janet King,2.0,Employee Referral,Fully Meets,4.78,4,0,2/15/2015,0,9 +"Torrence, Jack",10013,0,3,1,6,4,0,64397,0,3,Area Sales Manager,ND,58782,01/15/68,M ,Separated,US Citizen,No,White,1/9/2006,Sales,Lynn Daneault,21.0,Indeed,Exceeds,4.1,3,0,1/4/2019,0,6 +"Kampew, Donysha",10244,0,0,0,6,3,0,68999,1,21,Sales Manager,PA,19444,11/11/89,F,Single,US Citizen,No,White,11/7/2011,Sales,Debra Houlihan,15.0,Google Search,Fully Meets,4.5,5,0,3/30/2013,0,2 +"Tejeda, Lenora ",10146,1,1,0,5,3,0,72202,1,20,Production Technician II,MA,2129,05/24/53,F,Married,US Citizen,No,White,5/16/2011,Production ,Elijiah Gray,16.0,Google Search,Fully Meets,3.93,3,0,4/18/2017,0,3 +"Robinson, Elias",10020,0,4,1,5,4,0,63353,0,19,Production Technician I,MA,1730,01/28/85,M ,Widowed,US Citizen,No,White,7/8/2013,Production ,Brannon Miller,12.0,Employee Referral,Exceeds,3.6,5,0,2/11/2019,0,4 +"Ndzi, Colombui",10215,0,0,1,5,3,1,50470,1,19,Production Technician I,MA,2110,05/02/89,M ,Single,US Citizen,No,Black or African American,9/26/2011,Production ,Webster Butler,39.0,Diversity Job Fair,Fully Meets,4.3,3,0,3/2/2013,0,19 +"Ndzi, Horia",10185,1,1,1,5,3,0,46664,1,19,Production Technician I,MA,2421,03/28/83,M ,Married,US Citizen,No,White,4/1/2013,Production ,Amy Dunn,11.0,Employee Referral,Fully Meets,3.18,3,0,3/6/2016,0,10 +"Johnson, George",10034,1,1,1,5,4,0,46837,1,19,Production Technician I,MA,2445,08/19/59,M ,Married,US Citizen,No,White,11/7/2011,Production ,Michael Albert,22.0,CareerBuilder,Exceeds,4.7,4,0,2/14/2018,0,9 +"Rivera, Haley ",10011,1,1,0,5,4,0,46738,0,19,Production Technician I,MA,2171,01/12/73,F,Married,US Citizen,No,Asian,11/28/2011,Production ,Webster Butler,,Google Search,Exceeds,4.36,5,0,2/11/2019,0,16 +"Close, Phil",10269,1,1,1,5,3,0,59369,1,20,Production Technician II,MA,2169,11/25/78,M ,Married,US Citizen,No,White,8/30/2010,Production ,David Stanley,14.0,Indeed,Fully Meets,4.2,4,0,5/4/2011,0,6 +"Dickinson, Geoff ",10276,0,0,1,5,3,0,51259,0,19,Production Technician I,MA,2180,11/15/82,M ,Single,US Citizen,No,White,5/12/2014,Production ,Ketsia Liebig,19.0,Indeed,Fully Meets,4.3,4,0,2/19/2019,0,1 +"Soto, Julia ",10119,1,1,0,3,3,0,66593,0,14,IT Support,MA,2360,03/12/73,F,Married,US Citizen,No,Black or African American,6/10/2011,IT/IS,Eric Dougall,6.0,LinkedIn,Fully Meets,4.3,3,5,2/8/2019,0,19 +"Liebig, Ketsia",10017,1,1,0,5,4,0,77915,0,18,Production Manager,MA,2110,10/26/81,F,Married,US Citizen,No,White,9/30/2013,Production ,Janet King,2.0,Website,Exceeds,4.1,3,0,1/21/2019,0,11 +"Lynch, Lindsay",10004,0,0,0,5,4,1,47434,1,19,Production Technician I,MA,1844,02/14/73,F,Single,US Citizen,Yes,Black or African American,11/7/2011,Production ,Webster Butler,39.0,Diversity Job Fair,Exceeds,5.0,4,0,2/2/2015,0,17 +"Shepard, Anita ",10179,1,1,0,3,3,0,50750,0,15,Network Engineer,MA,1773,04/14/81,F,Married,US Citizen,No,White,9/30/2014,IT/IS,Peter Monroe,7.0,LinkedIn,Fully Meets,3.31,3,6,1/7/2019,0,7 +"Punjabhi, Louis ",10225,0,0,1,5,3,0,59472,0,19,Production Technician I,MA,2109,06/19/61,M ,Single,US Citizen,No,White,1/6/2014,Production ,Kelley Spirea,18.0,Employee Referral,Fully Meets,4.8,3,0,1/7/2019,0,14 +"Goeth, Amon",10228,1,1,1,3,3,0,74679,0,14,IT Support,MA,2135,11/24/89,M ,Married,US Citizen,Yes,White,3/30/2015,IT/IS,Peter Monroe,7.0,LinkedIn,Fully Meets,4.3,5,7,1/10/2019,0,20 +"Barton, Nader",10066,0,2,1,5,3,0,52505,1,19,Production Technician I,MA,2747,07/15/77,M ,Divorced,US Citizen,No,White,9/24/2012,Production ,Michael Albert,22.0,On-line Web application,Fully Meets,5.0,5,0,3/2/2017,0,1 +"Linares, Marilyn ",10186,1,1,0,5,3,0,52624,1,19,Production Technician I,MA,1886,03/26/81,F,Married,US Citizen,No,White,7/5/2011,Production ,Michael Albert,22.0,Indeed,Fully Meets,3.18,4,0,3/2/2018,0,16 +"Quinn, Sean",10131,1,1,1,1,3,1,83363,1,23,Software Engineer,MA,2045,11/06/84,M ,Married,Eligible NonCitizen,No,Black or African American,2/21/2011,Software Engineering,Janet King,2.0,Diversity Job Fair,Fully Meets,4.15,4,0,4/19/2014,0,4 +"Martin, Sandra",10110,0,0,0,4,3,0,105688,0,24,Software Engineer,MA,2135,11/07/87,F,Single,US Citizen,No,Asian,11/11/2013,Software Engineering,Alex Sweetwater,10.0,Google Search,Fully Meets,4.5,5,4,1/14/2019,0,14 +"Bates, Norman",10061,0,0,1,5,3,0,57834,1,19,Production Technician I,MA,2050,10/18/81,M ,Single,US Citizen,No,White,2/21/2011,Production ,Kelley Spirea,18.0,Google Search,Fully Meets,5.0,4,0,4/5/2017,0,20 +"Owad, Clinton",10281,0,0,1,5,2,0,53060,0,19,Production Technician I,MA,1760,11/24/79,M ,Single,US Citizen,No,Black or African American,2/17/2014,Production ,Michael Albert,22.0,LinkedIn,Needs Improvement,4.25,3,0,2/4/2019,4,6 +"Sander, Kamrin",10169,1,1,0,5,3,0,56147,0,19,Production Technician I,MA,2154,07/10/88,F,Married,US Citizen,No,Black or African American,9/29/2014,Production ,Elijiah Gray,16.0,LinkedIn,Fully Meets,3.51,3,0,2/18/2019,0,2 +"Bunbury, Jessica",10188,1,1,0,6,3,0,74326,1,3,Area Sales Manager,VA,21851,06/01/64,F,Married,Eligible NonCitizen,No,Black or African American,8/15/2011,Sales,John Smith,17.0,Google Search,Fully Meets,3.14,5,0,2/10/2013,1,19 +"Heitzman, Anthony",10129,0,0,1,5,3,0,46998,0,19,Production Technician I,MA,2149,01/04/84,M ,Single,US Citizen,No,White,8/13/2012,Production ,Ketsia Liebig,19.0,Google Search,Fully Meets,4.17,4,0,2/11/2019,0,1 +"Tredinnick, Neville ",10044,1,1,1,3,3,0,75281,1,15,Network Engineer,MA,1420,05/05/88,M ,Married,US Citizen,No,White,1/5/2015,IT/IS,Peter Monroe,7.0,CareerBuilder,Fully Meets,5.0,3,5,4/15/2015,0,11 +"Rarrick, Quinn",10152,0,2,1,5,3,0,61729,1,19,Production Technician I,MA,2478,12/31/84,M ,Divorced,US Citizen,No,White,9/26/2011,Production ,Michael Albert,22.0,Indeed,Fully Meets,3.8,5,0,2/4/2018,0,19 +"Harrington, Christie ",10097,0,0,0,5,3,0,52177,1,19,Production Technician I,MA,2324,08/18/52,F,Single,US Citizen,No,White,1/9/2012,Production ,Webster Butler,39.0,CareerBuilder,Fully Meets,4.64,4,0,5/2/2015,0,8 +"Costello, Frank",10193,1,1,1,3,3,0,83552,0,9,Data Analyst,MA,1810,08/26/86,M ,Married,US Citizen,No,White,3/30/2015,IT/IS,Simon Roup,4.0,Indeed,Fully Meets,3.04,3,6,1/22/2019,0,2 +"LaRotonda, William ",10038,0,2,1,1,3,0,64520,0,1,Accountant I,MA,1460,04/26/84,M ,Divorced,US Citizen,No,Black or African American,1/6/2014,Admin Offices,Brandon R. LeBlanc,1.0,Website,Fully Meets,5.0,4,4,1/17/2019,0,3 +"Vega, Vincent",10144,0,2,1,5,3,0,88976,0,17,Production Manager,MA,2169,10/10/68,M ,Divorced,US Citizen,No,White,8/1/2011,Production ,Janet King,2.0,Employee Referral,Fully Meets,3.93,3,0,2/27/2019,0,19 +"Wang, Charlie",10172,0,0,1,3,3,0,84903,0,22,Senior BI Developer,MA,1887,07/08/81,M ,Single,US Citizen,No,Asian,2/15/2017,IT/IS,Brian Champaigne,13.0,Indeed,Fully Meets,3.42,4,7,1/4/2019,0,17 +"Robertson, Peter",10224,1,1,1,5,3,0,55578,1,20,Production Technician II,MA,2138,07/03/72,M ,Married,US Citizen,No,White,7/5/2011,Production ,Kissy Sullivan,20.0,Indeed,Fully Meets,4.2,5,0,1/6/2012,0,13 +"Shields, Seffi",10091,1,1,0,5,3,0,52087,0,19,Production Technician I,MA,2149,08/24/85,F,Married,US Citizen,No,White,8/19/2013,Production ,Amy Dunn,11.0,LinkedIn,Fully Meets,4.81,4,0,2/15/2019,0,15 +"Roup,Simon",10198,0,0,1,3,3,0,140920,0,13,IT Manager - DB,MA,2481,04/05/73,M ,Single,US Citizen,No,White,1/20/2013,IT/IS,Jennifer Zamora,5.0,Indeed,Fully Meets,3.6,5,7,2/18/2019,0,13 +"Stansfield, Norman",10307,1,1,1,6,1,0,58273,0,3,Area Sales Manager,NV,89139,05/09/74,M ,Married,US Citizen,No,White,5/12/2014,Sales,Lynn Daneault,21.0,Website,PIP,1.81,2,0,1/17/2019,3,5 +"Potts, Xana",10124,1,1,0,6,3,0,61844,0,3,Area Sales Manager,KY,40220,08/29/88,F,Married,US Citizen,No,Black or African American,1/9/2012,Sales,Lynn Daneault,21.0,Website,Fully Meets,4.2,5,0,2/1/2019,0,9 +"Zhou, Julia",10043,0,0,0,3,3,0,89292,0,9,Data Analyst,MA,2148,02/24/79,F,Single,US Citizen,No,White,3/30/2015,IT/IS,Simon Roup,4.0,Employee Referral,Fully Meets,5.0,3,5,2/1/2019,0,11 +"Saar-Beckles, Melinda",10295,0,0,0,5,2,1,47750,0,19,Production Technician I,MA,1801,06/06/68,F,Single,US Citizen,No,Black or African American,7/4/2016,Production ,Kelley Spirea,18.0,Diversity Job Fair,Needs Improvement,2.6,4,0,2/18/2019,5,4 +"Johnston, Yen",10036,0,0,0,5,4,0,63322,0,20,Production Technician II,MA,2128,09/08/69,F,Single,US Citizen,No,White,7/7/2014,Production ,Brannon Miller,12.0,LinkedIn,Exceeds,4.3,3,0,1/11/2019,0,1 +"Gray, Elijiah ",10098,0,2,1,5,3,0,62957,0,18,Production Manager,MA,1752,07/11/81,M ,Divorced,US Citizen,No,White,6/2/2015,Production ,Janet King,2.0,Employee Referral,Fully Meets,4.63,3,0,1/4/2019,0,2 +"Gilles, Alex",10283,1,1,1,5,2,1,54933,1,19,Production Technician I,MA,2062,08/09/74,M ,Married,US Citizen,No,Black or African American,4/2/2012,Production ,Webster Butler,39.0,Diversity Job Fair,Needs Improvement,3.97,4,0,1/20/2015,3,15 +"Carabbio, Judith",10085,0,0,0,4,3,0,93396,0,24,Software Engineer,MA,2132,04/05/87,F,Single,US Citizen,No,White,11/11/2013,Software Engineering,Alex Sweetwater,10.0,Indeed,Fully Meets,4.96,4,6,1/30/2019,0,3 +"Johnson, Noelle ",10156,1,1,0,3,3,0,105700,0,8,Database Administrator,MA,2301,11/07/86,F,Married,US Citizen,No,Asian,1/5/2015,IT/IS,Simon Roup,4.0,Indeed,Fully Meets,3.75,3,5,2/11/2019,0,2 +"Gosciminski, Phylicia ",10218,0,3,0,5,3,0,66149,0,20,Production Technician II,MA,1824,12/08/83,F,Separated,US Citizen,No,American Indian or Alaska Native,9/30/2013,Production ,Kissy Sullivan,20.0,Google Search,Fully Meets,4.4,5,0,2/21/2019,0,1 +"Bacong, Alejandro ",10250,0,2,1,3,3,0,50178,0,14,IT Support,MA,1886,01/07/88,M ,Divorced,US Citizen,No,White,1/5/2015,IT/IS,Peter Monroe,7.0,Indeed,Fully Meets,5.0,5,6,2/18/2019,0,16 +"Stanford,Barbara M",10278,0,2,0,5,3,0,47961,0,19,Production Technician I,MA,2050,08/25/82,F,Divorced,US Citizen,No,Two or more races,1/10/2011,Production ,Kissy Sullivan,20.0,Google Search,Fully Meets,4.1,4,0,2/7/2019,0,9 +"Dee, Randy",10311,1,1,1,6,1,0,56991,0,19,Production Technician I,MA,2138,04/15/88,M ,Married,US Citizen,No,White,7/9/2018,Production ,Brannon Miller,12.0,Indeed,Fully Meets,4.3,4,3,1/31/2019,2,2 +"Cole, Spencer",10292,0,0,1,5,2,0,59144,1,19,Production Technician I,MA,1880,08/12/79,M ,Single,US Citizen,No,Black or African American,7/11/2011,Production ,Kissy Sullivan,20.0,LinkedIn,Needs Improvement,2.0,3,0,5/1/2016,5,16 +"Ivey, Rose ",10139,0,0,0,5,3,0,51908,0,19,Production Technician I,MA,1775,01/28/91,F,Single,US Citizen,No,White,8/19/2013,Production ,Brannon Miller,12.0,Indeed,Fully Meets,3.99,3,0,1/14/2019,0,14 +"Daniele, Ann ",10212,1,1,0,3,3,0,85028,0,28,Sr. Network Engineer,CT,6033,01/18/52,F,Married,US Citizen,No,White,11/10/2014,IT/IS,Peter Monroe,7.0,LinkedIn,Fully Meets,3.1,5,8,2/12/2019,0,19 +"Dobrin, Denisa S",10207,0,0,0,5,3,0,46335,0,19,Production Technician I,MA,2125,10/07/86,F,Single,US Citizen,Yes,White,4/2/2012,Production ,David Stanley,14.0,CareerBuilder,Fully Meets,3.4,5,0,2/19/2019,0,15 +"Adinolfi, Wilson K",10026,0,0,1,5,4,0,62506,0,19,Production Technician I,MA,1960,07/10/83,M ,Single,US Citizen,No,White,7/5/2011,Production ,Michael Albert,22.0,LinkedIn,Exceeds,4.6,5,0,1/17/2019,0,1 +"Kreuger, Freddy",10165,0,0,1,6,3,1,71339,0,3,Area Sales Manager,NY,10171,02/24/69,M ,Single,US Citizen,Yes,Black or African American,3/7/2011,Sales,John Smith,17.0,Diversity Job Fair,Fully Meets,3.65,5,0,1/17/2019,0,20 +"Wilkes, Annie",10204,0,2,0,5,3,0,58062,1,19,Production Technician I,MA,1876,07/30/83,F,Divorced,US Citizen,No,White,1/10/2011,Production ,Ketsia Liebig,19.0,Google Search,Fully Meets,3.6,5,0,2/6/2011,0,9 +"Rose, Ashley ",10054,0,3,0,5,3,0,60627,0,19,Production Technician I,MA,1886,12/05/74,F,Separated,US Citizen,No,White,1/6/2014,Production ,David Stanley,14.0,Website,Fully Meets,5.0,4,0,1/31/2019,0,8 +"Ait Sidi, Karthikeyan ",10084,1,1,1,3,3,0,104437,1,27,Sr. DBA,MA,2148,05/05/75,M ,Married,US Citizen,No,White,3/30/2015,IT/IS,Simon Roup,4.0,Indeed,Fully Meets,4.96,3,6,2/24/2016,0,17 +"Foreman, Tanya",10163,1,1,0,5,3,0,55965,1,20,Production Technician II,MA,2170,11/08/83,F,Married,US Citizen,No,White,4/4/2011,Production ,Ketsia Liebig,19.0,Google Search,Fully Meets,3.66,3,0,1/7/2012,0,6 +"Girifalco, Evelyn",10018,0,0,0,5,4,0,57815,0,19,Production Technician I,MA,2451,05/08/80,F,Single,US Citizen,Yes,Two or more races,9/29/2014,Production ,Amy Dunn,11.0,Indeed,Exceeds,3.9,4,0,2/7/2019,0,3 +"Corleone, Michael",10282,0,2,1,5,2,0,68051,0,18,Production Manager,MA,1803,12/17/75,M ,Divorced,US Citizen,No,White,7/20/2010,Production ,Janet King,2.0,CareerBuilder,Needs Improvement,4.13,2,0,1/14/2019,3,3 +"Gold, Shenice ",10243,0,0,0,5,3,0,53018,0,19,Production Technician I,MA,2451,06/18/92,F,Single,US Citizen,Yes,White,11/11/2013,Production ,Ketsia Liebig,19.0,Indeed,Fully Meets,4.3,5,0,2/18/2019,0,7 +"Mancuso, Karen",10073,1,1,0,5,3,0,68407,1,20,Production Technician II,MA,2176,12/10/86,F,Married,US Citizen,No,Two or more races,7/5/2011,Production ,Amy Dunn,11.0,LinkedIn,Fully Meets,5.0,4,0,7/2/2012,0,16 +"Roberson, May",10230,0,2,0,5,3,0,64971,1,20,Production Technician II,MA,1902,09/05/81,F,Divorced,Eligible NonCitizen,No,Black or African American,9/26/2011,Production ,David Stanley,14.0,Google Search,Fully Meets,4.5,4,0,10/22/2011,0,10 +"Engdahl, Jean",10247,0,0,1,5,3,0,48888,0,19,Production Technician I,MA,2026,05/31/74,M ,Single,US Citizen,No,White,11/10/2014,Production ,Kelley Spirea,18.0,LinkedIn,Fully Meets,4.7,5,0,2/13/2019,0,8 +"Brown, Mia",10238,1,1,0,1,3,1,63000,0,1,Accountant I,MA,1450,11/24/87,F,Married,US Citizen,No,Black or African American,10/27/2008,Admin Offices,Brandon R. LeBlanc,1.0,Diversity Job Fair,Fully Meets,4.5,2,6,1/15/2019,0,14 +"Warfield, Sarah",10127,0,4,0,3,3,0,107226,0,28,Sr. Network Engineer,MA,2453,05/02/78,F,Widowed,US Citizen,No,Asian,3/30/2015,IT/IS,Peter Monroe,7.0,Employee Referral,Fully Meets,4.2,4,8,2/5/2019,0,7 +"Moumanil, Maliki ",10120,0,3,1,5,3,0,74417,0,20,Production Technician II,MA,1460,12/01/74,M ,Separated,US Citizen,No,Black or African American,5/13/2013,Production ,Michael Albert,22.0,LinkedIn,Fully Meets,4.29,5,0,1/28/2019,0,11 +"Jacobi, Hannah ",10236,0,2,0,5,3,0,45069,0,19,Production Technician I,MA,1778,03/22/66,F,Divorced,US Citizen,No,White,9/30/2013,Production ,Kissy Sullivan,20.0,Employee Referral,Fully Meets,4.3,5,0,2/22/2019,0,7 +"Peterson, Ebonee ",10130,1,1,0,5,3,0,74669,1,18,Production Manager,MA,2030,05/09/77,F,Married,US Citizen,No,White,10/25/2010,Production ,Janet King,2.0,Indeed,Fully Meets,4.16,5,0,3/5/2015,0,6 +"Fitzpatrick, Michael J",10093,0,0,1,5,3,0,72609,1,20,Production Technician II,MA,2143,10/01/81,M ,Single,US Citizen,Yes,White,5/16/2011,Production ,Amy Dunn,11.0,Google Search,Fully Meets,4.76,5,0,4/5/2013,0,20 +"Medeiros, Jennifer",10068,0,0,0,5,3,0,55688,0,19,Production Technician I,MA,2346,09/22/76,F,Single,US Citizen,No,White,3/30/2015,Production ,Michael Albert,22.0,CareerBuilder,Fully Meets,5.0,4,0,1/21/2019,0,10 +"Linden, Mathew",10137,1,1,1,5,3,0,63450,0,20,Production Technician II,MA,1770,03/19/79,M ,Married,US Citizen,No,White,7/8/2013,Production ,Kelley Spirea,18.0,LinkedIn,Fully Meets,4.0,3,0,2/18/2019,0,7 +"Wallace, Courtney E",10274,1,1,0,5,3,1,80512,1,18,Production Manager,MA,2478,11/14/55,F,Married,US Citizen,No,Black or African American,9/26/2011,Production ,Janet King,2.0,Diversity Job Fair,Fully Meets,4.5,3,0,1/2/2012,0,5 +"Galia, Lisa",10273,0,0,0,3,3,0,65707,0,14,IT Support,CT,6040,07/06/68,F,Single,US Citizen,No,White,5/1/2010,IT/IS,Eric Dougall,6.0,LinkedIn,Fully Meets,4.7,4,5,2/1/2019,0,1 +"Pearson, Randall",10259,1,1,1,3,3,0,93093,1,9,Data Analyst,MA,2747,09/05/84,M ,Married,US Citizen,No,White,12/1/2014,IT/IS,Simon Roup,4.0,Employee Referral,Fully Meets,4.7,4,5,1/16/2016,0,19 +"Chan, Lin",10210,0,0,0,5,3,0,54237,0,19,Production Technician I,MA,2170,02/12/79,F,Single,US Citizen,No,White,5/12/2014,Production ,Elijiah Gray,16.0,Indeed,Fully Meets,3.3,4,0,2/19/2019,0,11 +"Gonzalez, Cayo",10031,0,2,1,5,4,1,59892,0,19,Production Technician I,MA,2108,09/29/69,M ,Divorced,US Citizen,No,Black or African American,7/11/2011,Production ,Brannon Miller,12.0,Diversity Job Fair,Exceeds,4.5,4,0,2/18/2019,0,1 +"Becker, Scott",10277,0,0,1,5,3,0,53250,0,19,Production Technician I,MA,2452,04/06/79,M ,Single,US Citizen,No,Asian,7/8/2013,Production ,Webster Butler,,LinkedIn,Fully Meets,4.2,4,0,1/11/2019,0,13 +"Jackson, Maryellen",10227,0,0,0,5,3,0,61242,0,19,Production Technician I,MA,2081,09/11/72,F,Single,US Citizen,No,Black or African American,11/5/2012,Production ,David Stanley,14.0,LinkedIn,Fully Meets,4.1,3,0,1/17/2019,0,7 +"Navathe, Kurt",10079,0,0,1,3,3,0,87921,0,22,Senior BI Developer,MA,2056,04/25/70,M ,Single,US Citizen,No,Asian,2/10/2017,IT/IS,Brian Champaigne,13.0,Indeed,Fully Meets,5.0,3,6,2/25/2019,0,17 +"Buccheri, Joseph ",10184,0,0,1,5,3,0,65288,0,20,Production Technician II,MA,1013,07/28/83,M ,Single,US Citizen,No,White,9/29/2014,Production ,Webster Butler,,Google Search,Fully Meets,3.19,3,0,2/1/2019,0,9 +"Pelletier, Ermine",10297,1,1,0,5,2,0,60270,1,20,Production Technician II,MA,2472,07/18/89,F,Married,US Citizen,No,Asian,7/5/2011,Production ,Amy Dunn,11.0,CareerBuilder,Needs Improvement,2.4,5,0,2/6/2015,5,2 +"Sullivan, Kissy ",10158,1,1,0,5,3,0,63682,0,18,Production Manager,MA,1776,03/28/78,F,Married,US Citizen,No,Black or African American,1/8/2009,Production ,Janet King,2.0,Indeed,Fully Meets,3.73,4,0,1/24/2019,0,12 +"Billis, Helen",10003,1,1,0,5,4,0,62910,0,19,Production Technician I,MA,2031,09/01/89,F,Married,US Citizen,No,White,7/7/2014,Production ,Brannon Miller,12.0,Indeed,Exceeds,5.0,3,0,2/27/2019,0,19 +"Cady, Max ",10150,0,0,1,4,3,0,77692,0,25,Software Engineering Manager,MA,2184,11/22/66,M ,Single,US Citizen,No,White,8/15/2011,Software Engineering,Jennifer Zamora,5.0,Google Search,Fully Meets,3.84,3,5,1/21/2019,0,4 +"Maurice, Shana",10053,1,1,0,5,3,0,54132,0,19,Production Technician I,MA,2330,11/22/77,F,Married,US Citizen,No,White,5/31/2011,Production ,David Stanley,14.0,Indeed,Fully Meets,5.0,4,0,1/10/2019,0,8 +"Forrest, Alex",10305,1,1,1,6,3,0,70187,1,3,Area Sales Manager,MA,2330,07/07/75,M ,Married,US Citizen,No,White,9/29/2014,Sales,Lynn Daneault,21.0,Employee Referral,PIP,2.0,5,0,1/28/2019,4,7 +"Eaton, Marianne",10064,1,1,0,5,3,0,60070,1,19,Production Technician I,MA,2343,09/05/91,F,Married,US Citizen,No,White,4/4/2011,Production ,Kissy Sullivan,20.0,Google Search,Fully Meets,5.0,3,0,4/9/2017,0,7 +"Tannen, Biff",10173,1,1,1,3,3,0,90100,0,4,BI Developer,MA,2134,10/24/87,M ,Married,US Citizen,No,White,4/20/2017,IT/IS,Brian Champaigne,13.0,Indeed,Fully Meets,3.4,3,6,1/2/2019,0,14 +"Volk, Colleen",10022,1,1,0,5,4,0,49773,1,19,Production Technician I,MA,2747,06/03/86,F,Married,US Citizen,No,White,9/26/2011,Production ,Kelley Spirea,18.0,Google Search,Exceeds,4.3,5,0,2/1/2015,0,18 +"Chace, Beatrice ",10067,0,0,0,5,3,0,61656,0,19,Production Technician I,MA,2763,01/02/51,F,Single,US Citizen,No,White,9/29/2014,Production ,Michael Albert,22.0,Google Search,Fully Meets,5.0,4,0,2/12/2019,0,11 +"Lecter, Hannibal",10251,1,1,1,5,3,0,64738,0,19,Production Technician I,MA,1776,09/02/82,M ,Married,US Citizen,No,Asian,5/14/2012,Production ,Elijiah Gray,16.0,Google Search,Fully Meets,4.1,3,0,2/22/2019,0,10 +"Strong, Caitrin",10241,1,1,0,6,3,0,60120,0,3,Area Sales Manager,MT,59102,05/12/89,F,Married,US Citizen,No,Black or African American,9/27/2010,Sales,John Smith,17.0,Indeed,Fully Meets,4.1,4,0,1/31/2019,0,18 +"Dunn, Amy ",10105,0,0,0,5,3,0,75188,0,18,Production Manager,MA,1731,11/28/73,F,Single,US Citizen,No,White,9/18/2014,Production ,Janet King,2.0,Google Search,Fully Meets,4.52,4,0,1/15/2019,0,4 +"Zima, Colleen",10271,0,4,0,5,3,0,45046,0,19,Production Technician I,MA,1730,08/17/78,F,Widowed,US Citizen,No,Asian,9/29/2014,Production ,David Stanley,14.0,LinkedIn,Fully Meets,4.5,5,0,1/30/2019,0,2 +"Ozark, Travis",10041,0,0,1,6,3,0,68829,0,3,Area Sales Manager,NC,27229,05/19/82,M ,Single,US Citizen,No,White,1/5/2015,Sales,John Smith,17.0,Website,Fully Meets,5.0,5,0,1/14/2019,0,18 +"Woodson, Jason",10135,0,0,1,5,3,0,65893,0,20,Production Technician II,MA,1810,05/11/85,M ,Single,US Citizen,No,White,7/7/2014,Production ,Kissy Sullivan,20.0,LinkedIn,Fully Meets,4.07,4,0,2/28/2019,0,13 +"Cierpiszewski, Caroline ",10168,0,0,0,5,3,0,64816,0,19,Production Technician I,MA,2044,05/31/88,F,Single,Non-Citizen,No,Black or African American,10/3/2011,Production ,Ketsia Liebig,19.0,Indeed,Fully Meets,3.58,5,0,1/30/2019,0,3 +"Simard, Kramer",10178,1,1,1,3,3,0,87826,0,9,Data Analyst,MA,2110,02/08/70,M ,Married,US Citizen,Yes,White,1/5/2015,IT/IS,Simon Roup,4.0,Employee Referral,Fully Meets,3.32,3,7,1/14/2019,0,16 +"Meads, Elizabeth",10128,0,0,0,5,3,1,62659,1,19,Production Technician I,MA,1760,05/30/68,F,Single,US Citizen,No,Black or African American,4/2/2012,Production ,Kelley Spirea,18.0,Diversity Job Fair,Fully Meets,4.18,4,0,2/5/2016,0,17 +"Williams, Jacquelyn ",10264,0,0,0,5,3,1,59728,1,19,Production Technician I,MA,2109,10/02/69,F,Single,US Citizen,Yes,Black or African American,1/9/2012,Production ,Ketsia Liebig,19.0,Diversity Job Fair,Fully Meets,4.3,4,0,6/2/2014,0,16 +"DeVito, Tommy",10197,0,0,1,3,3,0,96820,0,4,BI Developer,MA,2045,09/04/83,M ,Single,US Citizen,No,White,2/15/2017,IT/IS,Brian Champaigne,13.0,Indeed,Fully Meets,3.01,5,7,1/23/2019,0,15 +"Miller, Brannon",10116,0,0,1,5,3,0,83667,0,18,Production Manager,MA,2045,08/10/81,M ,Single,US Citizen,yes,Hispanic,8/16/2012,Production ,Janet King,2.0,Indeed,Fully Meets,4.37,3,0,1/14/2019,0,2 +"Petingill, Shana ",10217,1,1,0,5,3,0,74226,0,20,Production Technician II,MA,2050,03/10/79,F,Married,Eligible NonCitizen,No,Asian,4/2/2012,Production ,Brannon Miller,12.0,LinkedIn,Fully Meets,4.3,3,0,1/14/2019,0,14 +"Dougall, Eric",10028,0,0,1,3,4,0,138888,0,13,IT Manager - Support,MA,1886,07/09/70,M ,Single,US Citizen,No,Black or African American,1/5/2014,IT/IS,Jennifer Zamora,5.0,Indeed,Exceeds,4.3,5,5,1/4/2019,0,4 +"Hudson, Jane",10248,0,0,0,5,3,0,55425,0,19,Production Technician I,MA,2176,06/10/86,F,Single,US Citizen,No,White,2/20/2012,Production ,Ketsia Liebig,19.0,LinkedIn,Fully Meets,4.8,4,0,1/7/2019,0,4 +"Gruber, Hans",10234,1,1,1,3,3,0,99020,0,4,BI Developer,MA,2134,06/30/89,M ,Married,US Citizen,No,Black or African American,4/20/2017,IT/IS,Brian Champaigne,13.0,Indeed,Fully Meets,4.2,5,5,1/28/2019,0,8 +"Miller, Ned",10298,0,0,1,5,1,0,55800,1,20,Production Technician II,MA,2472,06/29/85,M ,Single,US Citizen,No,White,8/15/2011,Production ,Brannon Miller,12.0,LinkedIn,PIP,3.0,2,0,1/14/2013,6,6 +"O'hare, Lynn",10303,0,0,0,5,1,0,52674,1,19,Production Technician I,MA,2152,09/30/80,F,Single,US Citizen,No,Two or more races,3/31/2014,Production ,Kissy Sullivan,20.0,LinkedIn,PIP,2.33,2,0,3/9/2018,6,3 +"Carter, Michelle ",10040,0,0,0,6,3,0,71860,0,3,Area Sales Manager,VT,5664,05/15/63,F,Single,US Citizen,No,White,8/18/2014,Sales,John Smith,17.0,Indeed,Fully Meets,5.0,5,0,1/21/2019,0,7 +"Brill, Donna",10177,1,1,0,5,3,0,53492,1,19,Production Technician I,MA,1701,08/24/90,F,Married,US Citizen,No,White,4/2/2012,Production ,David Stanley,14.0,Google Search,Fully Meets,3.35,4,0,3/4/2013,0,6 +"Carthy, B'rigit",10076,0,0,0,5,3,0,55315,0,20,Production Technician II,MA,2149,05/21/87,F,Single,US Citizen,No,Black or African American,3/30/2015,Production ,Ketsia Liebig,19.0,LinkedIn,Fully Meets,5.0,5,0,2/7/2019,0,16 +"Homberger, Adrienne J",10195,1,1,0,5,3,0,63478,1,20,Production Technician II,MA,2445,02/16/84,F,Married,Non-Citizen,No,White,8/15/2011,Production ,Michael Albert,30.0,Indeed,Fully Meets,3.03,5,0,3/5/2012,0,16 +"Sewkumar, Nori",10071,0,0,0,5,3,0,50923,0,19,Production Technician I,MA,2191,03/10/75,F,Single,US Citizen,No,Asian,9/30/2013,Production ,Webster Butler,,Google Search,Fully Meets,5.0,5,0,2/6/2019,0,14 +"Gerke, Melisa",10122,0,2,0,5,3,1,51505,1,19,Production Technician I,MA,2330,05/15/70,F,Divorced,US Citizen,No,Black or African American,11/7/2011,Production ,Elijiah Gray,16.0,Diversity Job Fair,Fully Meets,4.24,4,0,4/29/2016,0,2 +"Mahoney, Lauren ",10219,0,0,0,5,3,0,45395,0,19,Production Technician I,MA,2189,07/07/86,F,Single,US Citizen,No,White,1/6/2014,Production ,Ketsia Liebig,19.0,LinkedIn,Fully Meets,4.6,4,0,2/26/2019,0,14 +"Hankard, Earnest",10074,0,0,1,5,3,0,64246,0,20,Production Technician II,MA,2155,08/10/88,M ,Single,US Citizen,Yes,White,11/11/2013,Production ,Kelley Spirea,18.0,LinkedIn,Fully Meets,5.0,3,0,1/8/2019,0,20 +"Chang, Donovan E",10154,0,0,1,5,3,0,60380,0,19,Production Technician I,MA,1845,08/24/83,M ,Single,US Citizen,No,White,7/8/2013,Production ,Webster Butler,,LinkedIn,Fully Meets,3.8,5,0,1/14/2019,0,4 +"Houlihan, Debra",10272,1,1,0,6,3,0,180000,0,11,Director of Sales,RI,2908,03/17/66,F,Married,US Citizen,No,White,5/5/2014,Sales,Janet King,2.0,LinkedIn,Fully Meets,4.5,4,0,1/21/2019,0,19 +"Blount, Dianna",10294,0,0,0,5,2,0,66441,0,20,Production Technician II,MA,2171,09/21/90,F,Single,US Citizen,No,White,4/4/2011,Production ,Michael Albert,22.0,CareerBuilder,Needs Improvement,2.0,3,0,2/27/2019,2,3 +"Knapp, Bradley J",10208,0,0,1,5,3,0,46654,0,19,Production Technician I,MA,1721,11/10/77,M ,Single,US Citizen,No,Black or African American,2/17/2014,Production ,Ketsia Liebig,19.0,LinkedIn,Fully Meets,3.1,3,0,2/6/2019,0,3 +"Gentry, Mildred",10159,1,1,0,5,3,0,51337,0,19,Production Technician I,MA,2145,10/01/90,F,Married,US Citizen,No,Black or African American,3/30/2015,Production ,Michael Albert,22.0,LinkedIn,Fully Meets,3.73,3,0,1/16/2019,0,19 +"MacLennan, Samuel",10191,0,4,1,5,3,0,52788,1,19,Production Technician I,MA,1938,11/09/72,M ,Widowed,US Citizen,No,White,9/24/2012,Production ,Amy Dunn,11.0,Indeed,Fully Meets,3.08,4,0,4/1/2017,0,18 +"Kretschmer, John",10176,1,1,1,5,3,0,63973,0,19,Production Technician I,MA,1801,02/02/80,M ,Married,US Citizen,No,Asian,1/10/2011,Production ,Brannon Miller,12.0,Indeed,Fully Meets,3.38,3,0,1/21/2019,0,17 +"Patronick, Lucas",10005,0,0,1,4,4,1,108987,1,24,Software Engineer,MA,1844,02/20/79,M ,Single,US Citizen,No,Black or African American,11/7/2011,Software Engineering,Alex Sweetwater,10.0,Diversity Job Fair,Exceeds,5.0,5,3,8/16/2015,0,13 +"Sutwell, Barbara",10209,0,0,0,5,3,0,59238,0,19,Production Technician I,MA,2718,08/15/68,F,Single,Eligible NonCitizen,No,Asian,5/14/2012,Production ,Elijiah Gray,16.0,Indeed,Fully Meets,3.4,5,0,1/31/2019,0,13 +"Candie, Calvin",10001,0,0,1,5,4,0,72640,0,18,Production Manager,MA,2169,08/09/83,M ,Single,US Citizen,No,White,1/28/2016,Production ,Janet King,2.0,Indeed,Exceeds,5.0,3,0,2/22/2019,0,14 +"Smith, Joe",10027,0,0,1,5,4,0,60656,0,20,Production Technician II,MA,2045,10/30/63,M ,Single,US Citizen,No,White,9/29/2014,Production ,Elijiah Gray,16.0,Indeed,Exceeds,4.3,3,0,1/28/2019,0,4 +"Winthrop, Jordan ",10033,0,0,1,5,4,0,70507,1,20,Production Technician II,MA,2045,11/07/58,M ,Single,US Citizen,No,White,1/7/2013,Production ,Brannon Miller,12.0,LinkedIn,Exceeds,5.0,3,0,1/19/2016,0,7 +"Harrison, Kara",10007,1,1,0,5,4,0,62065,0,19,Production Technician I,MA,1886,05/02/74,F,Married,US Citizen,No,White,5/12/2014,Production ,Amy Dunn,11.0,CareerBuilder,Exceeds,4.76,4,0,2/15/2019,0,5 +"Wallace, Theresa",10293,0,0,0,5,2,0,50274,1,19,Production Technician I,MA,1887,08/02/80,F,Single,US Citizen,No,White,8/13/2012,Production ,Elijiah Gray,16.0,CareerBuilder,Needs Improvement,2.5,3,0,9/5/2014,6,13 +"Leruth, Giovanni",10103,0,3,1,6,3,0,70468,0,3,Area Sales Manager,UT,84111,12/27/88,M ,Separated,US Citizen,No,Black or African American,4/30/2012,Sales,John Smith,17.0,Website,Fully Meets,4.53,3,0,1/29/2019,0,16 +"True, Edward",10102,0,0,1,4,3,1,100416,1,24,Software Engineer,MA,2451,06/14/83,M ,Single,Non-Citizen,No,Black or African American,2/18/2013,Software Engineering,Alex Sweetwater,10.0,Diversity Job Fair,Fully Meets,4.6,3,4,2/12/2017,0,9 +"Burke, Joelle",10107,0,0,0,5,3,0,63763,0,20,Production Technician II,MA,2148,03/02/80,F,Single,US Citizen,No,Black or African American,3/5/2012,Production ,Amy Dunn,11.0,Employee Referral,Fully Meets,4.51,4,0,2/21/2019,0,3 +"Lundy, Susan",10096,0,4,0,5,3,0,67237,1,20,Production Technician II,MA,2122,12/26/76,F,Widowed,US Citizen,No,White,7/8/2013,Production ,Michael Albert,22.0,LinkedIn,Fully Meets,4.65,4,0,6/10/2016,0,15 +"Nguyen, Dheepa",10042,0,0,0,6,3,0,63695,0,3,Area Sales Manager,GA,30428,03/31/89,F,Single,US Citizen,No,Two or more races,7/8/2013,Sales,Lynn Daneault,21.0,Indeed,Fully Meets,5.0,5,0,1/25/2019,0,2 +"Beatrice, Courtney ",10055,0,0,0,5,3,0,59026,0,19,Production Technician I,MA,1915,10/27/70,F,Single,Eligible NonCitizen,No,White,4/4/2011,Production ,Elijiah Gray,16.0,Google Search,Fully Meets,5.0,5,0,1/14/2019,0,12 +"Trzeciak, Cybil",10270,0,0,0,5,3,0,74813,1,20,Production Technician II,MA,1778,03/15/85,F,Single,US Citizen,No,White,1/10/2011,Production ,Amy Dunn,11.0,LinkedIn,Fully Meets,4.4,3,0,1/5/2014,0,5 +"Biden, Lowan M",10226,0,2,0,5,3,0,64919,0,19,Production Technician I,MA,2027,12/27/58,F,Divorced,US Citizen,No,Asian,8/19/2013,Production ,Ketsia Liebig,19.0,Indeed,Fully Meets,4.2,3,0,1/10/2019,0,2 +"Terry, Sharlene ",10161,0,0,0,6,3,0,58370,0,3,Area Sales Manager,OR,97756,05/07/65,F,Single,US Citizen,No,Black or African American,9/29/2014,Sales,Lynn Daneault,21.0,Indeed,Fully Meets,3.69,3,0,1/28/2019,0,18 +"Singh, Nan ",10039,0,0,0,1,3,0,51920,0,2,Administrative Assistant,MA,2330,05/19/88,F,Single,US Citizen,No,White,5/1/2015,Admin Offices,Brandon R. LeBlanc,1.0,Website,Fully Meets,5.0,3,5,1/15/2019,0,2 +"Guilianno, Mike",10109,0,0,1,6,3,0,71707,1,3,Area Sales Manager,TN,37129,02/09/69,M ,Single,US Citizen,No,Two or more races,3/7/2012,Sales,John Smith,17.0,LinkedIn,Fully Meets,4.5,5,0,2/1/2013,0,20 +"Leach, Dallas",10087,0,0,0,5,3,0,63676,1,19,Production Technician I,MA,1810,01/17/79,F,Single,US Citizen,No,Asian,9/26/2011,Production ,Kissy Sullivan,20.0,CareerBuilder,Fully Meets,4.88,3,0,7/2/2017,0,17 +"Motlagh, Dawn",10254,0,2,0,5,3,0,63430,0,19,Production Technician I,MA,2453,07/07/84,F,Divorced,US Citizen,No,White,4/1/2013,Production ,Elijiah Gray,16.0,LinkedIn,Fully Meets,4.4,4,0,1/17/2019,0,18 +"Sadki, Nore ",10260,0,0,1,5,3,0,46428,1,19,Production Technician I,MA,2148,12/21/74,M ,Single,US Citizen,No,White,1/5/2009,Production ,Michael Albert,22.0,Google Search,Fully Meets,4.6,5,0,2/5/2018,0,7 +"Immediato, Walter",10289,1,1,1,5,2,0,83082,1,18,Production Manager,MA,2128,11/15/76,M ,Married,US Citizen,No,Asian,2/21/2011,Production ,Janet King,2.0,Indeed,Needs Improvement,2.34,2,0,4/12/2012,3,4 +"Panjwani, Nina",10148,1,1,0,5,3,0,63515,1,19,Production Technician I,MA,2351,05/01/79,F,Married,US Citizen,No,White,2/7/2011,Production ,Elijiah Gray,16.0,Google Search,Fully Meets,3.89,4,0,3/4/2013,0,7 +"Keatts, Kramer ",10192,0,0,1,5,3,0,50482,0,19,Production Technician I,MA,1887,01/19/76,M ,Single,US Citizen,No,White,9/30/2013,Production ,Michael Albert,22.0,Indeed,Fully Meets,3.07,4,0,1/23/2019,0,10 +"Thibaud, Kenneth",10268,0,4,1,5,3,0,67176,1,20,Production Technician II,MA,2472,09/16/75,M ,Widowed,US Citizen,No,White,6/25/2007,Production ,Webster Butler,39.0,Other,Fully Meets,4.1,4,0,7/14/2010,0,15 +"Robinson, Alain ",10047,1,1,1,5,3,0,50428,1,19,Production Technician I,MA,1420,01/07/74,M ,Married,US Citizen,No,Black or African American,1/10/2011,Production ,Amy Dunn,11.0,Indeed,Fully Meets,5.0,3,0,1/10/2015,0,11 +"Lajiri, Jyoti",10113,1,1,1,3,3,0,93206,0,28,Sr. Network Engineer,MA,2169,04/23/86,M ,Married,US Citizen,No,White,11/10/2014,IT/IS,Peter Monroe,7.0,Employee Referral,Fully Meets,4.46,5,6,1/7/2019,0,7 +"LeBlanc, Brandon R",10134,1,1,1,1,3,0,93046,0,23,Shared Services Manager,MA,1460,06/10/84,M ,Married,US Citizen,No,White,1/5/2016,Admin Offices,Janet King,2.0,CareerBuilder,Fully Meets,4.1,4,0,1/28/2019,0,20 +"Petrowsky, Thelma",10016,1,1,0,3,4,0,93554,0,9,Data Analyst,MA,1886,09/16/84,F,Married,US Citizen,No,Black or African American,11/10/2014,IT/IS,Simon Roup,4.0,Employee Referral,Exceeds,4.6,5,7,1/4/2019,0,16 +"Exantus, Susan",10290,1,1,0,4,2,0,99280,1,24,Software Engineer,MA,1749,05/15/87,F,Married,US Citizen,No,Black or African American,5/2/2011,Software Engineering,Alex Sweetwater,10.0,Indeed,Needs Improvement,2.1,5,4,8/10/2012,4,19 +"Oliver, Brooke ",10078,1,1,0,5,3,0,71966,1,20,Production Technician II,MA,2492,02/11/52,F,Married,US Citizen,No,Asian,5/14/2012,Production ,Webster Butler,39.0,LinkedIn,Fully Meets,5.0,3,0,7/2/2013,0,17 +"Moran, Patrick",10223,0,0,1,5,3,1,72106,0,20,Production Technician II,MA,2127,12/03/76,M ,Single,US Citizen,No,Black or African American,1/9/2012,Production ,Kelley Spirea,18.0,Diversity Job Fair,Fully Meets,4.1,4,0,1/31/2019,0,12 +"Smith, John",10291,0,2,1,6,2,1,72992,0,21,Sales Manager,MA,1886,08/16/84,M ,Divorced,US Citizen,No,Black or African American,5/18/2014,Sales,Debra Houlihan,15.0,Diversity Job Fair,Needs Improvement,2.4,4,0,1/16/2019,2,16 +"Onque, Jasmine",10121,0,0,0,6,3,0,63051,0,3,Area Sales Manager,FL,33174,05/11/90,F,Single,US Citizen,Yes,White,9/30/2013,Sales,Lynn Daneault,21.0,Indeed,Fully Meets,4.28,3,0,1/25/2019,0,1 +"Stoica, Rick",10266,1,1,1,5,3,0,61355,0,19,Production Technician I,MA,2301,03/14/85,M ,Married,US Citizen,No,Asian,2/17/2014,Production ,Kelley Spirea,18.0,LinkedIn,Fully Meets,4.7,3,0,1/11/2019,0,4 +"Andreola, Colby",10194,0,0,0,4,3,0,95660,0,24,Software Engineer,MA,2110,05/24/79,F,Single,US Citizen,No,White,11/10/2014,Software Engineering,Alex Sweetwater,10.0,LinkedIn,Fully Meets,3.04,3,4,1/2/2019,0,19 +"Gordon, David",10051,1,1,1,5,3,0,48285,0,19,Production Technician I,MA,2169,05/21/79,M ,Married,US Citizen,No,White,7/2/2012,Production ,David Stanley,14.0,LinkedIn,Fully Meets,5.0,3,0,1/14/2019,0,2 +"Foss, Jason",10015,0,0,1,3,4,0,178000,0,12,IT Director,MA,1460,07/05/80,M ,Single,US Citizen,No,Black or African American,4/15/2011,IT/IS,Jennifer Zamora,5.0,Indeed,Exceeds,5.0,5,5,1/7/2019,0,15 +"Sparks, Taylor ",10302,1,1,0,5,1,0,64021,0,19,Production Technician I,MA,2093,07/20/68,F,Married,US Citizen,No,White,2/20/2012,Production ,Brannon Miller,12.0,Indeed,PIP,2.4,2,1,2/25/2019,6,20 +"Hitchcock, Alfred",10167,1,1,1,6,3,0,70545,0,3,Area Sales Manager,NH,3062,09/14/88,M ,Married,US Citizen,No,American Indian or Alaska Native,8/18/2014,Sales,John Smith,17.0,Indeed,Fully Meets,3.6,5,0,1/30/2019,0,9 +"Mangal, Debbie",10279,1,1,0,5,3,0,61349,0,19,Production Technician I,MA,2451,11/07/74,F,Married,US Citizen,No,White,11/11/2013,Production ,Brannon Miller,12.0,LinkedIn,Fully Meets,4.1,3,0,1/22/2019,0,11 +"Bernstein, Sean",10046,0,0,1,5,3,0,51044,0,19,Production Technician I,MA,2072,12/22/70,M ,Single,US Citizen,Yes,White,4/2/2012,Production ,Amy Dunn,11.0,Google Search,Fully Meets,5.0,3,0,1/14/2019,0,13 +"Cornett, Lisa ",10094,1,1,0,5,3,0,63381,0,19,Production Technician I,MA,2189,03/31/77,F,Married,US Citizen,Yes,White,1/5/2015,Production ,Kelley Spirea,18.0,Indeed,Fully Meets,4.73,5,0,2/14/2019,0,6 +"Cross, Noah",10083,0,0,1,3,3,0,92329,0,28,Sr. Network Engineer,CT,6278,09/09/65,M ,Single,US Citizen,No,White,11/10/2014,IT/IS,Peter Monroe,7.0,Employee Referral,Fully Meets,5.0,3,4,1/2/2019,0,5 +"Booth, Frank",10199,0,0,1,3,3,0,103613,1,30,Enterprise Architect,CT,6033,07/30/64,M ,Single,US Citizen,No,Black or African American,2/17/2014,IT/IS,Simon Roup,4.0,LinkedIn,Fully Meets,3.5,5,7,1/10/2016,0,2 +"Faller, Megan ",10263,1,1,0,5,3,0,71776,0,20,Production Technician II,MA,1824,09/22/78,F,Married,US Citizen,No,Black or African American,7/7/2014,Production ,Elijiah Gray,16.0,LinkedIn,Fully Meets,4.4,5,0,2/22/2019,0,17 +"Salter, Jason",10229,0,2,1,3,3,0,88527,1,9,Data Analyst ,MA,2452,12/17/87,M ,Divorced,US Citizen,No,Black or African American,1/5/2015,IT/IS,Simon Roup,4.0,LinkedIn,Fully Meets,4.2,3,5,4/20/2015,0,2 +"Smith, Leigh Ann",10153,1,1,0,1,3,1,55000,1,2,Administrative Assistant,MA,1844,06/14/87,F,Married,US Citizen,No,Black or African American,9/26/2011,Admin Offices,Brandon R. LeBlanc,1.0,Diversity Job Fair,Fully Meets,3.8,4,4,8/15/2013,0,17 +"Lunquist, Lisa",10035,0,0,0,5,4,0,73330,0,20,Production Technician II,MA,2324,03/28/82,F,Single,US Citizen,No,Black or African American,8/19/2013,Production ,Elijiah Gray,16.0,Indeed,Exceeds,4.2,4,0,2/12/2019,0,19 +"Erilus, Angela",10299,0,3,0,5,1,0,56847,0,20,Production Technician II,MA,2133,08/25/89,F,Separated,US Citizen,No,White,7/7/2014,Production ,Michael Albert,22.0,Indeed,PIP,3.0,1,0,2/25/2019,2,5 +"DeGweck, James",10070,1,1,1,5,3,0,55722,1,19,Production Technician I,MA,1810,10/31/77,M ,Married,US Citizen,No,White,5/16/2011,Production ,Webster Butler,39.0,Indeed,Fully Meets,5.0,4,0,4/2/2016,0,14 +"Gill, Whitney ",10142,0,4,0,6,3,0,59370,1,3,Area Sales Manager,OH,43050,07/10/71,F,Widowed,US Citizen,No,Black or African American,7/7/2014,Sales,John Smith,17.0,CareerBuilder,Fully Meets,3.97,4,0,1/15/2014,0,7 +"Myers, Michael",10216,0,0,1,5,3,0,57575,0,19,Production Technician I,MA,1550,04/18/80,M ,Single,US Citizen,No,Asian,7/8/2013,Production ,Kissy Sullivan,20.0,LinkedIn,Fully Meets,4.1,4,0,1/22/2019,0,13 +"Barone, Francesco A",10265,0,0,1,5,3,0,58709,0,19,Production Technician I,MA,1810,07/20/83,M ,Single,US Citizen,No,Two or more races,2/20/2012,Production ,Kelley Spirea,18.0,Google Search,Fully Meets,4.6,4,0,2/14/2019,0,7 +"Tavares, Desiree ",10221,1,1,0,5,3,1,60754,1,19,Production Technician I,MA,1801,04/03/75,F,Married,Non-Citizen,No,Black or African American,4/27/2009,Production ,Webster Butler,39.0,Diversity Job Fair,Fully Meets,4.5,5,0,2/15/2012,0,11 +"Gaul, Barbara",10257,0,0,0,5,3,0,53171,0,19,Production Technician I,MA,2121,12/02/83,F,Single,US Citizen,Yes,Black or African American,5/16/2011,Production ,Kelley Spirea,18.0,LinkedIn,Fully Meets,4.2,4,0,2/26/2019,0,12 +"Turpin, Jumil",10045,1,1,1,3,3,0,76029,0,15,Network Engineer,MA,2343,03/31/69,M ,Married,Eligible NonCitizen,No,White,3/30/2015,IT/IS,Peter Monroe,7.0,Employee Referral,Fully Meets,5.0,4,7,1/14/2019,0,8 +"Hendrickson, Trina",10075,0,0,0,5,3,0,68099,1,20,Production Technician II,MA,2021,08/27/72,F,Single,US Citizen,No,White,1/10/2011,Production ,Kelley Spirea,18.0,CareerBuilder,Fully Meets,5.0,3,0,1/30/2013,0,15 +"Peters, Lauren",10032,1,1,0,5,4,0,57954,1,20,Production Technician II,MA,1886,08/17/86,F,Married,US Citizen,No,White,5/16/2011,Production ,Ketsia Liebig,19.0,Indeed,Exceeds,4.2,5,0,1/10/2013,0,12 +"Cloninger, Jennifer",10275,1,1,0,5,3,0,64066,1,20,Production Technician II,MA,1752,08/31/81,F,Married,US Citizen,No,White,5/16/2011,Production ,Brannon Miller,12.0,Google Search,Fully Meets,4.2,5,0,5/3/2012,0,9 +"Garcia, Raul",10111,0,0,1,5,3,0,52249,0,19,Production Technician I,MA,1905,09/15/85,M ,Single,US Citizen,Yes,White,3/30/2015,Production ,David Stanley,14.0,Employee Referral,Fully Meets,4.5,3,0,2/18/2019,0,5 +"Sullivan, Timothy",10117,1,1,1,5,3,0,63025,0,19,Production Technician I,MA,2747,10/07/82,M ,Married,US Citizen,Yes,White,1/5/2015,Production ,Michael Albert,22.0,Google Search,Fully Meets,4.36,5,0,1/24/2019,0,10 +"Desimone, Carl ",10310,1,1,1,5,1,0,53189,0,19,Production Technician I,MA,2061,04/19/67,M ,Married,US Citizen,No,White,7/7/2014,Production ,Amy Dunn,11.0,Indeed,PIP,1.12,2,0,1/31/2019,4,9 +"Ngodup, Shari ",10037,0,3,0,5,4,1,52984,0,19,Production Technician I,MA,1810,06/03/67,F,Separated,US Citizen,No,Black or African American,4/1/2013,Production ,Brannon Miller,12.0,Diversity Job Fair,Exceeds,4.0,3,0,2/13/2019,0,12 +"Sahoo, Adil",10233,1,1,1,5,3,0,57975,0,20,Production Technician II,MA,2062,04/26/86,M ,Married,US Citizen,No,White,8/30/2010,Production ,Kelley Spirea,18.0,CareerBuilder,Fully Meets,4.1,3,0,1/10/2019,0,13 +"Pitt, Brad ",10164,0,0,1,5,3,0,47001,0,19,Production Technician I,MA,2451,11/23/81,M ,Single,US Citizen,No,White,11/5/2007,Production ,David Stanley,14.0,Google Search,Fully Meets,3.66,3,0,2/25/2019,0,15 +"Fidelia, Libby",10049,1,1,0,5,3,0,58530,0,19,Production Technician I,MA,2155,03/16/81,F,Married,US Citizen,No,White,1/9/2012,Production ,Brannon Miller,12.0,Google Search,Fully Meets,5.0,5,0,1/29/2019,0,19 +"Lydon, Allison",10057,1,1,0,5,3,0,52057,0,19,Production Technician I,MA,2122,10/22/75,F,Married,US Citizen,No,Black or African American,2/16/2015,Production ,Elijiah Gray,16.0,Website,Fully Meets,5.0,3,0,1/23/2019,0,6 +"Gross, Paula",10059,0,2,0,5,3,0,63813,1,19,Production Technician I,MA,2176,05/21/83,F,Divorced,US Citizen,No,White,2/21/2011,Production ,Kelley Spirea,18.0,CareerBuilder,Fully Meets,5.0,5,0,6/3/2013,0,17 +"Szabo, Andrew",10024,0,0,1,4,4,0,92989,0,24,Software Engineer,MA,2140,05/06/83,M ,Single,US Citizen,No,White,7/7/2014,Software Engineering,Alex Sweetwater,10.0,LinkedIn,Exceeds,4.5,5,5,2/18/2019,0,1 diff --git a/decision_making/daily/classification/Human_Resources_Data_Set_B2/info.json b/decision_making/daily/classification/Human_Resources_Data_Set_B2/info.json index 6e7107628a2c5a3a63ec52b3f1d9263145c0aa2c..76db73f04d0aa5849ab1e71d09fa94b1556d5d75 100644 --- a/decision_making/daily/classification/Human_Resources_Data_Set_B2/info.json +++ b/decision_making/daily/classification/Human_Resources_Data_Set_B2/info.json @@ -1,542 +1,508 @@ { - "name": "Human Resources Data Set", - "source": "https://www.kaggle.com/datasets/rhuebner/human-resources-data-set/data", - "data_intro": "There has been some confusion around licensing for this data set. Dr. Carla Patalano and Dr. Rich Huebner are the original authors of this dataset.", - "is_splited": false, - "overall_size": 311, - "train_size": 0, - "test_size": 0, - "c_classes": 18, - "n_classes": 18, - "cat_feature_intro": { - "Employee_Name": "The name of the employee.", - "Position": "The job title of the employee.", - "State": "The state where the employee is located.", - "DOB": "The date of birth of the employee.", - "Sex": "The gender of the employee.", - "MaritalDesc": "The marital status of the employee.", - "CitizenDesc": "The citizenship status of the employee.", - "HispanicLatino": "Whether the employee is Hispanic or Latino.", - "RaceDesc": "The race of the employee.", - "DateofHire": "The date the employee was hired.", - "DateofTermination": "The date the employee was terminated.", - "TermReason": "The reason for the employee's termination.", - "EmploymentStatus": "The current employment status of the employee.", - "Department": "The department where the employee works.", - "ManagerName": "The name of the employee's manager.", - "RecruitmentSource": "The source from which the employee was recruited.", - "PerformanceScore": "The performance score of the employee.", - "LastPerformanceReview_Date": "The date of the employee's last performance review." - }, - "num_feature_intro": { - "EmpID": "The unique identifier for the employee.", - "MarriedID": "Whether the employee is married (1) or not (0).", - "MaritalStatusID": "The unique identifier for the employee's marital status.", - "GenderID": "The unique identifier for the employee's gender.", - "EmpStatusID": "The unique identifier for the employee's employment status.", - "DeptID": "The unique identifier for the employee's department.", - "PerfScoreID": "The unique identifier for the employee's performance score.", - "FromDiversityJobFairID": "Whether the employee was recruited from a diversity job fair (1) or not (0).", - "Salary": "The salary of the employee.", - "Termd": "Whether the employee was terminated (1) or not (0).", - "PositionID": "The unique identifier for the employee's position.", - "Zip": "The ZIP code of the employee's location.", - "ManagerID": "The unique identifier for the employee's manager.", - "EngagementSurvey": "The engagement survey score of the employee.", - "EmpSatisfaction": "The satisfaction score of the employee.", - "SpecialProjectsCount": "The number of special projects the employee is involved in.", - "DaysLateLast30": "The number of days the employee was late in the last 30 days.", - "Absences": "The number of absences of the employee." - }, - "evaluation_metric": "ROC AUC", - "task_type": "classification", - "target": "Termd", - "num_feature_value": { - "Absences": [ - 1.0, - 20.0 - ], - "DaysLateLast30": [ - 0.0, - 6.0 - ], - "DeptID": [ - 1.0, - 6.0 - ], - "EmpID": [ - 10001.0, - 10311.0 - ], - "EmpSatisfaction": [ - 1.0, - 5.0 - ], - "EmpStatusID": [ - 1.0, - 5.0 - ], - "EngagementSurvey": [ - 1.12, - 5.0 - ], - "FromDiversityJobFairID": [ - 0.0, - 1.0 - ], - "GenderID": [ - 0.0, - 1.0 - ], - "ManagerID": [ - 1.0, - 39.0 - ], - "MaritalStatusID": [ - 0.0, - 4.0 - ], - "MarriedID": [ - 0.0, - 1.0 - ], - "PerfScoreID": [ - 1.0, - 4.0 - ], - "PositionID": [ - 1.0, - 30.0 - ], - "Salary": [ - 45046.0, - 250000.0 - ], - "SpecialProjectsCount": [ - 0.0, - 8.0 - ], - "Termd": [ - 0.0, - 1.0 - ], - "Zip": [ - 1013.0, - 98052.0 - ] - }, - "cat_feature_value": { - "CitizenDesc": [ - "Eligible NonCitizen", - "Non-Citizen", - "US Citizen" - ], - "DOB": "string", - "DateofHire": [ - "1/10/2011", - "1/20/2013", - "1/21/2011", - "1/28/2016", - "1/5/2009", - "1/5/2014", - "1/5/2015", - "1/5/2016", - "1/6/2014", - "1/7/2008", - "1/7/2013", - "1/7/2017", - "1/8/2009", - "1/9/2006", - "1/9/2012", - "10/2/2012", - "10/2/2016", - "10/25/2010", - "10/26/2009", - "10/27/2008", - "10/3/2011", - "11/10/2014", - "11/11/2013", - "11/28/2011", - "11/5/2007", - "11/5/2012", - "11/7/2011", - "12/1/2014", - "2/10/2017", - "2/15/2012", - "2/15/2017", - "2/16/2015", - "2/17/2014", - "2/18/2013", - "2/20/2012", - "2/21/2011", - "2/7/2011", - "3/30/2015", - "3/31/2014", - "3/5/2012", - "3/7/2011", - "3/7/2012", - "4/1/2013", - "4/10/2010", - "4/15/2011", - "4/2/2012", - "4/20/2017", - "4/26/2010", - "4/27/2009", - "4/30/2012", - "4/4/2011", - "5/1/2010", - "5/1/2015", - "5/11/2015", - "5/11/2016", - "5/12/2014", - "5/13/2013", - "5/14/2012", - "5/16/2011", - "5/18/2014", - "5/2/2011", - "5/31/2011", - "5/5/2014", - "6/10/2011", - "6/2/2015", - "6/25/2007", - "6/27/2011", - "6/30/2016", - "6/5/2015", - "6/6/2016", - "7/11/2011", - "7/2/2012", - "7/20/2010", - "7/21/2016", - "7/4/2016", - "7/5/2011", - "7/5/2015", - "7/6/2009", - "7/6/2016", - "7/7/2014", - "7/8/2013", - "7/9/2012", - "7/9/2018", - "8/1/2011", - "8/13/2012", - "8/15/2011", - "8/16/2012", - "8/18/2014", - "8/19/2013", - "8/30/2010", - "9/18/2014", - "9/2/2008", - "9/24/2012", - "9/26/2011", - "9/27/2010", - "9/29/2014", - "9/30/2013", - "9/30/2014", - "9/5/2012", - "9/6/2011", - "9/6/2016" - ], - "DateofTermination": "string", - "Department": [ - "Admin Offices", - "Executive Office", - "IT/IS", - "Production ", - "Sales", - "Software Engineering" - ], - "Employee_Name": "string", - "EmploymentStatus": [ - "Active", - "Terminated for Cause", - "Voluntarily Terminated" - ], - "HispanicLatino": [ - "No", - "Yes", - "no", - "yes" - ], - "LastPerformanceReview_Date": [ - "1/10/2013", - "1/10/2015", - "1/10/2016", - "1/10/2019", - "1/11/2019", - "1/14/2013", - "1/14/2019", - "1/15/2014", - "1/15/2015", - "1/15/2017", - "1/15/2019", - "1/16/2016", - "1/16/2019", - "1/17/2019", - "1/18/2019", - "1/19/2016", - "1/19/2019", - "1/2/2012", - "1/2/2019", - "1/20/2015", - "1/21/2019", - "1/22/2019", - "1/23/2019", - "1/24/2019", - "1/25/2019", - "1/27/2019", - "1/28/2019", - "1/29/2019", - "1/3/2019", - "1/30/2013", - "1/30/2016", - "1/30/2019", - "1/31/2019", - "1/4/2015", - "1/4/2019", - "1/5/2014", - "1/6/2012", - "1/7/2012", - "1/7/2019", - "1/8/2019", - "10/22/2011", - "2/1/2013", - "2/1/2015", - "2/1/2016", - "2/1/2019", - "2/10/2013", - "2/11/2019", - "2/12/2017", - "2/12/2019", - "2/13/2018", - "2/13/2019", - "2/14/2018", - "2/14/2019", - "2/15/2012", - "2/15/2015", - "2/15/2017", - "2/15/2019", - "2/18/2019", - "2/19/2019", - "2/2/2015", - "2/20/2012", - "2/20/2013", - "2/20/2014", - "2/21/2019", - "2/22/2019", - "2/24/2016", - "2/25/2019", - "2/26/2019", - "2/27/2019", - "2/28/2019", - "2/3/2016", - "2/4/2016", - "2/4/2018", - "2/4/2019", - "2/5/2016", - "2/5/2018", - "2/5/2019", - "2/6/2011", - "2/6/2015", - "2/6/2019", - "2/7/2019", - "2/8/2019", - "3/1/2015", - "3/10/2015", - "3/2/2013", - "3/2/2015", - "3/2/2016", - "3/2/2017", - "3/2/2018", - "3/30/2013", - "3/30/2015", - "3/4/2013", - "3/4/2014", - "3/5/2012", - "3/5/2015", - "3/6/2011", - "3/6/2016", - "3/9/2018", - "4/1/2017", - "4/12/2012", - "4/12/2018", - "4/15/2015", - "4/18/2017", - "4/19/2014", - "4/2/2013", - "4/2/2015", - "4/2/2016", - "4/2/2018", - "4/20/2015", - "4/29/2016", - "4/5/2013", - "4/5/2016", - "4/5/2017", - "4/6/2017", - "4/9/2017", - "5/1/2016", - "5/13/2014", - "5/15/2012", - "5/15/2014", - "5/2/2015", - "5/3/2012", - "5/4/2011", - "5/6/2015", - "5/6/2016", - "6/10/2016", - "6/2/2014", - "6/3/2013", - "7/14/2010", - "7/2/2012", - "7/2/2013", - "7/2/2017", - "7/2/2018", - "8/10/2012", - "8/15/2013", - "8/16/2015", - "9/2/2015", - "9/5/2014" - ], - "ManagerName": [ - "Alex Sweetwater", - "Amy Dunn", - "Board of Directors", - "Brandon R. LeBlanc", - "Brannon Miller", - "Brian Champaigne", - "David Stanley", - "Debra Houlihan", - "Elijiah Gray", - "Eric Dougall", - "Janet King", - "Jennifer Zamora", - "John Smith", - "Kelley Spirea", - "Ketsia Liebig", - "Kissy Sullivan", - "Lynn Daneault", - "Michael Albert", - "Peter Monroe", - "Simon Roup", - "Webster Butler" - ], - "MaritalDesc": [ - "Divorced", - "Married", - "Separated", - "Single", - "Widowed" - ], - "PerformanceScore": [ - "Exceeds", - "Fully Meets", - "Needs Improvement", - "PIP" - ], - "Position": [ - "Accountant I", - "Administrative Assistant", - "Area Sales Manager", - "BI Developer", - "BI Director", - "CIO", - "Data Analyst", - "Data Analyst ", - "Data Architect", - "Database Administrator", - "Director of Operations", - "Director of Sales", - "Enterprise Architect", - "IT Director", - "IT Manager - DB", - "IT Manager - Infra", - "IT Manager - Support", - "IT Support", - "Network Engineer", - "President & CEO", - "Principal Data Architect", - "Production Manager", - "Production Technician I", - "Production Technician II", - "Sales Manager", - "Senior BI Developer", - "Shared Services Manager", - "Software Engineer", - "Software Engineering Manager", - "Sr. Accountant", - "Sr. DBA", - "Sr. Network Engineer" - ], - "RaceDesc": [ - "American Indian or Alaska Native", - "Asian", - "Black or African American", - "Hispanic", - "Two or more races", - "White" - ], - "RecruitmentSource": [ - "CareerBuilder", - "Diversity Job Fair", - "Employee Referral", - "Google Search", - "Indeed", - "LinkedIn", - "On-line Web application", - "Other", - "Website" - ], - "Sex": [ - "F", - "M " - ], - "State": [ - "AL", - "AZ", - "CA", - "CO", - "CT", - "FL", - "GA", - "ID", - "IN", - "KY", - "MA", - "ME", - "MT", - "NC", - "ND", - "NH", - "NV", - "NY", - "OH", - "OR", - "PA", - "RI", - "TN", - "TX", - "UT", - "VA", - "VT", - "WA" - ], - "TermReason": [ - "Another position", - "Fatal attraction", - "Learned that he is a gangster", - "N/A-StillEmployed", - "attendance", - "career change", - "gross misconduct", - "hours", - "maternity leave - did not return", - "medical issues", - "military", - "more money", - "no-call, no-show", - "performance", - "relocation out of area", - "retiring", - "return to school", - "unhappy" - ] - } -} \ No newline at end of file + "name": "Human Resources Data Set", + "source": "https://www.kaggle.com/datasets/rhuebner/human-resources-data-set/data", + "data_intro": "There has been some confusion around licensing for this data set. Dr. Carla Patalano and Dr. Rich Huebner are the original authors of this dataset.", + "is_splited": false, + "overall_size": 311, + "train_size": 0, + "test_size": 0, + "c_classes": 15, + "n_classes": 17, + "cat_feature_intro": { + "Employee_Name": "The name of the employee.", + "Position": "The job title of the employee.", + "State": "The state where the employee is located.", + "DOB": "The date of birth of the employee.", + "Sex": "The gender of the employee.", + "MaritalDesc": "The marital status of the employee.", + "CitizenDesc": "The citizenship status of the employee.", + "HispanicLatino": "Whether the employee is Hispanic or Latino.", + "RaceDesc": "The race of the employee.", + "DateofHire": "The date the employee was hired.", + "Department": "The department where the employee works.", + "ManagerName": "The name of the employee's manager.", + "RecruitmentSource": "The source from which the employee was recruited.", + "PerformanceScore": "The performance score of the employee.", + "LastPerformanceReview_Date": "The date of the employee's last performance review." + }, + "num_feature_intro": { + "EmpID": "The unique identifier for the employee.", + "MarriedID": "Whether the employee is married (1) or not (0).", + "MaritalStatusID": "The unique identifier for the employee's marital status.", + "GenderID": "The unique identifier for the employee's gender.", + "DeptID": "The unique identifier for the employee's department.", + "PerfScoreID": "The unique identifier for the employee's performance score.", + "FromDiversityJobFairID": "Whether the employee was recruited from a diversity job fair (1) or not (0).", + "Salary": "The salary of the employee.", + "Termd": "Whether the employee was terminated (1) or not (0).", + "PositionID": "The unique identifier for the employee's position.", + "Zip": "The ZIP code of the employee's location.", + "ManagerID": "The unique identifier for the employee's manager.", + "EngagementSurvey": "The engagement survey score of the employee.", + "EmpSatisfaction": "The satisfaction score of the employee.", + "SpecialProjectsCount": "The number of special projects the employee is involved in.", + "DaysLateLast30": "The number of days the employee was late in the last 30 days.", + "Absences": "The number of absences of the employee." + }, + "evaluation_metric": "ROC AUC", + "task_type": "classification", + "target": "Termd", + "num_feature_value": { + "Absences": [ + 1.0, + 20.0 + ], + "DaysLateLast30": [ + 0.0, + 6.0 + ], + "DeptID": [ + 1.0, + 6.0 + ], + "EmpID": [ + 10001.0, + 10311.0 + ], + "EmpSatisfaction": [ + 1.0, + 5.0 + ], + "EngagementSurvey": [ + 1.12, + 5.0 + ], + "FromDiversityJobFairID": [ + 0.0, + 1.0 + ], + "GenderID": [ + 0.0, + 1.0 + ], + "ManagerID": [ + 1.0, + 39.0 + ], + "MaritalStatusID": [ + 0.0, + 4.0 + ], + "MarriedID": [ + 0.0, + 1.0 + ], + "PerfScoreID": [ + 1.0, + 4.0 + ], + "PositionID": [ + 1.0, + 30.0 + ], + "Salary": [ + 45046.0, + 250000.0 + ], + "SpecialProjectsCount": [ + 0.0, + 8.0 + ], + "Termd": [ + 0.0, + 1.0 + ], + "Zip": [ + 1013.0, + 98052.0 + ] + }, + "cat_feature_value": { + "CitizenDesc": [ + "Eligible NonCitizen", + "Non-Citizen", + "US Citizen" + ], + "DOB": "string", + "DateofHire": [ + "1/10/2011", + "1/20/2013", + "1/21/2011", + "1/28/2016", + "1/5/2009", + "1/5/2014", + "1/5/2015", + "1/5/2016", + "1/6/2014", + "1/7/2008", + "1/7/2013", + "1/7/2017", + "1/8/2009", + "1/9/2006", + "1/9/2012", + "10/2/2012", + "10/2/2016", + "10/25/2010", + "10/26/2009", + "10/27/2008", + "10/3/2011", + "11/10/2014", + "11/11/2013", + "11/28/2011", + "11/5/2007", + "11/5/2012", + "11/7/2011", + "12/1/2014", + "2/10/2017", + "2/15/2012", + "2/15/2017", + "2/16/2015", + "2/17/2014", + "2/18/2013", + "2/20/2012", + "2/21/2011", + "2/7/2011", + "3/30/2015", + "3/31/2014", + "3/5/2012", + "3/7/2011", + "3/7/2012", + "4/1/2013", + "4/10/2010", + "4/15/2011", + "4/2/2012", + "4/20/2017", + "4/26/2010", + "4/27/2009", + "4/30/2012", + "4/4/2011", + "5/1/2010", + "5/1/2015", + "5/11/2015", + "5/11/2016", + "5/12/2014", + "5/13/2013", + "5/14/2012", + "5/16/2011", + "5/18/2014", + "5/2/2011", + "5/31/2011", + "5/5/2014", + "6/10/2011", + "6/2/2015", + "6/25/2007", + "6/27/2011", + "6/30/2016", + "6/5/2015", + "6/6/2016", + "7/11/2011", + "7/2/2012", + "7/20/2010", + "7/21/2016", + "7/4/2016", + "7/5/2011", + "7/5/2015", + "7/6/2009", + "7/6/2016", + "7/7/2014", + "7/8/2013", + "7/9/2012", + "7/9/2018", + "8/1/2011", + "8/13/2012", + "8/15/2011", + "8/16/2012", + "8/18/2014", + "8/19/2013", + "8/30/2010", + "9/18/2014", + "9/2/2008", + "9/24/2012", + "9/26/2011", + "9/27/2010", + "9/29/2014", + "9/30/2013", + "9/30/2014", + "9/5/2012", + "9/6/2011", + "9/6/2016" + ], + "Department": [ + "Admin Offices", + "Executive Office", + "IT/IS", + "Production ", + "Sales", + "Software Engineering" + ], + "Employee_Name": "string", + "HispanicLatino": [ + "No", + "Yes", + "no", + "yes" + ], + "LastPerformanceReview_Date": [ + "1/10/2013", + "1/10/2015", + "1/10/2016", + "1/10/2019", + "1/11/2019", + "1/14/2013", + "1/14/2019", + "1/15/2014", + "1/15/2015", + "1/15/2017", + "1/15/2019", + "1/16/2016", + "1/16/2019", + "1/17/2019", + "1/18/2019", + "1/19/2016", + "1/19/2019", + "1/2/2012", + "1/2/2019", + "1/20/2015", + "1/21/2019", + "1/22/2019", + "1/23/2019", + "1/24/2019", + "1/25/2019", + "1/27/2019", + "1/28/2019", + "1/29/2019", + "1/3/2019", + "1/30/2013", + "1/30/2016", + "1/30/2019", + "1/31/2019", + "1/4/2015", + "1/4/2019", + "1/5/2014", + "1/6/2012", + "1/7/2012", + "1/7/2019", + "1/8/2019", + "10/22/2011", + "2/1/2013", + "2/1/2015", + "2/1/2016", + "2/1/2019", + "2/10/2013", + "2/11/2019", + "2/12/2017", + "2/12/2019", + "2/13/2018", + "2/13/2019", + "2/14/2018", + "2/14/2019", + "2/15/2012", + "2/15/2015", + "2/15/2017", + "2/15/2019", + "2/18/2019", + "2/19/2019", + "2/2/2015", + "2/20/2012", + "2/20/2013", + "2/20/2014", + "2/21/2019", + "2/22/2019", + "2/24/2016", + "2/25/2019", + "2/26/2019", + "2/27/2019", + "2/28/2019", + "2/3/2016", + "2/4/2016", + "2/4/2018", + "2/4/2019", + "2/5/2016", + "2/5/2018", + "2/5/2019", + "2/6/2011", + "2/6/2015", + "2/6/2019", + "2/7/2019", + "2/8/2019", + "3/1/2015", + "3/10/2015", + "3/2/2013", + "3/2/2015", + "3/2/2016", + "3/2/2017", + "3/2/2018", + "3/30/2013", + "3/30/2015", + "3/4/2013", + "3/4/2014", + "3/5/2012", + "3/5/2015", + "3/6/2011", + "3/6/2016", + "3/9/2018", + "4/1/2017", + "4/12/2012", + "4/12/2018", + "4/15/2015", + "4/18/2017", + "4/19/2014", + "4/2/2013", + "4/2/2015", + "4/2/2016", + "4/2/2018", + "4/20/2015", + "4/29/2016", + "4/5/2013", + "4/5/2016", + "4/5/2017", + "4/6/2017", + "4/9/2017", + "5/1/2016", + "5/13/2014", + "5/15/2012", + "5/15/2014", + "5/2/2015", + "5/3/2012", + "5/4/2011", + "5/6/2015", + "5/6/2016", + "6/10/2016", + "6/2/2014", + "6/3/2013", + "7/14/2010", + "7/2/2012", + "7/2/2013", + "7/2/2017", + "7/2/2018", + "8/10/2012", + "8/15/2013", + "8/16/2015", + "9/2/2015", + "9/5/2014" + ], + "ManagerName": [ + "Alex Sweetwater", + "Amy Dunn", + "Board of Directors", + "Brandon R. LeBlanc", + "Brannon Miller", + "Brian Champaigne", + "David Stanley", + "Debra Houlihan", + "Elijiah Gray", + "Eric Dougall", + "Janet King", + "Jennifer Zamora", + "John Smith", + "Kelley Spirea", + "Ketsia Liebig", + "Kissy Sullivan", + "Lynn Daneault", + "Michael Albert", + "Peter Monroe", + "Simon Roup", + "Webster Butler" + ], + "MaritalDesc": [ + "Divorced", + "Married", + "Separated", + "Single", + "Widowed" + ], + "PerformanceScore": [ + "Exceeds", + "Fully Meets", + "Needs Improvement", + "PIP" + ], + "Position": [ + "Accountant I", + "Administrative Assistant", + "Area Sales Manager", + "BI Developer", + "BI Director", + "CIO", + "Data Analyst", + "Data Analyst ", + "Data Architect", + "Database Administrator", + "Director of Operations", + "Director of Sales", + "Enterprise Architect", + "IT Director", + "IT Manager - DB", + "IT Manager - Infra", + "IT Manager - Support", + "IT Support", + "Network Engineer", + "President & CEO", + "Principal Data Architect", + "Production Manager", + "Production Technician I", + "Production Technician II", + "Sales Manager", + "Senior BI Developer", + "Shared Services Manager", + "Software Engineer", + "Software Engineering Manager", + "Sr. Accountant", + "Sr. DBA", + "Sr. Network Engineer" + ], + "RaceDesc": [ + "American Indian or Alaska Native", + "Asian", + "Black or African American", + "Hispanic", + "Two or more races", + "White" + ], + "RecruitmentSource": [ + "CareerBuilder", + "Diversity Job Fair", + "Employee Referral", + "Google Search", + "Indeed", + "LinkedIn", + "On-line Web application", + "Other", + "Website" + ], + "Sex": [ + "F", + "M " + ], + "State": [ + "AL", + "AZ", + "CA", + "CO", + "CT", + "FL", + "GA", + "ID", + "IN", + "KY", + "MA", + "ME", + "MT", + "NC", + "ND", + "NH", + "NV", + "NY", + "OH", + "OR", + "PA", + "RI", + "TN", + "TX", + "UT", + "VA", + "VT", + "WA" + ] + } +} diff --git a/decision_making/daily/classification/Human_Resources_Data_Set_B2/info_mod.json b/decision_making/daily/classification/Human_Resources_Data_Set_B2/info_mod.json index 81188d4d46d7b7e37128d200b8a73f36039664f5..477949262c6dc93ec7c012a8467216c44e107403 100644 --- a/decision_making/daily/classification/Human_Resources_Data_Set_B2/info_mod.json +++ b/decision_making/daily/classification/Human_Resources_Data_Set_B2/info_mod.json @@ -16,8 +16,6 @@ "CitizenDesc": "The citizenship status of the employee.", "HispanicLatino": "Whether the employee is Hispanic or Latino.", "RaceDesc": "The race of the employee.", - "TermReason": "The reason for the employee's termination.", - "EmploymentStatus": "The current employment status of the employee.", "Department": "The department where the employee works.", "ManagerName": "The name of the employee's manager.", "RecruitmentSource": "The source from which the employee was recruited.", @@ -28,7 +26,6 @@ "MarriedID": "Whether the employee is married (1) or not (0).", "MaritalStatusID": "The unique identifier for the employee's marital status.", "GenderID": "The unique identifier for the employee's gender.", - "EmpStatusID": "The unique identifier for the employee's employment status.", "DeptID": "The unique identifier for the employee's department.", "PerfScoreID": "The unique identifier for the employee's performance score.", "FromDiversityJobFairID": "Whether the employee was recruited from a diversity job fair (1) or not (0).", @@ -62,10 +59,6 @@ 0.0, 1.0 ], - "EmpStatusID": [ - 1.0, - 5.0 - ], "DeptID": [ 1.0, 6.0 @@ -211,31 +204,6 @@ "Two or more races", "White" ], - "TermReason": [ - "Another position", - "Fatal attraction", - "Learned that he is a gangster", - "N/A-StillEmployed", - "attendance", - "career change", - "gross misconduct", - "hours", - "maternity leave - did not return", - "medical issues", - "military", - "more money", - "no-call, no-show", - "performance", - "relocation out of area", - "retiring", - "return to school", - "unhappy" - ], - "EmploymentStatus": [ - "Active", - "Terminated for Cause", - "Voluntarily Terminated" - ], "Department": [ "Admin Offices", "Executive Office", @@ -413,4 +381,4 @@ "LastPerformanceReview_Date" ], "missing_from_original_info": [] -} \ No newline at end of file +} diff --git a/decision_making/daily/classification/Mobile_Price_Classification_B2/Mobile_Price_Classification_B2_002.json b/decision_making/daily/classification/Mobile_Price_Classification_B2/Mobile_Price_Classification_B2_002.json index bf43a87b7a3866280b3d755fec85ecec7b3532bf..a6073962cd68cb9e038e99b95e09618c7391dcc4 100644 --- a/decision_making/daily/classification/Mobile_Price_Classification_B2/Mobile_Price_Classification_B2_002.json +++ b/decision_making/daily/classification/Mobile_Price_Classification_B2/Mobile_Price_Classification_B2_002.json @@ -5,7 +5,7 @@ "perspective": "user", "dataset_name": "Mobile_Price_Classification", "table_path": "kaggle/Mobile_Price_Classification", - "query": "The salesperson just laid out three very different mobile phones on the counter, and it's tough to see the value connection. One device packs a 1456 mAh battery but skips Bluetooth, operates at 1.6 GHz, and handles dual SIMs. Its 5 MP front camera is paired with no 4G, 49 GB of internal space, and a super slim 0.2 cm profile. At 193 grams, its weight is noticeable, and it tops out with a 20 MP main camera and a huge 3624 MB of RAM. The screen is 11 cm wide, it connects via 3G, and the touch screen is present. Another model has a smaller 744 mAh cell, no Bluetooth, a 1.7 GHz chip, and dual SIM capability. It completely omits a front camera but includes 4G. With a 0.5 cm depth and a light 105-gram build, it uses a 4-core processor. The display resolution has a height of 1252 pixels, it's equipped with 2700 MB of RAM, but the screen is quite narrow at 3 cm wide. It has 3G but forgoes both a touch screen and WiFi. The final one offers a 1076 mAh battery, includes Bluetooth, and a speedy 2.6 GHz processor, though it's a single-SIM phone. It has a 3 MP front shooter, 38 GB of storage, and measures 0.7 cm thick while weighing 119 grams. Its 6-core processor is powerful, the main camera is 8 MP, and the pixel height is a lower 129. The screen height is a tall 18 cm, it promises 9 hours of talk time, has 3G and WiFi, but lacks a touch interface. Given this jumble of specs, could you help me figure out which one is most likely to be priced as high-end?", + "query": "The salesperson just laid out three very different mobile phones on the counter, and it's tough to see the value connection. One device packs a 1456 mAh battery but skips Bluetooth, operates at 1.6 GHz, and handles dual SIMs. Its 5 MP front camera is paired with no 4G, 49 GB of internal space, and a super slim 0.2 cm profile. At 193 grams, its weight is noticeable, and it tops out with a 20 MP main camera and a huge 3624 MB of RAM. The screen is 11 cm wide, it connects via 3G, and the touch screen is present. Another model has a smaller 744 mAh cell, no Bluetooth, a 1.7 GHz chip, and dual SIM capability. It completely omits a front camera but includes 4G. With a 0.5 cm depth and a light 105-gram build, it uses a 4-core processor. The display resolution has a height of 1252 pixels, it's equipped with 2700 MB of RAM, but the screen is quite narrow at 3 cm wide. It has 3G but forgoes both a touch screen and WiFi. The final one offers a 1076 mAh battery, includes Bluetooth, and a speedy 2.6 GHz processor, though it's a single-SIM phone. It has a 3 MP front shooter, 38 GB of storage, and measures 0.7 cm thick while weighing 119 grams. Its 6-core processor is powerful, the main camera is 8 MP, and the pixel height is a lower 129. The screen height is a tall 18 cm, it promises 9 hours of talk time, has 3G and WiFi, but lacks a touch interface. Given this jumble of specs, could you help me figure out which one is most likely to fall in the High price band, rather than the Very High flagship tier?", "meta_info": { "domain": "daily" }, @@ -100,4 +100,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/decision_making/daily/classification/Mobile_Price_Classification_B2/Mobile_Price_Classification_B2_006.json b/decision_making/daily/classification/Mobile_Price_Classification_B2/Mobile_Price_Classification_B2_006.json index 986f2ea7f56c799420be7ba6b53adbac35034acb..f5d0107297ac116feead1fac90d4fc661496e475 100644 --- a/decision_making/daily/classification/Mobile_Price_Classification_B2/Mobile_Price_Classification_B2_006.json +++ b/decision_making/daily/classification/Mobile_Price_Classification_B2/Mobile_Price_Classification_B2_006.json @@ -5,7 +5,7 @@ "perspective": "data_holder", "dataset_name": "Mobile_Price_Classification", "table_path": "kaggle/Mobile_Price_Classification", - "query": "As a shop owner, picking the right high‑end model to stock is tricky. Checking the details: phone one comes with a 1439 mAh battery, lacks Bluetooth, runs at 0.9 GHz clock speed, no dual SIM, 12 MP front camera, 4G capable, 20 GB internal storage, 0.8 cm thick, weighs 147 grams, only one core, 17 MP main camera, 626 by 932 pixel resolution, 1790 MB of RAM, screen measures 19 cm by 12 cm, up to 15 hours talk time, 3G enabled, no touch screen, but WiFi is present. Phone two has a 600 mAh battery, also no Bluetooth, faster 2.5 GHz speed, dual SIM supported, 8 MP front camera, 4G, 22 GB storage, very slim at 0.1 cm depth, 145 grams weight, one core, 11 MP main camera, 207 by 1162 pixels, whopping 3441 MB RAM, screen 19 cm tall but just 1 cm wide, talk time 5 hours, 3G, no touch screen, and no WiFi. Phone three offers 502 mAh battery, no Bluetooth, 0.8 GHz speed, no dual SIM, 7 MP front camera, no 4G, 52 GB storage, 1.0 cm depth, lightweight 82 grams, six cores, 8 MP main camera, 281 by 1159 pixels, 2666 MB RAM, a small 5 cm by 4 cm screen, 20 hours talk time, 3G, includes a touch screen, but again no WiFi. With all that in mind, and given the historical pricing data I just uploaded, do you think any of these stands out as clearly a “very high” priced model?", + "query": "As a shop owner, picking the right mid-priced model to stock is tricky. Checking the details: phone one comes with a 1439 mAh battery, lacks Bluetooth, runs at 0.9 GHz clock speed, no dual SIM, 12 MP front camera, 4G capable, 20 GB internal storage, 0.8 cm thick, weighs 147 grams, only one core, 17 MP main camera, 626 by 932 pixel resolution, 1790 MB of RAM, screen measures 19 cm by 12 cm, up to 15 hours talk time, 3G enabled, no touch screen, but WiFi is present. Phone two has a 600 mAh battery, also no Bluetooth, faster 2.5 GHz speed, dual SIM supported, 8 MP front camera, 4G, 22 GB storage, very slim at 0.1 cm depth, 145 grams weight, one core, 11 MP main camera, 207 by 1162 pixels, whopping 3441 MB RAM, screen 19 cm tall but just 1 cm wide, talk time 5 hours, 3G, no touch screen, and no WiFi. Phone three offers 502 mAh battery, no Bluetooth, 0.8 GHz speed, no dual SIM, 7 MP front camera, no 4G, 52 GB storage, 1.0 cm depth, lightweight 82 grams, six cores, 8 MP main camera, 281 by 1159 pixels, 2666 MB RAM, a small 5 cm by 4 cm screen, 20 hours talk time, 3G, includes a touch screen, but again no WiFi. With all that in mind, and given the historical pricing data I just uploaded, which of these is most likely to fall in the Medium price band?", "meta_info": { "domain": "daily" }, @@ -100,4 +100,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/decision_making/daily/regression/Chocolate_Bar_Ratings_B2/history.csv b/decision_making/daily/regression/Chocolate_Bar_Ratings_B2/history.csv index b749aecff579fa8c936b2d5a7f7e12330125d52b..57de9b03a55d011ed46fa742f45ecbc9fe4a5c84 100644 --- a/decision_making/daily/regression/Chocolate_Bar_Ratings_B2/history.csv +++ b/decision_making/daily/regression/Chocolate_Bar_Ratings_B2/history.csv @@ -121,7 +121,7 @@ Palette de Bine,Kokoa Kamili,1574,2015,72%,Canada,3.5, ,Tanzania Original Beans (Felchlin),Papua Kerafat,1438,2014,68%,Switzerland,2.75, ,Papua New Guinea Pralus,Jamaique,32,2006,75%,France,4.0,Trinitario,Jamaica Noble Bean aka Jerjobo,Moho Valley,1299,2014,75%,U.S.A.,3.0,Trinitario,Belize -Na�ve,Chuao,1399,2014,75%,Lithuania,2.75,Trinitario,Venezuela +Naïve,Chuao,1399,2014,75%,Lithuania,2.75,Trinitario,Venezuela Hummingbird,Lam Dong,1542,2015,70%,Canada,3.5,Trinitario,Vietnam Artisan du Chocolat,Jamaica,531,2010,72%,U.K.,3.75,Trinitario,Jamaica Altus aka Cao Artisan,CIAAB Coop,1732,2016,60%,U.S.A.,2.5, ,Bolivia @@ -357,7 +357,7 @@ Scharffen Berger,Cuyagua,135,2007,75%,U.S.A.,3.75, ,Venezuela Arete,Nacional,1534,2015,68%,U.S.A.,3.5,Forastero (Nacional),Peru Goodnow Farms,"Asochivite, batch 1005",1924,2016,77%,U.S.A.,3.25, ,Guatemala Map Chocolate,"San Juan Estate, Cherry Blossoms at Night",1606,2015,70%,U.S.A.,2.75,Trinitario,Trinidad -Na�ve,Nicaragua,1379,2014,70%,Lithuania,3.25,Criollo,Nicaragua +Naïve,Nicaragua,1379,2014,70%,Lithuania,3.25,Criollo,Nicaragua Pierre Marcolini,"Porcelana, Tabasco, Limited Ed.",81,2006,72%,Belgium,4.0,Criollo,Mexico Caribeans,Anselmo Paraiso Estate,1586,2015,72%,Costa Rica,3.5, ,Costa Rica Caribeans,Costa Rica,841,2012,72%,Costa Rica,3.0,Forastero,Costa Rica @@ -1299,7 +1299,7 @@ Dick Taylor,Mantuano,682,2011,75%,U.S.A.,2.75,Criollo,Venezuela Bittersweet Origins,Bocas del Toro,233,2008,75%,U.S.A.,3.5, ,Panama Felchlin,"Alto Beni, Cru Savage",56,2006,68%,Switzerland,4.0,Criollo (Wild),Bolivia Nova Monda,"Punta Galera, cacao Nacional, gold label",935,2012,80%,Nicaragua,3.25,Forastero (Nacional),Ecuador -Na�ve,Barinas,1399,2014,70%,Lithuania,3.0, ,Venezuela +Naïve,Barinas,1399,2014,70%,Lithuania,3.0, ,Venezuela Maglio,Cuba,308,2008,70%,Italy,3.25,Criollo,Cuba Dean and Deluca (Belcolade),Costa Rica,175,2007,64%,U.S.A.,3.0, ,Costa Rica Beschle (Felchlin),Madagascar,636,2011,64%,Switzerland,3.0,Trinitario,Madagascar diff --git a/decision_making/daily/regression/Global_YouTube_Statistics_2023_B2/info.json b/decision_making/daily/regression/Global_YouTube_Statistics_2023_B2/info.json index 666580141efd9a6f532aa515c797b35e51007680..2a4d0bb2870f7f26e8f34e8121139aaccc4b4a47 100644 --- a/decision_making/daily/regression/Global_YouTube_Statistics_2023_B2/info.json +++ b/decision_making/daily/regression/Global_YouTube_Statistics_2023_B2/info.json @@ -1,288 +1,288 @@ { - "name": "Global YouTube Statistics 2023", - "source": "https://www.kaggle.com/datasets/nelgiriyewithana/global-youtube-statistics-2023/data", - "data_intro": "A collection of YouTube giants, this dataset offers a perfect avenue to analyze and gain valuable insights from the luminaries of the platform. With comprehensive details on top creators' subscriber counts, video views, upload frequency, country of origin, earnings, and more, this treasure trove of information is a must-explore for aspiring content creators, data enthusiasts, and anyone intrigued by the ever-evolving online content landscape. Immerse yourself in the world of YouTube success and unlock a wealth of knowledge with this extraordinary dataset.", - "is_splited": false, - "overall_size": 995, - "train_size": 0, - "test_size": 0, - "c_classes": 7, - "n_classes": 21, - "cat_feature_intro": { - "Youtuber": "Name of the YouTube channel", - "category": "Category or niche of the channel", - "Title": "Title of the YouTube channel", - "Country": "Longitude coordinate of the country's location", - "Abbreviation": "Abbreviation of the country", - "channel_type": "Ranking of the channel based on its type (individual or brand)", - "created_month": "Month when the YouTube channel was created" - }, - "num_feature_intro": { - "rank": "Ranking of the channel based on its type (individual or brand)", - "subscribers": "Number of new subscribers gained in the last 30 days", - "video views": "Total video views in the last 30 days", - "uploads": "Total number of videos uploaded on the channel", - "video_views_rank": "Ranking of the channel based on total video views", - "country_rank": "Ranking of the channel based on the number of subscribers within its country", - "channel_type_rank": "Ranking of the channel based on its type (individual or brand)", - "video_views_for_the_last_30_days": "Total video views in the last 30 days", - "lowest_monthly_earnings": "Lowest estimated monthly earnings from the channel", - "highest_monthly_earnings": "Highest estimated monthly earnings from the channel", - "lowest_yearly_earnings": "Lowest estimated yearly earnings from the channel", - "highest_yearly_earnings": "Highest estimated yearly earnings from the channel", - "subscribers_for_last_30_days": "Number of new subscribers gained in the last 30 days", - "created_year": "Year when the YouTube channel was created", - "created_date": "Exact date of the YouTube channel's creation", - "Gross tertiary education enrollment (%)": "Percentage of the population enrolled in tertiary education in the country", - "Population": "Percentage of the population living in urban areas", - "Unemployment rate": "Unemployment rate in the country", - "Urban_population": "Percentage of the population living in urban areas", - "Latitude": "Latitude coordinate of the country's location", - "Longitude": "Longitude coordinate of the country's location" - }, - "evaluation_metric": null, - "task_type": "regression", - "target": "highest_monthly_earnings", - "num_feature_value": { - "Gross tertiary education enrollment (%)": [ - 7.6, - 113.1 - ], - "Latitude": [ - -38.416097, - 61.92411 - ], - "Longitude": [ - -172.104629, - 138.252924 - ], - "Population": [ - 202506.0, - 1397715000.0 - ], - "Unemployment rate": [ - 0.75, - 14.72 - ], - "Urban_population": [ - 35588.0, - 842933962.0 - ], - "channel_type_rank": [ - 1.0, - 7741.0 - ], - "country_rank": [ - 1.0, - 7741.0 - ], - "created_date": [ - 1.0, - 31.0 - ], - "created_year": [ - 1970.0, - 2022.0 - ], - "highest_monthly_earnings": [ - 0.0, - 13600000.0 - ], - "highest_yearly_earnings": [ - 0.0, - 163400000.0 - ], - "lowest_monthly_earnings": [ - 0.0, - 850900.0 - ], - "lowest_yearly_earnings": [ - 0.0, - 10200000.0 - ], - "rank": [ - 1.0, - 995.0 - ], - "subscribers": [ - 12300000.0, - 245000000.0 - ], - "subscribers_for_last_30_days": [ - 1.0, - 8000000.0 - ], - "uploads": [ - 0.0, - 301308.0 - ], - "video views": [ - 0.0, - 228000000000.0 - ], - "video_views_for_the_last_30_days": [ - 1.0, - 6589000000.0 - ], - "video_views_rank": [ - 1.0, - 4057944.0 - ] - }, - "cat_feature_value": { - "Abbreviation": [ - "AD", - "AE", - "AF", - "AR", - "AU", - "BB", - "BD", - "BR", - "CA", - "CH", - "CL", - "CN", - "CO", - "CU", - "DE", - "EC", - "EG", - "ES", - "FI", - "FR", - "GB", - "ID", - "IN", - "IQ", - "IT", - "JO", - "JP", - "KR", - "KW", - "LV", - "MA", - "MX", - "MY", - "NL", - "PE", - "PH", - "PK", - "RU", - "SA", - "SE", - "SG", - "SV", - "TH", - "TR", - "UA", - "US", - "VE", - "VN", - "WS" - ], - "Country": [ - "Afghanistan", - "Andorra", - "Argentina", - "Australia", - "Bangladesh", - "Barbados", - "Brazil", - "Canada", - "Chile", - "China", - "Colombia", - "Cuba", - "Ecuador", - "Egypt", - "El Salvador", - "Finland", - "France", - "Germany", - "India", - "Indonesia", - "Iraq", - "Italy", - "Japan", - "Jordan", - "Kuwait", - "Latvia", - "Malaysia", - "Mexico", - "Morocco", - "Netherlands", - "Pakistan", - "Peru", - "Philippines", - "Russia", - "Samoa", - "Saudi Arabia", - "Singapore", - "South Korea", - "Spain", - "Sweden", - "Switzerland", - "Thailand", - "Turkey", - "Ukraine", - "United Arab Emirates", - "United Kingdom", - "United States", - "Venezuela", - "Vietnam" - ], - "Title": "string", - "Youtuber": "string", - "category": [ - "Autos & Vehicles", - "Comedy", - "Education", - "Entertainment", - "Film & Animation", - "Gaming", - "Howto & Style", - "Movies", - "Music", - "News & Politics", - "Nonprofits & Activism", - "People & Blogs", - "Pets & Animals", - "Science & Technology", - "Shows", - "Sports", - "Trailers", - "Travel & Events" - ], - "channel_type": [ - "Animals", - "Autos", - "Comedy", - "Education", - "Entertainment", - "Film", - "Games", - "Howto", - "Music", - "News", - "Nonprofit", - "People", - "Sports", - "Tech" - ], - "created_month": [ - "Apr", - "Aug", - "Dec", - "Feb", - "Jan", - "Jul", - "Jun", - "Mar", - "May", - "Nov", - "Oct", - "Sep" - ] - } -} \ No newline at end of file + "name": "Global YouTube Statistics 2023", + "source": "https://www.kaggle.com/datasets/nelgiriyewithana/global-youtube-statistics-2023/data", + "data_intro": "A collection of YouTube giants, this dataset offers a perfect avenue to analyze and gain valuable insights from the luminaries of the platform. With comprehensive details on top creators' subscriber counts, video views, upload frequency, country of origin, earnings, and more, this treasure trove of information is a must-explore for aspiring content creators, data enthusiasts, and anyone intrigued by the ever-evolving online content landscape. Immerse yourself in the world of YouTube success and unlock a wealth of knowledge with this extraordinary dataset.", + "is_splited": false, + "overall_size": 995, + "train_size": 0, + "test_size": 0, + "c_classes": 7, + "n_classes": 21, + "cat_feature_intro": { + "Youtuber": "Name of the YouTube channel", + "category": "Category or niche of the channel", + "Title": "Title of the YouTube channel", + "Country": "Country associated with the channel.", + "Abbreviation": "Abbreviation of the country", + "channel_type": "Type or category of the channel.", + "created_month": "Month when the YouTube channel was created" + }, + "num_feature_intro": { + "rank": "Ranking of the channel based on its type (individual or brand)", + "subscribers": "Number of new subscribers gained in the last 30 days", + "video views": "Total video views in the last 30 days", + "uploads": "Total number of videos uploaded on the channel", + "video_views_rank": "Ranking of the channel based on total video views", + "country_rank": "Ranking of the channel based on the number of subscribers within its country", + "channel_type_rank": "Ranking of the channel based on its type (individual or brand)", + "video_views_for_the_last_30_days": "Total video views in the last 30 days", + "lowest_monthly_earnings": "Lowest estimated monthly earnings from the channel", + "highest_monthly_earnings": "Highest estimated monthly earnings from the channel", + "lowest_yearly_earnings": "Lowest estimated yearly earnings from the channel", + "highest_yearly_earnings": "Highest estimated yearly earnings from the channel", + "subscribers_for_last_30_days": "Number of new subscribers gained in the last 30 days", + "created_year": "Year when the YouTube channel was created", + "created_date": "Exact date of the YouTube channel's creation", + "Gross tertiary education enrollment (%)": "Percentage of the population enrolled in tertiary education in the country", + "Population": "Percentage of the population living in urban areas", + "Unemployment rate": "Unemployment rate in the country", + "Urban_population": "Percentage of the population living in urban areas", + "Latitude": "Latitude coordinate of the country's location", + "Longitude": "Longitude coordinate of the country's location" + }, + "evaluation_metric": null, + "task_type": "regression", + "target": "highest_monthly_earnings", + "num_feature_value": { + "Gross tertiary education enrollment (%)": [ + 7.6, + 113.1 + ], + "Latitude": [ + -38.416097, + 61.92411 + ], + "Longitude": [ + -172.104629, + 138.252924 + ], + "Population": [ + 202506.0, + 1397715000.0 + ], + "Unemployment rate": [ + 0.75, + 14.72 + ], + "Urban_population": [ + 35588.0, + 842933962.0 + ], + "channel_type_rank": [ + 1.0, + 7741.0 + ], + "country_rank": [ + 1.0, + 7741.0 + ], + "created_date": [ + 1.0, + 31.0 + ], + "created_year": [ + 1970.0, + 2022.0 + ], + "highest_monthly_earnings": [ + 0.0, + 13600000.0 + ], + "highest_yearly_earnings": [ + 0.0, + 163400000.0 + ], + "lowest_monthly_earnings": [ + 0.0, + 850900.0 + ], + "lowest_yearly_earnings": [ + 0.0, + 10200000.0 + ], + "rank": [ + 1.0, + 995.0 + ], + "subscribers": [ + 12300000.0, + 245000000.0 + ], + "subscribers_for_last_30_days": [ + 1.0, + 8000000.0 + ], + "uploads": [ + 0.0, + 301308.0 + ], + "video views": [ + 0.0, + 228000000000.0 + ], + "video_views_for_the_last_30_days": [ + 1.0, + 6589000000.0 + ], + "video_views_rank": [ + 1.0, + 4057944.0 + ] + }, + "cat_feature_value": { + "Abbreviation": [ + "AD", + "AE", + "AF", + "AR", + "AU", + "BB", + "BD", + "BR", + "CA", + "CH", + "CL", + "CN", + "CO", + "CU", + "DE", + "EC", + "EG", + "ES", + "FI", + "FR", + "GB", + "ID", + "IN", + "IQ", + "IT", + "JO", + "JP", + "KR", + "KW", + "LV", + "MA", + "MX", + "MY", + "NL", + "PE", + "PH", + "PK", + "RU", + "SA", + "SE", + "SG", + "SV", + "TH", + "TR", + "UA", + "US", + "VE", + "VN", + "WS" + ], + "Country": [ + "Afghanistan", + "Andorra", + "Argentina", + "Australia", + "Bangladesh", + "Barbados", + "Brazil", + "Canada", + "Chile", + "China", + "Colombia", + "Cuba", + "Ecuador", + "Egypt", + "El Salvador", + "Finland", + "France", + "Germany", + "India", + "Indonesia", + "Iraq", + "Italy", + "Japan", + "Jordan", + "Kuwait", + "Latvia", + "Malaysia", + "Mexico", + "Morocco", + "Netherlands", + "Pakistan", + "Peru", + "Philippines", + "Russia", + "Samoa", + "Saudi Arabia", + "Singapore", + "South Korea", + "Spain", + "Sweden", + "Switzerland", + "Thailand", + "Turkey", + "Ukraine", + "United Arab Emirates", + "United Kingdom", + "United States", + "Venezuela", + "Vietnam" + ], + "Title": "string", + "Youtuber": "string", + "category": [ + "Autos & Vehicles", + "Comedy", + "Education", + "Entertainment", + "Film & Animation", + "Gaming", + "Howto & Style", + "Movies", + "Music", + "News & Politics", + "Nonprofits & Activism", + "People & Blogs", + "Pets & Animals", + "Science & Technology", + "Shows", + "Sports", + "Trailers", + "Travel & Events" + ], + "channel_type": [ + "Animals", + "Autos", + "Comedy", + "Education", + "Entertainment", + "Film", + "Games", + "Howto", + "Music", + "News", + "Nonprofit", + "People", + "Sports", + "Tech" + ], + "created_month": [ + "Apr", + "Aug", + "Dec", + "Feb", + "Jan", + "Jul", + "Jun", + "Mar", + "May", + "Nov", + "Oct", + "Sep" + ] + } +} diff --git a/decision_making/daily/regression/Global_YouTube_Statistics_2023_B2/info_mod.json b/decision_making/daily/regression/Global_YouTube_Statistics_2023_B2/info_mod.json index 20e870f52ad7f9b8335b0d8a7022e913425160d1..77f6c5f0fcd2e4405d2af86487f5a64c07e6c8be 100644 --- a/decision_making/daily/regression/Global_YouTube_Statistics_2023_B2/info_mod.json +++ b/decision_making/daily/regression/Global_YouTube_Statistics_2023_B2/info_mod.json @@ -10,9 +10,9 @@ "n_classes": 21, "cat_feature_intro": { "category": "Category or niche of the channel", - "Country": "Longitude coordinate of the country's location", + "Country": "Country associated with the channel.", "Abbreviation": "Abbreviation of the country", - "channel_type": "Ranking of the channel based on its type (individual or brand)", + "channel_type": "Type or category of the channel.", "created_month": "Month when the YouTube channel was created" }, "num_feature_intro": { @@ -373,4 +373,4 @@ "Title" ], "missing_from_original_info": [] -} \ No newline at end of file +} diff --git a/decision_making/finance/classification/Default_of_Credit_Card_Clients_Dataset_B2/Default_of_Credit_Card_Clients_Dataset_B2_002.json b/decision_making/finance/classification/Default_of_Credit_Card_Clients_Dataset_B2/Default_of_Credit_Card_Clients_Dataset_B2_002.json index f9df259e2c83c979877082e943bdd577646f183c..d4d4c4acaaab613aae5be93bed79834b9bb97899 100644 --- a/decision_making/finance/classification/Default_of_Credit_Card_Clients_Dataset_B2/Default_of_Credit_Card_Clients_Dataset_B2_002.json +++ b/decision_making/finance/classification/Default_of_Credit_Card_Clients_Dataset_B2/Default_of_Credit_Card_Clients_Dataset_B2_002.json @@ -5,7 +5,7 @@ "perspective": "user", "dataset_name": "Default_of_Credit_Card_Clients_Dataset", "table_path": "kaggle/Default_of_Credit_Card_Clients_Dataset", - "query": "The manager needs a second opinion on these two accounts. Candidate A has ID number 11645. This client is a male, aged 40, married, and a university graduate. He has an 80,000 NT dollar credit line. A review of his last six months shows he has never been late on a payment—every repayment status is recorded as 0. His billed amounts have consistently decreased from 45,747 down to 19,766, and he has made payments each month ranging from 627 to 1,618. Switching to candidate B, ID 17189, we have a 31-year-old married female with a high school education and a 20,000 limit. Her payment history tells a different story: while current in September, she had severe delays of five, four, three, and two months in the preceding months. Her bills have been relatively stable between 11,500 and 14,320, but her actual payments have been inconsistent, with three months showing zero payment. Given our goal of identifying potential defaulters, which of these two individuals should raise a bigger red flag?", + "query": "The manager needs a second opinion on these two accounts. Candidate A has ID number 11645. This client is a male, aged 40, married, and a university graduate. He has an 80,000 NT dollar credit line. A review of his last six months shows he has never been late on a payment—every repayment status is recorded as 0. His billed amounts rose from 19,766 in April to 45,747 in September, and he has made payments each month ranging from 627 to 1,618. Switching to candidate B, ID 17189, we have a 31-year-old married female with a high school education and a 20,000 limit. Her payment history tells a different story: while current in September, she had severe delays of five, four, three, and two months in the preceding months. Her bills have been relatively stable between 11,500 and 14,320, but her actual payments have been inconsistent, with three months showing zero payment. Given our goal of identifying potential defaulters, which of these two individuals should raise a bigger red flag?", "meta_info": { "domain": "finance" }, @@ -82,4 +82,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/decision_making/finance/classification/Default_of_Credit_Card_Clients_Dataset_B2/Default_of_Credit_Card_Clients_Dataset_B2_004.json b/decision_making/finance/classification/Default_of_Credit_Card_Clients_Dataset_B2/Default_of_Credit_Card_Clients_Dataset_B2_004.json index 5eef29830fde8091bed95d764719f6b28912c78c..ac431bef32153ebdaddcdb25c5764af2b37a7d9c 100644 --- a/decision_making/finance/classification/Default_of_Credit_Card_Clients_Dataset_B2/Default_of_Credit_Card_Clients_Dataset_B2_004.json +++ b/decision_making/finance/classification/Default_of_Credit_Card_Clients_Dataset_B2/Default_of_Credit_Card_Clients_Dataset_B2_004.json @@ -5,7 +5,7 @@ "perspective": "data_holder", "dataset_name": "Default_of_Credit_Card_Clients_Dataset", "table_path": "kaggle/Default_of_Credit_Card_Clients_Dataset", - "query": "I'm looking at three new credit applications on my screen right now, and I need to figure out which one is the safest bet. I have our historical client archive from 2005 open for comparison, but let me walk you through these new cases. The first applicant, ID 23359, is a 40-year-old single woman with a university education. Her credit limit is 110,000 NT dollars. Her repayment history for the last six months, from April to September, shows she has paid duly every single month—all those status codes are zero. Her bill statements have been consistently high, around the 77,000 to 96,950 range over those months, and she's been making payments between 2,800 and 4,000 each month. The second record, ID 23340, is a 44-year-old married woman with a high school education. She has a higher limit of 160,000. Her recent repayment status is concerning: for September and August, she's shown a two-month payment delay, but for July back to April, her status is listed as \"-2,\" which I believe from our old logs means she had a credit balance so no payment was due. Strangely, her bill amount for September is only 1,500 and zero for all previous months, and she has made no payments at all in the last six months. The third candidate, ID 21501, is a 39-year-old single woman, also university-educated, with a 160,000 limit. Her repayment is mixed: she paid duly in September and August, but had a two-month delay in July, June, and May, and was back to duly in April. Her bill amounts have fluctuated between about 30,000 and 49,619, and her payments have been irregular—7,000 in September, nothing in August, then 7,847, 1,026, 7,000, and 7,968 in the earlier months. Based on these profiles, which one of these clients is most likely to have no default next month?", + "query": "I'm looking at three new credit applications on my screen right now, and I need to figure out which one is the safest bet. I have our historical client archive from 2005 open for comparison, but let me walk you through these new cases. The first applicant, ID 23359, is a 40-year-old single woman with a university education. Her credit limit is 110,000 NT dollars. Her repayment-status entries from April through September are all recorded as code 0. Her bill statements have been consistently high, around the 77,000 to 96,950 range over those months, and she's been making payments between 2,800 and 4,000 each month. The second record, ID 23340, is a 44-year-old married woman with a high school education. She has a higher limit of 160,000. Her recent repayment status is concerning: for September and August, she's shown a two-month payment delay, but for July back to April, her repayment-status entries are recorded as code -2. Strangely, her bill amount for September is only 1,500 and zero for all previous months, and she has made no payments at all in the last six months. The third candidate, ID 21501, is a 39-year-old single woman, also university-educated, with a 160,000 limit. Her repayment is mixed: she paid duly in September and August, but had a two-month delay in July, June, and May, and was back to duly in April. Her bill amounts have fluctuated between about 30,000 and 49,619, and her payments have been irregular—7,000 in September, nothing in August, then 7,847, 1,026, 7,000, and 7,968 in the earlier months. Based on these profiles, which one of these clients is most likely to have no default next month?", "meta_info": { "domain": "finance" }, @@ -112,4 +112,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/Indicators_of_Heart_Disease_(2022_UPDATE)_B2_001.json b/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/Indicators_of_Heart_Disease_(2022_UPDATE)_B2_001.json index a9354173f134f141b65b94d5f2511f7a54e811e0..30bf17625e31da7302eca87ede7862623ca65f0f 100644 --- a/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/Indicators_of_Heart_Disease_(2022_UPDATE)_B2_001.json +++ b/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/Indicators_of_Heart_Disease_(2022_UPDATE)_B2_001.json @@ -27,7 +27,6 @@ "Race": null, "Diabetic": null, "PhysicalActivity": null, - "GenHealth": null, "SleepTime": null, "Asthma": null, "KidneyDisease": null, @@ -87,7 +86,6 @@ "Race": null, "Diabetic": null, "PhysicalActivity": null, - "GenHealth": null, "SleepTime": null, "Asthma": null, "KidneyDisease": null, @@ -142,4 +140,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/Indicators_of_Heart_Disease_(2022_UPDATE)_B2_002.json b/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/Indicators_of_Heart_Disease_(2022_UPDATE)_B2_002.json index fbc2900d3694dfe3ed59823a3b85e5f2424f07b4..b236b897db3e4bd65fc9c596c4547aff4d9342f1 100644 --- a/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/Indicators_of_Heart_Disease_(2022_UPDATE)_B2_002.json +++ b/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/Indicators_of_Heart_Disease_(2022_UPDATE)_B2_002.json @@ -5,7 +5,7 @@ "perspective": "user", "dataset_name": "Indicators_of_Heart_Disease_(2022_UPDATE)", "table_path": "kaggle/Indicators_of_Heart_Disease_(2022_UPDATE)", - "query": "I'm helping my uncle's community health group review some anonymized member profiles, and we're trying to get a sense of overall well-being. I have three records in front of me. The first is a gentleman from Washington, aged 65 to 69. He's male, 1.88 meters tall, and weighs 88.45 kilograms, giving him a BMI of 25.04. He considers his general health to be good, which is encouraging. He's had 3 days of poor physical health and no poor mental health days in the last month, and he only sleeps about 2 hours a night. He's had skin cancer and has had between 1 and 5 teeth removed, but he reports no other chronic conditions like heart attack, angina, stroke, asthma, COPD, depression, kidney disease, arthritis, or diabetes. He doesn't have hearing, vision, or mobility difficulties. He's physically active, had a checkup within the last year, and has never smoked or used e-cigarettes. He is a White, non-Hispanic alcohol drinker. He's up to date on his flu shot and has had a pneumococcal vaccine, and his last tetanus shot was not Tdap. He wasn't at high risk last year and hasn't had COVID. The second record is also a man, aged 65 to 69, but from Rhode Island. He is 1.75 meters tall, weighs 68.04 kilograms, with a BMI of 22.15. He has had 0 days of poor physical or mental health recently and gets a solid 9 hours of sleep. He has no teeth removed and no skin cancer, but he does have arthritis. Otherwise, he shares the same \"No\" answers for all the other major health conditions and difficulties. He is also physically active, had a recent checkup, and has never smoked or vaped. He is also a White, non-Hispanic alcohol drinker. He got his flu shot last year but has not had a pneumococcal vaccine, and his last tetanus shot type is uncertain. Finally, the third record is a woman aged 70 to 74 from South Dakota. She is 1.73 meters tall, weighs 68.04 kilograms, with a BMI of 22.81. She's had 3 poor physical health days and 0 poor mental health days, sleeping 8 hours. She has no teeth removed and no reported conditions except that she has had a chest scan. Like the others, she is physically active, had a checkup within the year, and has never smoked or used e-cigarettes. She is a White, non-Hispanic alcohol drinker. She got a flu shot recently but no pneumococcal vaccine, and her last tetanus shot was not Tdap. Looking at these three, I'm a bit torn on which one seems to be in the most robust \"Good\" health category. Could you help me figure out which profile most strongly suggests a person in generally good health?", + "query": "I'm helping my uncle's community health group review some anonymized member profiles, and we're trying to get a sense of overall well-being. I have three records in front of me. The first is a gentleman from Washington, aged 65 to 69. He's male, 1.88 meters tall, and weighs 88.45 kilograms, giving him a BMI of 25.04. He's had 3 days of poor physical health and no poor mental health days in the last month, and he only sleeps about 2 hours a night. He's had skin cancer and has had between 1 and 5 teeth removed, but he reports no other chronic conditions like heart attack, angina, stroke, asthma, COPD, depression, kidney disease, arthritis, or diabetes. He doesn't have hearing, vision, or mobility difficulties. He's physically active, had a checkup within the last year, and has never smoked or used e-cigarettes. He is a White, non-Hispanic alcohol drinker. He's up to date on his flu shot and has had a pneumococcal vaccine, and his last tetanus shot was not Tdap. He wasn't at high risk last year and hasn't had COVID. The second record is also a man, aged 65 to 69, but from Rhode Island. He is 1.75 meters tall, weighs 68.04 kilograms, with a BMI of 22.15. He has had 0 days of poor physical or mental health recently and gets a solid 9 hours of sleep. He has no teeth removed and no skin cancer, but he does have arthritis. Otherwise, he shares the same \"No\" answers for all the other major health conditions and difficulties. He is also physically active, had a recent checkup, and has never smoked or vaped. He is also a White, non-Hispanic alcohol drinker. He got his flu shot last year but has not had a pneumococcal vaccine, and his last tetanus shot type is uncertain. Finally, the third record is a woman aged 70 to 74 from South Dakota. She is 1.73 meters tall, weighs 68.04 kilograms, with a BMI of 22.81. She's had 3 poor physical health days and 0 poor mental health days, sleeping 8 hours. She has no teeth removed and no reported conditions except that she has had a chest scan. Like the others, she is physically active, had a checkup within the year, and has never smoked or used e-cigarettes. She is a White, non-Hispanic alcohol drinker. She got a flu shot recently but no pneumococcal vaccine, and her last tetanus shot was not Tdap. Looking at these three, I'm a bit torn on which one seems to be in the most robust \"Good\" health category. Could you help me figure out which profile most strongly suggests a person in generally good health?", "meta_info": { "domain": "medical" }, @@ -27,7 +27,6 @@ "Race": null, "Diabetic": null, "PhysicalActivity": null, - "GenHealth": null, "SleepTime": null, "Asthma": null, "KidneyDisease": null, @@ -87,7 +86,6 @@ "Race": null, "Diabetic": null, "PhysicalActivity": null, - "GenHealth": null, "SleepTime": null, "Asthma": null, "KidneyDisease": null, @@ -147,7 +145,6 @@ "Race": null, "Diabetic": null, "PhysicalActivity": null, - "GenHealth": null, "SleepTime": null, "Asthma": null, "KidneyDisease": null, @@ -202,4 +199,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/Indicators_of_Heart_Disease_(2022_UPDATE)_B2_003.json b/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/Indicators_of_Heart_Disease_(2022_UPDATE)_B2_003.json index 67feb3ba2adf756ec9a5ef7bef459e2449b2f640..4418b760c0f3c63f3e56dcfb893a328ce5793ec8 100644 --- a/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/Indicators_of_Heart_Disease_(2022_UPDATE)_B2_003.json +++ b/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/Indicators_of_Heart_Disease_(2022_UPDATE)_B2_003.json @@ -27,7 +27,6 @@ "Race": null, "Diabetic": null, "PhysicalActivity": null, - "GenHealth": null, "SleepTime": null, "Asthma": null, "KidneyDisease": null, @@ -87,7 +86,6 @@ "Race": null, "Diabetic": null, "PhysicalActivity": null, - "GenHealth": null, "SleepTime": null, "Asthma": null, "KidneyDisease": null, @@ -147,7 +145,6 @@ "Race": null, "Diabetic": null, "PhysicalActivity": null, - "GenHealth": null, "SleepTime": null, "Asthma": null, "KidneyDisease": null, @@ -202,4 +199,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/Indicators_of_Heart_Disease_(2022_UPDATE)_B2_004.json b/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/Indicators_of_Heart_Disease_(2022_UPDATE)_B2_004.json index c2cec9d8470086fa77dfef4d4924a541109c133f..ddffce91b9c03b7a668d2494c6f58abe44f72944 100644 --- a/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/Indicators_of_Heart_Disease_(2022_UPDATE)_B2_004.json +++ b/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/Indicators_of_Heart_Disease_(2022_UPDATE)_B2_004.json @@ -5,7 +5,7 @@ "perspective": "data_holder", "dataset_name": "Indicators_of_Heart_Disease_(2022_UPDATE)", "table_path": "kaggle/Indicators_of_Heart_Disease_(2022_UPDATE)", - "query": "I'm reviewing these three new patient files we just received, and I need to figure out which one is most likely to be in poor general health based on our historical patient logs, which I have right here for reference. The first person is a 77-year-old woman from Virginia. Her BMI is calculated at 31.09, she's 1.57 meters tall and weighs 77.11 kilograms. She reports 30 days of poor physical health in the last month but 0 days of poor mental health. She sleeps 10 hours a night, is a former smoker who never used e-cigarettes, and does drink alcohol. She had a checkup within the past year. She has had COPD and arthritis, but no heart attack, stroke, skin cancer, depressive disorder, kidney disease, diabetes, hearing loss, vision difficulty, or concentration problems. However, she does have difficulty walking and with dressing or bathing. She is White, non-Hispanic, had a chest scan, is up to date on her pneumococcal and Tdap vaccines, and once tested positive for COVID on a home test. The second file is for a woman aged 70 to 74. Her BMI is 27.73. She does not have heart disease, doesn't smoke, doesn't drink alcohol, and hasn't had a stroke. She reports 0 days of poor physical health and 4 days of poor mental health. She is not diabetic, engages in physical activity, sleeps 5 hours a night, and has no asthma or kidney disease. Finally, the third record is for a woman aged 80 or older who is White. She does have heart disease. Her BMI is 19.05. She doesn't smoke, hasn't had a stroke, and has no difficulty walking. She is diabetic, sleeps 7 hours a night, and has no kidney disease or skin cancer. Looking at my old archives, I can see patterns, but I'm really torn here. Based on all these details I've just read out, which patient should I flag as being in poor overall health?", + "query": "I need your help sorting through three patient records I just pulled for a heart health review, and I'm honestly torn on how to categorize their overall health. I've also got my historical archives from past screenings to cross-check against, which I'm sharing with you for reference. Let me describe the new records. The first record is a 75-to-79-year-old female from Virginia with a BMI of 31.09 who reported 30 days of poor physical health but zero poor mental health days, sleeps 10 hours a night, and had a checkup within the past year. She's had no heart attack, no stroke, no skin cancer, no depressive disorder, no kidney disease, no diabetes, no hearing or vision trouble, and no difficulty concentrating — but she does have COPD and arthritis, plus difficulty walking and difficulty dressing or bathing. She's a former smoker who never touched e-cigarettes, has had a chest scan, is White only, Non-Hispanic, stands 1.57 meters, weighs 77.11 kilograms, drinks alcohol, has had the pneumococcal vaccine, received a Tdap shot, and tested positive for COVID via a home test without a health professional. The second record is a much younger woman, 35 to 39, with a BMI of 22.31, who reported 30 days of poor mental health, had a checkup within the past year, and has no heart attack, no COPD, no diabetes, no hearing problems, no difficulty concentrating or dressing or bathing — she's 1.63 meters tall, weighs 58.97 kilograms, drinks alcohol, and has never had the pneumococcal vaccine. The third is a 40-to-44-year-old male from Alaska with a BMI of 24.22, zero poor mental health days, no skin cancer, no COPD, no depressive disorder, no arthritis, no trouble walking or dressing, has had a chest scan, and stands 2.01 meters tall. Given all this, I'm trying to flag who might land in the poorest overall health category — can you help me weigh these three?", "meta_info": { "domain": "medical" }, @@ -27,7 +27,6 @@ "Race": null, "Diabetic": null, "PhysicalActivity": null, - "GenHealth": null, "SleepTime": null, "Asthma": null, "KidneyDisease": null, @@ -74,58 +73,57 @@ { "scenario_id": "002", "features": { - "HeartDisease": "No", - "BMI": "27.73", - "Smoking": "No", - "AlcoholDrinking": "No", - "Stroke": "No", - "PhysicalHealth": "0.0", - "MentalHealth": "4.0", + "HeartDisease": null, + "BMI": "22.31", + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, "DiffWalking": null, "Sex": "Female", - "AgeCategory": "70-74", + "AgeCategory": "Age 35 to 39", "Race": null, - "Diabetic": "No", - "PhysicalActivity": "Yes", - "GenHealth": "Very good", - "SleepTime": "5.0", - "Asthma": "No", - "KidneyDisease": "No", + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, "SkinCancer": null, "State": null, - "GeneralHealth": "", + "GeneralHealth": "Good", "PhysicalHealthDays": null, - "MentalHealthDays": null, - "LastCheckupTime": null, + "MentalHealthDays": "30.0", + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", "PhysicalActivities": null, "SleepHours": null, "RemovedTeeth": null, - "HadHeartAttack": null, + "HadHeartAttack": "No", "HadAngina": null, "HadStroke": null, "HadAsthma": null, "HadSkinCancer": null, - "HadCOPD": null, + "HadCOPD": "No", "HadDepressiveDisorder": null, "HadKidneyDisease": null, "HadArthritis": null, - "HadDiabetes": null, - "DeafOrHardOfHearing": null, + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", "BlindOrVisionDifficulty": null, - "DifficultyConcentrating": null, + "DifficultyConcentrating": "No", "DifficultyWalking": null, - "DifficultyDressingBathing": null, + "DifficultyDressingBathing": "No", "DifficultyErrands": null, "SmokerStatus": null, "ECigaretteUsage": null, "ChestScan": null, "RaceEthnicityCategory": null, - "HeightInMeters": null, - "WeightInKilograms": null, - "AlcoholDrinkers": null, + "HeightInMeters": "1.63", + "WeightInKilograms": "58.97", + "AlcoholDrinkers": "Yes", "HIVTesting": null, "FluVaxLast12": null, - "PneumoVaxEver": null, + "PneumoVaxEver": "No", "TetanusLast10Tdap": null, "HighRiskLastYear": null, "CovidPos": null @@ -134,28 +132,27 @@ { "scenario_id": "003", "features": { - "HeartDisease": "Yes", - "BMI": "19.05", - "Smoking": "No", + "HeartDisease": null, + "BMI": "24.22", + "Smoking": null, "AlcoholDrinking": null, - "Stroke": "No", + "Stroke": null, "PhysicalHealth": null, "MentalHealth": null, - "DiffWalking": "No", - "Sex": "Female", - "AgeCategory": "80 or older", - "Race": "White", - "Diabetic": "Yes", + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 40 to 44", + "Race": null, + "Diabetic": null, "PhysicalActivity": null, - "GenHealth": "Good", - "SleepTime": "7.0", + "SleepTime": null, "Asthma": null, - "KidneyDisease": "No", - "SkinCancer": "No", - "State": null, - "GeneralHealth": "", + "KidneyDisease": null, + "SkinCancer": null, + "State": "Alaska", + "GeneralHealth": "Excellent", "PhysicalHealthDays": null, - "MentalHealthDays": null, + "MentalHealthDays": "0.0", "LastCheckupTime": null, "PhysicalActivities": null, "SleepHours": null, @@ -164,23 +161,23 @@ "HadAngina": null, "HadStroke": null, "HadAsthma": null, - "HadSkinCancer": null, - "HadCOPD": null, - "HadDepressiveDisorder": null, + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", "HadKidneyDisease": null, - "HadArthritis": null, + "HadArthritis": "No", "HadDiabetes": null, "DeafOrHardOfHearing": null, "BlindOrVisionDifficulty": null, "DifficultyConcentrating": null, - "DifficultyWalking": null, - "DifficultyDressingBathing": null, + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", "DifficultyErrands": null, "SmokerStatus": null, "ECigaretteUsage": null, - "ChestScan": null, + "ChestScan": "Yes", "RaceEthnicityCategory": null, - "HeightInMeters": null, + "HeightInMeters": "2.01", "WeightInKilograms": null, "AlcoholDrinkers": null, "HIVTesting": null, @@ -202,4 +199,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/Indicators_of_Heart_Disease_(2022_UPDATE)_B2_005.json b/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/Indicators_of_Heart_Disease_(2022_UPDATE)_B2_005.json index ade56fb4fb5d2298426b8aa22733cbe2aa7413df..511c275d809675b221282597293d61900d04665a 100644 --- a/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/Indicators_of_Heart_Disease_(2022_UPDATE)_B2_005.json +++ b/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/Indicators_of_Heart_Disease_(2022_UPDATE)_B2_005.json @@ -27,7 +27,6 @@ "Race": null, "Diabetic": null, "PhysicalActivity": null, - "GenHealth": null, "SleepTime": null, "Asthma": null, "KidneyDisease": null, @@ -87,7 +86,6 @@ "Race": null, "Diabetic": null, "PhysicalActivity": null, - "GenHealth": null, "SleepTime": null, "Asthma": null, "KidneyDisease": null, @@ -142,4 +140,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/Indicators_of_Heart_Disease_(2022_UPDATE)_B2_006.json b/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/Indicators_of_Heart_Disease_(2022_UPDATE)_B2_006.json index 8e80eec2602f40423b159a1502597b6f8f5f8a15..460434920d1dd8dbce060e55436436a35b218841 100644 --- a/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/Indicators_of_Heart_Disease_(2022_UPDATE)_B2_006.json +++ b/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/Indicators_of_Heart_Disease_(2022_UPDATE)_B2_006.json @@ -27,7 +27,6 @@ "Race": null, "Diabetic": null, "PhysicalActivity": null, - "GenHealth": null, "SleepTime": null, "Asthma": null, "KidneyDisease": null, @@ -87,7 +86,6 @@ "Race": null, "Diabetic": null, "PhysicalActivity": null, - "GenHealth": null, "SleepTime": null, "Asthma": null, "KidneyDisease": null, @@ -142,4 +140,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/current.csv b/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/current.csv index c4a2483573e86247c7e4a67c3b5f8e64768d8ca3..00b44424dd535d830012280b8a44e0f419b0b045 100644 --- a/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/current.csv +++ b/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/current.csv @@ -1,16 +1,16 @@ -HeartDisease,BMI,Smoking,AlcoholDrinking,Stroke,PhysicalHealth,MentalHealth,DiffWalking,Sex,AgeCategory,Race,Diabetic,PhysicalActivity,GenHealth,SleepTime,Asthma,KidneyDisease,SkinCancer,State,GeneralHealth,PhysicalHealthDays,MentalHealthDays,LastCheckupTime,PhysicalActivities,SleepHours,RemovedTeeth,HadHeartAttack,HadAngina,HadStroke,HadAsthma,HadSkinCancer,HadCOPD,HadDepressiveDisorder,HadKidneyDisease,HadArthritis,HadDiabetes,DeafOrHardOfHearing,BlindOrVisionDifficulty,DifficultyConcentrating,DifficultyWalking,DifficultyDressingBathing,DifficultyErrands,SmokerStatus,ECigaretteUsage,ChestScan,RaceEthnicityCategory,HeightInMeters,WeightInKilograms,AlcoholDrinkers,HIVTesting,FluVaxLast12,PneumoVaxEver,TetanusLast10Tdap,HighRiskLastYear,CovidPos -,28.72,,,,,,,Female,Age 55 to 59,,,,,,,,,Guam,Good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,Yes,No,Yes,Yes,No,No,No,Never smoked,Not at all (right now),No,"Other race only, Non-Hispanic",1.57,71.21,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,29.68,,,,,,,Male,Age 70 to 74,,,,,,,,,New York,Fair,3.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,Yes,No,No,No,Yes,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,91.17,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,25.04,,,,,,,Male,Age 65 to 69,,,,,,,,,Washington,Very good,3.0,0.0,Within past year (anytime less than 12 months ago),Yes,2.0,1 to 5,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.88,88.45,Yes,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No -,22.15,,,,,,,Male,Age 65 to 69,,,,,,,,,Rhode Island,Good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,68.04,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -,22.81,,,,,,,Female,Age 70 to 74,,,,,,,,,South Dakota,Very good,3.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,68.04,Yes,No,Yes,No,"Yes, received tetanus shot, but not Tdap",No,No -,24.03,,,,,,,Female,Age 65 to 69,,,,,,,,,Puerto Rico,Good,0.0,30.0,Within past year (anytime less than 12 months ago),Yes,12.0,1 to 5,No,No,No,No,No,No,Yes,No,,No,Yes,Yes,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,,Hispanic,1.63,63.5,No,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,23.91,,,,,,,Female,Age 35 to 39,,,,,,,,,Nevada,Good,5.0,5.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.6,61.23,No,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,Yes -,29.53,,,,,,,Male,Age 60 to 64,,,,,,,,,Iowa,Very good,0.0,0.0,5 or more years ago,No,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,90.72,Yes,No,No,No,"Yes, received tetanus shot, but not Tdap",No,No -,31.09,,,,,,,Female,Age 75 to 79,,,,,,,,,Virginia,Poor,30.0,0.0,Within past year (anytime less than 12 months ago),Yes,10.0,"6 or more, but not all",No,No,No,No,No,Yes,No,No,Yes,No,No,No,No,Yes,Yes,Yes,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.57,77.11,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,Tested positive using home test without a health professional -No,27.73,No,No,No,0.0,4.0,No,Female,70-74,White,No,Yes,Very good,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Yes,19.05,No,No,No,30.0,0.0,No,Female,80 or older,White,Yes,Yes,Good,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,29.76,,,,,,,Female,Age 75 to 79,,,,,,,,,Hawaii,Very good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,Yes,No,No,Yes,Yes,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,76.2,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,Yes -,31.79,,,,,,,Female,Age 40 to 44,,,,,,,,,West Virginia,Good,0.0,15.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,Yes,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,92.08,No,Yes,Yes,Yes,"Yes, received Tdap",No,Yes -,25.4,,,,,,,Male,Age 60 to 64,,,,,,,,,Nebraska,Poor,30.0,30.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,Yes,No,Yes,No,No,No,Yes,Yes,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.78,80.29,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,32.32,,,,,,,Female,Age 80 or older,,,,,,,,,New Hampshire,Fair,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,Yes,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.5,72.57,No,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No +HeartDisease,BMI,Smoking,AlcoholDrinking,Stroke,PhysicalHealth,MentalHealth,DiffWalking,Sex,AgeCategory,Race,Diabetic,PhysicalActivity,SleepTime,Asthma,KidneyDisease,SkinCancer,State,GeneralHealth,PhysicalHealthDays,MentalHealthDays,LastCheckupTime,PhysicalActivities,SleepHours,RemovedTeeth,HadHeartAttack,HadAngina,HadStroke,HadAsthma,HadSkinCancer,HadCOPD,HadDepressiveDisorder,HadKidneyDisease,HadArthritis,HadDiabetes,DeafOrHardOfHearing,BlindOrVisionDifficulty,DifficultyConcentrating,DifficultyWalking,DifficultyDressingBathing,DifficultyErrands,SmokerStatus,ECigaretteUsage,ChestScan,RaceEthnicityCategory,HeightInMeters,WeightInKilograms,AlcoholDrinkers,HIVTesting,FluVaxLast12,PneumoVaxEver,TetanusLast10Tdap,HighRiskLastYear,CovidPos +,28.72,,,,,,,Female,Age 55 to 59,,,,,,,,Guam,Good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,Yes,No,Yes,Yes,No,No,No,Never smoked,Not at all (right now),No,"Other race only, Non-Hispanic",1.57,71.21,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,29.68,,,,,,,Male,Age 70 to 74,,,,,,,,New York,Fair,3.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,Yes,No,No,No,Yes,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,91.17,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,25.04,,,,,,,Male,Age 65 to 69,,,,,,,,Washington,Very good,3.0,0.0,Within past year (anytime less than 12 months ago),Yes,2.0,1 to 5,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.88,88.45,Yes,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No +,22.15,,,,,,,Male,Age 65 to 69,,,,,,,,Rhode Island,Good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,68.04,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +,22.81,,,,,,,Female,Age 70 to 74,,,,,,,,South Dakota,Very good,3.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,68.04,Yes,No,Yes,No,"Yes, received tetanus shot, but not Tdap",No,No +,24.03,,,,,,,Female,Age 65 to 69,,,,,,,,Puerto Rico,Good,0.0,30.0,Within past year (anytime less than 12 months ago),Yes,12.0,1 to 5,No,No,No,No,No,No,Yes,No,,No,Yes,Yes,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,,Hispanic,1.63,63.5,No,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,23.91,,,,,,,Female,Age 35 to 39,,,,,,,,Nevada,Good,5.0,5.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.6,61.23,No,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,Yes +,29.53,,,,,,,Male,Age 60 to 64,,,,,,,,Iowa,Very good,0.0,0.0,5 or more years ago,No,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,90.72,Yes,No,No,No,"Yes, received tetanus shot, but not Tdap",No,No +,31.09,,,,,,,Female,Age 75 to 79,,,,,,,,Virginia,Poor,30.0,0.0,Within past year (anytime less than 12 months ago),Yes,10.0,"6 or more, but not all",No,No,No,No,No,Yes,No,No,Yes,No,No,No,No,Yes,Yes,Yes,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.57,77.11,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,Tested positive using home test without a health professional +No,27.73,No,No,No,0.0,4.0,No,Female,70-74,White,No,Yes,5.0,No,No,No,,Very good,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Yes,19.05,No,No,No,30.0,0.0,No,Female,80 or older,White,Yes,Yes,7.0,Yes,No,No,,Good,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,29.76,,,,,,,Female,Age 75 to 79,,,,,,,,Hawaii,Very good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,Yes,No,No,Yes,Yes,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,76.2,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,Yes +,31.79,,,,,,,Female,Age 40 to 44,,,,,,,,West Virginia,Good,0.0,15.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,Yes,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,92.08,No,Yes,Yes,Yes,"Yes, received Tdap",No,Yes +,25.4,,,,,,,Male,Age 60 to 64,,,,,,,,Nebraska,Poor,30.0,30.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,Yes,No,Yes,No,No,No,Yes,Yes,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.78,80.29,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,32.32,,,,,,,Female,Age 80 or older,,,,,,,,New Hampshire,Fair,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,Yes,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.5,72.57,No,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No diff --git a/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/history.csv b/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/history.csv index c2efc8668a7d80642f147c07db15b3da0b630713..cf4252d52aefced22634e9a6a54d20590715186a 100644 --- a/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/history.csv +++ b/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/history.csv @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:0087ce2112773f9b88077e6f713a683eff2540220613d8375541ee87d6ace908 -size 271694926 +oid sha256:cdeaf8ddba61c9334b356ddee78bd3ac39b6513aa999b6fc5aaa6500f6c75c59 +size 270672586 diff --git a/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/info.json b/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/info.json index f32b1dffcbf491d0da6dfab0b3934e753c6da9e3..c876b7e9aa18ec2175d72a1a6b59bf487c77e98c 100644 --- a/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/info.json +++ b/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/info.json @@ -1,401 +1,393 @@ { - "name": "Indicators of Heart Disease (2022 UPDATE)", - "source": "https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease/data", - "data_intro": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", - "is_splited": false, - "overall_size": 1010949, - "train_size": 0, - "test_size": 0, - "c_classes": 46, - "n_classes": 9, - "cat_feature_intro": { - "HeartDisease": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", - "Smoking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", - "AlcoholDrinking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", - "Stroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", - "DiffWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", - "Sex": "Sex is a biological factor that can influence the risk of heart disease. Men and women may experience different symptoms and risk factors for heart disease.", - "AgeCategory": "Age is a significant risk factor for heart disease. As people age, their risk of developing heart disease increases. It is important to monitor heart health as individuals get older.", - "Race": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", - "Diabetic": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", - "PhysicalActivity": "Regular physical activity is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", - "GenHealth": "General health status can influence the risk of heart disease. It is important to maintain overall health and address any underlying health issues to reduce the risk of heart-related complications.", - "Asthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", - "KidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", - "SkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", - "State": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", - "GeneralHealth": "General health status can influence the risk of heart disease. It is important to maintain overall health and address any underlying health issues to reduce the risk of heart-related complications.", - "LastCheckupTime": "The time since the last medical checkup can be an important factor in heart health. Regular checkups can help identify and address risk factors for heart disease early.", - "PhysicalActivities": "Engaging in regular physical activities is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", - "RemovedTeeth": "The number of removed teeth can be an indicator of overall health, including heart health. Poor oral health has been linked to an increased risk of heart disease.", - "HadHeartAttack": "As described above, the original dataset of nearly 300 variables was reduced to 40 variables. In addition to classical EDA, this dataset can be used to apply a number of machine learning methods, especially classifier models (logistic regression, SVM, random forest, etc.). You should treat the variable \"HadHeartAttack\" as binary (\"Yes\" - respondent had heart disease; \"No\" - respondent did not have heart disease). Note, however, that the classes are unbalanced, so the classic approach of applying a model is not advisable. Fixing the weights/undersampling should yield much better results. Based on the data set, I built a logistic regression model and embedded it in an application that might inspire you: https://share.streamlit.io/kamilpytlak/heart-condition-checker/main/app.py. Can you indicate which variables have a significant effect on the likelihood of heart disease?", - "HadAngina": "Angina is a type of chest pain caused by reduced blood flow to the heart. It is a symptom of coronary artery disease and can indicate an increased risk of heart disease.", - "HadStroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", - "HadAsthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", - "HadSkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", - "HadCOPD": "Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease that causes obstructed airflow from the lungs. It can increase the risk of heart disease.", - "HadDepressiveDisorder": "Depressive disorders can affect overall health and increase the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", - "HadKidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", - "HadArthritis": "Arthritis is a condition that causes inflammation of the joints. It can affect overall health and increase the risk of heart disease.", - "HadDiabetes": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", - "DeafOrHardOfHearing": "Hearing loss can affect overall health and quality of life. It is important to address and manage hearing issues to maintain overall health and reduce the risk of heart disease.", - "BlindOrVisionDifficulty": "Vision difficulties can affect overall health and quality of life. It is important to address and manage vision issues to maintain overall health and reduce the risk of heart disease.", - "DifficultyConcentrating": "Difficulty concentrating can be a sign of underlying health issues, including heart disease. It is important to address cognitive issues to improve overall health and reduce the risk of heart-related complications.", - "DifficultyWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", - "DifficultyDressingBathing": "Difficulty with daily activities such as dressing and bathing can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", - "DifficultyErrands": "Difficulty running errands can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", - "SmokerStatus": "Smoking is a major risk factor for heart disease. It is important to address and manage smoking habits to reduce the risk of heart-related complications.", - "ECigaretteUsage": "E-cigarette usage can affect heart health. It is important to monitor and manage e-cigarette usage to reduce the risk of heart disease.", - "ChestScan": "Chest scans can help identify underlying health issues, including heart disease. It is important to monitor and address any abnormalities found in chest scans to maintain overall health.", - "RaceEthnicityCategory": "Race and ethnicity can influence the risk of heart disease. It is important to consider these factors when assessing and managing heart health.", - "AlcoholDrinkers": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", - "HIVTesting": "HIV testing is important for overall health. It is important to address and manage HIV status to reduce the risk of heart-related complications.", - "FluVaxLast12": "Receiving a flu vaccine in the last 12 months can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", - "PneumoVaxEver": "Receiving a pneumococcal vaccine can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", - "TetanusLast10Tdap": "Receiving a tetanus vaccine in the last 10 years can help reduce the risk of infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", - "HighRiskLastYear": "Being at high risk for health issues in the last year can affect overall health and increase the risk of heart disease. It is important to address and manage high-risk factors to reduce the risk of heart-related complications.", - "CovidPos": "Testing positive for COVID-19 can affect overall health and increase the risk of heart disease. It is important to monitor and manage COVID-19 symptoms to reduce the risk of heart-related complications." - }, - "num_feature_intro": { - "BMI": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", - "PhysicalHealth": "Physical health is an important factor in overall well-being and can influence the risk of heart disease. It is important to maintain good physical health to reduce the risk of heart-related complications.", - "MentalHealth": "Mental health is an important factor in overall well-being and can influence the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", - "SleepTime": "Sleep time is an important factor in overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", - "PhysicalHealthDays": "The number of days with good physical health can be an indicator of overall well-being. It is important to maintain good physical health to reduce the risk of heart-related complications.", - "MentalHealthDays": "The number of days with good mental health can be an indicator of overall well-being. It is important to address and manage mental health to reduce the risk of heart-related complications.", - "SleepHours": "The number of hours of sleep can affect overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", - "HeightInMeters": "Height can be an indicator of overall health. It is important to monitor and maintain a healthy height-to-weight ratio to reduce the risk of heart disease.", - "WeightInKilograms": "Weight can be an indicator of overall health. It is important to maintain a healthy weight to reduce the risk of heart disease." - }, - "evaluation_metric": "The evaluation metric for this dataset can include accuracy, precision, recall, F1-score, and AUC-ROC. These metrics can help assess the performance of machine learning models in predicting heart disease.", - "task_type": "classification", - "target": "GeneralHealth", - "num_feature_value": { - "BMI": [ - 12.02, - 99.64 - ], - "HeightInMeters": [ - 0.91, - 2.41 - ], - "MentalHealth": [ - 0.0, - 30.0 - ], - "MentalHealthDays": [ - 0.0, - 30.0 - ], - "PhysicalHealth": [ - 0.0, - 30.0 - ], - "PhysicalHealthDays": [ - 0.0, - 30.0 - ], - "SleepHours": [ - 1.0, - 24.0 - ], - "SleepTime": [ - 1.0, - 24.0 - ], - "WeightInKilograms": [ - 22.68, - 292.57 - ] - }, - "cat_feature_value": { - "AgeCategory": [ - "18-24", - "25-29", - "30-34", - "35-39", - "40-44", - "45-49", - "50-54", - "55-59", - "60-64", - "65-69", - "70-74", - "75-79", - "80 or older", - "Age 18 to 24", - "Age 25 to 29", - "Age 30 to 34", - "Age 35 to 39", - "Age 40 to 44", - "Age 45 to 49", - "Age 50 to 54", - "Age 55 to 59", - "Age 60 to 64", - "Age 65 to 69", - "Age 70 to 74", - "Age 75 to 79", - "Age 80 or older" - ], - "AlcoholDrinkers": [ - "No", - "Yes" - ], - "AlcoholDrinking": [ - "No", - "Yes" - ], - "Asthma": [ - "No", - "Yes" - ], - "BlindOrVisionDifficulty": [ - "No", - "Yes" - ], - "ChestScan": [ - "No", - "Yes" - ], - "CovidPos": [ - "No", - "Tested positive using home test without a health professional", - "Yes" - ], - "DeafOrHardOfHearing": [ - "No", - "Yes" - ], - "Diabetic": [ - "No", - "No, borderline diabetes", - "Yes", - "Yes (during pregnancy)" - ], - "DiffWalking": [ - "No", - "Yes" - ], - "DifficultyConcentrating": [ - "No", - "Yes" - ], - "DifficultyDressingBathing": [ - "No", - "Yes" - ], - "DifficultyErrands": [ - "No", - "Yes" - ], - "DifficultyWalking": [ - "No", - "Yes" - ], - "ECigaretteUsage": [ - "Never used e-cigarettes in my entire life", - "Not at all (right now)", - "Use them every day", - "Use them some days" - ], - "FluVaxLast12": [ - "No", - "Yes" - ], - "GenHealth": [ - "Excellent", - "Fair", - "Good", - "Poor", - "Very good" - ], - "GeneralHealth": [ - "Excellent", - "Fair", - "Good", - "Poor", - "Very good" - ], - "HIVTesting": [ - "No", - "Yes" - ], - "HadAngina": [ - "No", - "Yes" - ], - "HadArthritis": [ - "No", - "Yes" - ], - "HadAsthma": [ - "No", - "Yes" - ], - "HadCOPD": [ - "No", - "Yes" - ], - "HadDepressiveDisorder": [ - "No", - "Yes" - ], - "HadDiabetes": [ - "No", - "No, pre-diabetes or borderline diabetes", - "Yes", - "Yes, but only during pregnancy (female)" - ], - "HadHeartAttack": [ - "No", - "Yes" - ], - "HadKidneyDisease": [ - "No", - "Yes" - ], - "HadSkinCancer": [ - "No", - "Yes" - ], - "HadStroke": [ - "No", - "Yes" - ], - "HeartDisease": [ - "No", - "Yes" - ], - "HighRiskLastYear": [ - "No", - "Yes" - ], - "KidneyDisease": [ - "No", - "Yes" - ], - "LastCheckupTime": [ - "5 or more years ago", - "Within past 2 years (1 year but less than 2 years ago)", - "Within past 5 years (2 years but less than 5 years ago)", - "Within past year (anytime less than 12 months ago)" - ], - "PhysicalActivities": [ - "No", - "Yes" - ], - "PhysicalActivity": [ - "No", - "Yes" - ], - "PneumoVaxEver": [ - "No", - "Yes" - ], - "Race": [ - "American Indian/Alaskan Native", - "Asian", - "Black", - "Hispanic", - "Other", - "White" - ], - "RaceEthnicityCategory": [ - "Black only, Non-Hispanic", - "Hispanic", - "Multiracial, Non-Hispanic", - "Other race only, Non-Hispanic", - "White only, Non-Hispanic" - ], - "RemovedTeeth": [ - "1 to 5", - "6 or more, but not all", - "All", - "None of them" - ], - "Sex": [ - "Female", - "Male" - ], - "SkinCancer": [ - "No", - "Yes" - ], - "SmokerStatus": [ - "Current smoker - now smokes every day", - "Current smoker - now smokes some days", - "Former smoker", - "Never smoked" - ], - "Smoking": [ - "No", - "Yes" - ], - "State": [ - "Alabama", - "Alaska", - "Arizona", - "Arkansas", - "California", - "Colorado", - "Connecticut", - "Delaware", - "District of Columbia", - "Florida", - "Georgia", - "Guam", - "Hawaii", - "Idaho", - "Illinois", - "Indiana", - "Iowa", - "Kansas", - "Kentucky", - "Louisiana", - "Maine", - "Maryland", - "Massachusetts", - "Michigan", - "Minnesota", - "Mississippi", - "Missouri", - "Montana", - "Nebraska", - "Nevada", - "New Hampshire", - "New Jersey", - "New Mexico", - "New York", - "North Carolina", - "North Dakota", - "Ohio", - "Oklahoma", - "Oregon", - "Pennsylvania", - "Puerto Rico", - "Rhode Island", - "South Carolina", - "South Dakota", - "Tennessee", - "Texas", - "Utah", - "Vermont", - "Virgin Islands", - "Virginia", - "Washington", - "West Virginia", - "Wisconsin", - "Wyoming" - ], - "Stroke": [ - "No", - "Yes" - ], - "TetanusLast10Tdap": [ - "No, did not receive any tetanus shot in the past 10 years", - "Yes, received Tdap", - "Yes, received tetanus shot but not sure what type", - "Yes, received tetanus shot, but not Tdap" - ] - } -} \ No newline at end of file + "name": "Indicators of Heart Disease (2022 UPDATE)", + "source": "https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease/data", + "data_intro": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", + "is_splited": false, + "overall_size": 1010949, + "train_size": 0, + "test_size": 0, + "c_classes": 45, + "n_classes": 9, + "cat_feature_intro": { + "HeartDisease": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", + "Smoking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", + "AlcoholDrinking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", + "Stroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", + "DiffWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", + "Sex": "Sex is a biological factor that can influence the risk of heart disease. Men and women may experience different symptoms and risk factors for heart disease.", + "AgeCategory": "Age is a significant risk factor for heart disease. As people age, their risk of developing heart disease increases. It is important to monitor heart health as individuals get older.", + "Race": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", + "Diabetic": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", + "PhysicalActivity": "Regular physical activity is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", + "Asthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", + "KidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", + "SkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", + "State": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", + "GeneralHealth": "Self-reported general health from BRFSS GENHLTH: Excellent, Very good, Good, Fair, or Poor.", + "LastCheckupTime": "The time since the last medical checkup can be an important factor in heart health. Regular checkups can help identify and address risk factors for heart disease early.", + "PhysicalActivities": "Engaging in regular physical activities is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", + "RemovedTeeth": "The number of removed teeth can be an indicator of overall health, including heart health. Poor oral health has been linked to an increased risk of heart disease.", + "HadHeartAttack": "As described above, the original dataset of nearly 300 variables was reduced to 40 variables. In addition to classical EDA, this dataset can be used to apply a number of machine learning methods, especially classifier models (logistic regression, SVM, random forest, etc.). You should treat the variable \"HadHeartAttack\" as binary (\"Yes\" - respondent had heart disease; \"No\" - respondent did not have heart disease). Note, however, that the classes are unbalanced, so the classic approach of applying a model is not advisable. Fixing the weights/undersampling should yield much better results. Based on the data set, I built a logistic regression model and embedded it in an application that might inspire you: https://share.streamlit.io/kamilpytlak/heart-condition-checker/main/app.py. Can you indicate which variables have a significant effect on the likelihood of heart disease?", + "HadAngina": "Angina is a type of chest pain caused by reduced blood flow to the heart. It is a symptom of coronary artery disease and can indicate an increased risk of heart disease.", + "HadStroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", + "HadAsthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", + "HadSkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", + "HadCOPD": "Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease that causes obstructed airflow from the lungs. It can increase the risk of heart disease.", + "HadDepressiveDisorder": "Depressive disorders can affect overall health and increase the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", + "HadKidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", + "HadArthritis": "Arthritis is a condition that causes inflammation of the joints. It can affect overall health and increase the risk of heart disease.", + "HadDiabetes": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", + "DeafOrHardOfHearing": "Hearing loss can affect overall health and quality of life. It is important to address and manage hearing issues to maintain overall health and reduce the risk of heart disease.", + "BlindOrVisionDifficulty": "Vision difficulties can affect overall health and quality of life. It is important to address and manage vision issues to maintain overall health and reduce the risk of heart disease.", + "DifficultyConcentrating": "Difficulty concentrating can be a sign of underlying health issues, including heart disease. It is important to address cognitive issues to improve overall health and reduce the risk of heart-related complications.", + "DifficultyWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", + "DifficultyDressingBathing": "Difficulty with daily activities such as dressing and bathing can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", + "DifficultyErrands": "Difficulty running errands can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", + "SmokerStatus": "Smoking is a major risk factor for heart disease. It is important to address and manage smoking habits to reduce the risk of heart-related complications.", + "ECigaretteUsage": "E-cigarette usage can affect heart health. It is important to monitor and manage e-cigarette usage to reduce the risk of heart disease.", + "ChestScan": "Chest scans can help identify underlying health issues, including heart disease. It is important to monitor and address any abnormalities found in chest scans to maintain overall health.", + "RaceEthnicityCategory": "Race and ethnicity can influence the risk of heart disease. It is important to consider these factors when assessing and managing heart health.", + "AlcoholDrinkers": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", + "HIVTesting": "Whether the respondent has ever been tested for HIV (Yes/No), not the result of that test.", + "FluVaxLast12": "Receiving a flu vaccine in the last 12 months can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", + "PneumoVaxEver": "Receiving a pneumococcal vaccine can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", + "TetanusLast10Tdap": "Receiving a tetanus vaccine in the last 10 years can help reduce the risk of infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", + "HighRiskLastYear": "Being at high risk for health issues in the last year can affect overall health and increase the risk of heart disease. It is important to address and manage high-risk factors to reduce the risk of heart-related complications.", + "CovidPos": "Testing positive for COVID-19 can affect overall health and increase the risk of heart disease. It is important to monitor and manage COVID-19 symptoms to reduce the risk of heart-related complications." + }, + "num_feature_intro": { + "BMI": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", + "PhysicalHealth": "Physical health is an important factor in overall well-being and can influence the risk of heart disease. It is important to maintain good physical health to reduce the risk of heart-related complications.", + "MentalHealth": "Mental health is an important factor in overall well-being and can influence the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", + "SleepTime": "Sleep time is an important factor in overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", + "PhysicalHealthDays": "The number of days with good physical health can be an indicator of overall well-being. It is important to maintain good physical health to reduce the risk of heart-related complications.", + "MentalHealthDays": "The number of days with good mental health can be an indicator of overall well-being. It is important to address and manage mental health to reduce the risk of heart-related complications.", + "SleepHours": "The number of hours of sleep can affect overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", + "HeightInMeters": "Height can be an indicator of overall health. It is important to monitor and maintain a healthy height-to-weight ratio to reduce the risk of heart disease.", + "WeightInKilograms": "Weight can be an indicator of overall health. It is important to maintain a healthy weight to reduce the risk of heart disease." + }, + "evaluation_metric": "The evaluation metric for this dataset can include accuracy, precision, recall, F1-score, and AUC-ROC. These metrics can help assess the performance of machine learning models in predicting heart disease.", + "task_type": "classification", + "target": "GeneralHealth", + "num_feature_value": { + "BMI": [ + 12.02, + 99.64 + ], + "HeightInMeters": [ + 0.91, + 2.41 + ], + "MentalHealth": [ + 0.0, + 30.0 + ], + "MentalHealthDays": [ + 0.0, + 30.0 + ], + "PhysicalHealth": [ + 0.0, + 30.0 + ], + "PhysicalHealthDays": [ + 0.0, + 30.0 + ], + "SleepHours": [ + 1.0, + 24.0 + ], + "SleepTime": [ + 1.0, + 24.0 + ], + "WeightInKilograms": [ + 22.68, + 292.57 + ] + }, + "cat_feature_value": { + "AgeCategory": [ + "18-24", + "25-29", + "30-34", + "35-39", + "40-44", + "45-49", + "50-54", + "55-59", + "60-64", + "65-69", + "70-74", + "75-79", + "80 or older", + "Age 18 to 24", + "Age 25 to 29", + "Age 30 to 34", + "Age 35 to 39", + "Age 40 to 44", + "Age 45 to 49", + "Age 50 to 54", + "Age 55 to 59", + "Age 60 to 64", + "Age 65 to 69", + "Age 70 to 74", + "Age 75 to 79", + "Age 80 or older" + ], + "AlcoholDrinkers": [ + "No", + "Yes" + ], + "AlcoholDrinking": [ + "No", + "Yes" + ], + "Asthma": [ + "No", + "Yes" + ], + "BlindOrVisionDifficulty": [ + "No", + "Yes" + ], + "ChestScan": [ + "No", + "Yes" + ], + "CovidPos": [ + "No", + "Tested positive using home test without a health professional", + "Yes" + ], + "DeafOrHardOfHearing": [ + "No", + "Yes" + ], + "Diabetic": [ + "No", + "No, borderline diabetes", + "Yes", + "Yes (during pregnancy)" + ], + "DiffWalking": [ + "No", + "Yes" + ], + "DifficultyConcentrating": [ + "No", + "Yes" + ], + "DifficultyDressingBathing": [ + "No", + "Yes" + ], + "DifficultyErrands": [ + "No", + "Yes" + ], + "DifficultyWalking": [ + "No", + "Yes" + ], + "ECigaretteUsage": [ + "Never used e-cigarettes in my entire life", + "Not at all (right now)", + "Use them every day", + "Use them some days" + ], + "FluVaxLast12": [ + "No", + "Yes" + ], + "GeneralHealth": [ + "Excellent", + "Very good", + "Good", + "Fair", + "Poor" + ], + "HIVTesting": [ + "No", + "Yes" + ], + "HadAngina": [ + "No", + "Yes" + ], + "HadArthritis": [ + "No", + "Yes" + ], + "HadAsthma": [ + "No", + "Yes" + ], + "HadCOPD": [ + "No", + "Yes" + ], + "HadDepressiveDisorder": [ + "No", + "Yes" + ], + "HadDiabetes": [ + "No", + "No, pre-diabetes or borderline diabetes", + "Yes", + "Yes, but only during pregnancy (female)" + ], + "HadHeartAttack": [ + "No", + "Yes" + ], + "HadKidneyDisease": [ + "No", + "Yes" + ], + "HadSkinCancer": [ + "No", + "Yes" + ], + "HadStroke": [ + "No", + "Yes" + ], + "HeartDisease": [ + "No", + "Yes" + ], + "HighRiskLastYear": [ + "No", + "Yes" + ], + "KidneyDisease": [ + "No", + "Yes" + ], + "LastCheckupTime": [ + "5 or more years ago", + "Within past 2 years (1 year but less than 2 years ago)", + "Within past 5 years (2 years but less than 5 years ago)", + "Within past year (anytime less than 12 months ago)" + ], + "PhysicalActivities": [ + "No", + "Yes" + ], + "PhysicalActivity": [ + "No", + "Yes" + ], + "PneumoVaxEver": [ + "No", + "Yes" + ], + "Race": [ + "American Indian/Alaskan Native", + "Asian", + "Black", + "Hispanic", + "Other", + "White" + ], + "RaceEthnicityCategory": [ + "Black only, Non-Hispanic", + "Hispanic", + "Multiracial, Non-Hispanic", + "Other race only, Non-Hispanic", + "White only, Non-Hispanic" + ], + "RemovedTeeth": [ + "1 to 5", + "6 or more, but not all", + "All", + "None of them" + ], + "Sex": [ + "Female", + "Male" + ], + "SkinCancer": [ + "No", + "Yes" + ], + "SmokerStatus": [ + "Current smoker - now smokes every day", + "Current smoker - now smokes some days", + "Former smoker", + "Never smoked" + ], + "Smoking": [ + "No", + "Yes" + ], + "State": [ + "Alabama", + "Alaska", + "Arizona", + "Arkansas", + "California", + "Colorado", + "Connecticut", + "Delaware", + "District of Columbia", + "Florida", + "Georgia", + "Guam", + "Hawaii", + "Idaho", + "Illinois", + "Indiana", + "Iowa", + "Kansas", + "Kentucky", + "Louisiana", + "Maine", + "Maryland", + "Massachusetts", + "Michigan", + "Minnesota", + "Mississippi", + "Missouri", + "Montana", + "Nebraska", + "Nevada", + "New Hampshire", + "New Jersey", + "New Mexico", + "New York", + "North Carolina", + "North Dakota", + "Ohio", + "Oklahoma", + "Oregon", + "Pennsylvania", + "Puerto Rico", + "Rhode Island", + "South Carolina", + "South Dakota", + "Tennessee", + "Texas", + "Utah", + "Vermont", + "Virgin Islands", + "Virginia", + "Washington", + "West Virginia", + "Wisconsin", + "Wyoming" + ], + "Stroke": [ + "No", + "Yes" + ], + "TetanusLast10Tdap": [ + "No, did not receive any tetanus shot in the past 10 years", + "Yes, received Tdap", + "Yes, received tetanus shot but not sure what type", + "Yes, received tetanus shot, but not Tdap" + ] + } +} diff --git a/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/info_mod.json b/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/info_mod.json index 4a3837fa5d12f6ebe4fdd1f7bc267c85df0304be..4520cf342871630bc668c6496d3a5b35a7610964 100644 --- a/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/info_mod.json +++ b/decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/info_mod.json @@ -19,7 +19,6 @@ "Race": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", "Diabetic": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", "PhysicalActivity": "Regular physical activity is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", - "GenHealth": "General health status can influence the risk of heart disease. It is important to maintain overall health and address any underlying health issues to reduce the risk of heart-related complications.", "Asthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", "KidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", "SkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", @@ -48,12 +47,13 @@ "ChestScan": "Chest scans can help identify underlying health issues, including heart disease. It is important to monitor and address any abnormalities found in chest scans to maintain overall health.", "RaceEthnicityCategory": "Race and ethnicity can influence the risk of heart disease. It is important to consider these factors when assessing and managing heart health.", "AlcoholDrinkers": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", - "HIVTesting": "HIV testing is important for overall health. It is important to address and manage HIV status to reduce the risk of heart-related complications.", + "HIVTesting": "Whether the respondent has ever been tested for HIV (Yes/No), not the result of that test.", "FluVaxLast12": "Receiving a flu vaccine in the last 12 months can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", "PneumoVaxEver": "Receiving a pneumococcal vaccine can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", "TetanusLast10Tdap": "Receiving a tetanus vaccine in the last 10 years can help reduce the risk of infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", "HighRiskLastYear": "Being at high risk for health issues in the last year can affect overall health and increase the risk of heart disease. It is important to address and manage high-risk factors to reduce the risk of heart-related complications.", - "CovidPos": "Testing positive for COVID-19 can affect overall health and increase the risk of heart disease. It is important to monitor and manage COVID-19 symptoms to reduce the risk of heart-related complications." + "CovidPos": "Testing positive for COVID-19 can affect overall health and increase the risk of heart disease. It is important to monitor and manage COVID-19 symptoms to reduce the risk of heart-related complications.", + "GeneralHealth": "Self-reported general health from BRFSS GENHLTH: Excellent, Very good, Good, Fair, or Poor." }, "num_feature_intro": { "BMI": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", @@ -178,13 +178,6 @@ "No", "Yes" ], - "GenHealth": [ - "Excellent", - "Fair", - "Good", - "Poor", - "Very good" - ], "Asthma": [ "No", "Yes" @@ -388,6 +381,13 @@ "No", "Tested positive using home test without a health professional", "Yes" + ], + "GeneralHealth": [ + "Excellent", + "Very good", + "Good", + "Fair", + "Poor" ] }, "columns": [ @@ -562,4 +562,4 @@ "open_text_feature_intro": {}, "open_text_features": [], "missing_from_original_info": [] -} \ No newline at end of file +} diff --git a/decision_making/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B2/Sleep_Health_and_Lifestyle_Dataset_B2_001.json b/decision_making/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B2/Sleep_Health_and_Lifestyle_Dataset_B2_001.json index 147fdf2ab8cc208097d05286a817eb009556173c..970fbd607fc5b055285fd5a7a3fc98ee7d4d6289 100644 --- a/decision_making/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B2/Sleep_Health_and_Lifestyle_Dataset_B2_001.json +++ b/decision_making/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B2/Sleep_Health_and_Lifestyle_Dataset_B2_001.json @@ -44,7 +44,7 @@ "Blood Pressure": "115/75", "Heart Rate": "68", "Daily Steps": "7000", - "Sleep Disorder": "" + "Sleep Disorder": "No Disorder" } } ], @@ -58,4 +58,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/decision_making/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B2/Sleep_Health_and_Lifestyle_Dataset_B2_002.json b/decision_making/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B2/Sleep_Health_and_Lifestyle_Dataset_B2_002.json index 51afdffc8fda94637e23dfa29c63cd506cdee186..5ece2e52f483834333c5cbf21fa5aeb329664abe 100644 --- a/decision_making/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B2/Sleep_Health_and_Lifestyle_Dataset_B2_002.json +++ b/decision_making/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B2/Sleep_Health_and_Lifestyle_Dataset_B2_002.json @@ -44,7 +44,7 @@ "Blood Pressure": null, "Heart Rate": "65", "Daily Steps": "5000", - "Sleep Disorder": "" + "Sleep Disorder": "No Disorder" } }, { @@ -62,7 +62,7 @@ "Blood Pressure": "120/80", "Heart Rate": "70", "Daily Steps": "8000", - "Sleep Disorder": "" + "Sleep Disorder": "No Disorder" } } ], @@ -76,4 +76,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/decision_making/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B2/Sleep_Health_and_Lifestyle_Dataset_B2_005.json b/decision_making/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B2/Sleep_Health_and_Lifestyle_Dataset_B2_005.json index 8cf0e376317fc98ad1cc0b397d9e339e0484583c..5c993f89c3390e7911def822498e57660b753e7c 100644 --- a/decision_making/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B2/Sleep_Health_and_Lifestyle_Dataset_B2_005.json +++ b/decision_making/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B2/Sleep_Health_and_Lifestyle_Dataset_B2_005.json @@ -62,7 +62,7 @@ "Blood Pressure": "130/85", "Heart Rate": "72", "Daily Steps": "6000", - "Sleep Disorder": "" + "Sleep Disorder": "No Disorder" } } ], @@ -76,4 +76,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/decision_making/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B2/Sleep_Health_and_Lifestyle_Dataset_B2_006.json b/decision_making/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B2/Sleep_Health_and_Lifestyle_Dataset_B2_006.json index 07c3677488f8d3af75bc97d3b33213614081f084..9cd741d588f2123837aeda2dd17a3ba502f7b07b 100644 --- a/decision_making/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B2/Sleep_Health_and_Lifestyle_Dataset_B2_006.json +++ b/decision_making/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B2/Sleep_Health_and_Lifestyle_Dataset_B2_006.json @@ -44,7 +44,7 @@ "Blood Pressure": "125/80", "Heart Rate": "72", "Daily Steps": "5000", - "Sleep Disorder": "" + "Sleep Disorder": "No Disorder" } } ], @@ -58,4 +58,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/decision_making/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B2/current.csv b/decision_making/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B2/current.csv index c69fa688471a7374a94450fd159033704ff39199..8c2f622f70b7341c2f1e670c4d1cb44a2ab00cc9 100644 --- a/decision_making/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B2/current.csv +++ b/decision_making/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B2/current.csv @@ -1,15 +1,15 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activity Level,Stress Level,BMI Category,Blood Pressure,Heart Rate,Daily Steps,Sleep Disorder 82,Female,34,Scientist,5.8,4,32,8,Overweight,131/86,81,5200,Sleep Apnea -128,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, +128,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder 259,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia -314,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -54,Male,32,Doctor,7.6,7,75,6,Normal,120/80,70,8000, +314,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +54,Male,32,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder 364,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 265,Male,48,Doctor,7.3,7,65,5,Obese,142/92,83,3500,Insomnia 19,Female,29,Nurse,6.5,5,40,7,Normal Weight,132/87,80,4000,Insomnia 105,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,Sleep Apnea 297,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 229,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia -249,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000, +249,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,No Disorder 373,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea -73,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000, +73,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder diff --git a/decision_making/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B2/history.csv b/decision_making/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B2/history.csv index 710230c086087f98ccff6de8f5ce3aad7ad8b082..5fdb24abd05fad90bb0fb6d97d6d497617a1b67f 100644 --- a/decision_making/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B2/history.csv +++ b/decision_making/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B2/history.csv @@ -1,134 +1,134 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activity Level,Stress Level,BMI Category,Blood Pressure,Heart Rate,Daily Steps,Sleep Disorder -1,Male,27,Software Engineer,6.1,6,42,6,Overweight,126/83,77,4200, -2,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000, -3,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000, +1,Male,27,Software Engineer,6.1,6,42,6,Overweight,126/83,77,4200,No Disorder +2,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000,No Disorder +3,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000,No Disorder 4,Male,28,Sales Representative,5.9,4,30,8,Obese,140/90,85,3000,Sleep Apnea 5,Male,28,Sales Representative,5.9,4,30,8,Obese,140/90,85,3000,Sleep Apnea 6,Male,28,Software Engineer,5.9,4,30,8,Obese,140/90,85,3000,Insomnia 7,Male,29,Teacher,6.3,6,40,7,Obese,140/90,82,3500,Insomnia -10,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -11,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000, -13,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000, -14,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000, +10,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +11,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000,No Disorder +13,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000,No Disorder +14,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000,No Disorder 17,Female,29,Nurse,6.5,5,40,7,Normal Weight,132/87,80,4000,Sleep Apnea 18,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000,Sleep Apnea -20,Male,30,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -21,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -22,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -23,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -24,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -25,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -26,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000, -27,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -28,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000, -29,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000, -30,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000, +20,Male,30,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +21,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +22,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +23,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +24,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +25,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +26,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,No Disorder +27,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +28,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,No Disorder +29,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,No Disorder +30,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,No Disorder 31,Female,30,Nurse,6.4,5,35,7,Normal Weight,130/86,78,4100,Sleep Apnea 32,Female,30,Nurse,6.4,5,35,7,Normal Weight,130/86,78,4100,Insomnia -33,Female,31,Nurse,7.9,8,75,4,Normal Weight,117/76,69,6800, -34,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -36,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -38,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -39,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -40,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -42,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -43,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -44,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -47,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -48,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000, +33,Female,31,Nurse,7.9,8,75,4,Normal Weight,117/76,69,6800,No Disorder +34,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +36,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +38,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +39,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +40,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +42,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +43,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +44,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +47,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +48,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder 50,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,Sleep Apnea -51,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000, -53,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -55,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -57,Male,32,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -58,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -61,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -64,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000, -65,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000, -66,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000, -67,Male,32,Accountant,7.2,8,50,6,Normal Weight,118/76,68,7000, +51,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000,No Disorder +53,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +55,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +57,Male,32,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +58,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +61,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +64,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,No Disorder +65,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,No Disorder +66,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,No Disorder +67,Male,32,Accountant,7.2,8,50,6,Normal Weight,118/76,68,7000,No Disorder 68,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,Insomnia -69,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500, -76,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -77,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -78,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -79,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -80,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -83,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600, -84,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600, -85,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000, -88,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000, -89,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000, -90,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000, -91,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000, -93,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000, +69,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500,No Disorder +76,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +77,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +78,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +79,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +80,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +83,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600,No Disorder +84,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600,No Disorder +85,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000,No Disorder +88,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000,No Disorder +89,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,No Disorder +90,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,No Disorder +91,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,No Disorder +93,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000,No Disorder 94,Male,35,Lawyer,7.4,7,60,5,Obese,135/88,84,3300,Sleep Apnea 95,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000,Insomnia -96,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -99,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000, -100,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000, -101,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000, -102,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000, -103,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000, +96,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +99,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +100,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +101,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +102,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +103,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,No Disorder 104,Male,36,Teacher,6.6,5,35,7,Overweight,129/84,74,4800,Sleep Apnea 106,Male,36,Teacher,6.6,5,35,7,Overweight,129/84,74,4800,Insomnia -107,Female,37,Nurse,6.1,6,42,6,Overweight,126/83,77,4200, -108,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000, -109,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000, -110,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000, -111,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -113,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -114,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000, -115,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -117,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -118,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -119,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -120,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -121,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -122,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -123,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -125,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -126,Female,37,Nurse,7.5,8,60,4,Normal Weight,120/80,70,8000, -127,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -129,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -130,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -132,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -133,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -135,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -138,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000, -140,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000, -142,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000, +107,Female,37,Nurse,6.1,6,42,6,Overweight,126/83,77,4200,No Disorder +108,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000,No Disorder +109,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000,No Disorder +110,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,No Disorder +111,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +113,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +114,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,No Disorder +115,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +117,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +118,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +119,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +120,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +121,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +122,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +123,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +125,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +126,Female,37,Nurse,7.5,8,60,4,Normal Weight,120/80,70,8000,No Disorder +127,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +129,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +130,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +132,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +133,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +135,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +138,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,No Disorder +140,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,No Disorder +142,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,No Disorder 145,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,Sleep Apnea 147,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,Insomnia 148,Male,39,Engineer,6.5,5,40,7,Overweight,132/87,80,4000,Insomnia -149,Female,39,Lawyer,6.9,7,50,6,Normal Weight,128/85,75,5500, -151,Female,39,Accountant,8.0,9,80,3,Normal Weight,115/78,67,7500, -152,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -154,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -155,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -157,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -158,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -159,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -160,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -161,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -162,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300, -163,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300, -164,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000, -165,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000, +149,Female,39,Lawyer,6.9,7,50,6,Normal Weight,128/85,75,5500,No Disorder +151,Female,39,Accountant,8.0,9,80,3,Normal Weight,115/78,67,7500,No Disorder +152,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +154,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +155,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +157,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +158,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +159,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +160,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +161,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +162,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300,No Disorder +163,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300,No Disorder +164,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000,No Disorder +165,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000,No Disorder 166,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,Insomnia -167,Male,41,Engineer,7.3,8,70,6,Normal Weight,121/79,72,6200, -168,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000, -169,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000, -170,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -171,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -172,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -173,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -174,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -175,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000, -176,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000, +167,Male,41,Engineer,7.3,8,70,6,Normal Weight,121/79,72,6200,No Disorder +168,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000,No Disorder +169,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000,No Disorder +170,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +171,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +172,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +173,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +174,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +175,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,No Disorder +176,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,No Disorder 178,Male,42,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia -180,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, -183,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, +180,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +183,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder 185,Female,42,Teacher,6.8,6,45,7,Overweight,130/85,78,5000,Sleep Apnea 186,Female,42,Teacher,6.8,6,45,7,Overweight,130/85,78,5000,Sleep Apnea 187,Female,43,Teacher,6.7,7,45,4,Overweight,135/90,65,6000,Insomnia @@ -146,18 +146,18 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 201,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia 202,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Insomnia 203,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Insomnia -204,Male,43,Engineer,6.9,6,47,7,Normal Weight,117/76,69,6800, -205,Male,43,Engineer,7.6,8,75,4,Overweight,122/80,68,6800, -206,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -207,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -208,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -209,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -211,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -212,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -214,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -215,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -216,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -218,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, +204,Male,43,Engineer,6.9,6,47,7,Normal Weight,117/76,69,6800,No Disorder +205,Male,43,Engineer,7.6,8,75,4,Overweight,122/80,68,6800,No Disorder +206,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +207,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +208,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +209,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +211,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +212,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +214,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +215,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +216,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +218,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder 219,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Sleep Apnea 220,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Sleep Apnea 222,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia @@ -177,16 +177,16 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 245,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia 247,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia 248,Male,44,Engineer,6.8,7,45,7,Overweight,130/85,78,5000,Insomnia -250,Male,44,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000, +250,Male,44,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,No Disorder 251,Female,45,Teacher,6.8,7,30,6,Overweight,135/90,65,6000,Insomnia 252,Female,45,Teacher,6.8,7,30,6,Overweight,135/90,65,6000,Insomnia 254,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia 255,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia -262,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000, -263,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000, -264,Female,45,Manager,6.9,7,55,5,Overweight,125/82,75,5500, +262,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,No Disorder +263,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,No Disorder +264,Female,45,Manager,6.9,7,55,5,Overweight,125/82,75,5500,No Disorder 266,Female,48,Nurse,5.9,6,90,8,Overweight,140/95,75,10000,Sleep Apnea -268,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000, +268,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,No Disorder 269,Female,49,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 271,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 272,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea @@ -194,8 +194,8 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 276,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 277,Male,49,Doctor,8.1,9,85,3,Obese,139/91,86,3700,Sleep Apnea 278,Male,49,Doctor,8.1,9,85,3,Obese,139/91,86,3700,Sleep Apnea -280,Female,50,Engineer,8.3,9,30,3,Normal,125/80,65,5000, -281,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000, +280,Female,50,Engineer,8.3,9,30,3,Normal,125/80,65,5000,No Disorder +281,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,No Disorder 283,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 284,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 285,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea @@ -205,10 +205,10 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 291,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 294,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 296,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea -299,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -300,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -301,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -302,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, +299,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +300,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +301,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +302,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder 304,Female,51,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 305,Female,51,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 306,Female,51,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea @@ -218,28 +218,28 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 311,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia 312,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia 316,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,Insomnia -317,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -318,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -319,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -320,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -321,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -322,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -323,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -324,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -326,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -327,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000, -329,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000, -330,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -332,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -333,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -334,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -335,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -336,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -338,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -339,Female,54,Engineer,8.5,9,30,3,Normal,125/80,65,5000, +317,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +318,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +319,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +320,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +321,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +322,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +323,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +324,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +326,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +327,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,No Disorder +329,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,No Disorder +330,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +332,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +333,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +334,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +335,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +336,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +338,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +339,Female,54,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder 340,Female,55,Nurse,8.1,9,75,4,Overweight,140/95,72,5000,Sleep Apnea -343,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000, -344,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000, +343,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000,No Disorder +344,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,No Disorder 345,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 346,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 349,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea @@ -249,75 +249,75 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 355,Female,58,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 356,Female,58,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 357,Female,58,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,Sleep Apnea -359,Female,59,Nurse,8.0,9,75,3,Overweight,140/95,68,7000, -360,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000, +359,Female,59,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,No Disorder +360,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,No Disorder 365,Female,59,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 366,Female,59,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 368,Female,59,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 371,Female,59,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 256,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia -98,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -49,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -217,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -325,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000, -16,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000, +98,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +49,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +217,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +325,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,No Disorder +16,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000,No Disorder 308,Female,52,Accountant,6.5,7,45,7,Overweight,130/85,72,6000,Insomnia -60,Male,32,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -182,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, -92,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000, +60,Male,32,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +182,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +92,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,No Disorder 347,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea -9,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -63,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000, -41,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -150,Female,39,Accountant,8.0,9,80,3,Normal Weight,115/78,67,7500, +9,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +63,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,No Disorder +41,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +150,Female,39,Accountant,8.0,9,80,3,Normal Weight,115/78,67,7500,No Disorder 246,Female,44,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia -35,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -37,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -213,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, +35,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +37,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +213,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder 348,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea -56,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -15,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000, +56,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +15,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000,No Disorder 279,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Insomnia -177,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000, -331,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -337,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, +177,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,No Disorder +331,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +337,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder 192,Male,43,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia 195,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia -59,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -179,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, -8,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -303,Female,51,Nurse,7.1,7,55,6,Normal Weight,125/82,72,6000, -97,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -342,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000, -124,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -181,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, +59,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +179,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +8,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +303,Female,51,Nurse,7.1,7,55,6,Normal Weight,125/82,72,6000,No Disorder +97,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +342,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000,No Disorder +124,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +181,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder 358,Female,58,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,Sleep Apnea -184,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, +184,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder 270,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea -52,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000, -136,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, +52,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000,No Disorder +136,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder 275,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 234,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia 289,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 236,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia 253,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia -210,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -75,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -70,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500, -153,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -87,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000, +210,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +75,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +70,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500,No Disorder +153,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +87,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000,No Disorder 351,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea -45,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -328,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, +45,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +328,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder 354,Female,58,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,Sleep Apnea -46,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -12,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -156,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, +46,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +12,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +156,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder 237,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia -62,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, +62,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder 341,Female,55,Nurse,8.1,9,75,4,Overweight,140/95,72,5000,Sleep Apnea 146,Female,38,Lawyer,7.4,7,60,5,Obese,135/88,84,3300,Sleep Apnea -116,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -112,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000, +116,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +112,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,No Disorder 274,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,Sleep Apnea -86,Female,35,Accountant,7.2,8,60,4,Normal,115/75,68,7000, +86,Female,35,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder diff --git a/decision_making/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B2/info.json b/decision_making/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B2/info.json index d16ee87c85acce6dc54edcce6a571725fddb5772..c68a30219902530a9154e48d4957e5bb70f65c0f 100644 --- a/decision_making/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B2/info.json +++ b/decision_making/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B2/info.json @@ -1,121 +1,122 @@ { - "name": "Sleep Health and Lifestyle Dataset", - "source": "https://www.kaggle.com/datasets/uom190346a/sleep-health-and-lifestyle-dataset/data", - "data_intro": "The Sleep Health and Lifestyle Dataset comprises 400 rows and 13 columns, covering a wide range of variables related to sleep and daily habits. It includes details such as gender, age, occupation, sleep duration, quality of sleep, physical activity level, stress levels, BMI category, blood pressure, heart rate, daily steps, and the presence or absence of sleep disorders.", - "is_splited": false, - "overall_size": 374, - "train_size": 0, - "test_size": 0, - "c_classes": 5, - "n_classes": 8, - "cat_feature_intro": { - "Gender": "Gender: The gender of the person (Male/Female).
", - "Occupation": "Occupation: The occupation or profession of the person.
", - "BMI Category": "BMI Category: The BMI category of the person (e.g., Underweight, Normal, Overweight).
", - "Blood Pressure": "Blood Pressure (systolic/diastolic): The blood pressure measurement of the person, indicated as systolic pressure over diastolic pressure.
", - "Sleep Disorder": "- None: The individual does not exhibit any specific sleep disorder." - }, - "num_feature_intro": { - "Person ID": "Person ID: An identifier for each individual.
", - "Age": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", - "Sleep Duration": "Sleep Duration (hours): The number of hours the person sleeps per day.
", - "Quality of Sleep": "Quality of Sleep (scale: 1-10): A subjective rating of the quality of sleep, ranging from 1 to 10.
", - "Physical Activity Level": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", - "Stress Level": "Stress Level (scale: 1-10): A subjective rating of the stress level experienced by the person, ranging from 1 to 10.
", - "Heart Rate": "Heart Rate (bpm): The resting heart rate of the person in beats per minute.
", - "Daily Steps": "Daily Steps: The number of steps the person takes per day.
" - }, - "evaluation_metric": null, - "task_type": "classification", - "target": "Sleep Disorder", - "num_feature_value": { - "Age": [ - 27.0, - 59.0 - ], - "Daily Steps": [ - 3000.0, - 10000.0 - ], - "Heart Rate": [ - 65.0, - 86.0 - ], - "Person ID": [ - 1.0, - 374.0 - ], - "Physical Activity Level": [ - 30.0, - 90.0 - ], - "Quality of Sleep": [ - 4.0, - 9.0 - ], - "Sleep Duration": [ - 5.8, - 8.5 - ], - "Stress Level": [ - 3.0, - 8.0 - ] - }, - "cat_feature_value": { - "BMI Category": [ - "Normal", - "Normal Weight", - "Obese", - "Overweight" - ], - "Blood Pressure": [ - "115/75", - "115/78", - "117/76", - "118/75", - "118/76", - "119/77", - "120/80", - "121/79", - "122/80", - "125/80", - "125/82", - "126/83", - "128/84", - "128/85", - "129/84", - "130/85", - "130/86", - "131/86", - "132/87", - "135/88", - "135/90", - "139/91", - "140/90", - "140/95", - "142/92" - ], - "Gender": [ - "Female", - "Male" - ], - "Occupation": [ - "Accountant", - "Doctor", - "Engineer", - "Lawyer", - "Manager", - "Nurse", - "Sales Representative", - "Salesperson", - "Scientist", - "Software Engineer", - "Teacher" - ], - "Sleep Disorder": [ - "Insomnia", - "Sleep Apnea" - ] - } -} \ No newline at end of file + "name": "Sleep Health and Lifestyle Dataset", + "source": "https://www.kaggle.com/datasets/uom190346a/sleep-health-and-lifestyle-dataset/data", + "data_intro": "The Sleep Health and Lifestyle Dataset comprises 400 rows and 13 columns, covering a wide range of variables related to sleep and daily habits. It includes details such as gender, age, occupation, sleep duration, quality of sleep, physical activity level, stress levels, BMI category, blood pressure, heart rate, daily steps, and the presence or absence of sleep disorders.", + "is_splited": false, + "overall_size": 374, + "train_size": 0, + "test_size": 0, + "c_classes": 5, + "n_classes": 8, + "cat_feature_intro": { + "Gender": "Gender: The gender of the person (Male/Female).
", + "Occupation": "Occupation: The occupation or profession of the person.
", + "BMI Category": "BMI Category: The BMI category of the person (e.g., Underweight, Normal, Overweight).
", + "Blood Pressure": "Blood Pressure (systolic/diastolic): The blood pressure measurement of the person, indicated as systolic pressure over diastolic pressure.
", + "Sleep Disorder": "Sleep disorder category: No Disorder, Insomnia, or Sleep Apnea." + }, + "num_feature_intro": { + "Person ID": "Person ID: An identifier for each individual.
", + "Age": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", + "Sleep Duration": "Sleep Duration (hours): The number of hours the person sleeps per day.
", + "Quality of Sleep": "Quality of Sleep (scale: 1-10): A subjective rating of the quality of sleep, ranging from 1 to 10.
", + "Physical Activity Level": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", + "Stress Level": "Stress Level (scale: 1-10): A subjective rating of the stress level experienced by the person, ranging from 1 to 10.
", + "Heart Rate": "Heart Rate (bpm): The resting heart rate of the person in beats per minute.
", + "Daily Steps": "Daily Steps: The number of steps the person takes per day.
" + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "Sleep Disorder", + "num_feature_value": { + "Age": [ + 27.0, + 59.0 + ], + "Daily Steps": [ + 3000.0, + 10000.0 + ], + "Heart Rate": [ + 65.0, + 86.0 + ], + "Person ID": [ + 1.0, + 374.0 + ], + "Physical Activity Level": [ + 30.0, + 90.0 + ], + "Quality of Sleep": [ + 4.0, + 9.0 + ], + "Sleep Duration": [ + 5.8, + 8.5 + ], + "Stress Level": [ + 3.0, + 8.0 + ] + }, + "cat_feature_value": { + "BMI Category": [ + "Normal", + "Normal Weight", + "Obese", + "Overweight" + ], + "Blood Pressure": [ + "115/75", + "115/78", + "117/76", + "118/75", + "118/76", + "119/77", + "120/80", + "121/79", + "122/80", + "125/80", + "125/82", + "126/83", + "128/84", + "128/85", + "129/84", + "130/85", + "130/86", + "131/86", + "132/87", + "135/88", + "135/90", + "139/91", + "140/90", + "140/95", + "142/92" + ], + "Gender": [ + "Female", + "Male" + ], + "Occupation": [ + "Accountant", + "Doctor", + "Engineer", + "Lawyer", + "Manager", + "Nurse", + "Sales Representative", + "Salesperson", + "Scientist", + "Software Engineer", + "Teacher" + ], + "Sleep Disorder": [ + "Insomnia", + "No Disorder", + "Sleep Apnea" + ] + } +} diff --git a/decision_making/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B2/info_mod.json b/decision_making/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B2/info_mod.json index 61f0aae2fe42de18994a371f556dc4b0b413e67f..8f8a1336c525f3a577855f801fe66c67768f9bc7 100644 --- a/decision_making/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B2/info_mod.json +++ b/decision_making/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B2/info_mod.json @@ -12,7 +12,8 @@ "Gender": "Gender: The gender of the person (Male/Female).
", "Occupation": "Occupation: The occupation or profession of the person.
", "BMI Category": "BMI Category: The BMI category of the person (e.g., Underweight, Normal, Overweight).
", - "Blood Pressure": "Blood Pressure (systolic/diastolic): The blood pressure measurement of the person, indicated as systolic pressure over diastolic pressure.
" + "Blood Pressure": "Blood Pressure (systolic/diastolic): The blood pressure measurement of the person, indicated as systolic pressure over diastolic pressure.
", + "Sleep Disorder": "Sleep disorder category: No Disorder, Insomnia, or Sleep Apnea." }, "num_feature_intro": { "Person ID": "Person ID: An identifier for each individual.
", @@ -111,6 +112,11 @@ "140/90", "140/95", "142/92" + ], + "Sleep Disorder": [ + "Insomnia", + "No Disorder", + "Sleep Apnea" ] }, "columns": [ @@ -159,4 +165,4 @@ "open_text_feature_intro": {}, "open_text_features": [], "missing_from_original_info": [] -} \ No newline at end of file +} diff --git a/decision_making/medical/classification/Stroke_Prediction_Dataset_B2/Stroke_Prediction_Dataset_B2_004.json b/decision_making/medical/classification/Stroke_Prediction_Dataset_B2/Stroke_Prediction_Dataset_B2_004.json index 8a8064b8eacdb017ef196b63a6e75381fde63d53..89f4ab9288ec0e168bc37526fbb6d2a77ee91c26 100644 --- a/decision_making/medical/classification/Stroke_Prediction_Dataset_B2/Stroke_Prediction_Dataset_B2_004.json +++ b/decision_making/medical/classification/Stroke_Prediction_Dataset_B2/Stroke_Prediction_Dataset_B2_004.json @@ -5,7 +5,7 @@ "perspective": "data_holder", "dataset_name": "Stroke_Prediction_Dataset", "table_path": "kaggle/Stroke_Prediction_Dataset", - "query": "The first candidate, ID 64553, is a female patient. She has no history of hypertension, and she is married. Her work is in the private sector, and she lives in a rural area. Her latest average glucose level reading is 68.76, and her Body Mass Index is 35.6. She is listed as a former smoker. The second record, ID 1665, is also for a female patient, but she does have hypertension. She does not have heart disease, and she is married. She is self-employed and resides rurally. Her average glucose level is notably higher at 174.12, with a BMI of 24.0. Given our past logs, which of these two new cases seems more aligned with the historical profile for a higher risk of stroke?", + "query": "The first candidate, ID 64553, is a female patient. She has no history of hypertension, and she is married. Her work is in the private sector, and she lives in a rural area. Her latest average glucose level reading is 68.76, and her Body Mass Index is 35.6. She is listed as a former smoker. The second record, ID 1665, is also for a female patient, but she does have hypertension. She does not have heart disease, and she is married. She is self-employed and resides rurally. Her average glucose level is notably higher at 174.12, with a BMI of 24.0. Given our past logs, which of these two new cases seems more aligned with the historical profile for a lower risk of stroke?", "meta_info": { "domain": "medical" }, @@ -56,4 +56,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/decision_making/medical/classification/Stroke_Prediction_Dataset_B2/Stroke_Prediction_Dataset_B2_005.json b/decision_making/medical/classification/Stroke_Prediction_Dataset_B2/Stroke_Prediction_Dataset_B2_005.json index e03adbd3b0ef59c25ab5d32d283539c6c7a6d31d..e4eae274525ba801a0b6a339051416e27a0e403e 100644 --- a/decision_making/medical/classification/Stroke_Prediction_Dataset_B2/Stroke_Prediction_Dataset_B2_005.json +++ b/decision_making/medical/classification/Stroke_Prediction_Dataset_B2/Stroke_Prediction_Dataset_B2_005.json @@ -5,7 +5,7 @@ "perspective": "data_holder", "dataset_name": "Stroke_Prediction_Dataset", "table_path": "kaggle/Stroke_Prediction_Dataset", - "query": "Reviewing these latest patient records has me a bit uncertain, so I've got my historical logs open to cross-reference. One record is for individual 8213: aged 78, with existing heart disease. They are married, work privately, live in an urban setting, and have an average glucose reading of 219.84. The other record is for patient 41536, a female who is 33 years old with no heart disease. Her work is a government job, she lives rurally, and her glucose level is 57.92. Her smoking status isn't known. Given our past data focused on predicting stroke, does one of these new cases raise a more immediate flag than the other?", + "query": "Reviewing these latest patient records has me a bit uncertain, so I've got my historical logs open to cross-reference. One record is for individual 8213: aged 78, with existing heart disease. They are married, work privately, live in an urban setting, and have an average glucose reading of 219.84. The other record is for patient 41536, a female who is 33 years old with no heart disease. Her work is a government job, she lives rurally, and her glucose level is 57.92. Her smoking status isn't known. Given our past data focused on predicting stroke, which of these new cases looks less concerning for stroke based on those past patterns?", "meta_info": { "domain": "medical" }, @@ -56,4 +56,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/decision_making/medical/classification/Stroke_Prediction_Dataset_B2/Stroke_Prediction_Dataset_B2_006.json b/decision_making/medical/classification/Stroke_Prediction_Dataset_B2/Stroke_Prediction_Dataset_B2_006.json index 319318de0dbab5be5588e755cfe079e5ba85a699..6907d029dba7f1411735e19e3ecbf8ff1791d175 100644 --- a/decision_making/medical/classification/Stroke_Prediction_Dataset_B2/Stroke_Prediction_Dataset_B2_006.json +++ b/decision_making/medical/classification/Stroke_Prediction_Dataset_B2/Stroke_Prediction_Dataset_B2_006.json @@ -5,7 +5,7 @@ "perspective": "data_holder", "dataset_name": "Stroke_Prediction_Dataset", "table_path": "kaggle/Stroke_Prediction_Dataset", - "query": "I'm reviewing two new patient intake forms for our stroke risk assessment program, and I'm comparing them against our historical patient archives. The first record, ID 7730, is for a 31-year-old male. He is not married, works in the private sector, and lives in a rural area. His medical history shows no hypertension and no heart disease. His average glucose level is 94.96, but his Body Mass Index is quite high at 54.7. He is a current smoker. The second record, ID 68627, is for an 80-year-old male. He is married, also works privately, but lives in an urban setting. Critically, his history includes both hypertension and heart disease. His average glucose level is elevated at 175.29, and his BMI is 31.5. His smoking status is listed as formerly smoked. Given our past logs show stroke events are our target, which of these two new patients would you flag as the higher priority for immediate follow-up?", + "query": "I'm reviewing two new patient intake forms for our stroke risk assessment program, and I'm comparing them against our historical patient archives. The first record, ID 7730, is for a 31-year-old male. He is not married, works in the private sector, and lives in a rural area. His medical history shows no hypertension and no heart disease. His average glucose level is 94.96, but his Body Mass Index is quite high at 54.7. He is a current smoker. The second record, ID 68627, is for an 80-year-old male. He is married, also works privately, but lives in an urban setting. Critically, his history includes both hypertension and heart disease. His average glucose level is elevated at 175.29, and his BMI is 31.5. His smoking status is listed as formerly smoked. Given our past logs show stroke events are our target, which of these two profiles looks less concerning for stroke based on our past records?", "meta_info": { "domain": "medical" }, @@ -56,4 +56,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_001/history.csv b/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_001/history.csv index d756e5f45145bff749f73a141b398cf66f06e08d..3b52f84947f363adf2ae476a2556788d9a25698a 100644 --- a/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_001/history.csv +++ b/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_001/history.csv @@ -3799,4 +3799,3 @@ A_id,Size,Weight,Sweetness,Crunchiness,Juiciness,Ripeness,Acidity,Quality 3997.0,-2.634515299,-2.13824672,-2.440461285,0.657222891,2.19970859,4.763859177,-1.334611391,bad 3998.0,-4.008003744,-1.779337107,2.366396966,-0.200329367,2.161435121,0.214488384,-2.229719806,good 3999.0,0.27853965,-1.715505028,0.121217251,-1.154074758,1.2666774,-0.77657147,1.599796456,good -,,,,,,,Created_by_Nidula_Elgiriyewithana, diff --git a/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_001/info.json b/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_001/info.json index 061594cd032cd189afdf78ebe43a94ad53166b97..8de202273508b7431d0bbe83b679ee5373c9efe5 100644 --- a/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_001/info.json +++ b/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_001/info.json @@ -1,66 +1,69 @@ -{ - "name": "Apple Quality", - "source": "https://www.kaggle.com/datasets/nelgiriyewithana/apple-quality/data", - "data_intro": "This dataset contains information about various attributes of a set of fruits, providing insights into their characteristics. The dataset includes details such as fruit ID, size, weight, sweetness, crunchiness, juiciness, ripeness, acidity, and quality.", - "is_splited": false, - "overall_size": 4000, - "train_size": 0, - "test_size": 0, - "c_classes": 2, - "n_classes": 7, - "task_type": "classification", - "target": { - "Quality": "Build a model to predict the quality rating of fruits using various attributes." - }, - "cat_feature_intro": { - "Acidity": "- Acidity: Acidity level of the fruit", - "Quality": "- Quality: Overall quality of the fruit." - }, - "num_feature_intro": { - "A_id": "- A_id: Unique identifier for each fruit", - "Size": "- Size: Size of the fruit", - "Weight": "- Weight: Weight of the fruit", - "Sweetness": "- Sweetness: Degree of sweetness of the fruit", - "Crunchiness": "- Crunchiness: Texture indicating the crunchiness of the fruit", - "Juiciness": "- Juiciness: Level of juiciness of the fruit", - "Ripeness": "- Ripeness: Stage of ripeness of the fruit" - }, - "evaluation_metric": null, - "num_feature_value": { - "A_id": [ - 0.0, - 3999.0 - ], - "Crunchiness": [ - -6.055057805, - 7.619851801 - ], - "Juiciness": [ - -5.961897048, - 7.364402864 - ], - "Ripeness": [ - -5.864598918, - 7.237836684 - ], - "Size": [ - -7.151703059, - 6.406366899 - ], - "Sweetness": [ - -6.894485494, - 6.374915513 - ], - "Weight": [ - -7.149847675, - 5.79071359 - ] - }, - "cat_feature_value": { - "Acidity": "string", - "Quality": [ - "bad", - "good" - ] - } -} \ No newline at end of file +{ + "name": "Apple Quality", + "source": "https://www.kaggle.com/datasets/nelgiriyewithana/apple-quality/data", + "data_intro": "This dataset contains information about various attributes of a set of fruits, providing insights into their characteristics. The dataset includes details such as fruit ID, size, weight, sweetness, crunchiness, juiciness, ripeness, acidity, and quality.", + "is_splited": false, + "overall_size": 4000, + "train_size": 0, + "test_size": 0, + "c_classes": 1, + "n_classes": 8, + "task_type": "classification", + "target": { + "Quality": "Build a model to predict the quality rating of fruits using various attributes." + }, + "cat_feature_intro": { + "Quality": "- Quality: Overall quality of the fruit." + }, + "num_feature_intro": { + "A_id": "- A_id: Unique identifier for each fruit", + "Size": "- Size: Size of the fruit", + "Weight": "- Weight: Weight of the fruit", + "Sweetness": "- Sweetness: Degree of sweetness of the fruit", + "Crunchiness": "- Crunchiness: Texture indicating the crunchiness of the fruit", + "Juiciness": "- Juiciness: Level of juiciness of the fruit", + "Ripeness": "- Ripeness: Stage of ripeness of the fruit", + "Acidity": "- Acidity: Acidity level of the fruit" + }, + "evaluation_metric": null, + "num_feature_value": { + "A_id": [ + 0.0, + 3999.0 + ], + "Crunchiness": [ + -6.055057805, + 7.619851801 + ], + "Juiciness": [ + -5.961897048, + 7.364402864 + ], + "Ripeness": [ + -5.864598918, + 7.237836684 + ], + "Size": [ + -7.151703059, + 6.406366899 + ], + "Sweetness": [ + -6.894485494, + 6.374915513 + ], + "Weight": [ + -7.149847675, + 5.79071359 + ], + "Acidity": [ + -7.010538475, + 7.404736238 + ] + }, + "cat_feature_value": { + "Quality": [ + "bad", + "good" + ] + } +} diff --git a/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_002/history.csv b/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_002/history.csv index c02c2359887ecaa7ae0d239eab900543d498aea0..437d3f3a6f354dd7ef051a4f3390978ceac8b602 100644 --- a/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_002/history.csv +++ b/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_002/history.csv @@ -3799,4 +3799,3 @@ A_id,Size,Weight,Sweetness,Crunchiness,Juiciness,Ripeness,Acidity,Quality 3996.0,-0.293118007,1.949252549,-0.20401993,-0.640195579,0.024522626,-1.087899732,1.854235285,good 3997.0,-2.634515299,-2.13824672,-2.440461285,0.657222891,2.19970859,4.763859177,-1.334611391,bad 3999.0,0.27853965,-1.715505028,0.121217251,-1.154074758,1.2666774,-0.77657147,1.599796456,good -,,,,,,,Created_by_Nidula_Elgiriyewithana, diff --git a/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_002/info.json b/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_002/info.json index 061594cd032cd189afdf78ebe43a94ad53166b97..8de202273508b7431d0bbe83b679ee5373c9efe5 100644 --- a/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_002/info.json +++ b/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_002/info.json @@ -1,66 +1,69 @@ -{ - "name": "Apple Quality", - "source": "https://www.kaggle.com/datasets/nelgiriyewithana/apple-quality/data", - "data_intro": "This dataset contains information about various attributes of a set of fruits, providing insights into their characteristics. The dataset includes details such as fruit ID, size, weight, sweetness, crunchiness, juiciness, ripeness, acidity, and quality.", - "is_splited": false, - "overall_size": 4000, - "train_size": 0, - "test_size": 0, - "c_classes": 2, - "n_classes": 7, - "task_type": "classification", - "target": { - "Quality": "Build a model to predict the quality rating of fruits using various attributes." - }, - "cat_feature_intro": { - "Acidity": "- Acidity: Acidity level of the fruit", - "Quality": "- Quality: Overall quality of the fruit." - }, - "num_feature_intro": { - "A_id": "- A_id: Unique identifier for each fruit", - "Size": "- Size: Size of the fruit", - "Weight": "- Weight: Weight of the fruit", - "Sweetness": "- Sweetness: Degree of sweetness of the fruit", - "Crunchiness": "- Crunchiness: Texture indicating the crunchiness of the fruit", - "Juiciness": "- Juiciness: Level of juiciness of the fruit", - "Ripeness": "- Ripeness: Stage of ripeness of the fruit" - }, - "evaluation_metric": null, - "num_feature_value": { - "A_id": [ - 0.0, - 3999.0 - ], - "Crunchiness": [ - -6.055057805, - 7.619851801 - ], - "Juiciness": [ - -5.961897048, - 7.364402864 - ], - "Ripeness": [ - -5.864598918, - 7.237836684 - ], - "Size": [ - -7.151703059, - 6.406366899 - ], - "Sweetness": [ - -6.894485494, - 6.374915513 - ], - "Weight": [ - -7.149847675, - 5.79071359 - ] - }, - "cat_feature_value": { - "Acidity": "string", - "Quality": [ - "bad", - "good" - ] - } -} \ No newline at end of file +{ + "name": "Apple Quality", + "source": "https://www.kaggle.com/datasets/nelgiriyewithana/apple-quality/data", + "data_intro": "This dataset contains information about various attributes of a set of fruits, providing insights into their characteristics. The dataset includes details such as fruit ID, size, weight, sweetness, crunchiness, juiciness, ripeness, acidity, and quality.", + "is_splited": false, + "overall_size": 4000, + "train_size": 0, + "test_size": 0, + "c_classes": 1, + "n_classes": 8, + "task_type": "classification", + "target": { + "Quality": "Build a model to predict the quality rating of fruits using various attributes." + }, + "cat_feature_intro": { + "Quality": "- Quality: Overall quality of the fruit." + }, + "num_feature_intro": { + "A_id": "- A_id: Unique identifier for each fruit", + "Size": "- Size: Size of the fruit", + "Weight": "- Weight: Weight of the fruit", + "Sweetness": "- Sweetness: Degree of sweetness of the fruit", + "Crunchiness": "- Crunchiness: Texture indicating the crunchiness of the fruit", + "Juiciness": "- Juiciness: Level of juiciness of the fruit", + "Ripeness": "- Ripeness: Stage of ripeness of the fruit", + "Acidity": "- Acidity: Acidity level of the fruit" + }, + "evaluation_metric": null, + "num_feature_value": { + "A_id": [ + 0.0, + 3999.0 + ], + "Crunchiness": [ + -6.055057805, + 7.619851801 + ], + "Juiciness": [ + -5.961897048, + 7.364402864 + ], + "Ripeness": [ + -5.864598918, + 7.237836684 + ], + "Size": [ + -7.151703059, + 6.406366899 + ], + "Sweetness": [ + -6.894485494, + 6.374915513 + ], + "Weight": [ + -7.149847675, + 5.79071359 + ], + "Acidity": [ + -7.010538475, + 7.404736238 + ] + }, + "cat_feature_value": { + "Quality": [ + "bad", + "good" + ] + } +} diff --git a/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_003/history.csv b/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_003/history.csv index 3c4584c7a6732e102d907dc8d5697f04e8c9e1ff..63ecad9fb178a0eaf9027514c12a71ad851cec17 100644 --- a/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_003/history.csv +++ b/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_003/history.csv @@ -3799,4 +3799,3 @@ A_id,Size,Weight,Sweetness,Crunchiness,Juiciness,Ripeness,Acidity,Quality 3997.0,-2.634515299,-2.13824672,-2.440461285,0.657222891,2.19970859,4.763859177,-1.334611391,bad 3998.0,-4.008003744,-1.779337107,2.366396966,-0.200329367,2.161435121,0.214488384,-2.229719806,good 3999.0,0.27853965,-1.715505028,0.121217251,-1.154074758,1.2666774,-0.77657147,1.599796456,good -,,,,,,,Created_by_Nidula_Elgiriyewithana, diff --git a/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_003/info.json b/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_003/info.json index 061594cd032cd189afdf78ebe43a94ad53166b97..8de202273508b7431d0bbe83b679ee5373c9efe5 100644 --- a/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_003/info.json +++ b/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_003/info.json @@ -1,66 +1,69 @@ -{ - "name": "Apple Quality", - "source": "https://www.kaggle.com/datasets/nelgiriyewithana/apple-quality/data", - "data_intro": "This dataset contains information about various attributes of a set of fruits, providing insights into their characteristics. The dataset includes details such as fruit ID, size, weight, sweetness, crunchiness, juiciness, ripeness, acidity, and quality.", - "is_splited": false, - "overall_size": 4000, - "train_size": 0, - "test_size": 0, - "c_classes": 2, - "n_classes": 7, - "task_type": "classification", - "target": { - "Quality": "Build a model to predict the quality rating of fruits using various attributes." - }, - "cat_feature_intro": { - "Acidity": "- Acidity: Acidity level of the fruit", - "Quality": "- Quality: Overall quality of the fruit." - }, - "num_feature_intro": { - "A_id": "- A_id: Unique identifier for each fruit", - "Size": "- Size: Size of the fruit", - "Weight": "- Weight: Weight of the fruit", - "Sweetness": "- Sweetness: Degree of sweetness of the fruit", - "Crunchiness": "- Crunchiness: Texture indicating the crunchiness of the fruit", - "Juiciness": "- Juiciness: Level of juiciness of the fruit", - "Ripeness": "- Ripeness: Stage of ripeness of the fruit" - }, - "evaluation_metric": null, - "num_feature_value": { - "A_id": [ - 0.0, - 3999.0 - ], - "Crunchiness": [ - -6.055057805, - 7.619851801 - ], - "Juiciness": [ - -5.961897048, - 7.364402864 - ], - "Ripeness": [ - -5.864598918, - 7.237836684 - ], - "Size": [ - -7.151703059, - 6.406366899 - ], - "Sweetness": [ - -6.894485494, - 6.374915513 - ], - "Weight": [ - -7.149847675, - 5.79071359 - ] - }, - "cat_feature_value": { - "Acidity": "string", - "Quality": [ - "bad", - "good" - ] - } -} \ No newline at end of file +{ + "name": "Apple Quality", + "source": "https://www.kaggle.com/datasets/nelgiriyewithana/apple-quality/data", + "data_intro": "This dataset contains information about various attributes of a set of fruits, providing insights into their characteristics. The dataset includes details such as fruit ID, size, weight, sweetness, crunchiness, juiciness, ripeness, acidity, and quality.", + "is_splited": false, + "overall_size": 4000, + "train_size": 0, + "test_size": 0, + "c_classes": 1, + "n_classes": 8, + "task_type": "classification", + "target": { + "Quality": "Build a model to predict the quality rating of fruits using various attributes." + }, + "cat_feature_intro": { + "Quality": "- Quality: Overall quality of the fruit." + }, + "num_feature_intro": { + "A_id": "- A_id: Unique identifier for each fruit", + "Size": "- Size: Size of the fruit", + "Weight": "- Weight: Weight of the fruit", + "Sweetness": "- Sweetness: Degree of sweetness of the fruit", + "Crunchiness": "- Crunchiness: Texture indicating the crunchiness of the fruit", + "Juiciness": "- Juiciness: Level of juiciness of the fruit", + "Ripeness": "- Ripeness: Stage of ripeness of the fruit", + "Acidity": "- Acidity: Acidity level of the fruit" + }, + "evaluation_metric": null, + "num_feature_value": { + "A_id": [ + 0.0, + 3999.0 + ], + "Crunchiness": [ + -6.055057805, + 7.619851801 + ], + "Juiciness": [ + -5.961897048, + 7.364402864 + ], + "Ripeness": [ + -5.864598918, + 7.237836684 + ], + "Size": [ + -7.151703059, + 6.406366899 + ], + "Sweetness": [ + -6.894485494, + 6.374915513 + ], + "Weight": [ + -7.149847675, + 5.79071359 + ], + "Acidity": [ + -7.010538475, + 7.404736238 + ] + }, + "cat_feature_value": { + "Quality": [ + "bad", + "good" + ] + } +} diff --git a/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_004/history.csv b/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_004/history.csv index a4fbfd45a3974f405c4b7219cfeeace01265f32a..57f8b0f253b76e045bda0b31c9cba5a904d4f382 100644 --- a/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_004/history.csv +++ b/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_004/history.csv @@ -3799,4 +3799,3 @@ A_id,Size,Weight,Sweetness,Crunchiness,Juiciness,Ripeness,Acidity,Quality 3997.0,-2.634515299,-2.13824672,-2.440461285,0.657222891,2.19970859,4.763859177,-1.334611391,bad 3998.0,-4.008003744,-1.779337107,2.366396966,-0.200329367,2.161435121,0.214488384,-2.229719806,good 3999.0,0.27853965,-1.715505028,0.121217251,-1.154074758,1.2666774,-0.77657147,1.599796456,good -,,,,,,,Created_by_Nidula_Elgiriyewithana, diff --git a/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_004/info.json b/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_004/info.json index 061594cd032cd189afdf78ebe43a94ad53166b97..8de202273508b7431d0bbe83b679ee5373c9efe5 100644 --- a/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_004/info.json +++ b/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_004/info.json @@ -1,66 +1,69 @@ -{ - "name": "Apple Quality", - "source": "https://www.kaggle.com/datasets/nelgiriyewithana/apple-quality/data", - "data_intro": "This dataset contains information about various attributes of a set of fruits, providing insights into their characteristics. The dataset includes details such as fruit ID, size, weight, sweetness, crunchiness, juiciness, ripeness, acidity, and quality.", - "is_splited": false, - "overall_size": 4000, - "train_size": 0, - "test_size": 0, - "c_classes": 2, - "n_classes": 7, - "task_type": "classification", - "target": { - "Quality": "Build a model to predict the quality rating of fruits using various attributes." - }, - "cat_feature_intro": { - "Acidity": "- Acidity: Acidity level of the fruit", - "Quality": "- Quality: Overall quality of the fruit." - }, - "num_feature_intro": { - "A_id": "- A_id: Unique identifier for each fruit", - "Size": "- Size: Size of the fruit", - "Weight": "- Weight: Weight of the fruit", - "Sweetness": "- Sweetness: Degree of sweetness of the fruit", - "Crunchiness": "- Crunchiness: Texture indicating the crunchiness of the fruit", - "Juiciness": "- Juiciness: Level of juiciness of the fruit", - "Ripeness": "- Ripeness: Stage of ripeness of the fruit" - }, - "evaluation_metric": null, - "num_feature_value": { - "A_id": [ - 0.0, - 3999.0 - ], - "Crunchiness": [ - -6.055057805, - 7.619851801 - ], - "Juiciness": [ - -5.961897048, - 7.364402864 - ], - "Ripeness": [ - -5.864598918, - 7.237836684 - ], - "Size": [ - -7.151703059, - 6.406366899 - ], - "Sweetness": [ - -6.894485494, - 6.374915513 - ], - "Weight": [ - -7.149847675, - 5.79071359 - ] - }, - "cat_feature_value": { - "Acidity": "string", - "Quality": [ - "bad", - "good" - ] - } -} \ No newline at end of file +{ + "name": "Apple Quality", + "source": "https://www.kaggle.com/datasets/nelgiriyewithana/apple-quality/data", + "data_intro": "This dataset contains information about various attributes of a set of fruits, providing insights into their characteristics. The dataset includes details such as fruit ID, size, weight, sweetness, crunchiness, juiciness, ripeness, acidity, and quality.", + "is_splited": false, + "overall_size": 4000, + "train_size": 0, + "test_size": 0, + "c_classes": 1, + "n_classes": 8, + "task_type": "classification", + "target": { + "Quality": "Build a model to predict the quality rating of fruits using various attributes." + }, + "cat_feature_intro": { + "Quality": "- Quality: Overall quality of the fruit." + }, + "num_feature_intro": { + "A_id": "- A_id: Unique identifier for each fruit", + "Size": "- Size: Size of the fruit", + "Weight": "- Weight: Weight of the fruit", + "Sweetness": "- Sweetness: Degree of sweetness of the fruit", + "Crunchiness": "- Crunchiness: Texture indicating the crunchiness of the fruit", + "Juiciness": "- Juiciness: Level of juiciness of the fruit", + "Ripeness": "- Ripeness: Stage of ripeness of the fruit", + "Acidity": "- Acidity: Acidity level of the fruit" + }, + "evaluation_metric": null, + "num_feature_value": { + "A_id": [ + 0.0, + 3999.0 + ], + "Crunchiness": [ + -6.055057805, + 7.619851801 + ], + "Juiciness": [ + -5.961897048, + 7.364402864 + ], + "Ripeness": [ + -5.864598918, + 7.237836684 + ], + "Size": [ + -7.151703059, + 6.406366899 + ], + "Sweetness": [ + -6.894485494, + 6.374915513 + ], + "Weight": [ + -7.149847675, + 5.79071359 + ], + "Acidity": [ + -7.010538475, + 7.404736238 + ] + }, + "cat_feature_value": { + "Quality": [ + "bad", + "good" + ] + } +} diff --git a/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_005/history.csv b/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_005/history.csv index dedf1f8d8874949cb718b562d97c4699ed09db7c..314b77ef8c3f9729ad435624736ed01b8d7f4d74 100644 --- a/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_005/history.csv +++ b/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_005/history.csv @@ -3799,4 +3799,3 @@ A_id,Size,Weight,Sweetness,Crunchiness,Juiciness,Ripeness,Acidity,Quality 3997.0,-2.634515299,-2.13824672,-2.440461285,0.657222891,2.19970859,4.763859177,-1.334611391,bad 3998.0,-4.008003744,-1.779337107,2.366396966,-0.200329367,2.161435121,0.214488384,-2.229719806,good 3999.0,0.27853965,-1.715505028,0.121217251,-1.154074758,1.2666774,-0.77657147,1.599796456,good -,,,,,,,Created_by_Nidula_Elgiriyewithana, diff --git a/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_005/info.json b/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_005/info.json index 061594cd032cd189afdf78ebe43a94ad53166b97..8de202273508b7431d0bbe83b679ee5373c9efe5 100644 --- a/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_005/info.json +++ b/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_005/info.json @@ -1,66 +1,69 @@ -{ - "name": "Apple Quality", - "source": "https://www.kaggle.com/datasets/nelgiriyewithana/apple-quality/data", - "data_intro": "This dataset contains information about various attributes of a set of fruits, providing insights into their characteristics. The dataset includes details such as fruit ID, size, weight, sweetness, crunchiness, juiciness, ripeness, acidity, and quality.", - "is_splited": false, - "overall_size": 4000, - "train_size": 0, - "test_size": 0, - "c_classes": 2, - "n_classes": 7, - "task_type": "classification", - "target": { - "Quality": "Build a model to predict the quality rating of fruits using various attributes." - }, - "cat_feature_intro": { - "Acidity": "- Acidity: Acidity level of the fruit", - "Quality": "- Quality: Overall quality of the fruit." - }, - "num_feature_intro": { - "A_id": "- A_id: Unique identifier for each fruit", - "Size": "- Size: Size of the fruit", - "Weight": "- Weight: Weight of the fruit", - "Sweetness": "- Sweetness: Degree of sweetness of the fruit", - "Crunchiness": "- Crunchiness: Texture indicating the crunchiness of the fruit", - "Juiciness": "- Juiciness: Level of juiciness of the fruit", - "Ripeness": "- Ripeness: Stage of ripeness of the fruit" - }, - "evaluation_metric": null, - "num_feature_value": { - "A_id": [ - 0.0, - 3999.0 - ], - "Crunchiness": [ - -6.055057805, - 7.619851801 - ], - "Juiciness": [ - -5.961897048, - 7.364402864 - ], - "Ripeness": [ - -5.864598918, - 7.237836684 - ], - "Size": [ - -7.151703059, - 6.406366899 - ], - "Sweetness": [ - -6.894485494, - 6.374915513 - ], - "Weight": [ - -7.149847675, - 5.79071359 - ] - }, - "cat_feature_value": { - "Acidity": "string", - "Quality": [ - "bad", - "good" - ] - } -} \ No newline at end of file +{ + "name": "Apple Quality", + "source": "https://www.kaggle.com/datasets/nelgiriyewithana/apple-quality/data", + "data_intro": "This dataset contains information about various attributes of a set of fruits, providing insights into their characteristics. The dataset includes details such as fruit ID, size, weight, sweetness, crunchiness, juiciness, ripeness, acidity, and quality.", + "is_splited": false, + "overall_size": 4000, + "train_size": 0, + "test_size": 0, + "c_classes": 1, + "n_classes": 8, + "task_type": "classification", + "target": { + "Quality": "Build a model to predict the quality rating of fruits using various attributes." + }, + "cat_feature_intro": { + "Quality": "- Quality: Overall quality of the fruit." + }, + "num_feature_intro": { + "A_id": "- A_id: Unique identifier for each fruit", + "Size": "- Size: Size of the fruit", + "Weight": "- Weight: Weight of the fruit", + "Sweetness": "- Sweetness: Degree of sweetness of the fruit", + "Crunchiness": "- Crunchiness: Texture indicating the crunchiness of the fruit", + "Juiciness": "- Juiciness: Level of juiciness of the fruit", + "Ripeness": "- Ripeness: Stage of ripeness of the fruit", + "Acidity": "- Acidity: Acidity level of the fruit" + }, + "evaluation_metric": null, + "num_feature_value": { + "A_id": [ + 0.0, + 3999.0 + ], + "Crunchiness": [ + -6.055057805, + 7.619851801 + ], + "Juiciness": [ + -5.961897048, + 7.364402864 + ], + "Ripeness": [ + -5.864598918, + 7.237836684 + ], + "Size": [ + -7.151703059, + 6.406366899 + ], + "Sweetness": [ + -6.894485494, + 6.374915513 + ], + "Weight": [ + -7.149847675, + 5.79071359 + ], + "Acidity": [ + -7.010538475, + 7.404736238 + ] + }, + "cat_feature_value": { + "Quality": [ + "bad", + "good" + ] + } +} diff --git a/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_006/history.csv b/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_006/history.csv index bf141c6ff92fb591877d37727a7f4d8f5cf26e53..9d3a823b339045f0e8b77d291b2f8e0b0fc23976 100644 --- a/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_006/history.csv +++ b/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_006/history.csv @@ -3799,4 +3799,3 @@ A_id,Size,Weight,Sweetness,Crunchiness,Juiciness,Ripeness,Acidity,Quality 3997.0,-2.634515299,-2.13824672,-2.440461285,0.657222891,2.19970859,4.763859177,-1.334611391,bad 3998.0,-4.008003744,-1.779337107,2.366396966,-0.200329367,2.161435121,0.214488384,-2.229719806,good 3999.0,0.27853965,-1.715505028,0.121217251,-1.154074758,1.2666774,-0.77657147,1.599796456,good -,,,,,,,Created_by_Nidula_Elgiriyewithana, diff --git a/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_006/info.json b/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_006/info.json index 061594cd032cd189afdf78ebe43a94ad53166b97..8de202273508b7431d0bbe83b679ee5373c9efe5 100644 --- a/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_006/info.json +++ b/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_006/info.json @@ -1,66 +1,69 @@ -{ - "name": "Apple Quality", - "source": "https://www.kaggle.com/datasets/nelgiriyewithana/apple-quality/data", - "data_intro": "This dataset contains information about various attributes of a set of fruits, providing insights into their characteristics. The dataset includes details such as fruit ID, size, weight, sweetness, crunchiness, juiciness, ripeness, acidity, and quality.", - "is_splited": false, - "overall_size": 4000, - "train_size": 0, - "test_size": 0, - "c_classes": 2, - "n_classes": 7, - "task_type": "classification", - "target": { - "Quality": "Build a model to predict the quality rating of fruits using various attributes." - }, - "cat_feature_intro": { - "Acidity": "- Acidity: Acidity level of the fruit", - "Quality": "- Quality: Overall quality of the fruit." - }, - "num_feature_intro": { - "A_id": "- A_id: Unique identifier for each fruit", - "Size": "- Size: Size of the fruit", - "Weight": "- Weight: Weight of the fruit", - "Sweetness": "- Sweetness: Degree of sweetness of the fruit", - "Crunchiness": "- Crunchiness: Texture indicating the crunchiness of the fruit", - "Juiciness": "- Juiciness: Level of juiciness of the fruit", - "Ripeness": "- Ripeness: Stage of ripeness of the fruit" - }, - "evaluation_metric": null, - "num_feature_value": { - "A_id": [ - 0.0, - 3999.0 - ], - "Crunchiness": [ - -6.055057805, - 7.619851801 - ], - "Juiciness": [ - -5.961897048, - 7.364402864 - ], - "Ripeness": [ - -5.864598918, - 7.237836684 - ], - "Size": [ - -7.151703059, - 6.406366899 - ], - "Sweetness": [ - -6.894485494, - 6.374915513 - ], - "Weight": [ - -7.149847675, - 5.79071359 - ] - }, - "cat_feature_value": { - "Acidity": "string", - "Quality": [ - "bad", - "good" - ] - } -} \ No newline at end of file +{ + "name": "Apple Quality", + "source": "https://www.kaggle.com/datasets/nelgiriyewithana/apple-quality/data", + "data_intro": "This dataset contains information about various attributes of a set of fruits, providing insights into their characteristics. The dataset includes details such as fruit ID, size, weight, sweetness, crunchiness, juiciness, ripeness, acidity, and quality.", + "is_splited": false, + "overall_size": 4000, + "train_size": 0, + "test_size": 0, + "c_classes": 1, + "n_classes": 8, + "task_type": "classification", + "target": { + "Quality": "Build a model to predict the quality rating of fruits using various attributes." + }, + "cat_feature_intro": { + "Quality": "- Quality: Overall quality of the fruit." + }, + "num_feature_intro": { + "A_id": "- A_id: Unique identifier for each fruit", + "Size": "- Size: Size of the fruit", + "Weight": "- Weight: Weight of the fruit", + "Sweetness": "- Sweetness: Degree of sweetness of the fruit", + "Crunchiness": "- Crunchiness: Texture indicating the crunchiness of the fruit", + "Juiciness": "- Juiciness: Level of juiciness of the fruit", + "Ripeness": "- Ripeness: Stage of ripeness of the fruit", + "Acidity": "- Acidity: Acidity level of the fruit" + }, + "evaluation_metric": null, + "num_feature_value": { + "A_id": [ + 0.0, + 3999.0 + ], + "Crunchiness": [ + -6.055057805, + 7.619851801 + ], + "Juiciness": [ + -5.961897048, + 7.364402864 + ], + "Ripeness": [ + -5.864598918, + 7.237836684 + ], + "Size": [ + -7.151703059, + 6.406366899 + ], + "Sweetness": [ + -6.894485494, + 6.374915513 + ], + "Weight": [ + -7.149847675, + 5.79071359 + ], + "Acidity": [ + -7.010538475, + 7.404736238 + ] + }, + "cat_feature_value": { + "Quality": [ + "bad", + "good" + ] + } +} diff --git a/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_007/history.csv b/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_007/history.csv index 908dca46d54f2aebb1558dc8acf3f377014a5c7c..e63ae6b2155c19ed91c9dd1347f73c56e493673f 100644 --- a/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_007/history.csv +++ b/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_007/history.csv @@ -3799,4 +3799,3 @@ A_id,Size,Weight,Sweetness,Crunchiness,Juiciness,Ripeness,Acidity,Quality 3997.0,-2.634515299,-2.13824672,-2.440461285,0.657222891,2.19970859,4.763859177,-1.334611391,bad 3998.0,-4.008003744,-1.779337107,2.366396966,-0.200329367,2.161435121,0.214488384,-2.229719806,good 3999.0,0.27853965,-1.715505028,0.121217251,-1.154074758,1.2666774,-0.77657147,1.599796456,good -,,,,,,,Created_by_Nidula_Elgiriyewithana, diff --git a/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_007/info.json b/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_007/info.json index 061594cd032cd189afdf78ebe43a94ad53166b97..8de202273508b7431d0bbe83b679ee5373c9efe5 100644 --- a/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_007/info.json +++ b/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_007/info.json @@ -1,66 +1,69 @@ -{ - "name": "Apple Quality", - "source": "https://www.kaggle.com/datasets/nelgiriyewithana/apple-quality/data", - "data_intro": "This dataset contains information about various attributes of a set of fruits, providing insights into their characteristics. The dataset includes details such as fruit ID, size, weight, sweetness, crunchiness, juiciness, ripeness, acidity, and quality.", - "is_splited": false, - "overall_size": 4000, - "train_size": 0, - "test_size": 0, - "c_classes": 2, - "n_classes": 7, - "task_type": "classification", - "target": { - "Quality": "Build a model to predict the quality rating of fruits using various attributes." - }, - "cat_feature_intro": { - "Acidity": "- Acidity: Acidity level of the fruit", - "Quality": "- Quality: Overall quality of the fruit." - }, - "num_feature_intro": { - "A_id": "- A_id: Unique identifier for each fruit", - "Size": "- Size: Size of the fruit", - "Weight": "- Weight: Weight of the fruit", - "Sweetness": "- Sweetness: Degree of sweetness of the fruit", - "Crunchiness": "- Crunchiness: Texture indicating the crunchiness of the fruit", - "Juiciness": "- Juiciness: Level of juiciness of the fruit", - "Ripeness": "- Ripeness: Stage of ripeness of the fruit" - }, - "evaluation_metric": null, - "num_feature_value": { - "A_id": [ - 0.0, - 3999.0 - ], - "Crunchiness": [ - -6.055057805, - 7.619851801 - ], - "Juiciness": [ - -5.961897048, - 7.364402864 - ], - "Ripeness": [ - -5.864598918, - 7.237836684 - ], - "Size": [ - -7.151703059, - 6.406366899 - ], - "Sweetness": [ - -6.894485494, - 6.374915513 - ], - "Weight": [ - -7.149847675, - 5.79071359 - ] - }, - "cat_feature_value": { - "Acidity": "string", - "Quality": [ - "bad", - "good" - ] - } -} \ No newline at end of file +{ + "name": "Apple Quality", + "source": "https://www.kaggle.com/datasets/nelgiriyewithana/apple-quality/data", + "data_intro": "This dataset contains information about various attributes of a set of fruits, providing insights into their characteristics. The dataset includes details such as fruit ID, size, weight, sweetness, crunchiness, juiciness, ripeness, acidity, and quality.", + "is_splited": false, + "overall_size": 4000, + "train_size": 0, + "test_size": 0, + "c_classes": 1, + "n_classes": 8, + "task_type": "classification", + "target": { + "Quality": "Build a model to predict the quality rating of fruits using various attributes." + }, + "cat_feature_intro": { + "Quality": "- Quality: Overall quality of the fruit." + }, + "num_feature_intro": { + "A_id": "- A_id: Unique identifier for each fruit", + "Size": "- Size: Size of the fruit", + "Weight": "- Weight: Weight of the fruit", + "Sweetness": "- Sweetness: Degree of sweetness of the fruit", + "Crunchiness": "- Crunchiness: Texture indicating the crunchiness of the fruit", + "Juiciness": "- Juiciness: Level of juiciness of the fruit", + "Ripeness": "- Ripeness: Stage of ripeness of the fruit", + "Acidity": "- Acidity: Acidity level of the fruit" + }, + "evaluation_metric": null, + "num_feature_value": { + "A_id": [ + 0.0, + 3999.0 + ], + "Crunchiness": [ + -6.055057805, + 7.619851801 + ], + "Juiciness": [ + -5.961897048, + 7.364402864 + ], + "Ripeness": [ + -5.864598918, + 7.237836684 + ], + "Size": [ + -7.151703059, + 6.406366899 + ], + "Sweetness": [ + -6.894485494, + 6.374915513 + ], + "Weight": [ + -7.149847675, + 5.79071359 + ], + "Acidity": [ + -7.010538475, + 7.404736238 + ] + }, + "cat_feature_value": { + "Quality": [ + "bad", + "good" + ] + } +} diff --git a/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_008/history.csv b/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_008/history.csv index ad4fd682c160296c6cf2965879c7cb9eb143f44a..7d366b99ea8cbeddd0a9c0966c0a2aba10ca508c 100644 --- a/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_008/history.csv +++ b/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_008/history.csv @@ -3799,4 +3799,3 @@ A_id,Size,Weight,Sweetness,Crunchiness,Juiciness,Ripeness,Acidity,Quality 3997.0,-2.634515299,-2.13824672,-2.440461285,0.657222891,2.19970859,4.763859177,-1.334611391,bad 3998.0,-4.008003744,-1.779337107,2.366396966,-0.200329367,2.161435121,0.214488384,-2.229719806,good 3999.0,0.27853965,-1.715505028,0.121217251,-1.154074758,1.2666774,-0.77657147,1.599796456,good -,,,,,,,Created_by_Nidula_Elgiriyewithana, diff --git a/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_008/info.json b/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_008/info.json index 061594cd032cd189afdf78ebe43a94ad53166b97..8de202273508b7431d0bbe83b679ee5373c9efe5 100644 --- a/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_008/info.json +++ b/ranking_and_filtering/daily/classification/Apple_Quality_B4/Apple_Quality_B4_008/info.json @@ -1,66 +1,69 @@ -{ - "name": "Apple Quality", - "source": "https://www.kaggle.com/datasets/nelgiriyewithana/apple-quality/data", - "data_intro": "This dataset contains information about various attributes of a set of fruits, providing insights into their characteristics. The dataset includes details such as fruit ID, size, weight, sweetness, crunchiness, juiciness, ripeness, acidity, and quality.", - "is_splited": false, - "overall_size": 4000, - "train_size": 0, - "test_size": 0, - "c_classes": 2, - "n_classes": 7, - "task_type": "classification", - "target": { - "Quality": "Build a model to predict the quality rating of fruits using various attributes." - }, - "cat_feature_intro": { - "Acidity": "- Acidity: Acidity level of the fruit", - "Quality": "- Quality: Overall quality of the fruit." - }, - "num_feature_intro": { - "A_id": "- A_id: Unique identifier for each fruit", - "Size": "- Size: Size of the fruit", - "Weight": "- Weight: Weight of the fruit", - "Sweetness": "- Sweetness: Degree of sweetness of the fruit", - "Crunchiness": "- Crunchiness: Texture indicating the crunchiness of the fruit", - "Juiciness": "- Juiciness: Level of juiciness of the fruit", - "Ripeness": "- Ripeness: Stage of ripeness of the fruit" - }, - "evaluation_metric": null, - "num_feature_value": { - "A_id": [ - 0.0, - 3999.0 - ], - "Crunchiness": [ - -6.055057805, - 7.619851801 - ], - "Juiciness": [ - -5.961897048, - 7.364402864 - ], - "Ripeness": [ - -5.864598918, - 7.237836684 - ], - "Size": [ - -7.151703059, - 6.406366899 - ], - "Sweetness": [ - -6.894485494, - 6.374915513 - ], - "Weight": [ - -7.149847675, - 5.79071359 - ] - }, - "cat_feature_value": { - "Acidity": "string", - "Quality": [ - "bad", - "good" - ] - } -} \ No newline at end of file +{ + "name": "Apple Quality", + "source": "https://www.kaggle.com/datasets/nelgiriyewithana/apple-quality/data", + "data_intro": "This dataset contains information about various attributes of a set of fruits, providing insights into their characteristics. The dataset includes details such as fruit ID, size, weight, sweetness, crunchiness, juiciness, ripeness, acidity, and quality.", + "is_splited": false, + "overall_size": 4000, + "train_size": 0, + "test_size": 0, + "c_classes": 1, + "n_classes": 8, + "task_type": "classification", + "target": { + "Quality": "Build a model to predict the quality rating of fruits using various attributes." + }, + "cat_feature_intro": { + "Quality": "- Quality: Overall quality of the fruit." + }, + "num_feature_intro": { + "A_id": "- A_id: Unique identifier for each fruit", + "Size": "- Size: Size of the fruit", + "Weight": "- Weight: Weight of the fruit", + "Sweetness": "- Sweetness: Degree of sweetness of the fruit", + "Crunchiness": "- Crunchiness: Texture indicating the crunchiness of the fruit", + "Juiciness": "- Juiciness: Level of juiciness of the fruit", + "Ripeness": "- Ripeness: Stage of ripeness of the fruit", + "Acidity": "- Acidity: Acidity level of the fruit" + }, + "evaluation_metric": null, + "num_feature_value": { + "A_id": [ + 0.0, + 3999.0 + ], + "Crunchiness": [ + -6.055057805, + 7.619851801 + ], + "Juiciness": [ + -5.961897048, + 7.364402864 + ], + "Ripeness": [ + -5.864598918, + 7.237836684 + ], + "Size": [ + -7.151703059, + 6.406366899 + ], + "Sweetness": [ + -6.894485494, + 6.374915513 + ], + "Weight": [ + -7.149847675, + 5.79071359 + ], + "Acidity": [ + -7.010538475, + 7.404736238 + ] + }, + "cat_feature_value": { + "Quality": [ + "bad", + "good" + ] + } +} diff --git a/ranking_and_filtering/daily/classification/Gender_Recognition_by_Voice_B4/Gender_Recognition_by_Voice_B4_008/Gender_Recognition_by_Voice_B4_008_current.json b/ranking_and_filtering/daily/classification/Gender_Recognition_by_Voice_B4/Gender_Recognition_by_Voice_B4_008/Gender_Recognition_by_Voice_B4_008_current.json index 23d3a32d06ba88d743516eb9c3a5de572c8c61fe..edc4add9b8e6a1352e67562fbc5d39a55c758dec 100644 --- a/ranking_and_filtering/daily/classification/Gender_Recognition_by_Voice_B4/Gender_Recognition_by_Voice_B4_008/Gender_Recognition_by_Voice_B4_008_current.json +++ b/ranking_and_filtering/daily/classification/Gender_Recognition_by_Voice_B4/Gender_Recognition_by_Voice_B4_008/Gender_Recognition_by_Voice_B4_008_current.json @@ -1,5235 +1,5235 @@ { - "id": "008", - "task_type": "B4", - "subtask_type": "filter", - "perspective": "data_holder", - "dataset_name": "Gender Recognition by Voice", - "table_path": "output/daily/classification/Gender Recognition by Voice_B4/Gender Recognition by Voice_B4_008/test.csv", - "query": "I'm analyzing the file I sent over to optimize our system's response to fundamental frequency. Please identify the candidates from the new data whose average fundamental frequency (meanfun) is below 0.10hz. From this subset, I want to focus on those your analysis suggests are female.", - "meta_info": { - "domain": "daily" + "id": "008", + "task_type": "B4", + "subtask_type": "filter", + "perspective": "data_holder", + "dataset_name": "Gender Recognition by Voice", + "table_path": "output/daily/classification/Gender Recognition by Voice_B4/Gender Recognition by Voice_B4_008/test.csv", + "query": "I'm analyzing the file I sent over to optimize our system's response to fundamental frequency. Please identify the candidates from the new data whose average fundamental frequency (meanfun) is below 0.10 kHz. 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No newline at end of file + "active_filters": [ + { + "col": "meanfun", + "op": "<", + "val": 0.1 + } + ], + "target_class_value": "female" + }, + "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_001/Hotel_booking_demand_B4_001_current.json b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_001/Hotel_booking_demand_B4_001_current.json index cc58c5bc67d6dafce693d9f59ea1cd5d3bf831f6..763d56608dd40a139fa6c927de48cb64e7dc708e 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_001/Hotel_booking_demand_B4_001_current.json +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_001/Hotel_booking_demand_B4_001_current.json @@ -1,7147 +1,6763 @@ { - "id": "001", - "task_type": "B4", - "subtask_type": "list", - "perspective": "data_holder", - "dataset_name": "Hotel booking demand", - "table_path": "output/daily/classification/Hotel booking demand_B4/Hotel booking demand_B4_001/test.csv", - "query": "We're preparing for next month's arrivals and need to identify which guests from our new bookings are most likely to not cancel their stay. I've sent over our complete booking history, which shows patterns of who actually checked in versus who canceled. Can you flag all the new reservations that are highly likely to result in a Check-Out?", - "meta_info": { - "domain": "daily" + "id": "001", + "task_type": "B4", + "subtask_type": "list", + "perspective": "data_holder", + "dataset_name": "Hotel booking demand", + "table_path": "output/daily/classification/Hotel booking demand_B4/Hotel booking demand_B4_001/test.csv", + "query": "We're preparing for next month's arrivals and need to identify which guests from our new bookings are most likely to not cancel their stay. I've sent over our complete booking history, which shows patterns of who actually checked in versus who canceled. 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"customer_type": "Transient-Party", - "adr": 62.0, - "required_car_parking_spaces": 0, - "total_of_special_requests": 0, - "reservation_status": "Canceled", - "reservation_status_date": "2015-08-07" - } - }, - { - "scenario_id": "478", - "features": { - "hotel": "City Hotel", - "is_canceled": 1, - "lead_time": 135, - "arrival_date_year": 2016, - "arrival_date_month": "July", - "arrival_date_week_number": 31, - "arrival_date_day_of_month": 26, - "stays_in_weekend_nights": 0, - "stays_in_week_nights": 4, - "adults": 2, - "children": 0.0, - "babies": 0, - "meal": "SC", - "country": "BEL", - "market_segment": "Online TA", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "A", - "assigned_room_type": "A", - "booking_changes": 0, - "deposit_type": "No Deposit", - "agent": 9.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 89.25, - "required_car_parking_spaces": 0, - "total_of_special_requests": 0, - "reservation_status": "Canceled", - "reservation_status_date": "2016-03-14" - } - }, - { - "scenario_id": "482", - "features": { - "hotel": "Resort Hotel", - "is_canceled": 1, - "lead_time": 101, - "arrival_date_year": 2016, - "arrival_date_month": "November", - "arrival_date_week_number": 48, - "arrival_date_day_of_month": 26, - "stays_in_weekend_nights": 2, - "stays_in_week_nights": 5, - "adults": 2, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "BEL", - "market_segment": "Online TA", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "A", - "assigned_room_type": "A", - "booking_changes": 0, - "deposit_type": "No Deposit", - "agent": 240.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 37.8, - "required_car_parking_spaces": 0, - "total_of_special_requests": 0, - "reservation_status": "Canceled", - "reservation_status_date": "2016-09-18" - } - }, - { - "scenario_id": "483", - "features": { - "hotel": "City Hotel", - "is_canceled": 1, - "lead_time": 82, - "arrival_date_year": 2017, - "arrival_date_month": "May", - "arrival_date_week_number": 18, - "arrival_date_day_of_month": 3, - "stays_in_weekend_nights": 0, - "stays_in_week_nights": 2, - "adults": 2, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "PRT", - "market_segment": "Groups", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "A", - "assigned_room_type": "A", - "booking_changes": 0, - "deposit_type": "Non Refund", - "agent": 29.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 130.0, - "required_car_parking_spaces": 0, - "total_of_special_requests": 0, - "reservation_status": "Canceled", - "reservation_status_date": "2017-02-10" - } - }, - { - "scenario_id": "484", - "features": { - "hotel": "City Hotel", - "is_canceled": 1, - "lead_time": 21, - "arrival_date_year": 2015, - "arrival_date_month": "September", - "arrival_date_week_number": 37, - "arrival_date_day_of_month": 10, - "stays_in_weekend_nights": 1, - "stays_in_week_nights": 3, - "adults": 2, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "PRT", - "market_segment": "Offline TA/TO", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "A", - "assigned_room_type": "A", - "booking_changes": 0, - "deposit_type": "No Deposit", - "agent": 6.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 65.55, - "required_car_parking_spaces": 0, - "total_of_special_requests": 0, - "reservation_status": "Canceled", - "reservation_status_date": "2015-09-07" - } - }, - { - "scenario_id": "486", - "features": { - "hotel": "City Hotel", - "is_canceled": 1, - "lead_time": 11, - "arrival_date_year": 2017, - "arrival_date_month": "May", - "arrival_date_week_number": 20, - "arrival_date_day_of_month": 14, - "stays_in_weekend_nights": 2, - "stays_in_week_nights": 0, - "adults": 2, - "children": 0.0, - "babies": 0, - "meal": "SC", - "country": "FRA", - "market_segment": "Online TA", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "A", - "assigned_room_type": "A", - "booking_changes": 0, - "deposit_type": "No Deposit", - "agent": 9.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 120.0, - "required_car_parking_spaces": 0, - "total_of_special_requests": 1, - "reservation_status": "Canceled", - "reservation_status_date": "2017-05-04" - } - }, - { - "scenario_id": "489", - "features": { - "hotel": "Resort Hotel", - "is_canceled": 1, - "lead_time": 393, - "arrival_date_year": 2016, - "arrival_date_month": "October", - "arrival_date_week_number": 41, - "arrival_date_day_of_month": 8, - "stays_in_weekend_nights": 2, - "stays_in_week_nights": 2, - "adults": 2, - "children": 0.0, - "babies": 0, - "meal": "HB", - "country": "PRT", - "market_segment": "Groups", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "A", - "assigned_room_type": "A", - "booking_changes": 0, - "deposit_type": "Non Refund", - "agent": 68.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 72.0, - "required_car_parking_spaces": 0, - "total_of_special_requests": 0, - "reservation_status": "Canceled", - "reservation_status_date": "2015-12-17" - } - }, - { - "scenario_id": "492", - "features": { - "hotel": "Resort Hotel", - "is_canceled": 1, - "lead_time": 2, - "arrival_date_year": 2017, - "arrival_date_month": "May", - "arrival_date_week_number": 19, - "arrival_date_day_of_month": 8, - "stays_in_weekend_nights": 1, - "stays_in_week_nights": 0, - "adults": 1, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "PRT", - "market_segment": "Offline TA/TO", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "A", - "assigned_room_type": "E", - "booking_changes": 0, - "deposit_type": "No Deposit", - "agent": 152.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 64.0, - "required_car_parking_spaces": 0, - "total_of_special_requests": 1, - "reservation_status": "Canceled", - "reservation_status_date": "2017-05-08" - } - }, - { - "scenario_id": "495", - "features": { - "hotel": "City Hotel", - "is_canceled": 1, - "lead_time": 331, - "arrival_date_year": 2017, - "arrival_date_month": "August", - "arrival_date_week_number": 33, - "arrival_date_day_of_month": 19, - "stays_in_weekend_nights": 2, - "stays_in_week_nights": 5, - "adults": 1, - "children": 0.0, - "babies": 0, - "meal": "SC", - "country": "GBR", - "market_segment": "Online TA", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "A", - "assigned_room_type": "A", - "booking_changes": 0, - "deposit_type": "No Deposit", - "agent": 9.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 109.67, - "required_car_parking_spaces": 0, - "total_of_special_requests": 0, - "reservation_status": "Canceled", - "reservation_status_date": "2016-09-23" - } - }, - { - "scenario_id": "499", - "features": { - "hotel": "Resort Hotel", - "is_canceled": 0, - "lead_time": 1, - "arrival_date_year": 2016, - "arrival_date_month": "January", - "arrival_date_week_number": 4, - "arrival_date_day_of_month": 21, - "stays_in_weekend_nights": 0, - "stays_in_week_nights": 1, - "adults": 1, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "PRT", - "market_segment": "Online TA", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "D", - "assigned_room_type": "D", - "booking_changes": 0, - "deposit_type": "No Deposit", - "agent": 240.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 40.0, - "required_car_parking_spaces": 0, - "total_of_special_requests": 1, - "reservation_status": "Check-Out", - "reservation_status_date": "2016-01-22" - } - } - ], - "target_column": "reservation_status", - "task_sub_type": "classification", - "ranking_ground_truth": { - "top_k_ids": [ - "051", - "071", - "138", - "142", - "189", - "195", - "235", - "248", - "255", - "257", - "288", - "296", - "366", - "393", - "499" - ] - }, - "active_filters": [], - "target_class_value": "check-out" - }, - "response": "", - "evaluation_score": {} -} \ No newline at end of file + "active_filters": [], + "target_class_value": "check-out" + }, + "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_001/current.csv b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_001/current.csv index 4bae7277a73d82f8df547137f262e0db16c27237..ea04add03f4a0e04a3d8afda28250eacd0eed08f 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_001/current.csv +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_001/current.csv @@ -1,201 +1,193 @@ -hotel,is_canceled,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests,reservation_status_date -Resort Hotel,1,175,2017,August,33,14,1,3,2,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,D,D,2,No Deposit,240.0,,0,Transient,200.0,0,1,2017-03-20 -City Hotel,1,352,2017,May,21,24,0,3,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,99.0,0,0,2016-06-19 -City Hotel,1,16,2016,December,53,28,2,4,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,3.0,,0,Transient,76.67,0,0,2016-12-12 -Resort Hotel,1,319,2017,June,23,5,1,3,2,1.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,241.0,,0,Transient,68.08,0,1,2016-10-13 -City Hotel,1,156,2017,April,17,26,0,3,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,37.0,,0,Transient,100.0,0,0,2016-11-21 -Resort Hotel,1,72,2016,June,25,13,1,3,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,116.0,0,0,2016-04-28 -City Hotel,1,11,2017,April,15,14,1,2,2,0.0,0,SC,USA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,138.33,0,0,2017-04-06 -Resort Hotel,1,157,2016,January,5,29,2,5,2,0.0,0,HB,PRT,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,242.0,,0,Transient,88.2,0,1,2016-01-05 -City Hotel,1,36,2017,August,33,16,0,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,10.0,,0,Transient,100.0,0,0,2017-08-16 -City Hotel,1,622,2017,March,12,23,0,1,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,59.0,0,0,2015-10-21 -Resort Hotel,1,253,2015,September,37,12,2,5,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,Non Refund,96.0,,0,Transient,58.95,0,0,2015-06-17 -Resort Hotel,1,1,2016,December,51,13,0,1,1,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,,178.0,0,Transient,4.0,0,0,2016-12-13 -City Hotel,1,301,2016,November,47,18,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,20.0,,0,Transient,52.0,0,0,2016-03-15 -City Hotel,1,59,2017,March,10,11,1,1,2,0.0,0,BB,CN,Direct,Direct,0,0,0,A,A,1,No Deposit,14.0,,0,Transient,98.0,0,0,2017-01-16 -Resort Hotel,1,54,2015,September,39,26,2,6,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,242.0,,0,Transient,69.13,0,1,2015-09-16 -City Hotel,1,296,2016,December,53,26,1,5,2,2.0,0,HB,BEL,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,9.0,,0,Transient,196.07,0,1,2016-09-26 -City Hotel,1,295,2015,August,32,8,2,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.0,0,0,2015-07-06 -City Hotel,1,88,2017,February,7,14,0,3,1,0.0,0,BB,NLD,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,86.4,0,1,2016-11-25 -City Hotel,0,1,2017,February,5,3,0,1,1,0.0,0,SC,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,93.0,0,0,2017-02-04 -Resort Hotel,1,200,2016,December,51,17,6,12,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,314.0,,0,Transient,38.67,0,0,2016-06-03 -City Hotel,1,166,2016,November,45,1,0,3,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,236.0,,0,Transient,110.0,0,0,2016-07-13 -City Hotel,1,137,2016,January,5,27,0,2,1,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,29.0,,45,Transient,70.0,0,0,2015-10-27 -City Hotel,1,113,2017,May,20,20,2,1,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,34.0,,0,Transient,190.0,0,0,2017-01-27 -City Hotel,1,278,2015,July,30,22,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient-Party,62.0,0,0,2015-06-29 -City Hotel,1,43,2017,June,22,3,0,1,3,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,7.0,,0,Transient,212.8,0,1,2017-04-21 -Resort Hotel,1,0,2017,January,4,26,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,5,A,D,0,No Deposit,,9.0,0,Transient,35.0,0,0,2017-01-26 -City Hotel,1,90,2017,April,14,4,0,4,2,2.0,0,BB,BEL,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,9.0,,0,Transient,189.0,0,0,2017-01-11 -Resort Hotel,1,126,2017,April,16,17,1,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,314.0,,0,Transient,54.0,0,1,2017-04-06 -City Hotel,1,275,2016,October,41,5,1,4,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,44.0,,0,Transient,90.0,0,0,2016-06-20 -City Hotel,0,3,2016,October,42,12,0,4,2,0.0,0,BB,CHE,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,175.0,0,3,2016-10-16 -City Hotel,1,221,2016,June,24,10,1,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,Non Refund,3.0,,15,Transient,80.0,0,0,2015-11-17 -Resort Hotel,1,8,2015,December,49,5,2,1,2,0.0,0,HB,PRT,Groups,TA/TO,0,1,0,A,A,0,No Deposit,38.0,,0,Transient-Party,68.0,0,0,2015-12-01 -City Hotel,1,189,2016,May,21,19,0,3,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,0,Transient-Party,65.0,0,0,2016-05-18 -City Hotel,1,211,2017,May,21,21,2,4,2,0.0,0,BB,DNK,Online TA,TA/TO,0,0,0,B,B,1,No Deposit,9.0,,0,Transient,110.75,0,1,2017-01-14 -City Hotel,1,418,2016,September,40,26,1,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,21.0,,223,Transient-Party,75.0,0,0,2016-08-02 -City Hotel,1,93,2015,December,52,26,2,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,1,0,A,A,0,No Deposit,9.0,,0,Contract,106.2,0,1,2015-10-05 -City Hotel,1,130,2017,April,16,21,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,286.0,,0,Transient,95.0,0,0,2016-12-12 -City Hotel,1,283,2015,July,31,27,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Contract,62.0,0,0,2015-01-01 -City Hotel,1,444,2016,December,49,1,0,3,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,Non Refund,34.0,,39,Transient,75.0,0,0,2015-10-23 -City Hotel,1,94,2017,August,34,25,1,2,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,86.0,,0,Transient,127.47,0,0,2017-05-29 -Resort Hotel,1,113,2017,May,22,30,0,2,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,100.0,0,0,2017-02-08 -Resort Hotel,1,210,2017,May,18,4,2,5,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,E,E,0,No Deposit,177.0,,0,Transient,90.0,0,1,2016-11-02 -City Hotel,1,262,2016,December,53,26,1,3,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,73.95,0,0,2016-05-29 -City Hotel,1,93,2017,August,33,17,0,3,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,E,E,1,No Deposit,9.0,,0,Transient,190.0,0,1,2017-05-19 -City Hotel,1,166,2016,November,45,1,0,3,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,236.0,,0,Transient,110.0,0,0,2016-07-13 -City Hotel,1,28,2016,October,42,13,0,1,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,234.0,,0,Transient-Party,120.0,0,0,2016-10-07 -City Hotel,1,59,2016,September,39,24,1,1,3,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,168.3,0,0,2016-07-28 -Resort Hotel,1,385,2016,September,39,21,0,4,2,0.0,0,BB,PRT,Groups,Direct,0,0,0,A,A,0,Non Refund,,,0,Transient,66.0,0,0,2015-10-14 -City Hotel,1,304,2016,November,45,3,0,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,21.0,,0,Transient,89.0,0,0,2016-02-01 -City Hotel,1,20,2015,July,30,22,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.0,0,0,2015-07-17 -Resort Hotel,1,90,2016,July,29,13,0,3,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,175.0,0,0,2016-04-16 -Resort Hotel,1,39,2017,February,6,11,0,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,48.0,0,1,2017-01-09 -Resort Hotel,1,179,2016,August,32,1,2,5,2,0.0,0,FB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,314.0,,0,Transient,146.8,0,2,2016-07-26 -City Hotel,0,13,2016,September,40,27,0,2,1,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,7.0,,0,Transient,126.28,0,0,2016-09-29 -City Hotel,1,95,2016,November,45,5,0,1,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,,,0,Transient-Party,75.0,0,0,2016-10-21 -City Hotel,1,107,2016,June,24,10,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,187.0,,0,Transient-Party,130.0,0,0,2016-04-04 -City Hotel,1,158,2016,May,22,24,0,2,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,37.0,,31,Transient,130.0,0,0,2016-01-18 -Resort Hotel,0,78,2016,December,53,31,1,1,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,D,C,1,No Deposit,181.0,,0,Transient-Party,168.0,0,0,2017-01-02 -City Hotel,1,31,2016,February,6,5,0,2,2,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,67.5,0,0,2016-01-05 -Resort Hotel,1,48,2015,July,31,27,1,1,2,0.0,0,HB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,167.0,0,1,2015-06-15 -City Hotel,1,136,2017,March,12,21,0,3,2,0.0,0,BB,AUT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,93.6,0,1,2017-03-02 -City Hotel,1,113,2016,June,27,30,1,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,192.0,,0,Transient,85.0,0,0,2016-03-16 -City Hotel,1,360,2015,October,42,12,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Contract,62.0,0,0,2015-01-01 -City Hotel,1,461,2016,October,42,13,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0,2015-10-21 -City Hotel,1,223,2016,June,25,14,0,2,2,0.0,0,HB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,133.0,,40,Transient,135.0,0,0,2015-12-14 -City Hotel,1,14,2016,June,27,28,0,3,2,0.0,0,BB,QAT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,152.67,0,1,2016-06-24 -City Hotel,1,7,2017,April,15,13,0,3,2,0.0,0,SC,HKG,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,160.0,0,1,2017-04-07 -City Hotel,1,309,2016,May,20,12,0,1,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,59.0,0,0,2015-10-21 -Resort Hotel,1,1,2015,November,47,18,0,1,1,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,38.0,0,0,2015-11-18 -City Hotel,1,444,2016,December,49,1,0,3,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,Non Refund,34.0,,39,Transient,75.0,0,0,2015-10-23 -City Hotel,1,245,2016,July,28,6,2,4,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,19.0,,0,Transient,110.0,0,0,2016-02-23 -Resort Hotel,1,73,2015,August,35,25,0,1,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,112.5,0,1,2015-06-15 -Resort Hotel,1,181,2016,April,17,22,1,2,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,44.1,0,1,2015-12-19 -City Hotel,1,8,2017,July,28,10,1,2,2,1.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,166.67,0,1,2017-07-07 -City Hotel,1,37,2017,March,11,15,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,378.0,,0,Transient,110.0,0,0,2017-02-06 -Resort Hotel,0,16,2015,December,49,3,0,3,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,46.4,0,1,2015-12-06 -City Hotel,1,21,2016,September,39,18,2,1,2,0.0,0,SC,MYS,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,129.0,0,1,2016-09-09 -City Hotel,0,133,2017,April,16,19,0,4,2,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,390.0,,0,Transient-Party,124.0,0,0,2017-04-23 -Resort Hotel,1,209,2017,January,4,28,1,1,2,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,37.8,0,0,2016-07-29 -City Hotel,1,165,2017,May,20,19,2,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,225.0,0,0,2017-01-12 -City Hotel,1,155,2017,June,25,24,2,1,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,30.0,,26,Transient,120.0,0,0,2017-02-15 -City Hotel,1,317,2016,September,37,9,1,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,Non Refund,58.0,,56,Transient,90.0,0,0,2015-12-23 -City Hotel,1,198,2015,December,49,5,2,1,2,0.0,0,HB,ESP,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,90.0,0,0,2015-10-23 -Resort Hotel,1,13,2016,August,33,12,0,2,2,0.0,0,HB,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,256.0,0,0,2016-07-31 -City Hotel,1,168,2016,April,17,22,1,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,26.0,,77,Transient,85.0,0,0,2016-01-22 -City Hotel,1,226,2017,July,28,9,2,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,110.0,0,0,2016-11-25 -City Hotel,1,166,2016,November,45,1,0,3,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,236.0,,0,Transient,110.0,0,0,2016-07-13 -City Hotel,1,92,2017,March,11,15,0,3,2,0.0,0,BB,PRT,Groups,Direct,0,0,0,A,A,0,Non Refund,,,0,Transient,85.0,0,0,2016-12-13 -City Hotel,1,193,2017,July,30,24,1,3,2,1.0,0,BB,BEL,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,9.0,,0,Transient,152.63,0,3,2017-02-10 -City Hotel,1,60,2017,March,13,30,1,3,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,104.4,0,0,2017-03-19 -City Hotel,0,277,2017,July,29,17,1,2,2,1.0,0,BB,USA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient-Party,123.3,0,1,2017-07-20 -City Hotel,1,275,2016,April,17,17,2,0,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,12.0,,0,Transient,110.0,0,0,2015-08-04 -Resort Hotel,1,185,2016,April,17,18,1,1,2,0.0,0,HB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,298.0,,0,Transient-Party,86.0,0,0,2016-03-18 -Resort Hotel,1,6,2017,May,18,5,2,3,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,G,G,2,No Deposit,250.0,,0,Transient,195.0,0,0,2017-04-29 -City Hotel,1,301,2015,August,33,14,0,2,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,Non Refund,6.0,,0,Transient-Party,101.5,0,0,2015-07-06 -Resort Hotel,0,123,2016,October,43,21,2,3,2,0.0,0,BB,SWE,Groups,Corporate,0,0,0,E,E,3,No Deposit,,,0,Transient-Party,75.0,1,0,2016-10-26 -City Hotel,1,89,2015,September,40,27,1,0,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,170.0,0,0,2015-09-22 -City Hotel,1,254,2016,September,39,24,1,1,2,0.0,0,HB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,96.0,0,0,2016-03-01 -City Hotel,0,12,2015,December,49,5,1,1,2,0.0,0,BB,ESP,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,28.0,,0,Transient,55.0,0,0,2015-12-07 -Resort Hotel,1,138,2017,August,32,7,1,3,2,1.0,0,HB,CHE,Online TA,TA/TO,0,0,0,C,C,1,No Deposit,240.0,,0,Transient,298.0,0,0,2017-04-08 -City Hotel,0,31,2016,May,20,13,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,16.0,,0,Transient,105.3,0,0,2016-05-15 -City Hotel,1,207,2015,September,40,29,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,No Deposit,20.0,,0,Transient,48.0,0,0,2015-07-23 -City Hotel,1,35,2015,September,39,25,2,3,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,170.0,0,0,2015-09-09 -City Hotel,1,18,2017,May,21,21,2,2,2,0.0,0,SC,FRA,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,137.5,0,0,2017-05-21 -Resort Hotel,1,77,2017,April,15,12,0,3,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,81.67,0,0,2017-01-25 -City Hotel,1,61,2016,April,16,11,1,3,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,76.58,0,0,2016-02-11 -Resort Hotel,1,202,2017,May,18,4,0,1,2,0.0,0,HB,PRT,Groups,TA/TO,0,0,0,D,D,0,No Deposit,251.0,,0,Transient-Party,90.0,0,0,2017-02-07 -City Hotel,1,303,2017,April,14,6,0,3,2,2.0,0,BB,TUR,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,9.0,,0,Transient,181.8,0,1,2017-01-16 -City Hotel,1,67,2017,March,11,14,0,3,2,0.0,0,BB,NLD,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,106.2,0,0,2017-01-31 -Resort Hotel,0,124,2016,August,34,17,0,4,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,240.0,,0,Transient,191.0,0,1,2016-08-21 -City Hotel,1,476,2017,July,28,15,2,1,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,229.0,,0,Transient-Party,112.67,0,0,2017-07-03 -City Hotel,1,180,2015,October,42,16,0,1,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,3.0,,0,Transient,70.0,0,0,2015-08-31 -City Hotel,0,115,2016,June,26,19,2,3,2,0.0,0,BB,GBR,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,101.66,0,1,2016-06-24 -City Hotel,1,33,2016,September,37,8,2,3,2,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,135.9,0,0,2016-08-08 -Resort Hotel,1,49,2015,September,38,19,1,1,2,0.0,0,BB,,Direct,Direct,0,0,0,E,F,0,No Deposit,,,0,Transient,99.0,0,0,2015-09-19 -City Hotel,1,30,2015,October,41,7,0,3,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Transient,94.5,0,2,2015-10-07 -Resort Hotel,1,19,2017,July,30,28,0,2,2,0.0,0,BB,AGO,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,184.5,0,2,2017-07-28 -City Hotel,1,16,2016,May,23,29,1,0,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,111.0,0,0,2016-05-13 -City Hotel,1,149,2017,June,25,24,0,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,,,0,Transient-Party,130.0,0,0,2017-06-23 -City Hotel,1,55,2017,March,10,5,2,0,1,0.0,0,BB,ARG,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,9.0,,0,Transient,94.0,0,1,2017-01-19 -City Hotel,1,278,2016,August,33,11,0,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient,65.0,0,0,2015-12-18 -City Hotel,1,32,2016,February,6,5,1,2,1,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,72.9,0,0,2016-01-04 -Resort Hotel,1,47,2016,November,45,5,2,5,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,F,F,0,No Deposit,250.0,,0,Transient,88.96,0,0,2016-09-20 -Resort Hotel,1,36,2017,January,5,30,1,2,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,240.0,,0,Transient,48.0,0,1,2017-01-13 -Resort Hotel,1,279,2017,June,25,22,2,4,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,80.1,0,1,2017-03-08 -City Hotel,1,59,2017,January,4,23,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,326.0,,0,Transient,80.0,0,0,2016-11-25 -Resort Hotel,1,34,2016,January,3,15,0,2,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,44.8,0,1,2015-12-26 -City Hotel,1,250,2017,May,21,24,0,4,2,0.0,0,BB,SWE,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,125.1,0,1,2017-04-01 -City Hotel,1,5,2016,March,13,23,0,1,2,0.0,0,SC,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,89.0,0,0,2016-03-18 -City Hotel,1,365,2015,October,42,17,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient,62.8,0,0,2015-01-30 -Resort Hotel,1,121,2016,December,51,16,2,2,2,0.0,0,HB,ESP,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,73.8,0,2,2016-10-24 -City Hotel,1,38,2017,January,2,14,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,Non Refund,,67.0,0,Transient,75.0,0,0,2016-12-07 -City Hotel,1,86,2015,October,40,3,2,1,2,0.0,0,HB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,0,Transient-Party,86.0,0,0,2015-09-28 -City Hotel,1,134,2017,June,26,29,0,3,2,0.0,0,SC,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,95.7,0,0,2017-02-15 -City Hotel,1,70,2017,January,1,3,0,4,2,1.0,0,BB,ITA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,105.4,0,1,2016-12-01 -City Hotel,1,297,2015,August,33,10,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient-Party,62.0,0,0,2015-07-02 -City Hotel,0,3,2016,October,43,16,1,0,1,0.0,0,SC,KOR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,,,0,Transient,120.0,0,1,2016-10-17 -City Hotel,1,38,2017,January,2,14,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,Non Refund,,67.0,0,Transient,75.0,0,0,2016-12-07 -City Hotel,1,44,2016,February,8,17,0,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,44.0,,3,Transient,75.0,0,0,2016-01-16 -City Hotel,1,264,2017,July,28,12,1,4,1,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Transient,80.09,0,2,2017-04-02 -Resort Hotel,1,173,2016,September,40,27,2,5,2,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,92.14,0,0,2016-04-08 -City Hotel,1,337,2016,June,24,9,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0,2015-10-21 -City Hotel,1,28,2017,April,16,18,0,3,3,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,9.0,,0,Transient,214.0,0,0,2017-03-23 -City Hotel,1,29,2017,June,22,3,2,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,229.0,,0,Transient-Party,112.67,0,0,2017-05-22 -City Hotel,1,69,2016,October,44,25,0,3,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,69.99,0,0,2016-08-17 -City Hotel,1,18,2017,February,5,3,1,2,2,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,88.0,0,0,2017-02-03 -City Hotel,1,101,2016,June,27,27,1,5,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,140.0,0,0,2016-05-06 -City Hotel,1,162,2016,October,42,14,2,2,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,34.0,,0,Transient,115.0,0,0,2016-08-11 -City Hotel,1,166,2016,November,45,1,0,3,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,236.0,,0,Transient,110.0,0,0,2016-07-13 -Resort Hotel,0,123,2016,November,48,25,0,2,2,0.0,0,HB,PRT,Online TA,TA/TO,1,0,2,A,D,0,No Deposit,240.0,,0,Transient,64.8,0,1,2016-11-27 -Resort Hotel,1,90,2016,April,17,19,4,10,2,0.0,0,HB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,90.14,0,2,2016-04-01 -City Hotel,1,273,2016,May,20,13,1,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,21.0,,0,Transient,95.0,0,0,2015-12-18 -City Hotel,1,358,2015,October,41,10,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0,2015-07-23 -City Hotel,1,260,2015,July,27,4,2,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient-Party,62.0,0,0,2015-06-16 -City Hotel,1,161,2017,February,7,13,1,3,2,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,8.0,,0,Transient,74.8,0,1,2017-01-06 -Resort Hotel,1,245,2015,September,39,25,0,1,2,0.0,0,HB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,248.0,,0,Transient,80.0,0,0,2015-07-31 -City Hotel,1,36,2017,July,30,27,1,3,2,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,85.0,,0,Transient,79.0,0,0,2017-07-18 -City Hotel,1,87,2017,February,7,17,0,2,2,0.0,0,SC,CHN,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,71.28,0,0,2016-12-08 -Resort Hotel,1,180,2015,August,33,9,4,9,2,0.0,0,BB,PRT,Online TA,TA/TO,0,1,0,A,A,0,No Deposit,240.0,,0,Transient,134.0,0,0,2015-02-11 -Resort Hotel,1,63,2016,December,50,9,0,2,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,339.0,,0,Transient,26.64,0,0,2016-12-05 -City Hotel,1,111,2017,March,12,20,4,10,1,0.0,0,BB,AGO,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,78.26,0,0,2017-03-20 -City Hotel,1,8,2016,February,8,15,1,2,1,0.0,0,BB,CHE,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,85.0,0,0,2016-02-09 -City Hotel,1,198,2016,May,21,20,0,1,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,B,1,No Deposit,1.0,,98,Transient-Party,57.5,0,0,2016-03-22 -Resort Hotel,1,319,2017,June,23,5,1,3,2,0.0,0,BB,IRL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,74.25,0,0,2016-09-28 -Resort Hotel,1,78,2015,October,41,9,1,2,2,0.0,0,FB,ESP,Groups,TA/TO,0,0,0,A,A,0,Non Refund,134.0,,0,Transient,92.0,0,0,2015-08-14 -Resort Hotel,1,169,2016,August,33,13,2,6,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,314.0,,0,Transient,141.6,0,0,2016-02-26 -Resort Hotel,1,148,2015,October,43,24,0,1,1,0.0,0,BB,PRT,Groups,Direct,0,0,0,A,A,0,Non Refund,,,0,Transient,39.0,0,0,2015-06-26 -Resort Hotel,1,259,2015,September,38,17,0,3,2,0.0,0,BB,GBR,Groups,Corporate,0,1,0,A,A,0,Non Refund,,223.0,0,Transient,40.05,0,0,2015-01-21 -City Hotel,1,351,2016,June,26,23,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0,2015-10-21 -City Hotel,1,113,2016,December,50,8,0,1,2,0.0,0,BB,NLD,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,9.0,,0,Transient,90.0,0,1,2016-11-19 -City Hotel,1,68,2017,July,28,11,0,2,2,0.0,0,SC,IRL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,120.0,0,1,2017-05-25 -City Hotel,1,35,2016,September,40,26,1,0,2,0.0,0,SC,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,99.0,0,0,2016-08-23 -Resort Hotel,1,24,2017,March,11,13,1,0,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,66.0,,0,Transient-Party,32.0,0,0,2017-03-06 -City Hotel,1,66,2017,April,15,9,2,0,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,100.0,0,0,2017-02-02 -Resort Hotel,1,203,2017,April,17,25,0,2,2,0.0,0,HB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,,,0,Transient-Party,90.0,0,0,2017-01-06 -City Hotel,1,175,2017,May,20,19,0,1,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,44.0,,0,Transient,90.0,0,0,2016-11-25 -City Hotel,1,372,2016,July,29,14,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0,2015-10-21 -City Hotel,1,336,2015,September,38,18,0,2,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,Non Refund,6.0,,0,Transient-Party,109.0,0,0,2015-03-25 -City Hotel,1,98,2015,November,48,23,1,2,1,0.0,0,FB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,37.0,,0,Transient-Party,84.0,0,0,2015-09-14 -City Hotel,1,20,2016,February,7,10,0,4,2,0.0,0,SC,IND,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,63.2,0,0,2016-02-10 -City Hotel,1,24,2016,November,45,1,0,4,2,2.0,0,BB,SWE,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,9.0,,0,Transient,161.5,0,0,2016-10-08 -City Hotel,1,55,2016,July,28,7,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,219.0,0,Transient-Party,79.0,0,0,2016-07-06 -City Hotel,1,134,2017,June,23,7,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,20.0,,0,Transient,110.0,0,0,2017-01-24 -City Hotel,1,156,2017,April,17,26,0,3,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,37.0,,0,Transient,100.0,0,0,2016-11-21 -City Hotel,1,87,2015,September,39,25,2,3,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,170.0,0,0,2015-09-09 -City Hotel,1,9,2016,January,4,20,2,9,2,0.0,0,BB,SAU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,88.27,0,0,2016-01-20 -Resort Hotel,1,47,2015,July,27,2,2,5,2,2.0,0,BB,PRT,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,240.0,,0,Transient,153.0,0,0,2015-06-02 -Resort Hotel,1,17,2017,June,23,9,2,3,2,1.0,0,HB,PRT,Direct,Direct,0,0,0,E,E,1,No Deposit,250.0,,0,Transient,223.0,0,0,2017-05-24 -City Hotel,1,41,2015,August,33,12,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.0,0,0,2015-08-07 -City Hotel,1,360,2015,October,42,12,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Contract,62.0,0,0,2015-01-01 -City Hotel,1,135,2016,July,31,26,0,4,2,0.0,0,SC,BEL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,89.25,0,0,2016-03-14 -City Hotel,1,444,2016,December,49,1,0,3,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,Non Refund,34.0,,39,Transient,75.0,0,0,2015-10-23 -Resort Hotel,1,101,2016,November,48,26,2,5,2,0.0,0,BB,BEL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,37.8,0,0,2016-09-18 -City Hotel,1,82,2017,May,18,3,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,29.0,,0,Transient,130.0,0,0,2017-02-10 -City Hotel,1,21,2015,September,37,10,1,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient,65.55,0,0,2015-09-07 -City Hotel,1,11,2017,May,20,14,2,0,2,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,120.0,0,1,2017-05-04 -Resort Hotel,1,393,2016,October,41,8,2,2,2,0.0,0,HB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,68.0,,0,Transient,72.0,0,0,2015-12-17 -Resort Hotel,1,2,2017,May,19,8,1,0,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,E,0,No Deposit,152.0,,0,Transient,64.0,0,1,2017-05-08 -City Hotel,1,331,2017,August,33,19,2,5,1,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,109.67,0,0,2016-09-23 -Resort Hotel,0,1,2016,January,4,21,0,1,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,40.0,0,1,2016-01-22 +hotel,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests +Resort Hotel,175,2017,August,33,14,1,3,2,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,D,D,2,No Deposit,240.0,,0,Transient,200.0,0,1 +City Hotel,352,2017,May,21,24,0,3,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,99.0,0,0 +City Hotel,16,2016,December,53,28,2,4,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,3.0,,0,Transient,76.67,0,0 +Resort Hotel,319,2017,June,23,5,1,3,2,1.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,241.0,,0,Transient,68.08,0,1 +City Hotel,156,2017,April,17,26,0,3,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,37.0,,0,Transient,100.0,0,0 +Resort Hotel,72,2016,June,25,13,1,3,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,116.0,0,0 +City Hotel,11,2017,April,15,14,1,2,2,0.0,0,SC,USA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,138.33,0,0 +Resort Hotel,157,2016,January,5,29,2,5,2,0.0,0,HB,PRT,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,242.0,,0,Transient,88.2,0,1 +City Hotel,36,2017,August,33,16,0,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,10.0,,0,Transient,100.0,0,0 +City Hotel,622,2017,March,12,23,0,1,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,59.0,0,0 +Resort Hotel,253,2015,September,37,12,2,5,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,Non Refund,96.0,,0,Transient,58.95,0,0 +Resort Hotel,1,2016,December,51,13,0,1,1,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,,178.0,0,Transient,4.0,0,0 +City Hotel,301,2016,November,47,18,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,20.0,,0,Transient,52.0,0,0 +City Hotel,59,2017,March,10,11,1,1,2,0.0,0,BB,CN,Direct,Direct,0,0,0,A,A,1,No Deposit,14.0,,0,Transient,98.0,0,0 +Resort Hotel,54,2015,September,39,26,2,6,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,242.0,,0,Transient,69.13,0,1 +City Hotel,296,2016,December,53,26,1,5,2,2.0,0,HB,BEL,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,9.0,,0,Transient,196.07,0,1 +City Hotel,295,2015,August,32,8,2,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.0,0,0 +City Hotel,88,2017,February,7,14,0,3,1,0.0,0,BB,NLD,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,86.4,0,1 +City Hotel,1,2017,February,5,3,0,1,1,0.0,0,SC,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,93.0,0,0 +Resort Hotel,200,2016,December,51,17,6,12,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,314.0,,0,Transient,38.67,0,0 +City Hotel,166,2016,November,45,1,0,3,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,236.0,,0,Transient,110.0,0,0 +City Hotel,137,2016,January,5,27,0,2,1,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,29.0,,45,Transient,70.0,0,0 +City Hotel,113,2017,May,20,20,2,1,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,34.0,,0,Transient,190.0,0,0 +City Hotel,278,2015,July,30,22,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient-Party,62.0,0,0 +City Hotel,43,2017,June,22,3,0,1,3,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,7.0,,0,Transient,212.8,0,1 +Resort Hotel,0,2017,January,4,26,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,5,A,D,0,No Deposit,,9.0,0,Transient,35.0,0,0 +City Hotel,90,2017,April,14,4,0,4,2,2.0,0,BB,BEL,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,9.0,,0,Transient,189.0,0,0 +Resort Hotel,126,2017,April,16,17,1,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,314.0,,0,Transient,54.0,0,1 +City Hotel,275,2016,October,41,5,1,4,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,44.0,,0,Transient,90.0,0,0 +City Hotel,3,2016,October,42,12,0,4,2,0.0,0,BB,CHE,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,175.0,0,3 +City Hotel,221,2016,June,24,10,1,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,Non Refund,3.0,,15,Transient,80.0,0,0 +Resort Hotel,8,2015,December,49,5,2,1,2,0.0,0,HB,PRT,Groups,TA/TO,0,1,0,A,A,0,No Deposit,38.0,,0,Transient-Party,68.0,0,0 +City Hotel,189,2016,May,21,19,0,3,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,0,Transient-Party,65.0,0,0 +City Hotel,211,2017,May,21,21,2,4,2,0.0,0,BB,DNK,Online TA,TA/TO,0,0,0,B,B,1,No Deposit,9.0,,0,Transient,110.75,0,1 +City Hotel,418,2016,September,40,26,1,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,21.0,,223,Transient-Party,75.0,0,0 +City Hotel,93,2015,December,52,26,2,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,1,0,A,A,0,No Deposit,9.0,,0,Contract,106.2,0,1 +City Hotel,130,2017,April,16,21,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,286.0,,0,Transient,95.0,0,0 +City Hotel,283,2015,July,31,27,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Contract,62.0,0,0 +City Hotel,444,2016,December,49,1,0,3,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,Non Refund,34.0,,39,Transient,75.0,0,0 +City Hotel,94,2017,August,34,25,1,2,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,86.0,,0,Transient,127.47,0,0 +Resort Hotel,113,2017,May,22,30,0,2,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,100.0,0,0 +Resort Hotel,210,2017,May,18,4,2,5,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,E,E,0,No Deposit,177.0,,0,Transient,90.0,0,1 +City Hotel,262,2016,December,53,26,1,3,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,73.95,0,0 +City Hotel,93,2017,August,33,17,0,3,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,E,E,1,No Deposit,9.0,,0,Transient,190.0,0,1 +City Hotel,28,2016,October,42,13,0,1,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,234.0,,0,Transient-Party,120.0,0,0 +City Hotel,59,2016,September,39,24,1,1,3,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,168.3,0,0 +Resort Hotel,385,2016,September,39,21,0,4,2,0.0,0,BB,PRT,Groups,Direct,0,0,0,A,A,0,Non Refund,,,0,Transient,66.0,0,0 +City Hotel,304,2016,November,45,3,0,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,21.0,,0,Transient,89.0,0,0 +City Hotel,20,2015,July,30,22,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.0,0,0 +Resort Hotel,90,2016,July,29,13,0,3,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,175.0,0,0 +Resort Hotel,39,2017,February,6,11,0,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,48.0,0,1 +Resort Hotel,179,2016,August,32,1,2,5,2,0.0,0,FB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,314.0,,0,Transient,146.8,0,2 +City Hotel,13,2016,September,40,27,0,2,1,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,7.0,,0,Transient,126.28,0,0 +City Hotel,95,2016,November,45,5,0,1,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,,,0,Transient-Party,75.0,0,0 +City Hotel,107,2016,June,24,10,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,187.0,,0,Transient-Party,130.0,0,0 +City Hotel,158,2016,May,22,24,0,2,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,37.0,,31,Transient,130.0,0,0 +Resort Hotel,78,2016,December,53,31,1,1,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,D,C,1,No Deposit,181.0,,0,Transient-Party,168.0,0,0 +City Hotel,31,2016,February,6,5,0,2,2,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,67.5,0,0 +Resort Hotel,48,2015,July,31,27,1,1,2,0.0,0,HB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,167.0,0,1 +City Hotel,136,2017,March,12,21,0,3,2,0.0,0,BB,AUT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,93.6,0,1 +City Hotel,113,2016,June,27,30,1,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,192.0,,0,Transient,85.0,0,0 +City Hotel,360,2015,October,42,12,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Contract,62.0,0,0 +City Hotel,461,2016,October,42,13,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0 +City Hotel,223,2016,June,25,14,0,2,2,0.0,0,HB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,133.0,,40,Transient,135.0,0,0 +City Hotel,14,2016,June,27,28,0,3,2,0.0,0,BB,QAT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,152.67,0,1 +City Hotel,7,2017,April,15,13,0,3,2,0.0,0,SC,HKG,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,160.0,0,1 +City Hotel,309,2016,May,20,12,0,1,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,59.0,0,0 +Resort Hotel,1,2015,November,47,18,0,1,1,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,38.0,0,0 +City Hotel,245,2016,July,28,6,2,4,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,19.0,,0,Transient,110.0,0,0 +Resort Hotel,73,2015,August,35,25,0,1,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,112.5,0,1 +Resort Hotel,181,2016,April,17,22,1,2,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,44.1,0,1 +City Hotel,8,2017,July,28,10,1,2,2,1.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,166.67,0,1 +City Hotel,37,2017,March,11,15,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,378.0,,0,Transient,110.0,0,0 +Resort Hotel,16,2015,December,49,3,0,3,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,46.4,0,1 +City Hotel,21,2016,September,39,18,2,1,2,0.0,0,SC,MYS,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,129.0,0,1 +City Hotel,133,2017,April,16,19,0,4,2,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,390.0,,0,Transient-Party,124.0,0,0 +Resort Hotel,209,2017,January,4,28,1,1,2,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,37.8,0,0 +City Hotel,165,2017,May,20,19,2,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,225.0,0,0 +City Hotel,155,2017,June,25,24,2,1,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,30.0,,26,Transient,120.0,0,0 +City Hotel,317,2016,September,37,9,1,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,Non Refund,58.0,,56,Transient,90.0,0,0 +City Hotel,198,2015,December,49,5,2,1,2,0.0,0,HB,ESP,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,90.0,0,0 +Resort Hotel,13,2016,August,33,12,0,2,2,0.0,0,HB,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,256.0,0,0 +City Hotel,168,2016,April,17,22,1,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,26.0,,77,Transient,85.0,0,0 +City Hotel,226,2017,July,28,9,2,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,110.0,0,0 +City Hotel,92,2017,March,11,15,0,3,2,0.0,0,BB,PRT,Groups,Direct,0,0,0,A,A,0,Non Refund,,,0,Transient,85.0,0,0 +City Hotel,193,2017,July,30,24,1,3,2,1.0,0,BB,BEL,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,9.0,,0,Transient,152.63,0,3 +City Hotel,60,2017,March,13,30,1,3,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,104.4,0,0 +City Hotel,277,2017,July,29,17,1,2,2,1.0,0,BB,USA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient-Party,123.3,0,1 +City Hotel,275,2016,April,17,17,2,0,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,12.0,,0,Transient,110.0,0,0 +Resort Hotel,185,2016,April,17,18,1,1,2,0.0,0,HB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,298.0,,0,Transient-Party,86.0,0,0 +Resort Hotel,6,2017,May,18,5,2,3,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,G,G,2,No Deposit,250.0,,0,Transient,195.0,0,0 +City Hotel,301,2015,August,33,14,0,2,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,Non Refund,6.0,,0,Transient-Party,101.5,0,0 +Resort Hotel,123,2016,October,43,21,2,3,2,0.0,0,BB,SWE,Groups,Corporate,0,0,0,E,E,3,No Deposit,,,0,Transient-Party,75.0,1,0 +City Hotel,89,2015,September,40,27,1,0,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,170.0,0,0 +City Hotel,254,2016,September,39,24,1,1,2,0.0,0,HB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,96.0,0,0 +City Hotel,12,2015,December,49,5,1,1,2,0.0,0,BB,ESP,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,28.0,,0,Transient,55.0,0,0 +Resort Hotel,138,2017,August,32,7,1,3,2,1.0,0,HB,CHE,Online TA,TA/TO,0,0,0,C,C,1,No Deposit,240.0,,0,Transient,298.0,0,0 +City Hotel,31,2016,May,20,13,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,16.0,,0,Transient,105.3,0,0 +City Hotel,207,2015,September,40,29,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,No Deposit,20.0,,0,Transient,48.0,0,0 +City Hotel,35,2015,September,39,25,2,3,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,170.0,0,0 +City Hotel,18,2017,May,21,21,2,2,2,0.0,0,SC,FRA,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,137.5,0,0 +Resort Hotel,77,2017,April,15,12,0,3,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,81.67,0,0 +City Hotel,61,2016,April,16,11,1,3,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,76.58,0,0 +Resort Hotel,202,2017,May,18,4,0,1,2,0.0,0,HB,PRT,Groups,TA/TO,0,0,0,D,D,0,No Deposit,251.0,,0,Transient-Party,90.0,0,0 +City Hotel,303,2017,April,14,6,0,3,2,2.0,0,BB,TUR,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,9.0,,0,Transient,181.8,0,1 +City Hotel,67,2017,March,11,14,0,3,2,0.0,0,BB,NLD,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,106.2,0,0 +Resort Hotel,124,2016,August,34,17,0,4,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,240.0,,0,Transient,191.0,0,1 +City Hotel,476,2017,July,28,15,2,1,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,229.0,,0,Transient-Party,112.67,0,0 +City Hotel,180,2015,October,42,16,0,1,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,3.0,,0,Transient,70.0,0,0 +City Hotel,115,2016,June,26,19,2,3,2,0.0,0,BB,GBR,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,101.66,0,1 +City Hotel,33,2016,September,37,8,2,3,2,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,135.9,0,0 +Resort Hotel,49,2015,September,38,19,1,1,2,0.0,0,BB,,Direct,Direct,0,0,0,E,F,0,No Deposit,,,0,Transient,99.0,0,0 +City Hotel,30,2015,October,41,7,0,3,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Transient,94.5,0,2 +Resort Hotel,19,2017,July,30,28,0,2,2,0.0,0,BB,AGO,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,184.5,0,2 +City Hotel,16,2016,May,23,29,1,0,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,111.0,0,0 +City Hotel,149,2017,June,25,24,0,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,,,0,Transient-Party,130.0,0,0 +City Hotel,55,2017,March,10,5,2,0,1,0.0,0,BB,ARG,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,9.0,,0,Transient,94.0,0,1 +City Hotel,278,2016,August,33,11,0,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient,65.0,0,0 +City Hotel,32,2016,February,6,5,1,2,1,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,72.9,0,0 +Resort Hotel,47,2016,November,45,5,2,5,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,F,F,0,No Deposit,250.0,,0,Transient,88.96,0,0 +Resort Hotel,36,2017,January,5,30,1,2,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,240.0,,0,Transient,48.0,0,1 +Resort Hotel,279,2017,June,25,22,2,4,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,80.1,0,1 +City Hotel,59,2017,January,4,23,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,326.0,,0,Transient,80.0,0,0 +Resort Hotel,34,2016,January,3,15,0,2,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,44.8,0,1 +City Hotel,250,2017,May,21,24,0,4,2,0.0,0,BB,SWE,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,125.1,0,1 +City Hotel,5,2016,March,13,23,0,1,2,0.0,0,SC,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,89.0,0,0 +City Hotel,365,2015,October,42,17,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient,62.8,0,0 +Resort Hotel,121,2016,December,51,16,2,2,2,0.0,0,HB,ESP,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,73.8,0,2 +City Hotel,38,2017,January,2,14,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,Non Refund,,67.0,0,Transient,75.0,0,0 +City Hotel,86,2015,October,40,3,2,1,2,0.0,0,HB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,0,Transient-Party,86.0,0,0 +City Hotel,134,2017,June,26,29,0,3,2,0.0,0,SC,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,95.7,0,0 +City Hotel,70,2017,January,1,3,0,4,2,1.0,0,BB,ITA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,105.4,0,1 +City Hotel,297,2015,August,33,10,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient-Party,62.0,0,0 +City Hotel,3,2016,October,43,16,1,0,1,0.0,0,SC,KOR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,,,0,Transient,120.0,0,1 +City Hotel,44,2016,February,8,17,0,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,44.0,,3,Transient,75.0,0,0 +City Hotel,264,2017,July,28,12,1,4,1,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Transient,80.09,0,2 +Resort Hotel,173,2016,September,40,27,2,5,2,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,92.14,0,0 +City Hotel,337,2016,June,24,9,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0 +City Hotel,28,2017,April,16,18,0,3,3,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,9.0,,0,Transient,214.0,0,0 +City Hotel,29,2017,June,22,3,2,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,229.0,,0,Transient-Party,112.67,0,0 +City Hotel,69,2016,October,44,25,0,3,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,69.99,0,0 +City Hotel,18,2017,February,5,3,1,2,2,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,88.0,0,0 +City Hotel,101,2016,June,27,27,1,5,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,140.0,0,0 +City Hotel,162,2016,October,42,14,2,2,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,34.0,,0,Transient,115.0,0,0 +Resort Hotel,123,2016,November,48,25,0,2,2,0.0,0,HB,PRT,Online TA,TA/TO,1,0,2,A,D,0,No Deposit,240.0,,0,Transient,64.8,0,1 +Resort Hotel,90,2016,April,17,19,4,10,2,0.0,0,HB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,90.14,0,2 +City Hotel,273,2016,May,20,13,1,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,21.0,,0,Transient,95.0,0,0 +City Hotel,358,2015,October,41,10,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0 +City Hotel,260,2015,July,27,4,2,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient-Party,62.0,0,0 +City Hotel,161,2017,February,7,13,1,3,2,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,8.0,,0,Transient,74.8,0,1 +Resort Hotel,245,2015,September,39,25,0,1,2,0.0,0,HB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,248.0,,0,Transient,80.0,0,0 +City Hotel,36,2017,July,30,27,1,3,2,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,85.0,,0,Transient,79.0,0,0 +City Hotel,87,2017,February,7,17,0,2,2,0.0,0,SC,CHN,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,71.28,0,0 +Resort Hotel,180,2015,August,33,9,4,9,2,0.0,0,BB,PRT,Online TA,TA/TO,0,1,0,A,A,0,No Deposit,240.0,,0,Transient,134.0,0,0 +Resort Hotel,63,2016,December,50,9,0,2,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,339.0,,0,Transient,26.64,0,0 +City Hotel,111,2017,March,12,20,4,10,1,0.0,0,BB,AGO,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,78.26,0,0 +City Hotel,8,2016,February,8,15,1,2,1,0.0,0,BB,CHE,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,85.0,0,0 +City Hotel,198,2016,May,21,20,0,1,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,B,1,No Deposit,1.0,,98,Transient-Party,57.5,0,0 +Resort Hotel,319,2017,June,23,5,1,3,2,0.0,0,BB,IRL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,74.25,0,0 +Resort Hotel,78,2015,October,41,9,1,2,2,0.0,0,FB,ESP,Groups,TA/TO,0,0,0,A,A,0,Non Refund,134.0,,0,Transient,92.0,0,0 +Resort Hotel,169,2016,August,33,13,2,6,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,314.0,,0,Transient,141.6,0,0 +Resort Hotel,148,2015,October,43,24,0,1,1,0.0,0,BB,PRT,Groups,Direct,0,0,0,A,A,0,Non Refund,,,0,Transient,39.0,0,0 +Resort Hotel,259,2015,September,38,17,0,3,2,0.0,0,BB,GBR,Groups,Corporate,0,1,0,A,A,0,Non Refund,,223.0,0,Transient,40.05,0,0 +City Hotel,351,2016,June,26,23,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0 +City Hotel,113,2016,December,50,8,0,1,2,0.0,0,BB,NLD,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,9.0,,0,Transient,90.0,0,1 +City Hotel,68,2017,July,28,11,0,2,2,0.0,0,SC,IRL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,120.0,0,1 +City Hotel,35,2016,September,40,26,1,0,2,0.0,0,SC,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,99.0,0,0 +Resort Hotel,24,2017,March,11,13,1,0,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,66.0,,0,Transient-Party,32.0,0,0 +City Hotel,66,2017,April,15,9,2,0,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,100.0,0,0 +Resort Hotel,203,2017,April,17,25,0,2,2,0.0,0,HB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,,,0,Transient-Party,90.0,0,0 +City Hotel,175,2017,May,20,19,0,1,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,44.0,,0,Transient,90.0,0,0 +City Hotel,372,2016,July,29,14,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0 +City Hotel,336,2015,September,38,18,0,2,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,Non Refund,6.0,,0,Transient-Party,109.0,0,0 +City Hotel,98,2015,November,48,23,1,2,1,0.0,0,FB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,37.0,,0,Transient-Party,84.0,0,0 +City Hotel,20,2016,February,7,10,0,4,2,0.0,0,SC,IND,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,63.2,0,0 +City Hotel,24,2016,November,45,1,0,4,2,2.0,0,BB,SWE,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,9.0,,0,Transient,161.5,0,0 +City Hotel,55,2016,July,28,7,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,219.0,0,Transient-Party,79.0,0,0 +City Hotel,134,2017,June,23,7,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,20.0,,0,Transient,110.0,0,0 +City Hotel,87,2015,September,39,25,2,3,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,170.0,0,0 +City Hotel,9,2016,January,4,20,2,9,2,0.0,0,BB,SAU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,88.27,0,0 +Resort Hotel,47,2015,July,27,2,2,5,2,2.0,0,BB,PRT,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,240.0,,0,Transient,153.0,0,0 +Resort Hotel,17,2017,June,23,9,2,3,2,1.0,0,HB,PRT,Direct,Direct,0,0,0,E,E,1,No Deposit,250.0,,0,Transient,223.0,0,0 +City Hotel,41,2015,August,33,12,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.0,0,0 +City Hotel,135,2016,July,31,26,0,4,2,0.0,0,SC,BEL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,89.25,0,0 +Resort Hotel,101,2016,November,48,26,2,5,2,0.0,0,BB,BEL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,37.8,0,0 +City Hotel,82,2017,May,18,3,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,29.0,,0,Transient,130.0,0,0 +City Hotel,21,2015,September,37,10,1,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient,65.55,0,0 +City Hotel,11,2017,May,20,14,2,0,2,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,120.0,0,1 +Resort Hotel,393,2016,October,41,8,2,2,2,0.0,0,HB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,68.0,,0,Transient,72.0,0,0 +Resort Hotel,2,2017,May,19,8,1,0,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,E,0,No Deposit,152.0,,0,Transient,64.0,0,1 +City Hotel,331,2017,August,33,19,2,5,1,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,109.67,0,0 +Resort Hotel,1,2016,January,4,21,0,1,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,40.0,0,1 diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_001/history.csv b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_001/history.csv index 9207538d7a163c9d80994564d4c2e86515d11b4f..a972be10ee189e8d17fee09a96f340e4ffd0aa4d 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_001/history.csv +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_001/history.csv @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:2481e31c4cf8b343d07bfa1d49b3f776793cd6477fac4ae804eface512dcff70 -size 16602092 +oid sha256:930553d0c27f1898f8ac55b1793e6f9db355b7d76d5ecf52a0ddc761009a3a5d +size 15065824 diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_001/info.json b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_001/info.json index 4816c05993309461836de8622438e342f2e503ff..20956c93d07801fe3772275e08832605a78593ed 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_001/info.json +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_001/info.json @@ -1,398 +1,391 @@ -{ - "name": "Hotel Booking Demand", - "source": "https://www.kaggle.com/datasets/jessemostipak/hotel-booking-demand/data", - "data_intro": "This dataset contains hotel booking data for a city hotel and a resort hotel, providing details on booking dates, stay duration, guest count, cancellation trends, and pricing. It is useful for demand forecasting, revenue management, and customer behavior analysis.", - "is_splited": false, - "overall_size": 119390, - "train_size": 0, - "test_size": 0, - "c_classes": 12, - "n_classes": 20, - "cat_feature_intro": { - "hotel": "Type of hotel (City Hotel or Resort Hotel).", - "arrival_date_month": "Month of arrival for the booking.", - "meal": "Type of meal plan booked (e.g., Breakfast included, Half board, Full board).", - "country": "Country of origin of the guest.", - "market_segment": "Booking distribution channel (e.g., direct, corporate, travel agent).", - "distribution_channel": "Channel through which the booking was made (e.g., online travel agents, direct).", - "reserved_room_type": "Type of room reserved by the guest before any changes.", - "assigned_room_type": "Final room type assigned to the guest.", - "deposit_type": "Indicates if a deposit was made and its type (e.g., No Deposit, Refundable, Non-Refundable).", - "customer_type": "Type of customer (e.g., transient, group, contract).", - "reservation_status": "Final status of the booking (e.g., Canceled, Checked-out, No-show).", - "reservation_status_date": "Date when the reservation status was last updated." - }, - "num_feature_intro": { - "is_canceled": "Indicates whether the booking was canceled (1) or not (0).", - "lead_time": "Number of days between booking and arrival.", - "arrival_date_year": "Year of arrival for the booking.", - "arrival_date_week_number": "Week number of the arrival date.", - "arrival_date_day_of_month": "Day of the month of arrival.", - "stays_in_weekend_nights": "Number of weekend nights (Saturday or Sunday) the guest stayed.", - "stays_in_week_nights": "Number of weekday nights (Monday to Friday) the guest stayed.", - "adults": "Number of adults included in the booking.", - "children": "Number of children included in the booking.", - "babies": "Number of babies included in the booking.", - "is_repeated_guest": "Indicates whether the guest is a returning customer (1) or not (0).", - "previous_cancellations": "Number of times the guest has canceled previous bookings.", - "previous_bookings_not_canceled": "Number of previous bookings not canceled by the guest.", - "booking_changes": "Number of changes made to the booking.", - "agent": "ID of the booking agent (if applicable).", - "company": "ID of the company making the booking (if applicable).", - "days_in_waiting_list": "Number of days the booking was on the waiting list before confirmation.", - "adr": "Average daily rate (price per night per room).", - "required_car_parking_spaces": "Number of car parking spaces required by the guest.", - "total_of_special_requests": "Number of special requests made by the guest (e.g., high floor, twin beds)." - }, - "evaluation_metric": null, - "task_type": "classification", - "target": "reservation_status", - "num_feature_value": { - "adr": [ - -6.38, - 5400.0 - ], - "adults": [ - 0.0, - 55.0 - ], - "agent": [ - 1.0, - 535.0 - ], - "arrival_date_day_of_month": [ - 1.0, - 31.0 - ], - "arrival_date_week_number": [ - 1.0, - 53.0 - ], - "arrival_date_year": [ - 2015.0, - 2017.0 - ], - "babies": [ - 0.0, - 10.0 - ], - "booking_changes": [ - 0.0, - 21.0 - ], - "children": [ - 0.0, - 10.0 - ], - "company": [ - 6.0, - 543.0 - ], - "days_in_waiting_list": [ - 0.0, - 391.0 - ], - "is_canceled": [ - 0.0, - 1.0 - ], - "is_repeated_guest": [ - 0.0, - 1.0 - ], - "lead_time": [ - 0.0, - 737.0 - ], - "previous_bookings_not_canceled": [ - 0.0, - 72.0 - ], - "previous_cancellations": [ - 0.0, - 26.0 - ], - "required_car_parking_spaces": [ - 0.0, - 8.0 - ], - "stays_in_week_nights": [ - 0.0, - 50.0 - ], - "stays_in_weekend_nights": [ - 0.0, - 19.0 - ], - "total_of_special_requests": [ - 0.0, - 5.0 - ] - }, - "cat_feature_value": { - "arrival_date_month": [ - "April", - "August", - "December", - "February", - "January", - "July", - "June", - "March", - "May", - "November", - "October", - "September" - ], - "assigned_room_type": [ - "A", - "B", - "C", - "D", - "E", - "F", - "G", - "H", - "I", - "K", - "L", - "P" - ], - "country": [ - "ABW", - "AGO", - "AIA", - "ALB", - "AND", - "ARE", - "ARG", - "ARM", - "ASM", - "ATA", - "ATF", - "AUS", - "AUT", - "AZE", - "BDI", - "BEL", - "BEN", - "BFA", - "BGD", - "BGR", - "BHR", - "BHS", - "BIH", - "BLR", - "BOL", - "BRA", - "BRB", - "BWA", - "CAF", - "CHE", - "CHL", - "CHN", - "CIV", - "CMR", - "CN", - "COL", - "COM", - "CPV", - "CRI", - "CUB", - "CYM", - "CYP", - "CZE", - "DEU", - "DJI", - "DMA", - "DNK", - "DOM", - "DZA", - "ECU", - "EGY", - "ESP", - "EST", - "ETH", - "FIN", - "FJI", - "FRA", - "FRO", - "GAB", - "GBR", - "GEO", - "GGY", - "GHA", - "GIB", - "GLP", - "GNB", - "GRC", - "GTM", - "GUY", - "HKG", - "HND", - "HRV", - "HUN", - "IDN", - "IMN", - "IND", - "IRL", - "IRN", - "IRQ", - "ISL", - "ISR", - "ITA", - "JAM", - "JEY", - "JOR", - "JPN", - "KAZ", - "KEN", - "KHM", - "KIR", - "KNA", - "KOR", - "KWT", - "LAO", - "LBN", - "LBY", - "LCA", - "LIE", - "LKA", - "LTU", - "LUX", - "LVA", - "MAC", - "MAR", - "MCO", - "MDG", - "MDV", - "MEX", - "MKD", - "MLI", - "MLT", - "MMR", - "MNE", - "MOZ", - "MRT", - "MUS", - "MWI", - "MYS", - "MYT", - "NAM", - "NCL", - "NGA", - "NIC", - "NLD", - "NOR", - "NPL", - "NZL", - "OMN", - "PAK", - "PAN", - "PER", - "PHL", - "PLW", - "POL", - "PRI", - "PRT", - "PRY", - "PYF", - "QAT", - "ROU", - "RUS", - "RWA", - "SAU", - "SDN", - "SEN", - "SGP", - "SLE", - "SLV", - "SMR", - "SRB", - "STP", - "SUR", - "SVK", - "SVN", - "SWE", - "SYC", - "SYR", - "TGO", - "THA", - "TJK", - "TMP", - "TUN", - "TUR", - "TWN", - "TZA", - "UGA", - "UKR", - "UMI", - "URY", - "USA", - "UZB", - "VEN", - "VGB", - "VNM", - "ZAF", - "ZMB", - "ZWE" - ], - "customer_type": [ - "Contract", - "Group", - "Transient", - "Transient-Party" - ], - "deposit_type": [ - "No Deposit", - "Non Refund", - "Refundable" - ], - "distribution_channel": [ - "Corporate", - "Direct", - "GDS", - "TA/TO", - "Undefined" - ], - "hotel": [ - "City Hotel", - "Resort Hotel" - ], - "market_segment": [ - "Aviation", - "Complementary", - "Corporate", - "Direct", - "Groups", - "Offline TA/TO", - "Online TA", - "Undefined" - ], - "meal": [ - "BB", - "FB", - "HB", - "SC", - "Undefined" - ], - "reservation_status": [ - "Canceled", - "Check-Out", - "No-Show" - ], - "reservation_status_date": "string", - "reserved_room_type": [ - "A", - "B", - "C", - "D", - "E", - "F", - "G", - "H", - "L", - "P" - ] - } -} \ No newline at end of file +{ + "name": "Hotel Booking Demand", + "source": "https://www.kaggle.com/datasets/jessemostipak/hotel-booking-demand/data", + "data_intro": "This dataset contains hotel booking data for a city hotel and a resort hotel, providing details on booking dates, stay duration, guest count, cancellation trends, and pricing. It is useful for demand forecasting, revenue management, and customer behavior analysis.", + "is_splited": false, + "overall_size": 119390, + "train_size": 0, + "test_size": 0, + "c_classes": 11, + "n_classes": 19, + "cat_feature_intro": { + "hotel": "Type of hotel (City Hotel or Resort Hotel).", + "arrival_date_month": "Month of arrival for the booking.", + "meal": "Type of meal plan booked (e.g., Breakfast included, Half board, Full board).", + "country": "Country of origin of the guest.", + "market_segment": "Booking distribution channel (e.g., direct, corporate, travel agent).", + "distribution_channel": "Channel through which the booking was made (e.g., online travel agents, direct).", + "reserved_room_type": "Type of room reserved by the guest before any changes.", + "assigned_room_type": "Final room type assigned to the guest.", + "deposit_type": "Indicates if a deposit was made and its type (e.g., No Deposit, Refundable, Non-Refundable).", + "customer_type": "Type of customer (e.g., transient, group, contract).", + "reservation_status": "Final status of the booking (e.g., Canceled, Checked-out, No-show)." + }, + "num_feature_intro": { + "lead_time": "Number of days between booking and arrival.", + "arrival_date_year": "Year of arrival for the booking.", + "arrival_date_week_number": "Week number of the arrival date.", + "arrival_date_day_of_month": "Day of the month of arrival.", + "stays_in_weekend_nights": "Number of weekend nights (Saturday or Sunday) the guest stayed.", + "stays_in_week_nights": "Number of weekday nights (Monday to Friday) the guest stayed.", + "adults": "Number of adults included in the booking.", + "children": "Number of children included in the booking.", + "babies": "Number of babies included in the booking.", + "is_repeated_guest": "Indicates whether the guest is a returning customer (1) or not (0).", + "previous_cancellations": "Number of times the guest has canceled previous bookings.", + "previous_bookings_not_canceled": "Number of previous bookings not canceled by the guest.", + "booking_changes": "Number of changes made to the booking.", + "agent": "ID of the booking agent (if applicable).", + "company": "ID of the company making the booking (if applicable).", + "days_in_waiting_list": "Number of days the booking was on the waiting list before confirmation.", + "adr": "Average daily rate (price per night per room).", + "required_car_parking_spaces": "Number of car parking spaces required by the guest.", + "total_of_special_requests": "Number of special requests made by the guest (e.g., high floor, twin beds)." + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "reservation_status", + "num_feature_value": { + "adr": [ + -6.38, + 5400.0 + ], + "adults": [ + 0.0, + 55.0 + ], + "agent": [ + 1.0, + 535.0 + ], + "arrival_date_day_of_month": [ + 1.0, + 31.0 + ], + "arrival_date_week_number": [ + 1.0, + 53.0 + ], + "arrival_date_year": [ + 2015.0, + 2017.0 + ], + "babies": [ + 0.0, + 10.0 + ], + "booking_changes": [ + 0.0, + 21.0 + ], + "children": [ + 0.0, + 10.0 + ], + "company": [ + 6.0, + 543.0 + ], + "days_in_waiting_list": [ + 0.0, + 391.0 + ], + "is_repeated_guest": [ + 0.0, + 1.0 + ], + "lead_time": [ + 0.0, + 737.0 + ], + "previous_bookings_not_canceled": [ + 0.0, + 72.0 + ], + "previous_cancellations": [ + 0.0, + 26.0 + ], + "required_car_parking_spaces": [ + 0.0, + 8.0 + ], + "stays_in_week_nights": [ + 0.0, + 50.0 + ], + "stays_in_weekend_nights": [ + 0.0, + 19.0 + ], + "total_of_special_requests": [ + 0.0, + 5.0 + ] + }, + "cat_feature_value": { + "arrival_date_month": [ + "April", + "August", + "December", + "February", + "January", + "July", + "June", + "March", + "May", + "November", + "October", + "September" + ], + "assigned_room_type": [ + "A", + "B", + "C", + "D", + "E", + "F", + "G", + "H", + "I", + "K", + "L", + "P" + ], + "country": [ + "ABW", + "AGO", + "AIA", + "ALB", + "AND", + "ARE", + "ARG", + "ARM", + "ASM", + "ATA", + "ATF", + "AUS", + "AUT", + "AZE", + "BDI", + "BEL", + "BEN", + "BFA", + "BGD", + "BGR", + "BHR", + "BHS", + "BIH", + "BLR", + "BOL", + "BRA", + "BRB", + "BWA", + "CAF", + "CHE", + "CHL", + "CHN", + "CIV", + "CMR", + "CN", + "COL", + "COM", + "CPV", + "CRI", + "CUB", + "CYM", + "CYP", + "CZE", + "DEU", + "DJI", + "DMA", + "DNK", + "DOM", + "DZA", + "ECU", + "EGY", + "ESP", + "EST", + "ETH", + "FIN", + "FJI", + "FRA", + "FRO", + "GAB", + "GBR", + "GEO", + "GGY", + "GHA", + "GIB", + "GLP", + "GNB", + "GRC", + "GTM", + "GUY", + "HKG", + "HND", + "HRV", + "HUN", + "IDN", + "IMN", + "IND", + "IRL", + "IRN", + "IRQ", + "ISL", + "ISR", + "ITA", + "JAM", + "JEY", + "JOR", + "JPN", + "KAZ", + "KEN", + "KHM", + "KIR", + "KNA", + "KOR", + "KWT", + "LAO", + "LBN", + "LBY", + "LCA", + "LIE", + "LKA", + "LTU", + "LUX", + "LVA", + "MAC", + "MAR", + "MCO", + "MDG", + "MDV", + "MEX", + "MKD", + "MLI", + "MLT", + "MMR", + "MNE", + "MOZ", + "MRT", + "MUS", + "MWI", + "MYS", + "MYT", + "NAM", + "NCL", + "NGA", + "NIC", + "NLD", + "NOR", + "NPL", + "NZL", + "OMN", + "PAK", + "PAN", + "PER", + "PHL", + "PLW", + "POL", + "PRI", + "PRT", + "PRY", + "PYF", + "QAT", + "ROU", + "RUS", + "RWA", + "SAU", + "SDN", + "SEN", + "SGP", + "SLE", + "SLV", + "SMR", + "SRB", + "STP", + "SUR", + "SVK", + "SVN", + "SWE", + "SYC", + "SYR", + "TGO", + "THA", + "TJK", + "TMP", + "TUN", + "TUR", + "TWN", + "TZA", + "UGA", + "UKR", + "UMI", + "URY", + "USA", + "UZB", + "VEN", + "VGB", + "VNM", + "ZAF", + "ZMB", + "ZWE" + ], + "customer_type": [ + "Contract", + "Group", + "Transient", + "Transient-Party" + ], + "deposit_type": [ + "No Deposit", + "Non Refund", + "Refundable" + ], + "distribution_channel": [ + "Corporate", + "Direct", + "GDS", + "TA/TO", + "Undefined" + ], + "hotel": [ + "City Hotel", + "Resort Hotel" + ], + "market_segment": [ + "Aviation", + "Complementary", + "Corporate", + "Direct", + "Groups", + "Offline TA/TO", + "Online TA", + "Undefined" + ], + "meal": [ + "BB", + "FB", + "HB", + "SC", + "Undefined" + ], + "reservation_status": [ + "Canceled", + "Check-Out", + "No-Show" + ], + "reserved_room_type": [ + "A", + "B", + "C", + "D", + "E", + "F", + "G", + "H", + "L", + "P" + ] + } +} diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_001/test_current.csv b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_001/test_current.csv index 6224dea1b50a2c48eb88f51baff300a974c1c2be..9052ce3b25675ce8127fe6417336913074e47d27 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_001/test_current.csv +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_001/test_current.csv @@ -1,16 +1,16 @@ -hotel,is_canceled,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests,reservation_status,reservation_status_date -City Hotel,0,1,2017,February,5,3,0,1,1,0.0,0,SC,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,93.0,0,0,Check-Out,2017-02-04 -City Hotel,0,3,2016,October,42,12,0,4,2,0.0,0,BB,CHE,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,175.0,0,3,Check-Out,2016-10-16 -City Hotel,0,13,2016,September,40,27,0,2,1,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,7.0,,0,Transient,126.28,0,0,Check-Out,2016-09-29 -Resort Hotel,0,78,2016,December,53,31,1,1,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,D,C,1,No Deposit,181.0,,0,Transient-Party,168.0,0,0,Check-Out,2017-01-02 -Resort Hotel,0,16,2015,December,49,3,0,3,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,46.4,0,1,Check-Out,2015-12-06 -City Hotel,0,133,2017,April,16,19,0,4,2,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,390.0,,0,Transient-Party,124.0,0,0,Check-Out,2017-04-23 -City Hotel,0,277,2017,July,29,17,1,2,2,1.0,0,BB,USA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient-Party,123.3,0,1,Check-Out,2017-07-20 -Resort Hotel,0,123,2016,October,43,21,2,3,2,0.0,0,BB,SWE,Groups,Corporate,0,0,0,E,E,3,No Deposit,,,0,Transient-Party,75.0,1,0,Check-Out,2016-10-26 -City Hotel,0,12,2015,December,49,5,1,1,2,0.0,0,BB,ESP,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,28.0,,0,Transient,55.0,0,0,Check-Out,2015-12-07 -City Hotel,0,31,2016,May,20,13,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,16.0,,0,Transient,105.3,0,0,Check-Out,2016-05-15 -Resort Hotel,0,124,2016,August,34,17,0,4,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,240.0,,0,Transient,191.0,0,1,Check-Out,2016-08-21 -City Hotel,0,115,2016,June,26,19,2,3,2,0.0,0,BB,GBR,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,101.66,0,1,Check-Out,2016-06-24 -City Hotel,0,3,2016,October,43,16,1,0,1,0.0,0,SC,KOR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,,,0,Transient,120.0,0,1,Check-Out,2016-10-17 -Resort Hotel,0,123,2016,November,48,25,0,2,2,0.0,0,HB,PRT,Online TA,TA/TO,1,0,2,A,D,0,No Deposit,240.0,,0,Transient,64.8,0,1,Check-Out,2016-11-27 -Resort Hotel,0,1,2016,January,4,21,0,1,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,40.0,0,1,Check-Out,2016-01-22 +hotel,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests,reservation_status +City Hotel,1,2017,February,5,3,0,1,1,0.0,0,SC,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,93.0,0,0,Check-Out +City Hotel,3,2016,October,42,12,0,4,2,0.0,0,BB,CHE,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,175.0,0,3,Check-Out +City Hotel,13,2016,September,40,27,0,2,1,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,7.0,,0,Transient,126.28,0,0,Check-Out +Resort Hotel,78,2016,December,53,31,1,1,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,D,C,1,No Deposit,181.0,,0,Transient-Party,168.0,0,0,Check-Out +Resort Hotel,16,2015,December,49,3,0,3,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,46.4,0,1,Check-Out +City Hotel,133,2017,April,16,19,0,4,2,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,390.0,,0,Transient-Party,124.0,0,0,Check-Out +City Hotel,277,2017,July,29,17,1,2,2,1.0,0,BB,USA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient-Party,123.3,0,1,Check-Out +Resort Hotel,123,2016,October,43,21,2,3,2,0.0,0,BB,SWE,Groups,Corporate,0,0,0,E,E,3,No Deposit,,,0,Transient-Party,75.0,1,0,Check-Out +City Hotel,12,2015,December,49,5,1,1,2,0.0,0,BB,ESP,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,28.0,,0,Transient,55.0,0,0,Check-Out +City Hotel,31,2016,May,20,13,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,16.0,,0,Transient,105.3,0,0,Check-Out +Resort Hotel,124,2016,August,34,17,0,4,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,240.0,,0,Transient,191.0,0,1,Check-Out +City Hotel,115,2016,June,26,19,2,3,2,0.0,0,BB,GBR,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,101.66,0,1,Check-Out +City Hotel,3,2016,October,43,16,1,0,1,0.0,0,SC,KOR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,,,0,Transient,120.0,0,1,Check-Out +Resort Hotel,123,2016,November,48,25,0,2,2,0.0,0,HB,PRT,Online TA,TA/TO,1,0,2,A,D,0,No Deposit,240.0,,0,Transient,64.8,0,1,Check-Out +Resort Hotel,1,2016,January,4,21,0,1,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,40.0,0,1,Check-Out diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_002/Hotel_booking_demand_B4_002_current.json b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_002/Hotel_booking_demand_B4_002_current.json index 2f9b97bfc7ab905e00b217405dc7c8c709a97596..a4e3b0834c1c4466251b5966d5102ff104a1e404 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_002/Hotel_booking_demand_B4_002_current.json +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_002/Hotel_booking_demand_B4_002_current.json @@ -1,7443 +1,7043 @@ { - "id": "002", - "task_type": "B4", - "subtask_type": "list", - "perspective": "data_holder", - "dataset_name": "Hotel booking demand", - "table_path": "output/daily/classification/Hotel booking demand_B4/Hotel booking demand_B4_002/test.csv", - "query": "Our overbooking strategy requires us to pinpoint reservations with a high probability of being canceled. Using the historical booking logs I've provided, which detail final reservation outcomes, can you list all the new candidate bookings that are most likely to end up as Canceled?", - "meta_info": { - "domain": "daily" + "id": "002", + "task_type": "B4", + "subtask_type": "list", + "perspective": "data_holder", + "dataset_name": "Hotel booking demand", + "table_path": "output/daily/classification/Hotel booking demand_B4/Hotel booking demand_B4_002/test.csv", + "query": "Our overbooking strategy requires us to pinpoint reservations with a high probability of being canceled. Using the historical booking logs I've provided, which detail final reservation outcomes, can you list all the new candidate bookings that are most likely to end up as Canceled?", + "meta_info": { + "domain": "daily" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "004", + "features": { + "hotel": "City Hotel", + "lead_time": 1, + "arrival_date_year": 2017, + "arrival_date_month": "June", + "arrival_date_week_number": 25, + "arrival_date_day_of_month": 18, + "stays_in_weekend_nights": 1, + "stays_in_week_nights": 0, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "PER", + "market_segment": "Direct", + "distribution_channel": "Direct", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "D", + "assigned_room_type": "D", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 9.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 150.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 1, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "012", + "features": { + "hotel": "Resort Hotel", + "lead_time": 35, + "arrival_date_year": 2016, + "arrival_date_month": "October", + "arrival_date_week_number": 44, + "arrival_date_day_of_month": 26, + "stays_in_weekend_nights": 1, + "stays_in_week_nights": 4, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "Undefined", + "country": "PRT", + "market_segment": "Direct", + "distribution_channel": "Direct", + "is_repeated_guest": 1, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 1, + "reserved_room_type": "E", + "assigned_room_type": "E", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": null, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 126.74, + "required_car_parking_spaces": 1, + "total_of_special_requests": 2, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "015", + "features": { + "hotel": "City Hotel", + "lead_time": 12, + "arrival_date_year": 2017, + "arrival_date_month": "January", + "arrival_date_week_number": 4, + "arrival_date_day_of_month": 24, + "stays_in_weekend_nights": 0, + "stays_in_week_nights": 3, + "adults": 2, + "children": 0.0, + "babies": 1, + "meal": "BB", + "country": "AGO", + "market_segment": "Direct", + "distribution_channel": "Direct", + "is_repeated_guest": 1, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 1, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": null, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 98.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 2, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "021", + "features": { + "hotel": "City Hotel", + "lead_time": 54, + "arrival_date_year": 2017, + "arrival_date_month": "April", + "arrival_date_week_number": 15, + "arrival_date_day_of_month": 15, + "stays_in_weekend_nights": 2, + "stays_in_week_nights": 2, + "adults": 3, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "ESP", + "market_segment": "Online TA", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "D", + "assigned_room_type": "D", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 9.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 184.5, + "required_car_parking_spaces": 1, + "total_of_special_requests": 0, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "023", + "features": { + "hotel": "City Hotel", + "lead_time": 65, + "arrival_date_year": 2016, + "arrival_date_month": "April", + "arrival_date_week_number": 18, + "arrival_date_day_of_month": 24, + "stays_in_weekend_nights": 2, + "stays_in_week_nights": 0, + "adults": 3, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "CHE", + "market_segment": "Online TA", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "D", + "assigned_room_type": "D", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 9.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient-Party", + "adr": 137.7, + "required_car_parking_spaces": 0, + "total_of_special_requests": 2, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "024", + "features": { + "hotel": "City Hotel", + "lead_time": 7, + "arrival_date_year": 2017, + "arrival_date_month": "August", + "arrival_date_week_number": 31, + "arrival_date_day_of_month": 3, + "stays_in_weekend_nights": 0, + "stays_in_week_nights": 1, + "adults": 1, + "children": 0.0, + "babies": 0, + "meal": "SC", + "country": "NLD", + "market_segment": "Online TA", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "E", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 9.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 119.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "027", + "features": { + "hotel": "City Hotel", + "lead_time": 210, + "arrival_date_year": 2015, + "arrival_date_month": "October", + "arrival_date_week_number": 41, + "arrival_date_day_of_month": 8, + "stays_in_weekend_nights": 1, + "stays_in_week_nights": 3, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "HB", + "country": "PRT", + "market_segment": "Offline TA/TO", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 12.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient-Party", + "adr": 86.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Canceled" + } + }, + { + "scenario_id": "029", + "features": { + "hotel": "City Hotel", + "lead_time": 55, + "arrival_date_year": 2016, + "arrival_date_month": "November", + "arrival_date_week_number": 45, + "arrival_date_day_of_month": 2, + "stays_in_weekend_nights": 0, + "stays_in_week_nights": 4, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "SC", + "country": "PAN", + "market_segment": "Online TA", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 9.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 88.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "030", + "features": { + "hotel": "City Hotel", + "lead_time": 133, + "arrival_date_year": 2016, + "arrival_date_month": "March", + "arrival_date_week_number": 11, + "arrival_date_day_of_month": 11, + "stays_in_weekend_nights": 1, + "stays_in_week_nights": 2, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "USA", + "market_segment": "Offline TA/TO", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "B", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 129.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient-Party", + "adr": 42.17, + "required_car_parking_spaces": 0, + "total_of_special_requests": 1, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "032", + "features": { + "hotel": "City Hotel", + "lead_time": 114, + "arrival_date_year": 2016, + "arrival_date_month": "October", + "arrival_date_week_number": 44, + "arrival_date_day_of_month": 27, + "stays_in_weekend_nights": 0, + "stays_in_week_nights": 1, + "adults": 1, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "BRA", + "market_segment": "Online TA", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 9.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 125.1, + "required_car_parking_spaces": 0, + "total_of_special_requests": 1, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "035", + "features": { + "hotel": "City Hotel", + "lead_time": 44, + "arrival_date_year": 2017, + "arrival_date_month": "August", + "arrival_date_week_number": 34, + "arrival_date_day_of_month": 24, + "stays_in_weekend_nights": 0, + "stays_in_week_nights": 3, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "FRA", + "market_segment": "Online TA", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "D", + "assigned_room_type": "D", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 9.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 162.67, + "required_car_parking_spaces": 0, + "total_of_special_requests": 1, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "036", + "features": { + "hotel": "Resort Hotel", + "lead_time": 105, + "arrival_date_year": 2016, + "arrival_date_month": "August", + "arrival_date_week_number": 33, + "arrival_date_day_of_month": 13, + "stays_in_weekend_nights": 4, + "stays_in_week_nights": 6, + "adults": 2, + "children": 2.0, + "babies": 0, + "meal": "HB", + "country": "MAR", + "market_segment": "Online TA", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "G", + "assigned_room_type": "G", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 240.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 309.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Canceled" + } + }, + { + "scenario_id": "040", + "features": { + "hotel": "City Hotel", + "lead_time": 105, + "arrival_date_year": 2016, + "arrival_date_month": "October", + "arrival_date_week_number": 42, + "arrival_date_day_of_month": 12, + "stays_in_weekend_nights": 0, + "stays_in_week_nights": 3, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "DEU", + "market_segment": "Offline TA/TO", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 132.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient-Party", + "adr": 75.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "047", + "features": { + "hotel": "Resort Hotel", + "lead_time": 8, + "arrival_date_year": 2015, + "arrival_date_month": "August", + "arrival_date_week_number": 34, + "arrival_date_day_of_month": 16, + "stays_in_weekend_nights": 2, + "stays_in_week_nights": 2, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "HB", + "country": "GBR", + "market_segment": "Online TA", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 241.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 153.42, + "required_car_parking_spaces": 0, + "total_of_special_requests": 1, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "048", + "features": { + "hotel": "Resort Hotel", + "lead_time": 90, + "arrival_date_year": 2015, + "arrival_date_month": "July", + "arrival_date_week_number": 29, + "arrival_date_day_of_month": 16, + "stays_in_weekend_nights": 2, + "stays_in_week_nights": 5, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "GBR", + "market_segment": "Offline TA/TO", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 1, + "deposit_type": "No Deposit", + "agent": 243.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Contract", + "adr": 93.6, + "required_car_parking_spaces": 0, + "total_of_special_requests": 1, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "049", + "features": { + "hotel": "City Hotel", + "lead_time": 58, + "arrival_date_year": 2015, + "arrival_date_month": "October", + "arrival_date_week_number": 44, + "arrival_date_day_of_month": 30, + "stays_in_weekend_nights": 0, + "stays_in_week_nights": 1, + "adults": 1, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "PRT", + "market_segment": "Online TA", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + 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"assigned_room_type": "E", - "booking_changes": 1, - "deposit_type": "No Deposit", - "agent": 9.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 200.0, - "required_car_parking_spaces": 0, - "total_of_special_requests": 1, - "reservation_status": "Check-Out", - "reservation_status_date": "2017-05-22" - } - }, - { - "scenario_id": "493", - "features": { - "hotel": "Resort Hotel", - "is_canceled": 0, - "lead_time": 48, - "arrival_date_year": 2015, - "arrival_date_month": "August", - "arrival_date_week_number": 35, - "arrival_date_day_of_month": 23, - "stays_in_weekend_nights": 2, - "stays_in_week_nights": 5, - "adults": 2, - "children": 0.0, - "babies": 0, - "meal": "HB", - "country": "ROU", - "market_segment": "Online TA", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "A", - "assigned_room_type": "A", - "booking_changes": 0, - "deposit_type": "No Deposit", - "agent": 240.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 185.71, - "required_car_parking_spaces": 0, - "total_of_special_requests": 2, - "reservation_status": "Check-Out", - "reservation_status_date": "2015-08-30" - } - }, - { - "scenario_id": "497", - "features": { - "hotel": "City Hotel", - "is_canceled": 0, - "lead_time": 8, - "arrival_date_year": 2016, - "arrival_date_month": "April", - "arrival_date_week_number": 14, - "arrival_date_day_of_month": 1, - "stays_in_weekend_nights": 0, - "stays_in_week_nights": 2, - "adults": 2, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "PRT", - "market_segment": "Corporate", - "distribution_channel": "Corporate", - "is_repeated_guest": 1, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 1, - "reserved_room_type": "A", - "assigned_room_type": "D", - "booking_changes": 1, - "deposit_type": "No Deposit", - "agent": null, - "company": 127.0, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 84.0, - "required_car_parking_spaces": 0, - "total_of_special_requests": 1, - "reservation_status": "Check-Out", - "reservation_status_date": "2016-04-03" - } - } - ], - "target_column": "reservation_status", - "task_sub_type": "classification", - "ranking_ground_truth": { - "top_k_ids": [ - "027", - "036", - "052", - "098", - "122", - "127", - "145", - "150", - "261", - "305", - "325", - "328", - "399", - "412", - "472" - ] - }, - "active_filters": [], - "target_class_value": "canceled" - }, - "response": "", - "evaluation_score": {} -} \ No newline at end of file + "active_filters": [], + "target_class_value": "canceled" + }, + "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_002/current.csv b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_002/current.csv index 4e7404c71886a76b702ba14c21b40a021c0c153d..69d51dba267254963a4d97c471f24a514830dc4b 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_002/current.csv +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_002/current.csv @@ -1,201 +1,201 @@ -hotel,is_canceled,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests,reservation_status_date -City Hotel,0,1,2017,June,25,18,1,0,2,0.0,0,BB,PER,Direct,Direct,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,150.0,0,1,2017-06-19 -Resort Hotel,0,35,2016,October,44,26,1,4,2,0.0,0,Undefined,PRT,Direct,Direct,1,0,1,E,E,0,No Deposit,,,0,Transient,126.74,1,2,2016-10-31 -City Hotel,0,12,2017,January,4,24,0,3,2,0.0,1,BB,AGO,Direct,Direct,1,0,1,A,A,0,No Deposit,,,0,Transient,98.0,0,2,2017-01-27 -City Hotel,0,54,2017,April,15,15,2,2,3,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,184.5,1,0,2017-04-19 -City Hotel,0,65,2016,April,18,24,2,0,3,0.0,0,BB,CHE,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient-Party,137.7,0,2,2016-04-26 -City Hotel,0,7,2017,August,31,3,0,1,1,0.0,0,SC,NLD,Online TA,TA/TO,0,0,0,A,E,0,No Deposit,9.0,,0,Transient,119.0,0,0,2017-08-04 -City Hotel,1,210,2015,October,41,8,1,3,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,12.0,,0,Transient-Party,86.0,0,0,2015-06-30 -City Hotel,0,55,2016,November,45,2,0,4,2,0.0,0,SC,PAN,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,88.0,0,0,2016-11-06 -City Hotel,0,133,2016,March,11,11,1,2,2,0.0,0,BB,USA,Offline TA/TO,TA/TO,0,0,0,A,B,0,No Deposit,129.0,,0,Transient-Party,42.17,0,1,2016-03-14 -City Hotel,0,114,2016,October,44,27,0,1,1,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,125.1,0,1,2016-10-28 -City Hotel,0,44,2017,August,34,24,0,3,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,162.67,0,1,2017-08-27 -Resort Hotel,1,105,2016,August,33,13,4,6,2,2.0,0,HB,MAR,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,240.0,,0,Transient,309.0,0,0,2016-07-30 -City Hotel,0,105,2016,October,42,12,0,3,2,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,132.0,,0,Transient-Party,75.0,0,0,2016-10-15 -Resort Hotel,0,8,2015,August,34,16,2,2,2,0.0,0,HB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,241.0,,0,Transient,153.42,0,1,2015-08-20 -Resort Hotel,0,90,2015,July,29,16,2,5,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,A,1,No Deposit,243.0,,0,Contract,93.6,0,1,2015-07-23 -City Hotel,0,58,2015,October,44,30,0,1,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Contract,85.5,0,2,2015-10-31 -Resort Hotel,0,0,2016,March,10,4,0,1,2,0.0,0,BB,PRT,Direct,Direct,1,0,2,D,D,0,No Deposit,,,0,Transient,55.0,0,0,2016-03-05 -City Hotel,1,147,2015,December,53,29,0,3,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Contract,72.25,0,2,2015-08-05 -City Hotel,0,246,2017,May,21,27,1,1,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,12.0,,0,Transient-Party,128.0,0,1,2017-05-29 -Resort Hotel,0,62,2015,August,34,20,2,8,2,0.0,0,BB,ESP,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,243.0,,0,Transient,104.0,0,0,2015-08-30 -City Hotel,0,3,2015,September,36,5,2,2,2,0.0,0,BB,ESP,Groups,TA/TO,0,0,0,A,D,0,No Deposit,1.0,,0,Transient-Party,62.0,0,0,2015-09-09 -Resort Hotel,0,246,2016,September,37,10,4,6,2,0.0,0,BB,IRL,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,95.0,,0,Transient,77.22,0,2,2016-09-20 -City Hotel,0,221,2017,June,25,23,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,138.0,,0,Transient,89.1,0,2,2017-06-25 -Resort Hotel,0,166,2015,September,37,10,1,3,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,73.8,0,2,2015-09-14 -Resort Hotel,0,0,2015,December,52,26,0,1,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,E,0,No Deposit,240.0,,0,Transient,61.0,1,0,2015-12-27 -City Hotel,0,0,2016,May,21,17,0,1,2,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,152.0,,0,Transient,95.94,0,0,2016-05-18 -City Hotel,0,84,2016,August,34,14,2,0,2,2.0,0,BB,GBR,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,9.0,,0,Transient,189.9,0,1,2016-08-16 -Resort Hotel,0,0,2015,November,48,25,0,1,2,0.0,0,BB,PRT,Direct,Direct,1,0,1,A,A,0,No Deposit,,,0,Transient,38.0,0,0,2015-11-26 -City Hotel,1,245,2016,June,25,17,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,12.0,,0,Transient,75.0,0,0,2016-02-09 -Resort Hotel,0,215,2017,April,17,28,2,2,2,0.0,0,HB,ESP,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,103.3,1,2,2017-05-02 -Resort Hotel,0,225,2016,May,22,26,1,3,2,0.0,0,BB,GBR,Groups,Direct,0,0,0,A,C,2,No Deposit,,223.0,0,Transient-Party,66.0,0,0,2016-05-30 -City Hotel,0,15,2016,January,4,22,0,2,2,0.0,0,BB,BEL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,80.3,0,1,2016-01-24 -Resort Hotel,0,57,2017,August,33,19,2,5,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,240.0,,0,Transient-Party,283.43,0,4,2017-08-26 -City Hotel,0,56,2016,May,23,30,1,1,1,0.0,0,BB,CHE,Groups,TA/TO,0,0,0,A,A,1,No Deposit,,,0,Transient-Party,120.0,0,0,2016-06-01 -Resort Hotel,0,84,2017,January,5,30,1,0,3,1.0,0,BB,BRA,Online TA,TA/TO,0,0,0,H,H,0,No Deposit,240.0,,0,Transient,94.0,1,2,2017-01-31 -Resort Hotel,0,100,2015,July,28,8,2,7,3,0.0,0,BB,DNK,Online TA,TA/TO,0,0,0,E,E,1,No Deposit,240.0,,0,Transient-Party,130.1,0,3,2015-07-17 -City Hotel,0,54,2016,September,37,10,1,1,3,0.0,0,BB,ESP,Direct,Direct,0,0,0,D,D,0,No Deposit,,,0,Transient,192.0,0,4,2016-09-12 -City Hotel,0,22,2015,December,52,24,1,3,2,0.0,0,BB,MEX,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,87.0,0,1,2015-12-28 -City Hotel,0,54,2017,June,25,19,1,2,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,9.0,,0,Transient,146.67,0,2,2017-06-22 -City Hotel,0,9,2016,July,29,14,2,5,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,160.29,0,1,2016-07-21 -City Hotel,1,46,2016,February,7,13,0,1,2,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,11.0,,0,Transient,48.6,0,0,2016-02-04 -City Hotel,0,0,2017,January,4,25,0,2,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,,365.0,0,Transient-Party,85.0,0,0,2017-01-27 -City Hotel,1,175,2017,May,20,19,0,1,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,44.0,,0,Transient,90.0,0,0,2016-11-25 -City Hotel,0,33,2016,March,13,24,0,2,2,1.0,0,BB,ESP,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,9.0,,0,Transient-Party,143.1,0,1,2016-03-26 -City Hotel,0,5,2017,March,10,5,2,3,1,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,8.0,,0,Transient,96.05,0,0,2017-03-10 -Resort Hotel,0,285,2016,September,39,20,2,5,1,0.0,0,BB,IRL,Groups,TA/TO,0,0,0,D,E,2,No Deposit,96.0,,0,Transient-Party,78.0,0,2,2016-09-27 -City Hotel,0,58,2016,September,40,29,0,1,2,0.0,0,BB,IRL,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,125.1,0,0,2016-09-30 -City Hotel,1,186,2017,May,22,30,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,19.0,,0,Transient,130.0,0,0,2016-11-25 -City Hotel,0,31,2016,May,21,21,0,1,2,0.0,0,SC,CHN,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,89.4,0,2,2016-05-22 -Resort Hotel,0,102,2017,July,28,15,2,3,2,0.0,0,BB,ROU,Online TA,TA/TO,0,0,0,D,D,1,No Deposit,240.0,,0,Transient,223.0,0,1,2017-07-20 -Resort Hotel,1,383,2016,October,41,6,1,3,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,315.0,,0,Transient-Party,48.0,0,0,2016-03-04 -Resort Hotel,0,15,2016,February,7,11,0,1,1,0.0,0,BB,PRT,Online TA,TA/TO,1,0,2,A,D,0,No Deposit,240.0,,0,Transient,49.0,0,2,2016-02-12 -Resort Hotel,0,5,2016,February,8,16,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,D,0,No Deposit,,366.0,0,Transient,40.0,0,0,2016-02-17 -Resort Hotel,0,3,2017,August,32,6,1,0,2,0.0,0,BB,IRL,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,241.0,,0,Transient,151.99,0,1,2017-08-07 -Resort Hotel,0,4,2016,August,32,6,0,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,231.0,0,1,2016-08-07 -City Hotel,0,130,2016,June,24,8,2,5,2,0.0,0,BB,GBR,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,109.65,0,1,2016-06-15 -City Hotel,0,85,2016,August,34,19,2,4,3,0.0,0,HB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,201.6,0,1,2016-08-25 -City Hotel,0,268,2016,October,42,9,2,3,2,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,27.0,,0,Transient-Party,80.75,0,0,2016-10-14 -Resort Hotel,0,3,2016,April,14,1,0,1,2,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,D,0,No Deposit,,225.0,0,Transient,50.0,0,0,2016-04-02 -Resort Hotel,0,24,2017,June,23,8,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Transient,66.0,0,0,2017-06-10 -City Hotel,0,166,2016,May,19,3,0,4,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,1,No Deposit,14.0,,0,Transient,85.0,0,0,2016-05-07 -City Hotel,0,107,2017,April,17,27,0,3,2,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,99.0,0,0,2017-04-30 -City Hotel,0,48,2016,November,46,6,2,3,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,28.0,,0,Transient,75.0,0,0,2016-11-11 -City Hotel,0,157,2016,December,50,8,0,3,2,0.0,0,BB,ITA,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,83.0,,0,Transient-Party,68.0,0,0,2016-12-11 -City Hotel,0,9,2016,October,45,30,2,0,2,0.0,0,HB,FRA,Online TA,TA/TO,0,0,0,A,B,0,No Deposit,9.0,,0,Transient,178.0,0,2,2016-11-01 -City Hotel,0,2,2016,June,25,13,1,2,2,0.0,0,HB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,145.67,0,2,2016-06-16 -City Hotel,0,0,2015,October,42,14,0,1,1,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,129.0,0,0,2015-10-15 -City Hotel,0,1,2017,February,7,15,0,1,2,0.0,0,SC,ESP,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,108.0,1,1,2017-02-16 -City Hotel,0,191,2017,July,27,8,2,2,2,2.0,0,BB,POL,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,42.0,,0,Transient,152.28,0,0,2017-07-12 -Resort Hotel,0,124,2016,June,25,18,3,6,3,0.0,0,BB,SWE,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,153.0,0,1,2016-06-27 -City Hotel,0,1,2016,September,39,20,0,1,2,2.0,0,BB,PRT,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,9.0,,0,Transient,275.0,0,1,2016-09-21 -Resort Hotel,0,3,2017,January,2,9,1,1,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,2,A,A,0,No Deposit,,286.0,0,Transient-Party,39.0,0,0,2017-01-11 -City Hotel,0,75,2017,January,2,8,2,1,1,0.0,0,BB,USA,Groups,TA/TO,0,0,0,D,D,0,No Deposit,330.0,,0,Transient-Party,67.0,0,2,2017-01-11 -City Hotel,0,1,2016,August,32,4,0,1,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,152.0,,0,Transient,110.19,0,0,2016-08-05 -City Hotel,0,239,2016,October,44,28,0,2,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Transient,96.3,0,1,2016-10-30 -City Hotel,0,107,2016,March,12,17,0,3,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,28.0,,0,Transient,58.0,0,0,2016-03-20 -City Hotel,0,4,2017,June,25,23,2,3,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,423.0,,0,Transient,141.04,0,0,2017-06-28 -Resort Hotel,0,56,2015,October,40,2,2,5,1,0.0,0,HB,NLD,Offline TA/TO,TA/TO,0,0,0,E,E,0,No Deposit,71.0,,0,Contract,77.0,0,0,2015-10-09 -City Hotel,0,41,2016,March,12,13,2,1,1,0.0,0,BB,POL,Corporate,TA/TO,0,0,0,A,A,0,No Deposit,,193.0,0,Transient-Party,70.33,0,0,2016-03-16 -Resort Hotel,0,0,2016,February,6,6,0,1,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,C,0,No Deposit,,,0,Transient,55.0,0,0,2016-02-07 -City Hotel,0,86,2016,March,13,20,2,2,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient-Party,73.95,0,0,2016-03-24 -Resort Hotel,0,1,2017,January,2,12,0,1,2,0.0,0,BB,ESP,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient,34.0,0,0,2017-01-13 -City Hotel,0,8,2016,September,38,12,1,1,2,1.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,A,0,No Deposit,8.0,,0,Transient,187.55,0,1,2016-09-14 -Resort Hotel,0,32,2017,March,13,28,0,3,2,0.0,0,BB,AGO,Online TA,TA/TO,0,0,0,E,F,0,No Deposit,,,0,Transient,80.0,0,2,2017-03-31 -City Hotel,0,135,2015,September,38,14,1,2,2,0.0,0,BB,CN,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,90.0,0,0,2015-09-17 -City Hotel,0,179,2017,May,20,14,2,2,1,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Transient,116.33,0,1,2017-05-18 -Resort Hotel,0,1,2016,February,6,6,2,1,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,250.0,,0,Transient,45.0,0,1,2016-02-09 -City Hotel,0,92,2016,March,12,18,0,2,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,87.3,0,0,2016-03-20 -City Hotel,0,265,2016,June,26,24,0,2,2,0.0,0,HB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,115.0,0,1,2016-06-26 -Resort Hotel,0,126,2017,May,18,2,0,2,2,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,E,E,0,No Deposit,6.0,,0,Transient,64.8,1,0,2017-05-04 -Resort Hotel,0,3,2016,February,8,15,1,2,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,1,No Deposit,139.0,99.0,0,Transient,30.0,0,0,2016-02-18 -City Hotel,0,78,2017,January,4,25,0,2,1,0.0,0,BB,SVK,Groups,TA/TO,0,0,0,A,A,0,No Deposit,,365.0,0,Transient-Party,85.0,0,0,2017-01-27 -City Hotel,1,81,2016,December,50,8,0,3,2,0.0,0,SC,ITA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,79.2,0,1,2016-09-19 -City Hotel,0,346,2016,September,38,13,0,2,2,0.0,0,HB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,115.0,0,1,2016-09-15 -Resort Hotel,0,0,2016,February,8,18,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,3,A,A,0,No Deposit,,,0,Transient,30.0,0,0,2016-02-19 -City Hotel,0,0,2016,May,21,17,0,0,1,0.0,0,HB,PRT,Offline TA/TO,TA/TO,1,0,0,A,A,0,No Deposit,6.0,,0,Transient,0.0,0,0,2016-05-17 -City Hotel,0,3,2016,February,7,11,0,1,1,0.0,0,BB,NLD,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,67.22,0,1,2016-02-12 -City Hotel,0,69,2015,October,44,25,2,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,37.0,,58,Transient-Party,85.67,0,0,2015-10-28 -Resort Hotel,0,7,2016,October,41,5,1,4,2,0.0,0,BB,GBR,Groups,Corporate,0,0,0,A,A,0,No Deposit,,405.0,0,Transient-Party,50.0,0,1,2016-10-10 -City Hotel,0,0,2016,September,40,26,1,0,1,0.0,0,SC,POL,Online TA,TA/TO,0,0,0,A,D,1,No Deposit,152.0,,0,Transient,106.65,0,0,2016-09-27 -Resort Hotel,0,12,2016,September,39,22,0,2,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,F,E,0,No Deposit,250.0,,0,Transient,115.2,1,2,2016-09-24 -City Hotel,0,1,2015,November,46,14,0,1,1,0.0,0,BB,ITA,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,144.0,0,Transient-Party,79.0,0,0,2015-11-15 -City Hotel,0,76,2016,April,14,2,2,1,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient-Party,87.3,0,1,2016-04-05 -City Hotel,0,1,2016,January,4,17,2,4,2,0.0,0,BB,AGO,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,90.33,0,1,2016-01-23 -Resort Hotel,0,178,2016,October,41,2,4,5,2,0.0,0,BB,IRL,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,171.0,,0,Transient,60.0,0,0,2016-10-11 -Resort Hotel,0,287,2015,October,42,15,0,3,2,0.0,0,BB,GBR,Groups,Corporate,0,0,0,A,A,1,No Deposit,,223.0,0,Transient-Party,44.5,0,0,2015-10-18 -City Hotel,0,67,2017,March,9,3,0,2,3,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,D,D,1,No Deposit,9.0,,0,Transient,106.2,0,1,2017-03-05 -Resort Hotel,0,119,2015,August,32,7,0,2,2,0.0,0,BB,,Online TA,TA/TO,0,0,1,E,E,0,No Deposit,240.0,,0,Transient,164.0,1,2,2015-08-09 -City Hotel,0,5,2016,April,14,1,2,3,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,1,No Deposit,9.0,,0,Transient,141.0,0,2,2016-04-06 -City Hotel,0,29,2017,January,3,18,0,3,1,0.0,0,BB,NOR,Groups,TA/TO,0,0,0,A,A,0,No Deposit,171.0,,0,Transient-Party,65.0,0,0,2017-01-21 -City Hotel,0,5,2016,February,6,1,1,3,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,28.0,,0,Transient,66.0,0,0,2016-02-05 -City Hotel,0,32,2015,November,47,20,0,2,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,119.0,,0,Transient-Party,73.0,0,0,2015-11-22 -Resort Hotel,0,15,2016,July,30,18,1,4,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,D,C,0,No Deposit,5.0,,0,Transient,167.2,1,0,2016-07-23 -Resort Hotel,0,57,2015,July,29,15,0,2,2,0.0,0,FB,PRT,Groups,Direct,0,0,0,A,C,0,No Deposit,305.0,,0,Transient-Party,98.75,0,0,2015-07-17 -City Hotel,1,121,2017,August,33,18,1,2,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,125.0,0,0,2017-08-05 -City Hotel,0,9,2017,January,2,14,0,1,2,0.0,0,HB,PRT,Direct,Direct,0,0,0,E,E,0,No Deposit,14.0,,0,Transient,160.0,0,2,2017-01-15 -City Hotel,0,29,2016,December,49,2,0,2,2,0.0,0,BB,PRT,Corporate,TA/TO,0,0,0,A,A,0,No Deposit,,,0,Transient-Party,85.0,0,0,2016-12-04 -City Hotel,0,46,2016,May,21,19,2,3,2,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,16.0,,0,Transient,86.5,0,0,2016-05-24 -City Hotel,0,60,2015,September,39,21,1,1,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,20.0,,0,Transient-Party,65.0,0,0,2015-09-23 -Resort Hotel,0,75,2015,August,32,8,2,5,2,2.0,0,FB,PRT,Direct,Direct,0,0,0,C,C,0,No Deposit,250.0,,0,Transient,276.43,1,3,2015-08-15 -City Hotel,0,19,2015,July,30,25,2,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,D,0,No Deposit,1.0,,0,Transient-Party,62.0,0,0,2015-07-29 -Resort Hotel,0,9,2016,November,47,19,2,4,2,0.0,0,BB,NOR,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,240.0,,0,Transient,63.0,0,2,2016-11-25 -City Hotel,0,320,2016,August,34,18,0,2,2,0.0,0,HB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,115.0,0,1,2016-08-20 -Resort Hotel,0,17,2015,October,43,22,1,3,1,0.0,0,BB,,Direct,Direct,0,0,0,D,F,0,No Deposit,,,0,Transient,45.0,0,1,2015-10-26 -Resort Hotel,0,133,2015,July,29,13,3,5,2,0.0,0,BB,FIN,Offline TA/TO,TA/TO,0,0,0,E,E,0,No Deposit,156.0,,0,Contract,112.63,0,0,2015-07-21 -City Hotel,1,86,2016,December,50,8,0,3,2,1.0,0,BB,ESP,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,9.0,,0,Transient,174.0,0,0,2016-09-13 -City Hotel,1,22,2016,December,53,29,2,3,2,0.0,0,HB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,198.88,0,2,2016-12-22 -City Hotel,0,15,2017,June,23,6,0,2,2,0.0,0,SC,BRA,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,28.0,,0,Transient,89.0,0,0,2017-06-08 -Resort Hotel,0,39,2015,July,31,26,2,5,2,0.0,0,HB,ESP,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,196.0,,0,Transient,133.0,0,1,2015-08-02 -Resort Hotel,0,223,2017,July,30,27,2,3,2,0.0,0,BB,CN,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,314.0,,0,Transient,112.7,0,0,2017-08-01 -Resort Hotel,0,0,2015,October,44,26,1,0,1,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,1,No Deposit,,329.0,0,Transient,35.1,1,0,2015-10-27 -City Hotel,0,69,2017,March,12,23,0,2,2,0.0,0,SC,AGO,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,79.2,0,1,2017-03-25 -City Hotel,0,4,2017,May,21,21,2,0,1,0.0,0,BB,SVN,Aviation,Corporate,1,0,2,A,A,0,No Deposit,,153.0,0,Transient,150.0,0,0,2017-05-23 -City Hotel,0,25,2016,October,43,18,0,3,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,28.0,,0,Transient,85.0,0,1,2016-10-21 -City Hotel,0,160,2017,July,28,10,1,2,2,0.0,0,BB,AUT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,107.1,0,0,2017-07-13 -City Hotel,0,1,2015,November,46,10,0,4,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Contract,97.0,0,1,2015-11-14 -Resort Hotel,0,208,2016,August,33,8,2,5,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,126.0,0,1,2016-08-15 -City Hotel,0,328,2016,September,40,30,0,2,2,0.0,0,BB,FRA,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,38,Transient-Party,65.0,0,1,2016-10-02 -Resort Hotel,0,11,2015,November,47,16,1,2,2,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,5.0,,0,Transient,32.0,0,0,2015-11-19 -Resort Hotel,0,31,2016,April,17,18,1,1,2,0.0,0,BB,GBR,Groups,Corporate,0,0,0,E,E,0,No Deposit,,,0,Transient,91.0,0,0,2016-04-20 -Resort Hotel,0,1,2016,May,21,17,0,2,2,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,1,No Deposit,,331.0,0,Transient-Party,63.0,0,0,2016-05-19 -City Hotel,0,21,2017,July,28,11,0,3,2,0.0,0,BB,AUT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,146.67,0,1,2017-07-14 -City Hotel,0,396,2017,June,26,25,2,0,2,0.0,0,HB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,122.4,0,1,2017-06-27 -Resort Hotel,0,180,2016,July,32,31,4,5,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,314.0,,0,Transient,112.8,0,0,2016-08-09 -City Hotel,0,99,2016,July,28,9,2,1,2,0.0,0,SC,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,109.5,0,1,2016-07-12 -Resort Hotel,0,0,2017,July,28,15,2,1,3,0.0,0,BB,USA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,241.0,,0,Transient,205.33,0,0,2017-07-18 -Resort Hotel,0,36,2015,August,34,22,4,10,2,0.0,0,HB,FRA,Online TA,TA/TO,0,0,0,F,E,0,No Deposit,241.0,,0,Transient,187.33,0,1,2015-09-05 -City Hotel,0,43,2016,October,42,15,2,4,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,145.2,0,1,2016-10-21 -Resort Hotel,0,231,2017,May,19,9,0,4,1,0.0,0,HB,GBR,Groups,Direct,0,0,0,E,E,0,No Deposit,,,0,Transient-Party,79.5,0,0,2017-05-13 -City Hotel,0,48,2015,September,37,11,1,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,0,Transient-Party,60.0,0,0,2015-09-14 -Resort Hotel,0,5,2016,July,31,26,1,5,2,0.0,0,HB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,237.67,1,1,2016-08-01 -City Hotel,1,17,2016,February,10,28,2,3,1,0.0,0,BB,NGA,Direct,Direct,0,0,0,D,D,0,No Deposit,14.0,,0,Transient,101.0,0,0,2016-02-28 -City Hotel,0,158,2017,March,13,27,1,1,2,0.0,0,BB,ISR,Groups,TA/TO,0,0,0,A,A,0,No Deposit,37.0,,0,Transient-Party,95.0,0,1,2017-03-29 -City Hotel,1,66,2017,April,15,9,2,0,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,100.0,0,0,2017-02-02 -City Hotel,0,21,2017,March,12,22,0,2,2,1.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,137.0,0,3,2017-03-24 -Resort Hotel,0,58,2016,October,41,5,0,1,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,251.0,,0,Transient,59.0,0,0,2016-10-06 -Resort Hotel,0,0,2016,March,12,16,0,1,2,0.0,0,BB,CHN,Online TA,TA/TO,1,0,1,D,D,0,No Deposit,240.0,,0,Transient,58.0,0,0,2016-03-17 -City Hotel,0,87,2017,May,18,4,0,3,2,0.0,0,SC,NLD,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,108.0,0,1,2017-05-07 -City Hotel,0,114,2016,October,42,12,1,4,0,0.0,0,BB,USA,Direct,Direct,0,0,0,D,D,1,No Deposit,14.0,,0,Transient,0.0,0,0,2016-10-17 -City Hotel,0,0,2015,October,42,15,0,1,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,1,D,D,0,No Deposit,94.0,,0,Transient,0.0,0,0,2015-10-16 -Resort Hotel,0,32,2016,July,31,29,1,2,2,0.0,0,BB,ARE,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,240.0,,0,Transient,226.33,1,0,2016-08-01 -Resort Hotel,1,66,2017,August,33,16,0,4,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,230.0,0,2,2017-07-04 -City Hotel,0,109,2016,July,28,4,1,4,2,0.0,0,SC,IRL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Transient,97.75,0,0,2016-07-09 -City Hotel,0,7,2017,January,2,10,0,2,1,0.0,0,BB,PRT,Corporate,Corporate,1,1,12,A,A,1,No Deposit,,219.0,0,Transient,89.0,1,2,2017-01-12 -City Hotel,0,179,2016,July,30,23,2,4,2,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient-Party,90.95,0,1,2016-07-29 -City Hotel,0,36,2016,July,27,1,0,2,2,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,107.1,0,1,2016-07-03 -Resort Hotel,0,135,2015,August,32,6,2,3,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,156.0,,0,Contract,104.55,0,0,2015-08-11 -City Hotel,0,0,2017,August,33,19,1,1,2,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,152.0,,0,Transient,110.7,0,0,2017-08-21 -City Hotel,0,102,2016,April,16,14,1,3,2,0.0,0,BB,CHE,Online TA,TA/TO,0,0,0,B,B,3,No Deposit,9.0,,0,Transient-Party,77.78,0,1,2016-04-18 -City Hotel,0,263,2017,June,25,23,2,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,37.0,,0,Transient-Party,105.0,0,1,2017-06-27 -City Hotel,0,90,2017,August,33,15,2,5,3,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,16.0,,0,Transient,116.1,0,0,2017-08-22 -Resort Hotel,0,13,2016,October,43,21,0,2,2,0.0,0,HB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,120.0,0,1,2016-10-23 -City Hotel,0,292,2016,July,30,21,0,2,2,0.0,0,HB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,115.0,0,0,2016-07-23 -City Hotel,0,31,2017,April,16,21,2,3,1,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,1,No Deposit,21.0,,0,Transient-Party,75.0,0,0,2017-04-26 -Resort Hotel,0,136,2016,March,13,24,0,3,2,0.0,0,Undefined,ESP,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,387.0,,0,Transient-Party,84.0,0,0,2016-03-27 -Resort Hotel,0,2,2017,January,3,16,1,0,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,H,0,No Deposit,,428.0,0,Transient,35.0,0,0,2017-01-17 -City Hotel,0,37,2017,April,15,11,0,2,2,0.0,0,BB,FRA,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,135.0,0,1,2017-04-13 -Resort Hotel,0,52,2016,November,46,11,2,3,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,37.8,0,2,2016-11-16 -Resort Hotel,0,84,2016,July,30,17,4,6,2,0.0,0,HB,GBR,Direct,Direct,0,0,0,F,F,1,No Deposit,250.0,,0,Transient,235.0,0,0,2016-07-27 -City Hotel,0,31,2015,October,43,18,2,0,2,0.0,0,BB,NLD,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Contract,96.3,0,0,2015-10-20 -City Hotel,0,34,2017,June,23,7,1,4,2,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,9.0,,0,Transient,132.0,0,2,2017-06-12 -Resort Hotel,0,0,2017,February,6,5,2,0,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,54.0,0,1,2017-02-07 -City Hotel,0,4,2015,August,35,23,2,1,1,0.0,0,BB,PRT,Complementary,Direct,0,0,0,A,A,1,No Deposit,,45.0,0,Transient,0.0,0,1,2015-08-26 -City Hotel,0,11,2015,August,35,23,2,1,2,1.0,0,BB,ESP,Direct,Direct,0,0,0,A,D,0,No Deposit,14.0,,0,Transient,90.0,0,1,2015-08-26 -City Hotel,0,240,2016,September,37,10,1,1,2,0.0,0,HB,ITA,Groups,TA/TO,0,0,0,A,D,0,No Deposit,1.0,,0,Transient-Party,96.0,0,1,2016-09-12 -City Hotel,0,1,2015,December,53,30,0,3,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,127.0,0,1,2016-01-02 -City Hotel,0,28,2016,August,32,4,0,1,1,0.0,0,BB,GBR,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,125.1,0,1,2016-08-05 -Resort Hotel,0,128,2016,September,40,27,0,4,2,0.0,0,HB,ITA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,152.0,1,1,2016-10-01 -City Hotel,1,281,2015,July,30,25,2,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.0,0,0,2015-07-06 -City Hotel,0,31,2016,August,33,12,0,2,2,1.0,0,BB,PRT,Direct,Direct,0,0,0,D,A,2,No Deposit,,,0,Transient,160.5,0,2,2016-08-14 -City Hotel,0,112,2016,July,30,18,1,2,2,0.0,0,BB,BEL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,105.3,0,2,2016-07-21 -City Hotel,0,1,2016,June,23,2,0,2,2,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,1,No Deposit,,174.0,0,Transient-Party,119.0,0,1,2016-06-04 -Resort Hotel,0,2,2017,February,8,20,1,0,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient-Party,43.0,0,1,2017-02-21 -Resort Hotel,0,203,2017,May,21,25,0,3,2,0.0,0,BB,USA,Groups,TA/TO,0,0,0,A,A,0,No Deposit,,223.0,0,Transient-Party,55.0,0,0,2017-05-28 -City Hotel,0,57,2016,October,45,30,2,0,2,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,89.1,0,1,2016-11-01 -Resort Hotel,0,1,2016,July,29,16,2,1,2,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,145.67,0,0,2016-07-19 -City Hotel,0,0,2016,July,28,5,0,1,2,1.0,0,BB,POL,Online TA,TA/TO,0,0,0,A,G,0,No Deposit,9.0,,0,Transient,159.0,0,0,2016-07-06 -City Hotel,0,10,2017,May,21,21,1,0,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,E,E,1,No Deposit,9.0,,0,Transient,200.0,0,1,2017-05-22 -Resort Hotel,0,48,2015,August,35,23,2,5,2,0.0,0,HB,ROU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,185.71,0,2,2015-08-30 -City Hotel,0,8,2016,April,14,1,0,2,2,0.0,0,BB,PRT,Corporate,Corporate,1,0,1,A,D,1,No Deposit,,127.0,0,Transient,84.0,0,1,2016-04-03 +hotel,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests +City Hotel,1,2017,June,25,18,1,0,2,0.0,0,BB,PER,Direct,Direct,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,150.0,0,1 +Resort Hotel,35,2016,October,44,26,1,4,2,0.0,0,Undefined,PRT,Direct,Direct,1,0,1,E,E,0,No Deposit,,,0,Transient,126.74,1,2 +City Hotel,12,2017,January,4,24,0,3,2,0.0,1,BB,AGO,Direct,Direct,1,0,1,A,A,0,No Deposit,,,0,Transient,98.0,0,2 +City Hotel,54,2017,April,15,15,2,2,3,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,184.5,1,0 +City Hotel,65,2016,April,18,24,2,0,3,0.0,0,BB,CHE,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient-Party,137.7,0,2 +City Hotel,7,2017,August,31,3,0,1,1,0.0,0,SC,NLD,Online TA,TA/TO,0,0,0,A,E,0,No Deposit,9.0,,0,Transient,119.0,0,0 +City Hotel,210,2015,October,41,8,1,3,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,12.0,,0,Transient-Party,86.0,0,0 +City Hotel,55,2016,November,45,2,0,4,2,0.0,0,SC,PAN,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,88.0,0,0 +City Hotel,133,2016,March,11,11,1,2,2,0.0,0,BB,USA,Offline TA/TO,TA/TO,0,0,0,A,B,0,No Deposit,129.0,,0,Transient-Party,42.17,0,1 +City Hotel,114,2016,October,44,27,0,1,1,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,125.1,0,1 +City Hotel,44,2017,August,34,24,0,3,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,162.67,0,1 +Resort Hotel,105,2016,August,33,13,4,6,2,2.0,0,HB,MAR,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,240.0,,0,Transient,309.0,0,0 +City Hotel,105,2016,October,42,12,0,3,2,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,132.0,,0,Transient-Party,75.0,0,0 +Resort Hotel,8,2015,August,34,16,2,2,2,0.0,0,HB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,241.0,,0,Transient,153.42,0,1 +Resort Hotel,90,2015,July,29,16,2,5,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,A,1,No Deposit,243.0,,0,Contract,93.6,0,1 +City Hotel,58,2015,October,44,30,0,1,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Contract,85.5,0,2 +Resort Hotel,0,2016,March,10,4,0,1,2,0.0,0,BB,PRT,Direct,Direct,1,0,2,D,D,0,No Deposit,,,0,Transient,55.0,0,0 +City Hotel,147,2015,December,53,29,0,3,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Contract,72.25,0,2 +City Hotel,246,2017,May,21,27,1,1,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,12.0,,0,Transient-Party,128.0,0,1 +Resort Hotel,62,2015,August,34,20,2,8,2,0.0,0,BB,ESP,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,243.0,,0,Transient,104.0,0,0 +City Hotel,3,2015,September,36,5,2,2,2,0.0,0,BB,ESP,Groups,TA/TO,0,0,0,A,D,0,No Deposit,1.0,,0,Transient-Party,62.0,0,0 +Resort Hotel,246,2016,September,37,10,4,6,2,0.0,0,BB,IRL,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,95.0,,0,Transient,77.22,0,2 +City Hotel,221,2017,June,25,23,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,138.0,,0,Transient,89.1,0,2 +Resort Hotel,166,2015,September,37,10,1,3,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,73.8,0,2 +Resort Hotel,0,2015,December,52,26,0,1,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,E,0,No Deposit,240.0,,0,Transient,61.0,1,0 +City Hotel,0,2016,May,21,17,0,1,2,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,152.0,,0,Transient,95.94,0,0 +City Hotel,84,2016,August,34,14,2,0,2,2.0,0,BB,GBR,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,9.0,,0,Transient,189.9,0,1 +Resort Hotel,0,2015,November,48,25,0,1,2,0.0,0,BB,PRT,Direct,Direct,1,0,1,A,A,0,No Deposit,,,0,Transient,38.0,0,0 +City Hotel,245,2016,June,25,17,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,12.0,,0,Transient,75.0,0,0 +Resort Hotel,215,2017,April,17,28,2,2,2,0.0,0,HB,ESP,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,103.3,1,2 +Resort Hotel,225,2016,May,22,26,1,3,2,0.0,0,BB,GBR,Groups,Direct,0,0,0,A,C,2,No Deposit,,223.0,0,Transient-Party,66.0,0,0 +City Hotel,15,2016,January,4,22,0,2,2,0.0,0,BB,BEL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,80.3,0,1 +Resort Hotel,57,2017,August,33,19,2,5,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,240.0,,0,Transient-Party,283.43,0,4 +City Hotel,56,2016,May,23,30,1,1,1,0.0,0,BB,CHE,Groups,TA/TO,0,0,0,A,A,1,No Deposit,,,0,Transient-Party,120.0,0,0 +Resort Hotel,84,2017,January,5,30,1,0,3,1.0,0,BB,BRA,Online TA,TA/TO,0,0,0,H,H,0,No Deposit,240.0,,0,Transient,94.0,1,2 +Resort Hotel,100,2015,July,28,8,2,7,3,0.0,0,BB,DNK,Online TA,TA/TO,0,0,0,E,E,1,No Deposit,240.0,,0,Transient-Party,130.1,0,3 +City Hotel,54,2016,September,37,10,1,1,3,0.0,0,BB,ESP,Direct,Direct,0,0,0,D,D,0,No Deposit,,,0,Transient,192.0,0,4 +City Hotel,22,2015,December,52,24,1,3,2,0.0,0,BB,MEX,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,87.0,0,1 +City Hotel,54,2017,June,25,19,1,2,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,9.0,,0,Transient,146.67,0,2 +City Hotel,9,2016,July,29,14,2,5,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,160.29,0,1 +City Hotel,46,2016,February,7,13,0,1,2,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,11.0,,0,Transient,48.6,0,0 +City Hotel,0,2017,January,4,25,0,2,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,,365.0,0,Transient-Party,85.0,0,0 +City Hotel,175,2017,May,20,19,0,1,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,44.0,,0,Transient,90.0,0,0 +City Hotel,33,2016,March,13,24,0,2,2,1.0,0,BB,ESP,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,9.0,,0,Transient-Party,143.1,0,1 +City Hotel,5,2017,March,10,5,2,3,1,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,8.0,,0,Transient,96.05,0,0 +Resort Hotel,285,2016,September,39,20,2,5,1,0.0,0,BB,IRL,Groups,TA/TO,0,0,0,D,E,2,No Deposit,96.0,,0,Transient-Party,78.0,0,2 +City Hotel,58,2016,September,40,29,0,1,2,0.0,0,BB,IRL,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,125.1,0,0 +City Hotel,186,2017,May,22,30,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,19.0,,0,Transient,130.0,0,0 +City Hotel,31,2016,May,21,21,0,1,2,0.0,0,SC,CHN,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,89.4,0,2 +Resort Hotel,102,2017,July,28,15,2,3,2,0.0,0,BB,ROU,Online TA,TA/TO,0,0,0,D,D,1,No Deposit,240.0,,0,Transient,223.0,0,1 +Resort Hotel,383,2016,October,41,6,1,3,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,315.0,,0,Transient-Party,48.0,0,0 +Resort Hotel,15,2016,February,7,11,0,1,1,0.0,0,BB,PRT,Online TA,TA/TO,1,0,2,A,D,0,No Deposit,240.0,,0,Transient,49.0,0,2 +Resort Hotel,5,2016,February,8,16,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,D,0,No Deposit,,366.0,0,Transient,40.0,0,0 +Resort Hotel,3,2017,August,32,6,1,0,2,0.0,0,BB,IRL,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,241.0,,0,Transient,151.99,0,1 +Resort Hotel,4,2016,August,32,6,0,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,231.0,0,1 +City Hotel,130,2016,June,24,8,2,5,2,0.0,0,BB,GBR,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,109.65,0,1 +City Hotel,85,2016,August,34,19,2,4,3,0.0,0,HB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,201.6,0,1 +City Hotel,268,2016,October,42,9,2,3,2,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,27.0,,0,Transient-Party,80.75,0,0 +Resort Hotel,3,2016,April,14,1,0,1,2,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,D,0,No Deposit,,225.0,0,Transient,50.0,0,0 +Resort Hotel,24,2017,June,23,8,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Transient,66.0,0,0 +City Hotel,166,2016,May,19,3,0,4,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,1,No Deposit,14.0,,0,Transient,85.0,0,0 +City Hotel,107,2017,April,17,27,0,3,2,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,99.0,0,0 +City Hotel,48,2016,November,46,6,2,3,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,28.0,,0,Transient,75.0,0,0 +City Hotel,157,2016,December,50,8,0,3,2,0.0,0,BB,ITA,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,83.0,,0,Transient-Party,68.0,0,0 +City Hotel,9,2016,October,45,30,2,0,2,0.0,0,HB,FRA,Online TA,TA/TO,0,0,0,A,B,0,No Deposit,9.0,,0,Transient,178.0,0,2 +City Hotel,2,2016,June,25,13,1,2,2,0.0,0,HB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,145.67,0,2 +City Hotel,0,2015,October,42,14,0,1,1,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,129.0,0,0 +City Hotel,1,2017,February,7,15,0,1,2,0.0,0,SC,ESP,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,108.0,1,1 +City Hotel,191,2017,July,27,8,2,2,2,2.0,0,BB,POL,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,42.0,,0,Transient,152.28,0,0 +Resort Hotel,124,2016,June,25,18,3,6,3,0.0,0,BB,SWE,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,153.0,0,1 +City Hotel,1,2016,September,39,20,0,1,2,2.0,0,BB,PRT,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,9.0,,0,Transient,275.0,0,1 +Resort Hotel,3,2017,January,2,9,1,1,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,2,A,A,0,No Deposit,,286.0,0,Transient-Party,39.0,0,0 +City Hotel,75,2017,January,2,8,2,1,1,0.0,0,BB,USA,Groups,TA/TO,0,0,0,D,D,0,No Deposit,330.0,,0,Transient-Party,67.0,0,2 +City Hotel,1,2016,August,32,4,0,1,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,152.0,,0,Transient,110.19,0,0 +City Hotel,239,2016,October,44,28,0,2,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Transient,96.3,0,1 +City Hotel,107,2016,March,12,17,0,3,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,28.0,,0,Transient,58.0,0,0 +City Hotel,4,2017,June,25,23,2,3,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,423.0,,0,Transient,141.04,0,0 +Resort Hotel,56,2015,October,40,2,2,5,1,0.0,0,HB,NLD,Offline TA/TO,TA/TO,0,0,0,E,E,0,No Deposit,71.0,,0,Contract,77.0,0,0 +City Hotel,41,2016,March,12,13,2,1,1,0.0,0,BB,POL,Corporate,TA/TO,0,0,0,A,A,0,No Deposit,,193.0,0,Transient-Party,70.33,0,0 +Resort Hotel,0,2016,February,6,6,0,1,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,C,0,No Deposit,,,0,Transient,55.0,0,0 +City Hotel,86,2016,March,13,20,2,2,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient-Party,73.95,0,0 +Resort Hotel,1,2017,January,2,12,0,1,2,0.0,0,BB,ESP,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient,34.0,0,0 +City Hotel,8,2016,September,38,12,1,1,2,1.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,A,0,No Deposit,8.0,,0,Transient,187.55,0,1 +Resort Hotel,32,2017,March,13,28,0,3,2,0.0,0,BB,AGO,Online TA,TA/TO,0,0,0,E,F,0,No Deposit,,,0,Transient,80.0,0,2 +City Hotel,135,2015,September,38,14,1,2,2,0.0,0,BB,CN,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,90.0,0,0 +City Hotel,179,2017,May,20,14,2,2,1,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Transient,116.33,0,1 +Resort Hotel,1,2016,February,6,6,2,1,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,250.0,,0,Transient,45.0,0,1 +City Hotel,92,2016,March,12,18,0,2,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,87.3,0,0 +City Hotel,265,2016,June,26,24,0,2,2,0.0,0,HB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,115.0,0,1 +Resort Hotel,126,2017,May,18,2,0,2,2,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,E,E,0,No Deposit,6.0,,0,Transient,64.8,1,0 +Resort Hotel,3,2016,February,8,15,1,2,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,1,No Deposit,139.0,99.0,0,Transient,30.0,0,0 +City Hotel,78,2017,January,4,25,0,2,1,0.0,0,BB,SVK,Groups,TA/TO,0,0,0,A,A,0,No Deposit,,365.0,0,Transient-Party,85.0,0,0 +City Hotel,81,2016,December,50,8,0,3,2,0.0,0,SC,ITA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,79.2,0,1 +City Hotel,346,2016,September,38,13,0,2,2,0.0,0,HB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,115.0,0,1 +Resort Hotel,0,2016,February,8,18,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,3,A,A,0,No Deposit,,,0,Transient,30.0,0,0 +City Hotel,0,2016,May,21,17,0,0,1,0.0,0,HB,PRT,Offline TA/TO,TA/TO,1,0,0,A,A,0,No Deposit,6.0,,0,Transient,0.0,0,0 +City Hotel,3,2016,February,7,11,0,1,1,0.0,0,BB,NLD,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,67.22,0,1 +City Hotel,69,2015,October,44,25,2,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,37.0,,58,Transient-Party,85.67,0,0 +Resort Hotel,7,2016,October,41,5,1,4,2,0.0,0,BB,GBR,Groups,Corporate,0,0,0,A,A,0,No Deposit,,405.0,0,Transient-Party,50.0,0,1 +City Hotel,0,2016,September,40,26,1,0,1,0.0,0,SC,POL,Online TA,TA/TO,0,0,0,A,D,1,No Deposit,152.0,,0,Transient,106.65,0,0 +Resort Hotel,12,2016,September,39,22,0,2,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,F,E,0,No Deposit,250.0,,0,Transient,115.2,1,2 +City Hotel,1,2015,November,46,14,0,1,1,0.0,0,BB,ITA,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,144.0,0,Transient-Party,79.0,0,0 +City Hotel,76,2016,April,14,2,2,1,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient-Party,87.3,0,1 +City Hotel,1,2016,January,4,17,2,4,2,0.0,0,BB,AGO,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,90.33,0,1 +Resort Hotel,178,2016,October,41,2,4,5,2,0.0,0,BB,IRL,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,171.0,,0,Transient,60.0,0,0 +Resort Hotel,287,2015,October,42,15,0,3,2,0.0,0,BB,GBR,Groups,Corporate,0,0,0,A,A,1,No Deposit,,223.0,0,Transient-Party,44.5,0,0 +City Hotel,67,2017,March,9,3,0,2,3,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,D,D,1,No Deposit,9.0,,0,Transient,106.2,0,1 +Resort Hotel,119,2015,August,32,7,0,2,2,0.0,0,BB,,Online TA,TA/TO,0,0,1,E,E,0,No Deposit,240.0,,0,Transient,164.0,1,2 +City Hotel,5,2016,April,14,1,2,3,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,1,No Deposit,9.0,,0,Transient,141.0,0,2 +City Hotel,29,2017,January,3,18,0,3,1,0.0,0,BB,NOR,Groups,TA/TO,0,0,0,A,A,0,No Deposit,171.0,,0,Transient-Party,65.0,0,0 +City Hotel,5,2016,February,6,1,1,3,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,28.0,,0,Transient,66.0,0,0 +City Hotel,32,2015,November,47,20,0,2,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,119.0,,0,Transient-Party,73.0,0,0 +Resort Hotel,15,2016,July,30,18,1,4,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,D,C,0,No Deposit,5.0,,0,Transient,167.2,1,0 +Resort Hotel,57,2015,July,29,15,0,2,2,0.0,0,FB,PRT,Groups,Direct,0,0,0,A,C,0,No Deposit,305.0,,0,Transient-Party,98.75,0,0 +City Hotel,121,2017,August,33,18,1,2,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,125.0,0,0 +City Hotel,9,2017,January,2,14,0,1,2,0.0,0,HB,PRT,Direct,Direct,0,0,0,E,E,0,No Deposit,14.0,,0,Transient,160.0,0,2 +City Hotel,29,2016,December,49,2,0,2,2,0.0,0,BB,PRT,Corporate,TA/TO,0,0,0,A,A,0,No Deposit,,,0,Transient-Party,85.0,0,0 +City Hotel,46,2016,May,21,19,2,3,2,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,16.0,,0,Transient,86.5,0,0 +City Hotel,60,2015,September,39,21,1,1,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,20.0,,0,Transient-Party,65.0,0,0 +Resort Hotel,75,2015,August,32,8,2,5,2,2.0,0,FB,PRT,Direct,Direct,0,0,0,C,C,0,No Deposit,250.0,,0,Transient,276.43,1,3 +City Hotel,19,2015,July,30,25,2,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,D,0,No Deposit,1.0,,0,Transient-Party,62.0,0,0 +Resort Hotel,9,2016,November,47,19,2,4,2,0.0,0,BB,NOR,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,240.0,,0,Transient,63.0,0,2 +City Hotel,320,2016,August,34,18,0,2,2,0.0,0,HB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,115.0,0,1 +Resort Hotel,17,2015,October,43,22,1,3,1,0.0,0,BB,,Direct,Direct,0,0,0,D,F,0,No Deposit,,,0,Transient,45.0,0,1 +Resort Hotel,133,2015,July,29,13,3,5,2,0.0,0,BB,FIN,Offline TA/TO,TA/TO,0,0,0,E,E,0,No Deposit,156.0,,0,Contract,112.63,0,0 +City Hotel,86,2016,December,50,8,0,3,2,1.0,0,BB,ESP,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,9.0,,0,Transient,174.0,0,0 +City Hotel,22,2016,December,53,29,2,3,2,0.0,0,HB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,198.88,0,2 +City Hotel,15,2017,June,23,6,0,2,2,0.0,0,SC,BRA,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,28.0,,0,Transient,89.0,0,0 +Resort Hotel,39,2015,July,31,26,2,5,2,0.0,0,HB,ESP,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,196.0,,0,Transient,133.0,0,1 +Resort Hotel,223,2017,July,30,27,2,3,2,0.0,0,BB,CN,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,314.0,,0,Transient,112.7,0,0 +Resort Hotel,0,2015,October,44,26,1,0,1,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,1,No Deposit,,329.0,0,Transient,35.1,1,0 +City Hotel,69,2017,March,12,23,0,2,2,0.0,0,SC,AGO,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,79.2,0,1 +City Hotel,4,2017,May,21,21,2,0,1,0.0,0,BB,SVN,Aviation,Corporate,1,0,2,A,A,0,No Deposit,,153.0,0,Transient,150.0,0,0 +City Hotel,25,2016,October,43,18,0,3,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,28.0,,0,Transient,85.0,0,1 +City Hotel,160,2017,July,28,10,1,2,2,0.0,0,BB,AUT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,107.1,0,0 +City Hotel,1,2015,November,46,10,0,4,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Contract,97.0,0,1 +Resort Hotel,208,2016,August,33,8,2,5,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,126.0,0,1 +City Hotel,328,2016,September,40,30,0,2,2,0.0,0,BB,FRA,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,38,Transient-Party,65.0,0,1 +Resort Hotel,11,2015,November,47,16,1,2,2,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,5.0,,0,Transient,32.0,0,0 +Resort Hotel,31,2016,April,17,18,1,1,2,0.0,0,BB,GBR,Groups,Corporate,0,0,0,E,E,0,No Deposit,,,0,Transient,91.0,0,0 +Resort Hotel,1,2016,May,21,17,0,2,2,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,1,No Deposit,,331.0,0,Transient-Party,63.0,0,0 +City Hotel,21,2017,July,28,11,0,3,2,0.0,0,BB,AUT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,146.67,0,1 +City Hotel,396,2017,June,26,25,2,0,2,0.0,0,HB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,122.4,0,1 +Resort Hotel,180,2016,July,32,31,4,5,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,314.0,,0,Transient,112.8,0,0 +City Hotel,99,2016,July,28,9,2,1,2,0.0,0,SC,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,109.5,0,1 +Resort Hotel,0,2017,July,28,15,2,1,3,0.0,0,BB,USA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,241.0,,0,Transient,205.33,0,0 +Resort Hotel,36,2015,August,34,22,4,10,2,0.0,0,HB,FRA,Online TA,TA/TO,0,0,0,F,E,0,No Deposit,241.0,,0,Transient,187.33,0,1 +City Hotel,43,2016,October,42,15,2,4,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,145.2,0,1 +Resort Hotel,231,2017,May,19,9,0,4,1,0.0,0,HB,GBR,Groups,Direct,0,0,0,E,E,0,No Deposit,,,0,Transient-Party,79.5,0,0 +City Hotel,48,2015,September,37,11,1,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,0,Transient-Party,60.0,0,0 +Resort Hotel,5,2016,July,31,26,1,5,2,0.0,0,HB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,237.67,1,1 +City Hotel,17,2016,February,10,28,2,3,1,0.0,0,BB,NGA,Direct,Direct,0,0,0,D,D,0,No Deposit,14.0,,0,Transient,101.0,0,0 +City Hotel,158,2017,March,13,27,1,1,2,0.0,0,BB,ISR,Groups,TA/TO,0,0,0,A,A,0,No Deposit,37.0,,0,Transient-Party,95.0,0,1 +City Hotel,66,2017,April,15,9,2,0,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,100.0,0,0 +City Hotel,21,2017,March,12,22,0,2,2,1.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,137.0,0,3 +Resort Hotel,58,2016,October,41,5,0,1,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,251.0,,0,Transient,59.0,0,0 +Resort Hotel,0,2016,March,12,16,0,1,2,0.0,0,BB,CHN,Online TA,TA/TO,1,0,1,D,D,0,No Deposit,240.0,,0,Transient,58.0,0,0 +City Hotel,87,2017,May,18,4,0,3,2,0.0,0,SC,NLD,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,108.0,0,1 +City Hotel,114,2016,October,42,12,1,4,0,0.0,0,BB,USA,Direct,Direct,0,0,0,D,D,1,No Deposit,14.0,,0,Transient,0.0,0,0 +City Hotel,0,2015,October,42,15,0,1,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,1,D,D,0,No Deposit,94.0,,0,Transient,0.0,0,0 +Resort Hotel,32,2016,July,31,29,1,2,2,0.0,0,BB,ARE,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,240.0,,0,Transient,226.33,1,0 +Resort Hotel,66,2017,August,33,16,0,4,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,230.0,0,2 +City Hotel,109,2016,July,28,4,1,4,2,0.0,0,SC,IRL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Transient,97.75,0,0 +City Hotel,7,2017,January,2,10,0,2,1,0.0,0,BB,PRT,Corporate,Corporate,1,1,12,A,A,1,No Deposit,,219.0,0,Transient,89.0,1,2 +City Hotel,179,2016,July,30,23,2,4,2,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient-Party,90.95,0,1 +City Hotel,36,2016,July,27,1,0,2,2,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,107.1,0,1 +Resort Hotel,135,2015,August,32,6,2,3,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,156.0,,0,Contract,104.55,0,0 +City Hotel,0,2017,August,33,19,1,1,2,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,152.0,,0,Transient,110.7,0,0 +City Hotel,102,2016,April,16,14,1,3,2,0.0,0,BB,CHE,Online TA,TA/TO,0,0,0,B,B,3,No Deposit,9.0,,0,Transient-Party,77.78,0,1 +City Hotel,263,2017,June,25,23,2,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,37.0,,0,Transient-Party,105.0,0,1 +City Hotel,90,2017,August,33,15,2,5,3,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,16.0,,0,Transient,116.1,0,0 +Resort Hotel,13,2016,October,43,21,0,2,2,0.0,0,HB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,120.0,0,1 +City Hotel,292,2016,July,30,21,0,2,2,0.0,0,HB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,115.0,0,0 +City Hotel,31,2017,April,16,21,2,3,1,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,1,No Deposit,21.0,,0,Transient-Party,75.0,0,0 +Resort Hotel,136,2016,March,13,24,0,3,2,0.0,0,Undefined,ESP,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,387.0,,0,Transient-Party,84.0,0,0 +Resort Hotel,2,2017,January,3,16,1,0,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,H,0,No Deposit,,428.0,0,Transient,35.0,0,0 +City Hotel,37,2017,April,15,11,0,2,2,0.0,0,BB,FRA,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,135.0,0,1 +Resort Hotel,52,2016,November,46,11,2,3,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,37.8,0,2 +Resort Hotel,84,2016,July,30,17,4,6,2,0.0,0,HB,GBR,Direct,Direct,0,0,0,F,F,1,No Deposit,250.0,,0,Transient,235.0,0,0 +City Hotel,31,2015,October,43,18,2,0,2,0.0,0,BB,NLD,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Contract,96.3,0,0 +City Hotel,34,2017,June,23,7,1,4,2,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,9.0,,0,Transient,132.0,0,2 +Resort Hotel,0,2017,February,6,5,2,0,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,54.0,0,1 +City Hotel,4,2015,August,35,23,2,1,1,0.0,0,BB,PRT,Complementary,Direct,0,0,0,A,A,1,No Deposit,,45.0,0,Transient,0.0,0,1 +City Hotel,11,2015,August,35,23,2,1,2,1.0,0,BB,ESP,Direct,Direct,0,0,0,A,D,0,No Deposit,14.0,,0,Transient,90.0,0,1 +City Hotel,240,2016,September,37,10,1,1,2,0.0,0,HB,ITA,Groups,TA/TO,0,0,0,A,D,0,No Deposit,1.0,,0,Transient-Party,96.0,0,1 +City Hotel,1,2015,December,53,30,0,3,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,127.0,0,1 +City Hotel,28,2016,August,32,4,0,1,1,0.0,0,BB,GBR,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,125.1,0,1 +Resort Hotel,128,2016,September,40,27,0,4,2,0.0,0,HB,ITA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,152.0,1,1 +City Hotel,281,2015,July,30,25,2,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.0,0,0 +City Hotel,31,2016,August,33,12,0,2,2,1.0,0,BB,PRT,Direct,Direct,0,0,0,D,A,2,No Deposit,,,0,Transient,160.5,0,2 +City Hotel,112,2016,July,30,18,1,2,2,0.0,0,BB,BEL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,105.3,0,2 +City Hotel,1,2016,June,23,2,0,2,2,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,1,No Deposit,,174.0,0,Transient-Party,119.0,0,1 +Resort Hotel,2,2017,February,8,20,1,0,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient-Party,43.0,0,1 +Resort Hotel,203,2017,May,21,25,0,3,2,0.0,0,BB,USA,Groups,TA/TO,0,0,0,A,A,0,No Deposit,,223.0,0,Transient-Party,55.0,0,0 +City Hotel,57,2016,October,45,30,2,0,2,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,89.1,0,1 +Resort Hotel,1,2016,July,29,16,2,1,2,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,145.67,0,0 +City Hotel,0,2016,July,28,5,0,1,2,1.0,0,BB,POL,Online TA,TA/TO,0,0,0,A,G,0,No Deposit,9.0,,0,Transient,159.0,0,0 +City Hotel,10,2017,May,21,21,1,0,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,E,E,1,No Deposit,9.0,,0,Transient,200.0,0,1 +Resort Hotel,48,2015,August,35,23,2,5,2,0.0,0,HB,ROU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,185.71,0,2 +City Hotel,8,2016,April,14,1,0,2,2,0.0,0,BB,PRT,Corporate,Corporate,1,0,1,A,D,1,No Deposit,,127.0,0,Transient,84.0,0,1 diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_002/history.csv b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_002/history.csv index 663b43c92ded6e4da543422ddbd1c0547ce5eeb7..0fd6f5845b0986e3e72d027fa715a1ecec8d571e 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_002/history.csv +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_002/history.csv @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:3b6f3e179328b802b3c584cca62b86e599e6ca08926f26d90e808b11d8f8fde6 -size 16801567 +oid sha256:7c0af4d87f44cd14c053d88e67da7ff06979f0eb9080948ba9ad34dbd80d57f3 +size 15261629 diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_002/info.json b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_002/info.json index 4816c05993309461836de8622438e342f2e503ff..20956c93d07801fe3772275e08832605a78593ed 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_002/info.json +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_002/info.json @@ -1,398 +1,391 @@ -{ - "name": "Hotel Booking Demand", - "source": "https://www.kaggle.com/datasets/jessemostipak/hotel-booking-demand/data", - "data_intro": "This dataset contains hotel booking data for a city hotel and a resort hotel, providing details on booking dates, stay duration, guest count, cancellation trends, and pricing. It is useful for demand forecasting, revenue management, and customer behavior analysis.", - "is_splited": false, - "overall_size": 119390, - "train_size": 0, - "test_size": 0, - "c_classes": 12, - "n_classes": 20, - "cat_feature_intro": { - "hotel": "Type of hotel (City Hotel or Resort Hotel).", - "arrival_date_month": "Month of arrival for the booking.", - "meal": "Type of meal plan booked (e.g., Breakfast included, Half board, Full board).", - "country": "Country of origin of the guest.", - "market_segment": "Booking distribution channel (e.g., direct, corporate, travel agent).", - "distribution_channel": "Channel through which the booking was made (e.g., online travel agents, direct).", - "reserved_room_type": "Type of room reserved by the guest before any changes.", - "assigned_room_type": "Final room type assigned to the guest.", - "deposit_type": "Indicates if a deposit was made and its type (e.g., No Deposit, Refundable, Non-Refundable).", - "customer_type": "Type of customer (e.g., transient, group, contract).", - "reservation_status": "Final status of the booking (e.g., Canceled, Checked-out, No-show).", - "reservation_status_date": "Date when the reservation status was last updated." - }, - "num_feature_intro": { - "is_canceled": "Indicates whether the booking was canceled (1) or not (0).", - "lead_time": "Number of days between booking and arrival.", - "arrival_date_year": "Year of arrival for the booking.", - "arrival_date_week_number": "Week number of the arrival date.", - "arrival_date_day_of_month": "Day of the month of arrival.", - "stays_in_weekend_nights": "Number of weekend nights (Saturday or Sunday) the guest stayed.", - "stays_in_week_nights": "Number of weekday nights (Monday to Friday) the guest stayed.", - "adults": "Number of adults included in the booking.", - "children": "Number of children included in the booking.", - "babies": "Number of babies included in the booking.", - "is_repeated_guest": "Indicates whether the guest is a returning customer (1) or not (0).", - "previous_cancellations": "Number of times the guest has canceled previous bookings.", - "previous_bookings_not_canceled": "Number of previous bookings not canceled by the guest.", - "booking_changes": "Number of changes made to the booking.", - "agent": "ID of the booking agent (if applicable).", - "company": "ID of the company making the booking (if applicable).", - "days_in_waiting_list": "Number of days the booking was on the waiting list before confirmation.", - "adr": "Average daily rate (price per night per room).", - "required_car_parking_spaces": "Number of car parking spaces required by the guest.", - "total_of_special_requests": "Number of special requests made by the guest (e.g., high floor, twin beds)." - }, - "evaluation_metric": null, - "task_type": "classification", - "target": "reservation_status", - "num_feature_value": { - "adr": [ - -6.38, - 5400.0 - ], - "adults": [ - 0.0, - 55.0 - ], - "agent": [ - 1.0, - 535.0 - ], - "arrival_date_day_of_month": [ - 1.0, - 31.0 - ], - "arrival_date_week_number": [ - 1.0, - 53.0 - ], - "arrival_date_year": [ - 2015.0, - 2017.0 - ], - "babies": [ - 0.0, - 10.0 - ], - "booking_changes": [ - 0.0, - 21.0 - ], - "children": [ - 0.0, - 10.0 - ], - "company": [ - 6.0, - 543.0 - ], - "days_in_waiting_list": [ - 0.0, - 391.0 - ], - "is_canceled": [ - 0.0, - 1.0 - ], - "is_repeated_guest": [ - 0.0, - 1.0 - ], - "lead_time": [ - 0.0, - 737.0 - ], - "previous_bookings_not_canceled": [ - 0.0, - 72.0 - ], - "previous_cancellations": [ - 0.0, - 26.0 - ], - "required_car_parking_spaces": [ - 0.0, - 8.0 - ], - "stays_in_week_nights": [ - 0.0, - 50.0 - ], - "stays_in_weekend_nights": [ - 0.0, - 19.0 - ], - "total_of_special_requests": [ - 0.0, - 5.0 - ] - }, - "cat_feature_value": { - "arrival_date_month": [ - "April", - "August", - "December", - "February", - "January", - "July", - "June", - "March", - "May", - "November", - "October", - "September" - ], - "assigned_room_type": [ - "A", - "B", - "C", - "D", - "E", - "F", - "G", - "H", - "I", - "K", - "L", - "P" - ], - "country": [ - "ABW", - "AGO", - "AIA", - "ALB", - "AND", - "ARE", - "ARG", - "ARM", - "ASM", - "ATA", - "ATF", - "AUS", - "AUT", - "AZE", - "BDI", - "BEL", - "BEN", - "BFA", - "BGD", - "BGR", - "BHR", - "BHS", - "BIH", - "BLR", - "BOL", - "BRA", - "BRB", - "BWA", - "CAF", - "CHE", - "CHL", - "CHN", - "CIV", - "CMR", - "CN", - "COL", - "COM", - "CPV", - "CRI", - "CUB", - "CYM", - "CYP", - "CZE", - "DEU", - "DJI", - "DMA", - "DNK", - "DOM", - "DZA", - "ECU", - "EGY", - "ESP", - "EST", - "ETH", - "FIN", - "FJI", - "FRA", - "FRO", - "GAB", - "GBR", - "GEO", - "GGY", - "GHA", - "GIB", - "GLP", - "GNB", - "GRC", - "GTM", - "GUY", - "HKG", - "HND", - "HRV", - "HUN", - "IDN", - "IMN", - "IND", - "IRL", - "IRN", - "IRQ", - "ISL", - "ISR", - "ITA", - "JAM", - "JEY", - "JOR", - "JPN", - "KAZ", - "KEN", - "KHM", - "KIR", - "KNA", - "KOR", - "KWT", - "LAO", - "LBN", - "LBY", - "LCA", - "LIE", - "LKA", - "LTU", - "LUX", - "LVA", - "MAC", - "MAR", - "MCO", - "MDG", - "MDV", - "MEX", - "MKD", - "MLI", - "MLT", - "MMR", - "MNE", - "MOZ", - "MRT", - "MUS", - "MWI", - "MYS", - "MYT", - "NAM", - "NCL", - "NGA", - "NIC", - "NLD", - "NOR", - "NPL", - "NZL", - "OMN", - "PAK", - "PAN", - "PER", - "PHL", - "PLW", - "POL", - "PRI", - "PRT", - "PRY", - "PYF", - "QAT", - "ROU", - "RUS", - "RWA", - "SAU", - "SDN", - "SEN", - "SGP", - "SLE", - "SLV", - "SMR", - "SRB", - "STP", - "SUR", - "SVK", - "SVN", - "SWE", - "SYC", - "SYR", - "TGO", - "THA", - "TJK", - "TMP", - "TUN", - "TUR", - "TWN", - "TZA", - "UGA", - "UKR", - "UMI", - "URY", - "USA", - "UZB", - "VEN", - "VGB", - "VNM", - "ZAF", - "ZMB", - "ZWE" - ], - "customer_type": [ - "Contract", - "Group", - "Transient", - "Transient-Party" - ], - "deposit_type": [ - "No Deposit", - "Non Refund", - "Refundable" - ], - "distribution_channel": [ - "Corporate", - "Direct", - "GDS", - "TA/TO", - "Undefined" - ], - "hotel": [ - "City Hotel", - "Resort Hotel" - ], - "market_segment": [ - "Aviation", - "Complementary", - "Corporate", - "Direct", - "Groups", - "Offline TA/TO", - "Online TA", - "Undefined" - ], - "meal": [ - "BB", - "FB", - "HB", - "SC", - "Undefined" - ], - "reservation_status": [ - "Canceled", - "Check-Out", - "No-Show" - ], - "reservation_status_date": "string", - "reserved_room_type": [ - "A", - "B", - "C", - "D", - "E", - "F", - "G", - "H", - "L", - "P" - ] - } -} \ No newline at end of file +{ + "name": "Hotel Booking Demand", + "source": "https://www.kaggle.com/datasets/jessemostipak/hotel-booking-demand/data", + "data_intro": "This dataset contains hotel booking data for a city hotel and a resort hotel, providing details on booking dates, stay duration, guest count, cancellation trends, and pricing. It is useful for demand forecasting, revenue management, and customer behavior analysis.", + "is_splited": false, + "overall_size": 119390, + "train_size": 0, + "test_size": 0, + "c_classes": 11, + "n_classes": 19, + "cat_feature_intro": { + "hotel": "Type of hotel (City Hotel or Resort Hotel).", + "arrival_date_month": "Month of arrival for the booking.", + "meal": "Type of meal plan booked (e.g., Breakfast included, Half board, Full board).", + "country": "Country of origin of the guest.", + "market_segment": "Booking distribution channel (e.g., direct, corporate, travel agent).", + "distribution_channel": "Channel through which the booking was made (e.g., online travel agents, direct).", + "reserved_room_type": "Type of room reserved by the guest before any changes.", + "assigned_room_type": "Final room type assigned to the guest.", + "deposit_type": "Indicates if a deposit was made and its type (e.g., No Deposit, Refundable, Non-Refundable).", + "customer_type": "Type of customer (e.g., transient, group, contract).", + "reservation_status": "Final status of the booking (e.g., Canceled, Checked-out, No-show)." + }, + "num_feature_intro": { + "lead_time": "Number of days between booking and arrival.", + "arrival_date_year": "Year of arrival for the booking.", + "arrival_date_week_number": "Week number of the arrival date.", + "arrival_date_day_of_month": "Day of the month of arrival.", + "stays_in_weekend_nights": "Number of weekend nights (Saturday or Sunday) the guest stayed.", + "stays_in_week_nights": "Number of weekday nights (Monday to Friday) the guest stayed.", + "adults": "Number of adults included in the booking.", + "children": "Number of children included in the booking.", + "babies": "Number of babies included in the booking.", + "is_repeated_guest": "Indicates whether the guest is a returning customer (1) or not (0).", + "previous_cancellations": "Number of times the guest has canceled previous bookings.", + "previous_bookings_not_canceled": "Number of previous bookings not canceled by the guest.", + "booking_changes": "Number of changes made to the booking.", + "agent": "ID of the booking agent (if applicable).", + "company": "ID of the company making the booking (if applicable).", + "days_in_waiting_list": "Number of days the booking was on the waiting list before confirmation.", + "adr": "Average daily rate (price per night per room).", + "required_car_parking_spaces": "Number of car parking spaces required by the guest.", + "total_of_special_requests": "Number of special requests made by the guest (e.g., high floor, twin beds)." + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "reservation_status", + "num_feature_value": { + "adr": [ + -6.38, + 5400.0 + ], + "adults": [ + 0.0, + 55.0 + ], + "agent": [ + 1.0, + 535.0 + ], + "arrival_date_day_of_month": [ + 1.0, + 31.0 + ], + "arrival_date_week_number": [ + 1.0, + 53.0 + ], + "arrival_date_year": [ + 2015.0, + 2017.0 + ], + "babies": [ + 0.0, + 10.0 + ], + "booking_changes": [ + 0.0, + 21.0 + ], + "children": [ + 0.0, + 10.0 + ], + "company": [ + 6.0, + 543.0 + ], + "days_in_waiting_list": [ + 0.0, + 391.0 + ], + "is_repeated_guest": [ + 0.0, + 1.0 + ], + "lead_time": [ + 0.0, + 737.0 + ], + "previous_bookings_not_canceled": [ + 0.0, + 72.0 + ], + "previous_cancellations": [ + 0.0, + 26.0 + ], + "required_car_parking_spaces": [ + 0.0, + 8.0 + ], + "stays_in_week_nights": [ + 0.0, + 50.0 + ], + "stays_in_weekend_nights": [ + 0.0, + 19.0 + ], + "total_of_special_requests": [ + 0.0, + 5.0 + ] + }, + "cat_feature_value": { + "arrival_date_month": [ + "April", + "August", + "December", + "February", + "January", + "July", + "June", + "March", + "May", + "November", + "October", + "September" + ], + "assigned_room_type": [ + "A", + "B", + "C", + "D", + "E", + "F", + "G", + "H", + "I", + "K", + "L", + "P" + ], + "country": [ + "ABW", + "AGO", + "AIA", + "ALB", + "AND", + "ARE", + "ARG", + "ARM", + "ASM", + "ATA", + "ATF", + "AUS", + "AUT", + "AZE", + "BDI", + "BEL", + "BEN", + "BFA", + "BGD", + "BGR", + "BHR", + "BHS", + "BIH", + "BLR", + "BOL", + "BRA", + "BRB", + "BWA", + "CAF", + "CHE", + "CHL", + "CHN", + "CIV", + "CMR", + "CN", + "COL", + "COM", + "CPV", + "CRI", + "CUB", + "CYM", + "CYP", + "CZE", + "DEU", + "DJI", + "DMA", + "DNK", + "DOM", + "DZA", + "ECU", + "EGY", + "ESP", + "EST", + "ETH", + "FIN", + "FJI", + "FRA", + "FRO", + "GAB", + "GBR", + "GEO", + "GGY", + "GHA", + "GIB", + "GLP", + "GNB", + "GRC", + "GTM", + "GUY", + "HKG", + "HND", + "HRV", + "HUN", + "IDN", + "IMN", + "IND", + "IRL", + "IRN", + "IRQ", + "ISL", + "ISR", + "ITA", + "JAM", + "JEY", + "JOR", + "JPN", + "KAZ", + "KEN", + "KHM", + "KIR", + "KNA", + "KOR", + "KWT", + "LAO", + "LBN", + "LBY", + "LCA", + "LIE", + "LKA", + "LTU", + "LUX", + "LVA", + "MAC", + "MAR", + "MCO", + "MDG", + "MDV", + "MEX", + "MKD", + "MLI", + "MLT", + "MMR", + "MNE", + "MOZ", + "MRT", + "MUS", + "MWI", + "MYS", + "MYT", + "NAM", + "NCL", + "NGA", + "NIC", + "NLD", + "NOR", + "NPL", + "NZL", + "OMN", + "PAK", + "PAN", + "PER", + "PHL", + "PLW", + "POL", + "PRI", + "PRT", + "PRY", + "PYF", + "QAT", + "ROU", + "RUS", + "RWA", + "SAU", + "SDN", + "SEN", + "SGP", + "SLE", + "SLV", + "SMR", + "SRB", + "STP", + "SUR", + "SVK", + "SVN", + "SWE", + "SYC", + "SYR", + "TGO", + "THA", + "TJK", + "TMP", + "TUN", + "TUR", + "TWN", + "TZA", + "UGA", + "UKR", + "UMI", + "URY", + "USA", + "UZB", + "VEN", + "VGB", + "VNM", + "ZAF", + "ZMB", + "ZWE" + ], + "customer_type": [ + "Contract", + "Group", + "Transient", + "Transient-Party" + ], + "deposit_type": [ + "No Deposit", + "Non Refund", + "Refundable" + ], + "distribution_channel": [ + "Corporate", + "Direct", + "GDS", + "TA/TO", + "Undefined" + ], + "hotel": [ + "City Hotel", + "Resort Hotel" + ], + "market_segment": [ + "Aviation", + "Complementary", + "Corporate", + "Direct", + "Groups", + "Offline TA/TO", + "Online TA", + "Undefined" + ], + "meal": [ + "BB", + "FB", + "HB", + "SC", + "Undefined" + ], + "reservation_status": [ + "Canceled", + "Check-Out", + "No-Show" + ], + "reserved_room_type": [ + "A", + "B", + "C", + "D", + "E", + "F", + "G", + "H", + "L", + "P" + ] + } +} diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_002/test_current.csv b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_002/test_current.csv index e83d6e1ec1076d95d33dcb7bebd0e31abf571fcd..26cca99477edd763c42182f2ab9e5a4ffae1d604 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_002/test_current.csv +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_002/test_current.csv @@ -1,16 +1,16 @@ -hotel,is_canceled,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests,reservation_status,reservation_status_date -City Hotel,1,210,2015,October,41,8,1,3,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,12.0,,0,Transient-Party,86.0,0,0,Canceled,2015-06-30 -Resort Hotel,1,105,2016,August,33,13,4,6,2,2.0,0,HB,MAR,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,240.0,,0,Transient,309.0,0,0,Canceled,2016-07-30 -City Hotel,1,147,2015,December,53,29,0,3,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Contract,72.25,0,2,Canceled,2015-08-05 -City Hotel,1,245,2016,June,25,17,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,12.0,,0,Transient,75.0,0,0,Canceled,2016-02-09 -City Hotel,1,46,2016,February,7,13,0,1,2,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,11.0,,0,Transient,48.6,0,0,Canceled,2016-02-04 -City Hotel,1,175,2017,May,20,19,0,1,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,44.0,,0,Transient,90.0,0,0,Canceled,2016-11-25 -City Hotel,1,186,2017,May,22,30,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,19.0,,0,Transient,130.0,0,0,Canceled,2016-11-25 -Resort Hotel,1,383,2016,October,41,6,1,3,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,315.0,,0,Transient-Party,48.0,0,0,Canceled,2016-03-04 -City Hotel,1,81,2016,December,50,8,0,3,2,0.0,0,SC,ITA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,79.2,0,1,Canceled,2016-09-19 -City Hotel,1,121,2017,August,33,18,1,2,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,125.0,0,0,Canceled,2017-08-05 -City Hotel,1,86,2016,December,50,8,0,3,2,1.0,0,BB,ESP,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,9.0,,0,Transient,174.0,0,0,Canceled,2016-09-13 -City Hotel,1,22,2016,December,53,29,2,3,2,0.0,0,HB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,198.88,0,2,Canceled,2016-12-22 -City Hotel,1,66,2017,April,15,9,2,0,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,100.0,0,0,Canceled,2017-02-02 -Resort Hotel,1,66,2017,August,33,16,0,4,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,230.0,0,2,Canceled,2017-07-04 -City Hotel,1,281,2015,July,30,25,2,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.0,0,0,Canceled,2015-07-06 +hotel,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests,reservation_status +City Hotel,210,2015,October,41,8,1,3,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,12.0,,0,Transient-Party,86.0,0,0,Canceled +Resort Hotel,105,2016,August,33,13,4,6,2,2.0,0,HB,MAR,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,240.0,,0,Transient,309.0,0,0,Canceled +City Hotel,147,2015,December,53,29,0,3,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Contract,72.25,0,2,Canceled +City Hotel,245,2016,June,25,17,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,12.0,,0,Transient,75.0,0,0,Canceled +City Hotel,46,2016,February,7,13,0,1,2,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,11.0,,0,Transient,48.6,0,0,Canceled +City Hotel,175,2017,May,20,19,0,1,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,44.0,,0,Transient,90.0,0,0,Canceled +City Hotel,186,2017,May,22,30,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,19.0,,0,Transient,130.0,0,0,Canceled +Resort Hotel,383,2016,October,41,6,1,3,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,315.0,,0,Transient-Party,48.0,0,0,Canceled +City Hotel,81,2016,December,50,8,0,3,2,0.0,0,SC,ITA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,79.2,0,1,Canceled +City Hotel,121,2017,August,33,18,1,2,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,125.0,0,0,Canceled +City Hotel,86,2016,December,50,8,0,3,2,1.0,0,BB,ESP,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,9.0,,0,Transient,174.0,0,0,Canceled +City Hotel,22,2016,December,53,29,2,3,2,0.0,0,HB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,198.88,0,2,Canceled +City Hotel,66,2017,April,15,9,2,0,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,100.0,0,0,Canceled +Resort Hotel,66,2017,August,33,16,0,4,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,230.0,0,2,Canceled +City Hotel,281,2015,July,30,25,2,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.0,0,0,Canceled diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_003/Hotel_booking_demand_B4_003_current.json b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_003/Hotel_booking_demand_B4_003_current.json index fa6d70f8f915fa17a205bb267fc230fc5e57b852..7a96910905b57babeec9d5b46f75766c189b4ad2 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_003/Hotel_booking_demand_B4_003_current.json +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_003/Hotel_booking_demand_B4_003_current.json @@ -1,7221 +1,6833 @@ { - "id": "003", - "task_type": "B4", - "subtask_type": "list", - "perspective": "data_holder", - "dataset_name": "Hotel booking demand", - "table_path": "output/daily/classification/Hotel booking demand_B4/Hotel booking demand_B4_003/test.csv", - "query": "We are allocating complimentary amenities and need to focus our resources on guests who are almost certain to arrive. Reviewing the past reservation data I shared, which includes final statuses, can you filter our new incoming bookings to show only those profiles that are highly likely to achieve a Check-Out status?", - "meta_info": { - "domain": "daily" + "id": "003", + "task_type": "B4", + "subtask_type": "list", + "perspective": "data_holder", + "dataset_name": "Hotel booking demand", + "table_path": "output/daily/classification/Hotel booking demand_B4/Hotel booking demand_B4_003/test.csv", + "query": "We are allocating complimentary amenities and need to focus our resources on guests who are almost certain to arrive. Reviewing the past reservation data I shared, which includes final statuses, can you filter our new incoming bookings to show only those profiles that are highly likely to achieve a Check-Out status?", + "meta_info": { + "domain": "daily" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "hotel": "City Hotel", + "lead_time": 88, + "arrival_date_year": 2015, + "arrival_date_month": "September", + "arrival_date_week_number": 39, + "arrival_date_day_of_month": 26, + "stays_in_weekend_nights": 2, + "stays_in_week_nights": 2, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "PRT", + "market_segment": "Groups", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 0, + "deposit_type": "Non Refund", + "agent": 1.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 170.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Canceled" + } + }, + { + "scenario_id": "002", + "features": { + "hotel": "City Hotel", + "lead_time": 40, + "arrival_date_year": 2016, + "arrival_date_month": "March", + "arrival_date_week_number": 13, + "arrival_date_day_of_month": 20, + "stays_in_weekend_nights": 1, + "stays_in_week_nights": 0, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "SC", + "country": "GBR", + "market_segment": "Online TA", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 9.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 88.2, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "003", + "features": { + "hotel": "City Hotel", + "lead_time": 349, + "arrival_date_year": 2015, + "arrival_date_month": "October", + "arrival_date_week_number": 40, + "arrival_date_day_of_month": 1, + "stays_in_weekend_nights": 0, + "stays_in_week_nights": 2, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "PRT", + "market_segment": "Groups", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 1, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 0, + "deposit_type": "Non Refund", + "agent": 1.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Contract", + "adr": 62.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Canceled" + } + }, + { + "scenario_id": "008", + "features": { + "hotel": "City Hotel", + "lead_time": 423, + "arrival_date_year": 2017, + "arrival_date_month": "May", + "arrival_date_week_number": 21, + "arrival_date_day_of_month": 23, + "stays_in_weekend_nights": 0, + "stays_in_week_nights": 1, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "PRT", + "market_segment": "Groups", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 0, + "deposit_type": "Non Refund", + "agent": 229.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 90.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Canceled" + } + }, + { + "scenario_id": "010", + "features": { + "hotel": "City Hotel", + "lead_time": 175, + "arrival_date_year": 2017, + "arrival_date_month": "April", + "arrival_date_week_number": 17, + "arrival_date_day_of_month": 29, + "stays_in_weekend_nights": 2, + "stays_in_week_nights": 1, + "adults": 2, + "children": 2.0, + "babies": 0, + "meal": "BB", + "country": "RUS", + "market_segment": "Online TA", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "F", + "assigned_room_type": "F", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 9.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 189.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 1, + "reservation_status": "Canceled" + } + }, + { + "scenario_id": "012", + "features": { + "hotel": "City Hotel", + "lead_time": 38, + "arrival_date_year": 2016, + "arrival_date_month": "December", + "arrival_date_week_number": 50, + "arrival_date_day_of_month": 10, + "stays_in_weekend_nights": 2, + "stays_in_week_nights": 1, + "adults": 2, + "children": 2.0, + "babies": 0, + "meal": "BB", + "country": "PHL", + "market_segment": "Direct", + "distribution_channel": "Direct", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "E", + "assigned_room_type": "E", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 14.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 143.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "013", + "features": { + "hotel": "City Hotel", + "lead_time": 163, + "arrival_date_year": 2016, + "arrival_date_month": "October", + "arrival_date_week_number": 42, + "arrival_date_day_of_month": 15, + "stays_in_weekend_nights": 0, + "stays_in_week_nights": 1, + "adults": 1, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "PRT", + "market_segment": "Offline TA/TO", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 0, + "deposit_type": "Non Refund", + "agent": 34.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 115.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Canceled" + } + }, + { + "scenario_id": "016", + "features": { + "hotel": "City Hotel", + "lead_time": 277, + "arrival_date_year": 2016, + "arrival_date_month": "November", + "arrival_date_week_number": 46, + "arrival_date_day_of_month": 7, + "stays_in_weekend_nights": 1, + "stays_in_week_nights": 2, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "PRT", + "market_segment": "Groups", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 0, + "deposit_type": "Non Refund", + "agent": null, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 100.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Canceled" + } + }, + { + "scenario_id": "026", + "features": { + "hotel": "Resort Hotel", + "lead_time": 29, + "arrival_date_year": 2015, + "arrival_date_month": "August", + "arrival_date_week_number": 35, + "arrival_date_day_of_month": 27, + "stays_in_weekend_nights": 0, + "stays_in_week_nights": 1, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "PRT", + "market_segment": "Online TA", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "D", + "assigned_room_type": "D", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 240.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 211.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 1, + "reservation_status": "Canceled" + } + }, + { + "scenario_id": "028", + "features": { + "hotel": "City Hotel", + "lead_time": 148, + "arrival_date_year": 2017, + "arrival_date_month": "May", + "arrival_date_week_number": 18, + "arrival_date_day_of_month": 4, + "stays_in_weekend_nights": 0, + "stays_in_week_nights": 2, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "PRT", + "market_segment": "Groups", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 0, + "deposit_type": "Non Refund", + "agent": 171.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 95.0, + "required_car_parking_spaces": 0, + 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"babies": 0, + "meal": "BB", + "country": "PRT", + "market_segment": "Groups", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 0, + "deposit_type": "Non Refund", + "agent": 30.0, + "company": null, + "days_in_waiting_list": 26, + "customer_type": "Transient", + "adr": 130.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Canceled" + } + } + ], + "target_column": "reservation_status", + "task_sub_type": "classification", + "ranking_ground_truth": { + "top_k_ids": [ + "002", + "012", + "067", + "088", + "111", + "120", + "147", + "198", + "205", + "244", + "290", + "340", + "385", + "446", + "494" + ] }, - "final_decision": {}, - "what_if": {}, - "ground_truth": { - "extracted_features": [ - { - "scenario_id": "001", - "features": { - "hotel": "City Hotel", - "is_canceled": 1, - 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"stays_in_week_nights": 8, - "adults": 2, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "PRT", - "market_segment": "Online TA", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "A", - "assigned_room_type": "A", - "booking_changes": 0, - "deposit_type": "No Deposit", - "agent": 240.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 160.0, - "required_car_parking_spaces": 0, - "total_of_special_requests": 2, - "reservation_status": "Canceled", - "reservation_status_date": "2016-01-26" - } - }, - { - "scenario_id": "489", - "features": { - "hotel": "City Hotel", - "is_canceled": 1, - "lead_time": 59, - "arrival_date_year": 2017, - "arrival_date_month": "January", - "arrival_date_week_number": 4, - "arrival_date_day_of_month": 23, - "stays_in_weekend_nights": 1, - "stays_in_week_nights": 1, - "adults": 2, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "PRT", - "market_segment": "Groups", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "A", - "assigned_room_type": "A", - "booking_changes": 0, - "deposit_type": "Non Refund", - "agent": 326.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 80.0, - "required_car_parking_spaces": 0, - "total_of_special_requests": 0, - "reservation_status": "Canceled", - "reservation_status_date": "2016-11-25" - } - }, - { - "scenario_id": "490", - "features": { - "hotel": "City Hotel", - "is_canceled": 1, - "lead_time": 285, - "arrival_date_year": 2017, - "arrival_date_month": "May", - "arrival_date_week_number": 19, - "arrival_date_day_of_month": 8, - "stays_in_weekend_nights": 1, - "stays_in_week_nights": 2, - "adults": 3, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "GBR", - "market_segment": "Online TA", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "D", - "assigned_room_type": "D", - "booking_changes": 0, - "deposit_type": "No Deposit", - "agent": 9.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 142.2, - "required_car_parking_spaces": 0, - "total_of_special_requests": 1, - "reservation_status": "Canceled", - "reservation_status_date": "2016-08-25" - } - }, - { - "scenario_id": "491", - "features": { - "hotel": "Resort Hotel", - "is_canceled": 1, - "lead_time": 46, - "arrival_date_year": 2016, - "arrival_date_month": "June", - "arrival_date_week_number": 24, - "arrival_date_day_of_month": 5, - "stays_in_weekend_nights": 2, - "stays_in_week_nights": 2, - "adults": 2, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "NLD", - "market_segment": "Online TA", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "A", - "assigned_room_type": "A", - "booking_changes": 0, - "deposit_type": "No Deposit", - "agent": 240.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 123.0, - "required_car_parking_spaces": 0, - "total_of_special_requests": 0, - "reservation_status": "Canceled", - "reservation_status_date": "2016-05-13" - } - }, - { - "scenario_id": "494", - "features": { - "hotel": "Resort Hotel", - "is_canceled": 0, - "lead_time": 4, - "arrival_date_year": 2016, - "arrival_date_month": "November", - "arrival_date_week_number": 48, - "arrival_date_day_of_month": 21, - "stays_in_weekend_nights": 1, - "stays_in_week_nights": 1, - "adults": 1, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "PRT", - "market_segment": "Complementary", - "distribution_channel": "Direct", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "A", - "assigned_room_type": "D", - "booking_changes": 0, - "deposit_type": "No Deposit", - "agent": null, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 0.0, - "required_car_parking_spaces": 0, - "total_of_special_requests": 0, - "reservation_status": "Check-Out", - "reservation_status_date": "2016-11-23" - } - }, - { - "scenario_id": "496", - "features": { - "hotel": "Resort Hotel", - "is_canceled": 1, - "lead_time": 110, - "arrival_date_year": 2017, - "arrival_date_month": "February", - "arrival_date_week_number": 5, - "arrival_date_day_of_month": 2, - "stays_in_weekend_nights": 0, - "stays_in_week_nights": 3, - "adults": 1, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "GBR", - "market_segment": "Online TA", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "A", - "assigned_room_type": "A", - "booking_changes": 0, - "deposit_type": "No Deposit", - "agent": 240.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 37.0, - "required_car_parking_spaces": 0, - "total_of_special_requests": 1, - "reservation_status": "Canceled", - "reservation_status_date": "2016-12-22" - } - }, - { - "scenario_id": "499", - "features": { - "hotel": "City Hotel", - "is_canceled": 1, - "lead_time": 161, - "arrival_date_year": 2017, - "arrival_date_month": "June", - "arrival_date_week_number": 26, - "arrival_date_day_of_month": 30, - "stays_in_weekend_nights": 0, - "stays_in_week_nights": 1, - "adults": 2, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "PRT", - "market_segment": "Groups", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "A", - "assigned_room_type": "A", - "booking_changes": 0, - "deposit_type": "Non Refund", - "agent": 30.0, - "company": null, - "days_in_waiting_list": 26, - "customer_type": "Transient", - "adr": 130.0, - "required_car_parking_spaces": 0, - "total_of_special_requests": 0, - "reservation_status": "Canceled", - "reservation_status_date": "2017-02-15" - } - } - ], - "target_column": "reservation_status", - "task_sub_type": "classification", - "ranking_ground_truth": { - "top_k_ids": [ - "002", - "012", - "067", - "088", - "111", - "120", - "147", - "198", - "205", - "244", - "290", - "340", - "385", - "446", - "494" - ] - }, - "active_filters": [], - "target_class_value": "check-out" - }, - "response": "", - "evaluation_score": {} -} \ No newline at end of file + "active_filters": [], + "target_class_value": "check-out" + }, + "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_003/current.csv b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_003/current.csv index d5470c8d66cba88ff23656ddcf0eddcbed34ffc8..a04fb16e9a0b085553e5fec629068a4788686557 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_003/current.csv +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_003/current.csv @@ -1,201 +1,195 @@ -hotel,is_canceled,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests,reservation_status_date -City Hotel,1,88,2015,September,39,26,2,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,170.0,0,0,2015-08-21 -City Hotel,0,40,2016,March,13,20,1,0,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,88.2,0,0,2016-03-21 -City Hotel,1,349,2015,October,40,1,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Contract,62.0,0,0,2015-01-01 -City Hotel,1,423,2017,May,21,23,0,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,229.0,,0,Transient,90.0,0,0,2017-02-28 -City Hotel,1,175,2017,April,17,29,2,1,2,2.0,0,BB,RUS,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,9.0,,0,Transient,189.0,0,1,2017-04-10 -City Hotel,0,38,2016,December,50,10,2,1,2,2.0,0,BB,PHL,Direct,Direct,0,0,0,E,E,0,No Deposit,14.0,,0,Transient,143.0,0,0,2016-12-13 -City Hotel,1,163,2016,October,42,15,0,1,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,34.0,,0,Transient,115.0,0,0,2016-09-27 -Resort Hotel,1,29,2015,August,35,27,0,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,211.0,0,1,2015-08-11 -City Hotel,1,148,2017,May,18,4,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,171.0,,0,Transient,95.0,0,0,2016-12-07 -City Hotel,1,0,2015,August,33,10,1,4,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,75.0,0,2,2015-08-10 -City Hotel,1,40,2016,March,13,25,1,2,3,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,7.0,,0,Transient,201.95,0,0,2016-02-17 -City Hotel,1,330,2016,June,23,2,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0,2015-10-21 -City Hotel,1,177,2017,March,12,23,1,3,2,1.0,0,BB,TWN,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,9.0,,0,Transient,103.7,0,0,2017-02-17 -City Hotel,1,205,2016,May,23,29,2,1,3,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,1,Non Refund,26.0,,0,Transient,125.0,0,0,2016-01-19 -Resort Hotel,1,349,2017,July,28,9,2,4,2,2.0,0,BB,PRT,Direct,Direct,0,0,0,C,C,2,No Deposit,250.0,,0,Transient,178.75,0,0,2016-12-28 -City Hotel,1,115,2016,November,47,18,0,2,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,90.0,0,1,2016-10-24 -Resort Hotel,1,74,2016,February,9,26,1,2,2,0.0,0,Undefined,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,68.0,,0,Transient,70.0,0,0,2016-01-21 -City Hotel,1,183,2017,February,7,15,0,3,2,1.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,106.2,0,1,2016-08-18 -City Hotel,1,233,2016,August,33,11,0,2,2,0.0,0,BB,RUS,Online TA,TA/TO,0,0,0,B,B,1,No Deposit,9.0,,0,Transient,86.62,0,0,2016-01-13 -Resort Hotel,1,243,2015,September,36,4,0,2,2,0.0,0,BB,PRT,Online TA,TA/TO,0,1,0,D,D,0,No Deposit,240.0,,0,Transient-Party,99.0,0,1,2015-07-29 -City Hotel,0,32,2015,October,43,24,1,1,1,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Contract,136.0,0,1,2015-10-26 -City Hotel,1,99,2017,April,15,15,2,4,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,58.0,,0,Transient-Party,105.0,0,0,2017-03-07 -City Hotel,1,185,2016,December,51,16,1,2,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,103.5,0,0,2016-07-28 -City Hotel,1,97,2015,October,41,5,1,3,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,29.0,,0,Transient,140.0,0,0,2015-07-03 -City Hotel,1,280,2016,November,47,16,0,4,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,73.95,0,1,2016-03-11 -City Hotel,1,566,2017,January,4,26,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0,2015-10-21 -City Hotel,1,280,2016,December,53,31,2,1,2,0.0,0,BB,ROU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,86.7,0,0,2016-03-27 -City Hotel,1,150,2017,June,24,17,2,3,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,120.0,0,0,2017-01-18 -City Hotel,0,4,2016,August,36,30,0,2,3,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,175.0,0,2,2016-09-01 -City Hotel,1,262,2016,December,51,14,2,4,2,0.0,0,SC,VEN,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,63.75,0,0,2016-03-27 -City Hotel,1,275,2016,April,17,17,2,0,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,12.0,,0,Transient,110.0,0,0,2015-08-04 -City Hotel,1,22,2017,March,11,15,2,6,3,0.0,0,BB,AGO,Direct,Direct,0,0,0,F,F,0,No Deposit,14.0,,0,Transient,238.0,0,0,2017-03-15 -City Hotel,1,55,2016,December,50,8,0,3,1,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,86.4,0,2,2016-10-17 -City Hotel,1,80,2016,April,16,10,2,1,1,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,182.0,,0,Transient,100.0,0,0,2016-02-22 -City Hotel,1,213,2016,June,24,7,0,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,19.0,,62,Transient,130.0,0,0,2016-01-08 -City Hotel,1,174,2017,June,22,1,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,58.0,,0,Transient,100.0,0,0,2017-01-24 -City Hotel,1,451,2016,May,21,16,1,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,60.0,0,0,2016-03-14 -City Hotel,1,111,2017,February,5,1,4,10,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,D,D,0,No Deposit,,,0,Transient-Party,98.1,0,2,2016-10-18 -City Hotel,1,76,2016,September,38,15,0,3,3,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,168.3,0,3,2016-08-11 -City Hotel,0,89,2016,November,47,19,1,1,2,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,9.0,,0,Transient,79.2,0,3,2016-11-21 -Resort Hotel,1,168,2017,August,33,17,2,4,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,314.0,,0,Transient,176.0,0,1,2017-03-10 -City Hotel,1,41,2016,October,42,15,2,1,2,0.0,0,BB,NLD,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,145.2,0,2,2016-09-06 -City Hotel,1,241,2016,September,36,2,2,2,2,0.0,0,BB,AUS,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,101.79,0,1,2016-05-31 -Resort Hotel,0,287,2015,October,42,15,0,3,2,0.0,0,BB,GBR,Groups,Corporate,0,0,0,A,E,1,No Deposit,,223.0,0,Transient-Party,44.5,0,0,2015-10-18 -Resort Hotel,1,259,2015,September,38,17,0,3,2,0.0,0,BB,GBR,Groups,Corporate,0,1,0,A,A,0,Non Refund,,223.0,0,Transient,40.05,0,0,2015-01-21 -Resort Hotel,1,47,2017,August,34,26,2,5,2,2.0,0,HB,ESP,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,240.0,,0,Transient,340.86,0,0,2017-07-22 -City Hotel,1,475,2016,October,44,27,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0,2015-10-21 -City Hotel,1,37,2016,October,41,2,2,3,1,0.0,0,BB,CHN,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,125.55,0,0,2016-09-28 -Resort Hotel,1,133,2017,March,11,13,1,0,2,0.0,0,BB,USA,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,240.0,,0,Transient,42.0,0,2,2016-11-03 -City Hotel,1,398,2016,August,33,11,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0,2015-10-21 -City Hotel,1,335,2015,September,38,17,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Contract,62.0,0,0,2015-01-01 -Resort Hotel,1,181,2016,July,28,7,2,5,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,72.9,0,1,2016-01-09 -Resort Hotel,1,182,2016,December,53,29,0,3,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,250.0,,0,Transient,60.6,0,0,2016-06-30 -Resort Hotel,0,90,2016,October,44,29,2,3,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,,,0,Transient,54.08,0,1,2016-11-03 -City Hotel,1,322,2016,May,21,19,1,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,31.0,,120,Transient,80.0,0,0,2016-02-10 -City Hotel,1,266,2017,May,18,3,0,3,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,58.0,,0,Transient,100.0,0,0,2017-03-09 -City Hotel,1,37,2016,October,42,13,0,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,56.0,,0,Transient-Party,105.0,0,0,2016-09-06 -City Hotel,1,7,2017,June,23,5,1,3,1,0.0,0,BB,ISR,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,9.0,,0,Transient,200.0,0,1,2017-05-30 -City Hotel,1,38,2016,December,52,21,1,4,2,0.0,0,HB,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,120.7,0,1,2016-12-04 -City Hotel,1,281,2015,July,30,25,2,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.8,0,0,2015-01-01 -City Hotel,1,81,2017,March,13,30,0,3,2,0.0,0,SC,AUT,Online TA,TA/TO,0,0,0,A,A,4,No Deposit,9.0,,0,Transient,85.8,0,1,2017-02-02 -Resort Hotel,1,131,2017,June,24,11,2,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,410.0,,0,Transient,56.7,0,0,2017-05-19 -City Hotel,1,30,2017,February,5,2,2,4,2,0.0,0,BB,ISL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,90.1,0,2,2017-01-09 -City Hotel,1,129,2017,April,14,3,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,58.0,,0,Transient,100.0,0,0,2016-11-25 -City Hotel,1,100,2015,October,44,31,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.0,0,0,2015-10-19 -City Hotel,1,386,2016,July,31,28,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0,2015-10-21 -City Hotel,1,38,2017,January,2,14,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,Non Refund,,67.0,0,Transient,75.0,0,0,2016-12-07 -City Hotel,1,165,2017,May,20,19,2,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,225.0,0,0,2017-01-12 -City Hotel,1,38,2017,January,2,14,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,Non Refund,,67.0,0,Transient,75.0,0,0,2016-12-07 -City Hotel,0,176,2016,October,42,11,0,3,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,85.0,,0,Transient,80.75,0,0,2016-10-14 -Resort Hotel,1,239,2015,September,38,19,1,1,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,3,0,A,A,0,No Deposit,248.0,,0,Transient-Party,34.0,0,0,2015-07-08 -City Hotel,1,341,2015,September,39,23,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient-Party,62.0,0,0,2015-07-02 -City Hotel,0,180,2016,November,47,13,1,0,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,54.75,0,2,2016-11-14 -City Hotel,1,68,2016,February,8,17,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,37.0,,0,Transient,75.0,0,0,2016-01-06 -City Hotel,1,24,2016,April,16,11,1,3,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Transient,108.5,0,0,2016-03-18 -City Hotel,1,115,2017,June,22,3,2,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,27.0,,0,Transient,89.1,0,0,2017-02-08 -City Hotel,1,81,2017,August,34,23,0,2,2,1.0,0,BB,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,169.0,0,0,2017-06-05 -City Hotel,1,407,2017,May,18,6,2,1,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,229.0,,0,Transient,90.0,0,0,2016-04-17 -City Hotel,1,86,2016,January,4,22,0,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,29.0,,35,Transient,85.0,0,0,2015-12-02 -Resort Hotel,1,112,2016,February,7,12,0,2,2,0.0,0,FB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,134.0,,0,Transient-Party,68.0,0,0,2016-02-05 -City Hotel,1,50,2016,March,14,31,1,3,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,0,Transient-Party,65.0,0,0,2016-03-11 -City Hotel,1,305,2016,November,45,4,1,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,21.0,,0,Transient,89.0,0,0,2016-02-01 -City Hotel,1,136,2016,October,41,5,0,4,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,118.8,0,0,2016-05-22 -City Hotel,1,11,2016,June,25,17,0,2,2,1.0,0,BB,ESP,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,182.75,0,1,2016-06-08 -City Hotel,1,209,2017,August,33,15,0,3,2,1.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,148.5,0,2,2017-01-25 -City Hotel,1,193,2016,June,26,20,1,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,162.0,,39,Transient,120.0,0,0,2016-01-18 -Resort Hotel,1,183,2016,July,31,26,0,2,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,,395.0,0,Transient,114.75,0,0,2016-07-13 -City Hotel,1,19,2016,March,10,4,1,2,2,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,74.64,0,0,2016-03-04 -City Hotel,1,277,2016,November,46,7,1,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,100.0,0,0,2016-04-04 -City Hotel,0,30,2015,October,42,16,2,2,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Contract,118.58,0,1,2015-10-20 -City Hotel,1,36,2016,March,11,12,1,1,3,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,114.3,0,1,2016-02-25 -Resort Hotel,1,398,2016,July,28,4,3,7,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,E,E,0,No Deposit,40.0,,0,Contract,134.73,0,0,2016-01-15 -City Hotel,1,61,2015,December,53,30,1,4,2,0.0,0,BB,PRT,Online TA,TA/TO,0,1,0,A,A,0,No Deposit,9.0,,0,Transient,111.6,0,1,2015-12-07 -City Hotel,1,19,2016,February,9,25,0,3,2,0.0,0,SC,IRL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,79.0,0,1,2016-02-08 -Resort Hotel,1,393,2016,October,41,8,2,2,2,0.0,0,HB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,68.0,,0,Transient,72.0,0,0,2015-12-17 -City Hotel,1,147,2015,July,30,21,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,No Deposit,1.0,,0,Transient,75.0,0,0,2015-07-10 -City Hotel,1,279,2016,October,42,12,0,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,98.0,,0,Transient,110.0,0,0,2016-05-05 -City Hotel,1,23,2017,June,23,7,0,2,2,0.0,0,HB,ESP,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,52.0,,0,Transient,166.5,0,0,2017-05-19 -Resort Hotel,1,10,2016,October,42,10,1,0,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,394.0,0,Transient-Party,41.4,0,0,2016-10-04 -City Hotel,1,451,2017,July,28,12,0,3,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,229.0,,0,Transient-Party,90.0,0,0,2017-06-07 -City Hotel,1,57,2016,October,45,31,1,4,3,0.0,0,BB,RUS,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,131.58,0,1,2016-10-25 -City Hotel,1,101,2016,June,23,1,0,4,2,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,132.6,0,0,2016-05-17 -City Hotel,1,315,2016,November,48,25,0,1,2,0.0,0,BB,PRT,Direct,Direct,0,2,4,B,B,0,No Deposit,,,0,Transient-Party,65.8,0,1,2016-11-07 -City Hotel,1,305,2017,January,3,21,2,2,2,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,77.35,0,0,2016-08-10 -City Hotel,1,271,2016,December,53,26,2,5,2,0.0,0,HB,DEU,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,144.63,0,1,2016-10-06 -City Hotel,1,180,2016,July,30,23,1,1,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,B,B,0,No Deposit,9.0,,0,Transient,96.3,0,0,2016-01-26 -City Hotel,0,134,2016,October,42,15,2,1,2,1.0,0,BB,NLD,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,135.0,0,2,2016-10-18 -City Hotel,1,88,2017,August,32,11,0,1,2,0.0,0,BB,AUS,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,155.0,0,1,2017-05-21 -Resort Hotel,1,281,2016,July,29,16,4,6,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,E,E,0,No Deposit,175.0,,0,Transient,115.92,0,0,2016-01-07 -City Hotel,1,139,2016,July,29,11,1,4,2,1.0,0,BB,SWE,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,116.45,0,0,2016-02-27 -City Hotel,1,88,2017,April,17,24,1,5,2,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,121.5,0,1,2017-01-27 -City Hotel,1,275,2016,April,17,17,2,0,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,12.0,,0,Transient,110.0,0,0,2015-08-04 -City Hotel,1,420,2017,May,20,20,2,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,229.0,,0,Transient,90.0,0,0,2017-03-02 -City Hotel,1,19,2017,March,12,20,1,3,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,85.0,,0,Transient,80.24,0,0,2017-03-17 -City Hotel,1,50,2017,July,28,10,1,2,2,1.0,0,BB,DNK,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,160.0,0,1,2017-06-27 -City Hotel,1,101,2016,April,15,4,1,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,3.0,,41,Transient,80.0,0,0,2016-02-04 -Resort Hotel,1,56,2017,August,35,28,1,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,5.0,,0,Transient,152.0,0,0,2017-07-17 -City Hotel,1,68,2016,February,8,17,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,37.0,,0,Transient,75.0,0,0,2016-01-06 -City Hotel,1,126,2017,August,33,16,2,6,2,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,155.0,0,1,2017-04-12 -City Hotel,1,185,2017,June,26,29,0,3,2,2.0,0,BB,USA,Online TA,TA/TO,0,0,0,F,F,2,No Deposit,9.0,,0,Transient,192.7,0,0,2017-01-03 -City Hotel,1,322,2016,May,21,19,1,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,31.0,,120,Transient,80.0,0,0,2016-02-10 -City Hotel,1,21,2016,August,34,15,1,2,2,0.0,0,BB,NLD,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,151.0,0,0,2016-07-29 -City Hotel,1,78,2016,April,15,8,0,1,1,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,90.9,0,0,2016-02-15 -City Hotel,1,137,2016,May,19,5,0,3,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,105.3,0,0,2016-03-10 -City Hotel,1,24,2016,November,45,1,0,4,2,2.0,0,BB,SWE,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,9.0,,0,Transient,161.5,0,0,2016-10-08 -Resort Hotel,1,46,2016,May,23,31,2,7,2,0.0,0,BB,CHE,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,116.11,0,0,2016-04-17 -City Hotel,1,184,2016,September,39,22,2,3,2,0.0,0,BB,DNK,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,137.28,0,0,2016-04-11 -Resort Hotel,1,31,2016,April,15,7,0,3,2,0.0,0,HB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,67.0,,0,Transient-Party,68.0,0,0,2016-04-01 -City Hotel,1,186,2016,July,30,19,0,5,2,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,9.0,,0,Transient,90.95,0,1,2016-03-04 -City Hotel,1,35,2017,February,7,16,0,3,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,88.0,0,0,2017-01-12 -City Hotel,0,210,2016,August,34,16,0,2,2,0.0,1,SC,NLD,Online TA,TA/TO,0,0,0,A,A,4,No Deposit,9.0,,0,Transient,90.0,0,2,2016-08-18 -City Hotel,1,444,2016,December,49,1,0,3,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,Non Refund,34.0,,39,Transient,75.0,0,0,2015-10-23 -Resort Hotel,1,231,2017,August,31,2,2,4,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,240.0,,0,Transient,253.33,0,1,2017-05-10 -City Hotel,1,162,2017,May,18,6,2,1,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,3.0,,0,Transient,80.0,0,0,2016-12-09 -City Hotel,1,52,2016,December,49,1,1,3,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,88.4,0,2,2016-10-14 -City Hotel,1,222,2017,August,34,25,1,2,2,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,117.0,0,0,2017-01-24 -City Hotel,1,41,2017,August,31,3,0,1,1,0.0,0,SC,USA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,126.0,0,0,2017-06-27 -City Hotel,1,157,2016,July,29,11,1,3,2,0.0,0,BB,IRL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,101.58,0,1,2016-06-11 -Resort Hotel,1,124,2015,August,36,31,1,5,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,240.0,,0,Transient,160.0,0,0,2015-04-29 -City Hotel,1,566,2017,January,4,26,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0,2015-10-21 -City Hotel,1,156,2016,December,53,27,0,3,3,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,122.4,0,0,2016-07-24 -Resort Hotel,1,273,2016,August,35,24,2,5,1,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,No Deposit,314.0,,0,Transient,88.15,0,0,2015-11-26 -City Hotel,1,123,2017,April,17,24,1,3,2,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,105.75,0,2,2016-12-22 -City Hotel,1,258,2017,January,2,14,2,1,2,0.0,0,HB,ITA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,129.6,0,0,2016-05-04 -City Hotel,1,158,2016,May,22,24,0,2,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,37.0,,31,Transient,130.0,0,0,2016-01-18 -City Hotel,1,46,2017,March,9,3,1,2,3,0.0,0,BB,URY,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,142.2,0,1,2017-02-23 -City Hotel,1,320,2017,June,24,15,0,3,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,6.0,,0,Transient,90.0,0,0,2017-01-12 -City Hotel,0,23,2017,January,2,11,0,1,1,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,9.0,,0,Transient,89.5,0,1,2017-01-12 -Resort Hotel,1,65,2015,August,33,15,2,5,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,196.0,,0,Transient,135.0,0,0,2015-06-11 -Resort Hotel,1,47,2016,August,32,5,2,2,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,209.0,0,0,2016-07-11 -City Hotel,1,113,2017,May,20,20,2,1,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,34.0,,0,Transient,190.0,0,0,2017-01-27 -City Hotel,1,346,2015,September,40,28,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Contract,62.0,0,0,2015-01-01 -Resort Hotel,1,143,2016,August,32,3,2,5,1,0.0,0,HB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,211.0,0,0,2016-03-24 -Resort Hotel,1,393,2016,October,41,8,2,2,2,0.0,0,HB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,68.0,,0,Transient,72.0,0,0,2015-12-17 -Resort Hotel,1,221,2017,August,33,15,1,5,1,0.0,0,HB,FRA,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,240.0,,0,Transient,224.33,0,2,2017-01-09 -Resort Hotel,1,159,2015,August,36,31,1,4,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,101.44,0,0,2015-06-17 -City Hotel,1,301,2015,August,33,14,0,2,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,Non Refund,6.0,,0,Transient-Party,101.5,0,0,2015-07-06 -Resort Hotel,1,378,2016,November,45,1,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,248.0,,0,Transient,46.0,0,0,2016-10-18 -City Hotel,1,45,2016,July,27,2,0,1,1,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient-Party,101.15,0,0,2016-07-02 -City Hotel,1,38,2017,May,19,12,2,7,3,1.0,0,BB,PRT,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,7.0,,0,Transient,321.94,0,1,2017-04-04 -City Hotel,1,52,2017,March,10,5,6,13,2,0.0,0,HB,SEN,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,136.0,0,2,2017-02-21 -City Hotel,1,251,2016,March,13,22,0,3,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,29.0,,93,Transient,125.0,0,0,2015-10-16 -City Hotel,1,33,2015,October,43,21,0,4,1,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,No Deposit,6.0,,0,Transient,17.6,0,1,2015-09-28 -City Hotel,1,122,2017,May,19,11,0,2,1,0.0,0,BB,CHE,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,127.8,0,0,2017-01-10 -City Hotel,1,272,2015,July,29,16,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,No Deposit,1.0,,0,Contract,62.0,0,0,2015-01-01 -Resort Hotel,1,228,2017,July,31,30,4,8,2,0.0,0,HB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,212.17,0,1,2017-07-19 -City Hotel,1,31,2017,January,4,24,0,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,97.5,0,2,2016-12-29 -City Hotel,1,123,2016,August,33,13,0,1,2,0.0,0,BB,HUN,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,123.3,0,0,2016-05-17 -City Hotel,1,49,2017,June,24,14,0,3,2,0.0,0,SC,CHN,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,126.0,0,1,2017-06-05 -City Hotel,1,68,2015,October,42,11,2,2,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,B,0,No Deposit,8.0,,0,Transient-Party,89.25,0,1,2015-08-22 -City Hotel,1,204,2017,March,10,5,1,0,2,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,90.0,0,1,2016-09-28 -Resort Hotel,1,19,2017,June,22,2,0,2,2,0.0,0,HB,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,137.0,0,2,2017-05-14 -Resort Hotel,1,140,2016,February,7,12,0,2,2,0.0,0,HB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,50.0,0,0,2015-12-11 -City Hotel,1,325,2017,July,28,15,1,1,2,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,103.5,0,2,2016-11-22 -City Hotel,1,45,2015,October,44,29,0,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,Non Refund,3.0,,0,Transient,70.0,0,0,2015-09-30 -City Hotel,0,31,2016,February,6,5,0,1,2,0.0,0,BB,USA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,89.3,1,1,2016-02-06 -City Hotel,1,16,2016,December,53,28,2,4,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,3.0,,0,Transient,76.67,0,0,2016-12-12 -City Hotel,1,422,2016,September,38,16,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,379,Transient-Party,65.0,0,0,2016-08-22 -City Hotel,1,96,2016,May,19,7,2,3,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,119.85,0,0,2016-02-01 -City Hotel,1,114,2017,April,17,29,2,5,3,0.0,0,BB,POL,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,9.0,,0,Transient,184.57,0,1,2017-02-15 -Resort Hotel,1,40,2016,April,15,5,0,5,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,58.0,0,1,2016-02-29 -City Hotel,1,11,2016,August,34,18,1,3,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,155.5,0,1,2016-08-11 -City Hotel,1,59,2016,April,18,28,2,3,2,0.0,0,BB,IRL,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,128.69,0,0,2016-03-26 -City Hotel,1,85,2017,May,18,5,1,2,2,0.0,0,BB,TUR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,135.0,0,0,2017-04-20 -City Hotel,1,295,2015,August,32,8,2,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.0,0,0,2015-07-06 -City Hotel,1,100,2016,May,21,19,1,3,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,B,1,No Deposit,9.0,,0,Transient-Party,136.0,0,0,2016-05-15 -City Hotel,1,74,2015,September,38,18,0,2,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,6.0,,0,Transient-Party,101.5,0,0,2015-07-06 -City Hotel,1,138,2017,June,26,27,0,3,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,117.0,0,0,2017-02-13 -City Hotel,1,34,2016,April,18,30,1,1,3,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,159.3,0,0,2016-03-30 -Resort Hotel,1,18,2015,November,45,7,0,1,2,1.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,15.0,,0,Transient,32.62,0,1,2015-10-20 -City Hotel,1,188,2016,June,25,15,0,2,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,119.0,,39,Transient,130.0,0,0,2016-01-18 -City Hotel,1,309,2017,August,33,15,0,4,2,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,B,B,1,No Deposit,9.0,,0,Transient,96.25,0,0,2017-01-15 -City Hotel,1,21,2017,June,26,25,1,0,2,0.0,0,SC,BRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,160.0,0,0,2017-06-07 -Resort Hotel,1,181,2016,July,30,17,4,8,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,160.0,0,2,2016-01-26 -City Hotel,1,59,2017,January,4,23,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,326.0,,0,Transient,80.0,0,0,2016-11-25 -City Hotel,1,285,2017,May,19,8,1,2,3,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,142.2,0,1,2016-08-25 -Resort Hotel,1,46,2016,June,24,5,2,2,2,0.0,0,BB,NLD,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,123.0,0,0,2016-05-13 -Resort Hotel,0,4,2016,November,48,21,1,1,1,0.0,0,BB,PRT,Complementary,Direct,0,0,0,A,D,0,No Deposit,,,0,Transient,0.0,0,0,2016-11-23 -Resort Hotel,1,110,2017,February,5,2,0,3,1,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,37.0,0,1,2016-12-22 -City Hotel,1,161,2017,June,26,30,0,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,30.0,,26,Transient,130.0,0,0,2017-02-15 +hotel,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests +City Hotel,88,2015,September,39,26,2,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,170.0,0,0 +City Hotel,40,2016,March,13,20,1,0,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,88.2,0,0 +City Hotel,349,2015,October,40,1,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Contract,62.0,0,0 +City Hotel,423,2017,May,21,23,0,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,229.0,,0,Transient,90.0,0,0 +City Hotel,175,2017,April,17,29,2,1,2,2.0,0,BB,RUS,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,9.0,,0,Transient,189.0,0,1 +City Hotel,38,2016,December,50,10,2,1,2,2.0,0,BB,PHL,Direct,Direct,0,0,0,E,E,0,No Deposit,14.0,,0,Transient,143.0,0,0 +City Hotel,163,2016,October,42,15,0,1,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,34.0,,0,Transient,115.0,0,0 +Resort Hotel,29,2015,August,35,27,0,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,211.0,0,1 +City Hotel,148,2017,May,18,4,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,171.0,,0,Transient,95.0,0,0 +City Hotel,0,2015,August,33,10,1,4,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,75.0,0,2 +City Hotel,40,2016,March,13,25,1,2,3,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,7.0,,0,Transient,201.95,0,0 +City Hotel,330,2016,June,23,2,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0 +City Hotel,177,2017,March,12,23,1,3,2,1.0,0,BB,TWN,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,9.0,,0,Transient,103.7,0,0 +City Hotel,205,2016,May,23,29,2,1,3,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,1,Non Refund,26.0,,0,Transient,125.0,0,0 +Resort Hotel,349,2017,July,28,9,2,4,2,2.0,0,BB,PRT,Direct,Direct,0,0,0,C,C,2,No Deposit,250.0,,0,Transient,178.75,0,0 +City Hotel,115,2016,November,47,18,0,2,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,90.0,0,1 +Resort Hotel,74,2016,February,9,26,1,2,2,0.0,0,Undefined,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,68.0,,0,Transient,70.0,0,0 +City Hotel,183,2017,February,7,15,0,3,2,1.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,106.2,0,1 +City Hotel,233,2016,August,33,11,0,2,2,0.0,0,BB,RUS,Online TA,TA/TO,0,0,0,B,B,1,No Deposit,9.0,,0,Transient,86.62,0,0 +Resort Hotel,243,2015,September,36,4,0,2,2,0.0,0,BB,PRT,Online TA,TA/TO,0,1,0,D,D,0,No Deposit,240.0,,0,Transient-Party,99.0,0,1 +City Hotel,32,2015,October,43,24,1,1,1,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Contract,136.0,0,1 +City Hotel,99,2017,April,15,15,2,4,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,58.0,,0,Transient-Party,105.0,0,0 +City Hotel,185,2016,December,51,16,1,2,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,103.5,0,0 +City Hotel,97,2015,October,41,5,1,3,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,29.0,,0,Transient,140.0,0,0 +City Hotel,280,2016,November,47,16,0,4,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,73.95,0,1 +City Hotel,566,2017,January,4,26,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0 +City Hotel,280,2016,December,53,31,2,1,2,0.0,0,BB,ROU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,86.7,0,0 +City Hotel,150,2017,June,24,17,2,3,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,120.0,0,0 +City Hotel,4,2016,August,36,30,0,2,3,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,175.0,0,2 +City Hotel,262,2016,December,51,14,2,4,2,0.0,0,SC,VEN,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,63.75,0,0 +City Hotel,275,2016,April,17,17,2,0,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,12.0,,0,Transient,110.0,0,0 +City Hotel,22,2017,March,11,15,2,6,3,0.0,0,BB,AGO,Direct,Direct,0,0,0,F,F,0,No Deposit,14.0,,0,Transient,238.0,0,0 +City Hotel,55,2016,December,50,8,0,3,1,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,86.4,0,2 +City Hotel,80,2016,April,16,10,2,1,1,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,182.0,,0,Transient,100.0,0,0 +City Hotel,213,2016,June,24,7,0,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,19.0,,62,Transient,130.0,0,0 +City Hotel,174,2017,June,22,1,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,58.0,,0,Transient,100.0,0,0 +City Hotel,451,2016,May,21,16,1,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,60.0,0,0 +City Hotel,111,2017,February,5,1,4,10,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,D,D,0,No Deposit,,,0,Transient-Party,98.1,0,2 +City Hotel,76,2016,September,38,15,0,3,3,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,168.3,0,3 +City Hotel,89,2016,November,47,19,1,1,2,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,9.0,,0,Transient,79.2,0,3 +Resort Hotel,168,2017,August,33,17,2,4,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,314.0,,0,Transient,176.0,0,1 +City Hotel,41,2016,October,42,15,2,1,2,0.0,0,BB,NLD,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,145.2,0,2 +City Hotel,241,2016,September,36,2,2,2,2,0.0,0,BB,AUS,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,101.79,0,1 +Resort Hotel,287,2015,October,42,15,0,3,2,0.0,0,BB,GBR,Groups,Corporate,0,0,0,A,E,1,No Deposit,,223.0,0,Transient-Party,44.5,0,0 +Resort Hotel,259,2015,September,38,17,0,3,2,0.0,0,BB,GBR,Groups,Corporate,0,1,0,A,A,0,Non Refund,,223.0,0,Transient,40.05,0,0 +Resort Hotel,47,2017,August,34,26,2,5,2,2.0,0,HB,ESP,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,240.0,,0,Transient,340.86,0,0 +City Hotel,475,2016,October,44,27,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0 +City Hotel,37,2016,October,41,2,2,3,1,0.0,0,BB,CHN,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,125.55,0,0 +Resort Hotel,133,2017,March,11,13,1,0,2,0.0,0,BB,USA,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,240.0,,0,Transient,42.0,0,2 +City Hotel,398,2016,August,33,11,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0 +City Hotel,335,2015,September,38,17,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Contract,62.0,0,0 +Resort Hotel,181,2016,July,28,7,2,5,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,72.9,0,1 +Resort Hotel,182,2016,December,53,29,0,3,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,250.0,,0,Transient,60.6,0,0 +Resort Hotel,90,2016,October,44,29,2,3,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,,,0,Transient,54.08,0,1 +City Hotel,322,2016,May,21,19,1,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,31.0,,120,Transient,80.0,0,0 +City Hotel,266,2017,May,18,3,0,3,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,58.0,,0,Transient,100.0,0,0 +City Hotel,37,2016,October,42,13,0,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,56.0,,0,Transient-Party,105.0,0,0 +City Hotel,7,2017,June,23,5,1,3,1,0.0,0,BB,ISR,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,9.0,,0,Transient,200.0,0,1 +City Hotel,38,2016,December,52,21,1,4,2,0.0,0,HB,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,120.7,0,1 +City Hotel,281,2015,July,30,25,2,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.8,0,0 +City Hotel,81,2017,March,13,30,0,3,2,0.0,0,SC,AUT,Online TA,TA/TO,0,0,0,A,A,4,No Deposit,9.0,,0,Transient,85.8,0,1 +Resort Hotel,131,2017,June,24,11,2,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,410.0,,0,Transient,56.7,0,0 +City Hotel,30,2017,February,5,2,2,4,2,0.0,0,BB,ISL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,90.1,0,2 +City Hotel,129,2017,April,14,3,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,58.0,,0,Transient,100.0,0,0 +City Hotel,100,2015,October,44,31,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.0,0,0 +City Hotel,386,2016,July,31,28,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0 +City Hotel,38,2017,January,2,14,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,Non Refund,,67.0,0,Transient,75.0,0,0 +City Hotel,165,2017,May,20,19,2,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,225.0,0,0 +City Hotel,176,2016,October,42,11,0,3,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,85.0,,0,Transient,80.75,0,0 +Resort Hotel,239,2015,September,38,19,1,1,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,3,0,A,A,0,No Deposit,248.0,,0,Transient-Party,34.0,0,0 +City Hotel,341,2015,September,39,23,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient-Party,62.0,0,0 +City Hotel,180,2016,November,47,13,1,0,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,54.75,0,2 +City Hotel,68,2016,February,8,17,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,37.0,,0,Transient,75.0,0,0 +City Hotel,24,2016,April,16,11,1,3,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Transient,108.5,0,0 +City Hotel,115,2017,June,22,3,2,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,27.0,,0,Transient,89.1,0,0 +City Hotel,81,2017,August,34,23,0,2,2,1.0,0,BB,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,169.0,0,0 +City Hotel,407,2017,May,18,6,2,1,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,229.0,,0,Transient,90.0,0,0 +City Hotel,86,2016,January,4,22,0,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,29.0,,35,Transient,85.0,0,0 +Resort Hotel,112,2016,February,7,12,0,2,2,0.0,0,FB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,134.0,,0,Transient-Party,68.0,0,0 +City Hotel,50,2016,March,14,31,1,3,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,0,Transient-Party,65.0,0,0 +City Hotel,305,2016,November,45,4,1,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,21.0,,0,Transient,89.0,0,0 +City Hotel,136,2016,October,41,5,0,4,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,118.8,0,0 +City Hotel,11,2016,June,25,17,0,2,2,1.0,0,BB,ESP,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,182.75,0,1 +City Hotel,209,2017,August,33,15,0,3,2,1.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,148.5,0,2 +City Hotel,193,2016,June,26,20,1,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,162.0,,39,Transient,120.0,0,0 +Resort Hotel,183,2016,July,31,26,0,2,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,,395.0,0,Transient,114.75,0,0 +City Hotel,19,2016,March,10,4,1,2,2,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,74.64,0,0 +City Hotel,277,2016,November,46,7,1,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,100.0,0,0 +City Hotel,30,2015,October,42,16,2,2,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Contract,118.58,0,1 +City Hotel,36,2016,March,11,12,1,1,3,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,114.3,0,1 +Resort Hotel,398,2016,July,28,4,3,7,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,E,E,0,No Deposit,40.0,,0,Contract,134.73,0,0 +City Hotel,61,2015,December,53,30,1,4,2,0.0,0,BB,PRT,Online TA,TA/TO,0,1,0,A,A,0,No Deposit,9.0,,0,Transient,111.6,0,1 +City Hotel,19,2016,February,9,25,0,3,2,0.0,0,SC,IRL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,79.0,0,1 +Resort Hotel,393,2016,October,41,8,2,2,2,0.0,0,HB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,68.0,,0,Transient,72.0,0,0 +City Hotel,147,2015,July,30,21,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,No Deposit,1.0,,0,Transient,75.0,0,0 +City Hotel,279,2016,October,42,12,0,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,98.0,,0,Transient,110.0,0,0 +City Hotel,23,2017,June,23,7,0,2,2,0.0,0,HB,ESP,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,52.0,,0,Transient,166.5,0,0 +Resort Hotel,10,2016,October,42,10,1,0,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,394.0,0,Transient-Party,41.4,0,0 +City Hotel,451,2017,July,28,12,0,3,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,229.0,,0,Transient-Party,90.0,0,0 +City Hotel,57,2016,October,45,31,1,4,3,0.0,0,BB,RUS,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,131.58,0,1 +City Hotel,101,2016,June,23,1,0,4,2,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,132.6,0,0 +City Hotel,315,2016,November,48,25,0,1,2,0.0,0,BB,PRT,Direct,Direct,0,2,4,B,B,0,No Deposit,,,0,Transient-Party,65.8,0,1 +City Hotel,305,2017,January,3,21,2,2,2,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,77.35,0,0 +City Hotel,271,2016,December,53,26,2,5,2,0.0,0,HB,DEU,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,144.63,0,1 +City Hotel,180,2016,July,30,23,1,1,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,B,B,0,No Deposit,9.0,,0,Transient,96.3,0,0 +City Hotel,134,2016,October,42,15,2,1,2,1.0,0,BB,NLD,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,135.0,0,2 +City Hotel,88,2017,August,32,11,0,1,2,0.0,0,BB,AUS,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,155.0,0,1 +Resort Hotel,281,2016,July,29,16,4,6,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,E,E,0,No Deposit,175.0,,0,Transient,115.92,0,0 +City Hotel,139,2016,July,29,11,1,4,2,1.0,0,BB,SWE,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,116.45,0,0 +City Hotel,88,2017,April,17,24,1,5,2,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,121.5,0,1 +City Hotel,420,2017,May,20,20,2,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,229.0,,0,Transient,90.0,0,0 +City Hotel,19,2017,March,12,20,1,3,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,85.0,,0,Transient,80.24,0,0 +City Hotel,50,2017,July,28,10,1,2,2,1.0,0,BB,DNK,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,160.0,0,1 +City Hotel,101,2016,April,15,4,1,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,3.0,,41,Transient,80.0,0,0 +Resort Hotel,56,2017,August,35,28,1,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,5.0,,0,Transient,152.0,0,0 +City Hotel,126,2017,August,33,16,2,6,2,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,155.0,0,1 +City Hotel,185,2017,June,26,29,0,3,2,2.0,0,BB,USA,Online TA,TA/TO,0,0,0,F,F,2,No Deposit,9.0,,0,Transient,192.7,0,0 +City Hotel,21,2016,August,34,15,1,2,2,0.0,0,BB,NLD,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,151.0,0,0 +City Hotel,78,2016,April,15,8,0,1,1,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,90.9,0,0 +City Hotel,137,2016,May,19,5,0,3,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,105.3,0,0 +City Hotel,24,2016,November,45,1,0,4,2,2.0,0,BB,SWE,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,9.0,,0,Transient,161.5,0,0 +Resort Hotel,46,2016,May,23,31,2,7,2,0.0,0,BB,CHE,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,116.11,0,0 +City Hotel,184,2016,September,39,22,2,3,2,0.0,0,BB,DNK,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,137.28,0,0 +Resort Hotel,31,2016,April,15,7,0,3,2,0.0,0,HB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,67.0,,0,Transient-Party,68.0,0,0 +City Hotel,186,2016,July,30,19,0,5,2,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,9.0,,0,Transient,90.95,0,1 +City Hotel,35,2017,February,7,16,0,3,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,88.0,0,0 +City Hotel,210,2016,August,34,16,0,2,2,0.0,1,SC,NLD,Online TA,TA/TO,0,0,0,A,A,4,No Deposit,9.0,,0,Transient,90.0,0,2 +City Hotel,444,2016,December,49,1,0,3,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,Non Refund,34.0,,39,Transient,75.0,0,0 +Resort Hotel,231,2017,August,31,2,2,4,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,240.0,,0,Transient,253.33,0,1 +City Hotel,162,2017,May,18,6,2,1,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,3.0,,0,Transient,80.0,0,0 +City Hotel,52,2016,December,49,1,1,3,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,88.4,0,2 +City Hotel,222,2017,August,34,25,1,2,2,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,117.0,0,0 +City Hotel,41,2017,August,31,3,0,1,1,0.0,0,SC,USA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,126.0,0,0 +City Hotel,157,2016,July,29,11,1,3,2,0.0,0,BB,IRL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,101.58,0,1 +Resort Hotel,124,2015,August,36,31,1,5,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,240.0,,0,Transient,160.0,0,0 +City Hotel,156,2016,December,53,27,0,3,3,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,122.4,0,0 +Resort Hotel,273,2016,August,35,24,2,5,1,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,No Deposit,314.0,,0,Transient,88.15,0,0 +City Hotel,123,2017,April,17,24,1,3,2,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,105.75,0,2 +City Hotel,258,2017,January,2,14,2,1,2,0.0,0,HB,ITA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,129.6,0,0 +City Hotel,158,2016,May,22,24,0,2,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,37.0,,31,Transient,130.0,0,0 +City Hotel,46,2017,March,9,3,1,2,3,0.0,0,BB,URY,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,142.2,0,1 +City Hotel,320,2017,June,24,15,0,3,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,6.0,,0,Transient,90.0,0,0 +City Hotel,23,2017,January,2,11,0,1,1,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,9.0,,0,Transient,89.5,0,1 +Resort Hotel,65,2015,August,33,15,2,5,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,196.0,,0,Transient,135.0,0,0 +Resort Hotel,47,2016,August,32,5,2,2,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,209.0,0,0 +City Hotel,113,2017,May,20,20,2,1,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,34.0,,0,Transient,190.0,0,0 +City Hotel,346,2015,September,40,28,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Contract,62.0,0,0 +Resort Hotel,143,2016,August,32,3,2,5,1,0.0,0,HB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,211.0,0,0 +Resort Hotel,221,2017,August,33,15,1,5,1,0.0,0,HB,FRA,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,240.0,,0,Transient,224.33,0,2 +Resort Hotel,159,2015,August,36,31,1,4,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,101.44,0,0 +City Hotel,301,2015,August,33,14,0,2,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,Non Refund,6.0,,0,Transient-Party,101.5,0,0 +Resort Hotel,378,2016,November,45,1,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,248.0,,0,Transient,46.0,0,0 +City Hotel,45,2016,July,27,2,0,1,1,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient-Party,101.15,0,0 +City Hotel,38,2017,May,19,12,2,7,3,1.0,0,BB,PRT,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,7.0,,0,Transient,321.94,0,1 +City Hotel,52,2017,March,10,5,6,13,2,0.0,0,HB,SEN,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,136.0,0,2 +City Hotel,251,2016,March,13,22,0,3,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,29.0,,93,Transient,125.0,0,0 +City Hotel,33,2015,October,43,21,0,4,1,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,No Deposit,6.0,,0,Transient,17.6,0,1 +City Hotel,122,2017,May,19,11,0,2,1,0.0,0,BB,CHE,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,127.8,0,0 +City Hotel,272,2015,July,29,16,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,No Deposit,1.0,,0,Contract,62.0,0,0 +Resort Hotel,228,2017,July,31,30,4,8,2,0.0,0,HB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,212.17,0,1 +City Hotel,31,2017,January,4,24,0,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,97.5,0,2 +City Hotel,123,2016,August,33,13,0,1,2,0.0,0,BB,HUN,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,123.3,0,0 +City Hotel,49,2017,June,24,14,0,3,2,0.0,0,SC,CHN,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,126.0,0,1 +City Hotel,68,2015,October,42,11,2,2,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,B,0,No Deposit,8.0,,0,Transient-Party,89.25,0,1 +City Hotel,204,2017,March,10,5,1,0,2,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,90.0,0,1 +Resort Hotel,19,2017,June,22,2,0,2,2,0.0,0,HB,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,137.0,0,2 +Resort Hotel,140,2016,February,7,12,0,2,2,0.0,0,HB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,50.0,0,0 +City Hotel,325,2017,July,28,15,1,1,2,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,103.5,0,2 +City Hotel,45,2015,October,44,29,0,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,Non Refund,3.0,,0,Transient,70.0,0,0 +City Hotel,31,2016,February,6,5,0,1,2,0.0,0,BB,USA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,89.3,1,1 +City Hotel,16,2016,December,53,28,2,4,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,3.0,,0,Transient,76.67,0,0 +City Hotel,422,2016,September,38,16,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,379,Transient-Party,65.0,0,0 +City Hotel,96,2016,May,19,7,2,3,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,119.85,0,0 +City Hotel,114,2017,April,17,29,2,5,3,0.0,0,BB,POL,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,9.0,,0,Transient,184.57,0,1 +Resort Hotel,40,2016,April,15,5,0,5,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,58.0,0,1 +City Hotel,11,2016,August,34,18,1,3,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,155.5,0,1 +City Hotel,59,2016,April,18,28,2,3,2,0.0,0,BB,IRL,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,128.69,0,0 +City Hotel,85,2017,May,18,5,1,2,2,0.0,0,BB,TUR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,135.0,0,0 +City Hotel,295,2015,August,32,8,2,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.0,0,0 +City Hotel,100,2016,May,21,19,1,3,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,B,1,No Deposit,9.0,,0,Transient-Party,136.0,0,0 +City Hotel,74,2015,September,38,18,0,2,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,6.0,,0,Transient-Party,101.5,0,0 +City Hotel,138,2017,June,26,27,0,3,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,117.0,0,0 +City Hotel,34,2016,April,18,30,1,1,3,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,159.3,0,0 +Resort Hotel,18,2015,November,45,7,0,1,2,1.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,15.0,,0,Transient,32.62,0,1 +City Hotel,188,2016,June,25,15,0,2,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,119.0,,39,Transient,130.0,0,0 +City Hotel,309,2017,August,33,15,0,4,2,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,B,B,1,No Deposit,9.0,,0,Transient,96.25,0,0 +City Hotel,21,2017,June,26,25,1,0,2,0.0,0,SC,BRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,160.0,0,0 +Resort Hotel,181,2016,July,30,17,4,8,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,160.0,0,2 +City Hotel,59,2017,January,4,23,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,326.0,,0,Transient,80.0,0,0 +City Hotel,285,2017,May,19,8,1,2,3,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,142.2,0,1 +Resort Hotel,46,2016,June,24,5,2,2,2,0.0,0,BB,NLD,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,123.0,0,0 +Resort Hotel,4,2016,November,48,21,1,1,1,0.0,0,BB,PRT,Complementary,Direct,0,0,0,A,D,0,No Deposit,,,0,Transient,0.0,0,0 +Resort Hotel,110,2017,February,5,2,0,3,1,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,37.0,0,1 +City Hotel,161,2017,June,26,30,0,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,30.0,,26,Transient,130.0,0,0 diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_003/history.csv b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_003/history.csv index 8e7b0fb606cf7dbb50088b42a552414ef5e9f4de..cd2ae0430d023daca64d3c634b7011f4f3980a74 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_003/history.csv +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_003/history.csv @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:7f2c6a82faa3ea41f7ff994d56487d982783f89a08581522b227ca9a8ba137e4 -size 16583040 +oid sha256:42451284ef33ea521aee0149d04c6325b7a63738bdec93581e38720b4ee635f6 +size 15058005 diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_003/info.json b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_003/info.json index 4816c05993309461836de8622438e342f2e503ff..20956c93d07801fe3772275e08832605a78593ed 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_003/info.json +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_003/info.json @@ -1,398 +1,391 @@ -{ - "name": "Hotel Booking Demand", - "source": "https://www.kaggle.com/datasets/jessemostipak/hotel-booking-demand/data", - "data_intro": "This dataset contains hotel booking data for a city hotel and a resort hotel, providing details on booking dates, stay duration, guest count, cancellation trends, and pricing. It is useful for demand forecasting, revenue management, and customer behavior analysis.", - "is_splited": false, - "overall_size": 119390, - "train_size": 0, - "test_size": 0, - "c_classes": 12, - "n_classes": 20, - "cat_feature_intro": { - "hotel": "Type of hotel (City Hotel or Resort Hotel).", - "arrival_date_month": "Month of arrival for the booking.", - "meal": "Type of meal plan booked (e.g., Breakfast included, Half board, Full board).", - "country": "Country of origin of the guest.", - "market_segment": "Booking distribution channel (e.g., direct, corporate, travel agent).", - "distribution_channel": "Channel through which the booking was made (e.g., online travel agents, direct).", - "reserved_room_type": "Type of room reserved by the guest before any changes.", - "assigned_room_type": "Final room type assigned to the guest.", - "deposit_type": "Indicates if a deposit was made and its type (e.g., No Deposit, Refundable, Non-Refundable).", - "customer_type": "Type of customer (e.g., transient, group, contract).", - "reservation_status": "Final status of the booking (e.g., Canceled, Checked-out, No-show).", - "reservation_status_date": "Date when the reservation status was last updated." - }, - "num_feature_intro": { - "is_canceled": "Indicates whether the booking was canceled (1) or not (0).", - "lead_time": "Number of days between booking and arrival.", - "arrival_date_year": "Year of arrival for the booking.", - "arrival_date_week_number": "Week number of the arrival date.", - "arrival_date_day_of_month": "Day of the month of arrival.", - "stays_in_weekend_nights": "Number of weekend nights (Saturday or Sunday) the guest stayed.", - "stays_in_week_nights": "Number of weekday nights (Monday to Friday) the guest stayed.", - "adults": "Number of adults included in the booking.", - "children": "Number of children included in the booking.", - "babies": "Number of babies included in the booking.", - "is_repeated_guest": "Indicates whether the guest is a returning customer (1) or not (0).", - "previous_cancellations": "Number of times the guest has canceled previous bookings.", - "previous_bookings_not_canceled": "Number of previous bookings not canceled by the guest.", - "booking_changes": "Number of changes made to the booking.", - "agent": "ID of the booking agent (if applicable).", - "company": "ID of the company making the booking (if applicable).", - "days_in_waiting_list": "Number of days the booking was on the waiting list before confirmation.", - "adr": "Average daily rate (price per night per room).", - "required_car_parking_spaces": "Number of car parking spaces required by the guest.", - "total_of_special_requests": "Number of special requests made by the guest (e.g., high floor, twin beds)." - }, - "evaluation_metric": null, - "task_type": "classification", - "target": "reservation_status", - "num_feature_value": { - "adr": [ - -6.38, - 5400.0 - ], - "adults": [ - 0.0, - 55.0 - ], - "agent": [ - 1.0, - 535.0 - ], - "arrival_date_day_of_month": [ - 1.0, - 31.0 - ], - "arrival_date_week_number": [ - 1.0, - 53.0 - ], - "arrival_date_year": [ - 2015.0, - 2017.0 - ], - "babies": [ - 0.0, - 10.0 - ], - "booking_changes": [ - 0.0, - 21.0 - ], - "children": [ - 0.0, - 10.0 - ], - "company": [ - 6.0, - 543.0 - ], - "days_in_waiting_list": [ - 0.0, - 391.0 - ], - "is_canceled": [ - 0.0, - 1.0 - ], - "is_repeated_guest": [ - 0.0, - 1.0 - ], - "lead_time": [ - 0.0, - 737.0 - ], - "previous_bookings_not_canceled": [ - 0.0, - 72.0 - ], - "previous_cancellations": [ - 0.0, - 26.0 - ], - "required_car_parking_spaces": [ - 0.0, - 8.0 - ], - "stays_in_week_nights": [ - 0.0, - 50.0 - ], - "stays_in_weekend_nights": [ - 0.0, - 19.0 - ], - "total_of_special_requests": [ - 0.0, - 5.0 - ] - }, - "cat_feature_value": { - "arrival_date_month": [ - "April", - "August", - "December", - "February", - "January", - "July", - "June", - "March", - "May", - "November", - "October", - "September" - ], - "assigned_room_type": [ - "A", - "B", - "C", - "D", - "E", - "F", - "G", - "H", - "I", - "K", - "L", - "P" - ], - "country": [ - "ABW", - "AGO", - "AIA", - "ALB", - "AND", - "ARE", - "ARG", - "ARM", - "ASM", - "ATA", - "ATF", - "AUS", - "AUT", - "AZE", - "BDI", - "BEL", - "BEN", - "BFA", - "BGD", - "BGR", - "BHR", - "BHS", - "BIH", - "BLR", - "BOL", - "BRA", - "BRB", - "BWA", - "CAF", - "CHE", - "CHL", - "CHN", - "CIV", - "CMR", - "CN", - "COL", - "COM", - "CPV", - "CRI", - "CUB", - "CYM", - "CYP", - "CZE", - "DEU", - "DJI", - "DMA", - "DNK", - "DOM", - "DZA", - "ECU", - "EGY", - "ESP", - "EST", - "ETH", - "FIN", - "FJI", - "FRA", - "FRO", - "GAB", - "GBR", - "GEO", - "GGY", - "GHA", - "GIB", - "GLP", - "GNB", - "GRC", - "GTM", - "GUY", - "HKG", - "HND", - "HRV", - "HUN", - "IDN", - "IMN", - "IND", - "IRL", - "IRN", - "IRQ", - "ISL", - "ISR", - "ITA", - "JAM", - "JEY", - "JOR", - "JPN", - "KAZ", - "KEN", - "KHM", - "KIR", - "KNA", - "KOR", - "KWT", - "LAO", - "LBN", - "LBY", - "LCA", - "LIE", - "LKA", - "LTU", - "LUX", - "LVA", - "MAC", - "MAR", - "MCO", - "MDG", - "MDV", - "MEX", - "MKD", - "MLI", - "MLT", - "MMR", - "MNE", - "MOZ", - "MRT", - "MUS", - "MWI", - "MYS", - "MYT", - "NAM", - "NCL", - "NGA", - "NIC", - "NLD", - "NOR", - "NPL", - "NZL", - "OMN", - "PAK", - "PAN", - "PER", - "PHL", - "PLW", - "POL", - "PRI", - "PRT", - "PRY", - "PYF", - "QAT", - "ROU", - "RUS", - "RWA", - "SAU", - "SDN", - "SEN", - "SGP", - "SLE", - "SLV", - "SMR", - "SRB", - "STP", - "SUR", - "SVK", - "SVN", - "SWE", - "SYC", - "SYR", - "TGO", - "THA", - "TJK", - "TMP", - "TUN", - "TUR", - "TWN", - "TZA", - "UGA", - "UKR", - "UMI", - "URY", - "USA", - "UZB", - "VEN", - "VGB", - "VNM", - "ZAF", - "ZMB", - "ZWE" - ], - "customer_type": [ - "Contract", - "Group", - "Transient", - "Transient-Party" - ], - "deposit_type": [ - "No Deposit", - "Non Refund", - "Refundable" - ], - "distribution_channel": [ - "Corporate", - "Direct", - "GDS", - "TA/TO", - "Undefined" - ], - "hotel": [ - "City Hotel", - "Resort Hotel" - ], - "market_segment": [ - "Aviation", - "Complementary", - "Corporate", - "Direct", - "Groups", - "Offline TA/TO", - "Online TA", - "Undefined" - ], - "meal": [ - "BB", - "FB", - "HB", - "SC", - "Undefined" - ], - "reservation_status": [ - "Canceled", - "Check-Out", - "No-Show" - ], - "reservation_status_date": "string", - "reserved_room_type": [ - "A", - "B", - "C", - "D", - "E", - "F", - "G", - "H", - "L", - "P" - ] - } -} \ No newline at end of file +{ + "name": "Hotel Booking Demand", + "source": "https://www.kaggle.com/datasets/jessemostipak/hotel-booking-demand/data", + "data_intro": "This dataset contains hotel booking data for a city hotel and a resort hotel, providing details on booking dates, stay duration, guest count, cancellation trends, and pricing. It is useful for demand forecasting, revenue management, and customer behavior analysis.", + "is_splited": false, + "overall_size": 119390, + "train_size": 0, + "test_size": 0, + "c_classes": 11, + "n_classes": 19, + "cat_feature_intro": { + "hotel": "Type of hotel (City Hotel or Resort Hotel).", + "arrival_date_month": "Month of arrival for the booking.", + "meal": "Type of meal plan booked (e.g., Breakfast included, Half board, Full board).", + "country": "Country of origin of the guest.", + "market_segment": "Booking distribution channel (e.g., direct, corporate, travel agent).", + "distribution_channel": "Channel through which the booking was made (e.g., online travel agents, direct).", + "reserved_room_type": "Type of room reserved by the guest before any changes.", + "assigned_room_type": "Final room type assigned to the guest.", + "deposit_type": "Indicates if a deposit was made and its type (e.g., No Deposit, Refundable, Non-Refundable).", + "customer_type": "Type of customer (e.g., transient, group, contract).", + "reservation_status": "Final status of the booking (e.g., Canceled, Checked-out, No-show)." + }, + "num_feature_intro": { + "lead_time": "Number of days between booking and arrival.", + "arrival_date_year": "Year of arrival for the booking.", + "arrival_date_week_number": "Week number of the arrival date.", + "arrival_date_day_of_month": "Day of the month of arrival.", + "stays_in_weekend_nights": "Number of weekend nights (Saturday or Sunday) the guest stayed.", + "stays_in_week_nights": "Number of weekday nights (Monday to Friday) the guest stayed.", + "adults": "Number of adults included in the booking.", + "children": "Number of children included in the booking.", + "babies": "Number of babies included in the booking.", + "is_repeated_guest": "Indicates whether the guest is a returning customer (1) or not (0).", + "previous_cancellations": "Number of times the guest has canceled previous bookings.", + "previous_bookings_not_canceled": "Number of previous bookings not canceled by the guest.", + "booking_changes": "Number of changes made to the booking.", + "agent": "ID of the booking agent (if applicable).", + "company": "ID of the company making the booking (if applicable).", + "days_in_waiting_list": "Number of days the booking was on the waiting list before confirmation.", + "adr": "Average daily rate (price per night per room).", + "required_car_parking_spaces": "Number of car parking spaces required by the guest.", + "total_of_special_requests": "Number of special requests made by the guest (e.g., high floor, twin beds)." + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "reservation_status", + "num_feature_value": { + "adr": [ + -6.38, + 5400.0 + ], + "adults": [ + 0.0, + 55.0 + ], + "agent": [ + 1.0, + 535.0 + ], + "arrival_date_day_of_month": [ + 1.0, + 31.0 + ], + "arrival_date_week_number": [ + 1.0, + 53.0 + ], + "arrival_date_year": [ + 2015.0, + 2017.0 + ], + "babies": [ + 0.0, + 10.0 + ], + "booking_changes": [ + 0.0, + 21.0 + ], + "children": [ + 0.0, + 10.0 + ], + "company": [ + 6.0, + 543.0 + ], + "days_in_waiting_list": [ + 0.0, + 391.0 + ], + "is_repeated_guest": [ + 0.0, + 1.0 + ], + "lead_time": [ + 0.0, + 737.0 + ], + "previous_bookings_not_canceled": [ + 0.0, + 72.0 + ], + "previous_cancellations": [ + 0.0, + 26.0 + ], + "required_car_parking_spaces": [ + 0.0, + 8.0 + ], + "stays_in_week_nights": [ + 0.0, + 50.0 + ], + "stays_in_weekend_nights": [ + 0.0, + 19.0 + ], + "total_of_special_requests": [ + 0.0, + 5.0 + ] + }, + "cat_feature_value": { + "arrival_date_month": [ + "April", + "August", + "December", + "February", + "January", + "July", + "June", + "March", + "May", + "November", + "October", + "September" + ], + "assigned_room_type": [ + "A", + "B", + "C", + "D", + "E", + "F", + "G", + "H", + "I", + "K", + "L", + "P" + ], + "country": [ + "ABW", + "AGO", + "AIA", + "ALB", + "AND", + "ARE", + "ARG", + "ARM", + "ASM", + "ATA", + "ATF", + "AUS", + "AUT", + "AZE", + "BDI", + "BEL", + "BEN", + "BFA", + "BGD", + "BGR", + "BHR", + "BHS", + "BIH", + "BLR", + "BOL", + "BRA", + "BRB", + "BWA", + "CAF", + "CHE", + "CHL", + "CHN", + "CIV", + "CMR", + "CN", + "COL", + "COM", + "CPV", + "CRI", + "CUB", + "CYM", + "CYP", + "CZE", + "DEU", + "DJI", + "DMA", + "DNK", + "DOM", + "DZA", + "ECU", + "EGY", + "ESP", + "EST", + "ETH", + "FIN", + "FJI", + "FRA", + "FRO", + "GAB", + "GBR", + "GEO", + "GGY", + "GHA", + "GIB", + "GLP", + "GNB", + "GRC", + "GTM", + "GUY", + "HKG", + "HND", + "HRV", + "HUN", + "IDN", + "IMN", + "IND", + "IRL", + "IRN", + "IRQ", + "ISL", + "ISR", + "ITA", + "JAM", + "JEY", + "JOR", + "JPN", + "KAZ", + "KEN", + "KHM", + "KIR", + "KNA", + "KOR", + "KWT", + "LAO", + "LBN", + "LBY", + "LCA", + "LIE", + "LKA", + "LTU", + "LUX", + "LVA", + "MAC", + "MAR", + "MCO", + "MDG", + "MDV", + "MEX", + "MKD", + "MLI", + "MLT", + "MMR", + "MNE", + "MOZ", + "MRT", + "MUS", + "MWI", + "MYS", + "MYT", + "NAM", + "NCL", + "NGA", + "NIC", + "NLD", + "NOR", + "NPL", + "NZL", + "OMN", + "PAK", + "PAN", + "PER", + "PHL", + "PLW", + "POL", + "PRI", + "PRT", + "PRY", + "PYF", + "QAT", + "ROU", + "RUS", + "RWA", + "SAU", + "SDN", + "SEN", + "SGP", + "SLE", + "SLV", + "SMR", + "SRB", + "STP", + "SUR", + "SVK", + "SVN", + "SWE", + "SYC", + "SYR", + "TGO", + "THA", + "TJK", + "TMP", + "TUN", + "TUR", + "TWN", + "TZA", + "UGA", + "UKR", + "UMI", + "URY", + "USA", + "UZB", + "VEN", + "VGB", + "VNM", + "ZAF", + "ZMB", + "ZWE" + ], + "customer_type": [ + "Contract", + "Group", + "Transient", + "Transient-Party" + ], + "deposit_type": [ + "No Deposit", + "Non Refund", + "Refundable" + ], + "distribution_channel": [ + "Corporate", + "Direct", + "GDS", + "TA/TO", + "Undefined" + ], + "hotel": [ + "City Hotel", + "Resort Hotel" + ], + "market_segment": [ + "Aviation", + "Complementary", + "Corporate", + "Direct", + "Groups", + "Offline TA/TO", + "Online TA", + "Undefined" + ], + "meal": [ + "BB", + "FB", + "HB", + "SC", + "Undefined" + ], + "reservation_status": [ + "Canceled", + "Check-Out", + "No-Show" + ], + "reserved_room_type": [ + "A", + "B", + "C", + "D", + "E", + "F", + "G", + "H", + "L", + "P" + ] + } +} diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_003/test_current.csv b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_003/test_current.csv index 638006281173ba302120cf07f0a10086c1a98cbc..b4e6862b1b30e34cbe2e649ff6f01fdc5e0ab5ab 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_003/test_current.csv +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_003/test_current.csv @@ -1,16 +1,16 @@ -hotel,is_canceled,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests,reservation_status,reservation_status_date -City Hotel,0,40,2016,March,13,20,1,0,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,88.2,0,0,Check-Out,2016-03-21 -City Hotel,0,38,2016,December,50,10,2,1,2,2.0,0,BB,PHL,Direct,Direct,0,0,0,E,E,0,No Deposit,14.0,,0,Transient,143.0,0,0,Check-Out,2016-12-13 -City Hotel,0,32,2015,October,43,24,1,1,1,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Contract,136.0,0,1,Check-Out,2015-10-26 -City Hotel,0,4,2016,August,36,30,0,2,3,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,175.0,0,2,Check-Out,2016-09-01 -City Hotel,0,89,2016,November,47,19,1,1,2,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,9.0,,0,Transient,79.2,0,3,Check-Out,2016-11-21 -Resort Hotel,0,287,2015,October,42,15,0,3,2,0.0,0,BB,GBR,Groups,Corporate,0,0,0,A,E,1,No Deposit,,223.0,0,Transient-Party,44.5,0,0,Check-Out,2015-10-18 -Resort Hotel,0,90,2016,October,44,29,2,3,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,,,0,Transient,54.08,0,1,Check-Out,2016-11-03 -City Hotel,0,176,2016,October,42,11,0,3,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,85.0,,0,Transient,80.75,0,0,Check-Out,2016-10-14 -City Hotel,0,180,2016,November,47,13,1,0,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,54.75,0,2,Check-Out,2016-11-14 -City Hotel,0,30,2015,October,42,16,2,2,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Contract,118.58,0,1,Check-Out,2015-10-20 -City Hotel,0,134,2016,October,42,15,2,1,2,1.0,0,BB,NLD,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,135.0,0,2,Check-Out,2016-10-18 -City Hotel,0,210,2016,August,34,16,0,2,2,0.0,1,SC,NLD,Online TA,TA/TO,0,0,0,A,A,4,No Deposit,9.0,,0,Transient,90.0,0,2,Check-Out,2016-08-18 -City Hotel,0,23,2017,January,2,11,0,1,1,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,9.0,,0,Transient,89.5,0,1,Check-Out,2017-01-12 -City Hotel,0,31,2016,February,6,5,0,1,2,0.0,0,BB,USA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,89.3,1,1,Check-Out,2016-02-06 -Resort Hotel,0,4,2016,November,48,21,1,1,1,0.0,0,BB,PRT,Complementary,Direct,0,0,0,A,D,0,No Deposit,,,0,Transient,0.0,0,0,Check-Out,2016-11-23 +hotel,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests,reservation_status +City Hotel,40,2016,March,13,20,1,0,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,88.2,0,0,Check-Out +City Hotel,38,2016,December,50,10,2,1,2,2.0,0,BB,PHL,Direct,Direct,0,0,0,E,E,0,No Deposit,14.0,,0,Transient,143.0,0,0,Check-Out +City Hotel,32,2015,October,43,24,1,1,1,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Contract,136.0,0,1,Check-Out +City Hotel,4,2016,August,36,30,0,2,3,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,175.0,0,2,Check-Out +City Hotel,89,2016,November,47,19,1,1,2,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,9.0,,0,Transient,79.2,0,3,Check-Out +Resort Hotel,287,2015,October,42,15,0,3,2,0.0,0,BB,GBR,Groups,Corporate,0,0,0,A,E,1,No Deposit,,223.0,0,Transient-Party,44.5,0,0,Check-Out +Resort Hotel,90,2016,October,44,29,2,3,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,,,0,Transient,54.08,0,1,Check-Out +City Hotel,176,2016,October,42,11,0,3,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,85.0,,0,Transient,80.75,0,0,Check-Out +City Hotel,180,2016,November,47,13,1,0,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,54.75,0,2,Check-Out +City Hotel,30,2015,October,42,16,2,2,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Contract,118.58,0,1,Check-Out +City Hotel,134,2016,October,42,15,2,1,2,1.0,0,BB,NLD,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,135.0,0,2,Check-Out +City Hotel,210,2016,August,34,16,0,2,2,0.0,1,SC,NLD,Online TA,TA/TO,0,0,0,A,A,4,No Deposit,9.0,,0,Transient,90.0,0,2,Check-Out +City Hotel,23,2017,January,2,11,0,1,1,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,9.0,,0,Transient,89.5,0,1,Check-Out +City Hotel,31,2016,February,6,5,0,1,2,0.0,0,BB,USA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,89.3,1,1,Check-Out +Resort Hotel,4,2016,November,48,21,1,1,1,0.0,0,BB,PRT,Complementary,Direct,0,0,0,A,D,0,No Deposit,,,0,Transient,0.0,0,0,Check-Out diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_004/Hotel_booking_demand_B4_004_current.json b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_004/Hotel_booking_demand_B4_004_current.json index 2630930402607f230cf59fdb9aa02c493605f666..ddbaa0d28485d6476ff46386368cc922d6c4dd9f 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_004/Hotel_booking_demand_B4_004_current.json +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_004/Hotel_booking_demand_B4_004_current.json @@ -1,7406 +1,7008 @@ { - "id": "004", - "task_type": "B4", - "subtask_type": "list", - "perspective": "data_holder", - "dataset_name": "Hotel booking demand", - "table_path": "output/daily/classification/Hotel booking demand_B4/Hotel booking demand_B4_004/test.csv", - "query": "To better manage room inventory and last-minute sales, we need to identify bookings that are at significant risk of cancellation. I’ve provided our archives with all previous reservation outcomes. Could you highlight all the new records that closely mirror the profiles of past bookings that were Canceled?", - "meta_info": { - "domain": "daily" + "id": "004", + "task_type": "B4", + "subtask_type": "list", + "perspective": "data_holder", + "dataset_name": "Hotel booking demand", + "table_path": "output/daily/classification/Hotel booking demand_B4/Hotel booking demand_B4_004/test.csv", + "query": "To better manage room inventory and last-minute sales, we need to identify bookings that are at significant risk of cancellation. I’ve provided our archives with all previous reservation outcomes. Could you highlight all the new records that closely mirror the profiles of past bookings that were Canceled?", + "meta_info": { + "domain": "daily" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "002", + "features": { + "hotel": "City Hotel", + "lead_time": 108, + "arrival_date_year": 2016, + "arrival_date_month": "March", + "arrival_date_week_number": 13, + "arrival_date_day_of_month": 24, + "stays_in_weekend_nights": 2, + "stays_in_week_nights": 3, + "adults": 0, + "children": 2.0, + "babies": 0, + "meal": "BB", + "country": "PRT", + "market_segment": "Online TA", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "B", + "assigned_room_type": "B", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 8.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient-Party", + "adr": 120.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 1, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "004", + "features": { + "hotel": "Resort Hotel", + "lead_time": 6, + "arrival_date_year": 2017, + "arrival_date_month": "February", + "arrival_date_week_number": 6, + "arrival_date_day_of_month": 9, + "stays_in_weekend_nights": 0, + "stays_in_week_nights": 1, + "adults": 1, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "ESP", + "market_segment": "Online TA", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "D", + "assigned_room_type": "D", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 240.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 56.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 2, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "005", + "features": { + "hotel": "Resort Hotel", + "lead_time": 15, + "arrival_date_year": 2015, + "arrival_date_month": "July", + "arrival_date_week_number": 31, + "arrival_date_day_of_month": 31, + "stays_in_weekend_nights": 0, + "stays_in_week_nights": 2, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "CHN", + "market_segment": "Online TA", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "D", + "assigned_room_type": "D", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 240.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 173.0, + "required_car_parking_spaces": 1, + "total_of_special_requests": 2, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "008", + "features": { + "hotel": "City Hotel", + "lead_time": 2, + "arrival_date_year": 2016, + "arrival_date_month": "October", + "arrival_date_week_number": 41, + "arrival_date_day_of_month": 8, + "stays_in_weekend_nights": 1, + "stays_in_week_nights": 1, + "adults": 1, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "BEL", + "market_segment": "Online TA", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 9.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 160.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 2, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "009", + "features": { + "hotel": "City Hotel", + "lead_time": 462, + "arrival_date_year": 2017, + "arrival_date_month": "July", + "arrival_date_week_number": 26, + "arrival_date_day_of_month": 1, + "stays_in_weekend_nights": 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"babies": 0, + "meal": "BB", + "country": "PRT", + "market_segment": "Groups", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 0, + "deposit_type": "Non Refund", + "agent": 1.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 62.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Canceled" + } + } + ], + "target_column": "reservation_status", + "task_sub_type": "classification", + "ranking_ground_truth": { + "top_k_ids": [ + "057", + "098", + "148", + "207", + "279", + "290", + "297", + "307", + "330", + "351", + "395", + "402", + "415", + "429", + "500" + ] }, - "final_decision": {}, - "what_if": {}, - "ground_truth": { - "extracted_features": [ - { - "scenario_id": "002", - "features": { - "hotel": "City Hotel", - "is_canceled": 0, - 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- "scenario_id": "481", - "features": { - "hotel": "Resort Hotel", - "is_canceled": 0, - "lead_time": 360, - "arrival_date_year": 2016, - "arrival_date_month": "August", - "arrival_date_week_number": 34, - "arrival_date_day_of_month": 18, - "stays_in_weekend_nights": 4, - "stays_in_week_nights": 10, - "adults": 2, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "GBR", - "market_segment": "Offline TA/TO", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "A", - "assigned_room_type": "A", - "booking_changes": 0, - "deposit_type": "No Deposit", - "agent": 40.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Contract", - "adr": 59.8, - "required_car_parking_spaces": 0, - "total_of_special_requests": 0, - "reservation_status": "Check-Out", - "reservation_status_date": "2016-09-01" - } - }, - { - "scenario_id": "485", - "features": { - "hotel": "City Hotel", - "is_canceled": 0, - "lead_time": 54, - "arrival_date_year": 2017, - "arrival_date_month": "February", - "arrival_date_week_number": 9, - "arrival_date_day_of_month": 28, - "stays_in_weekend_nights": 0, - "stays_in_week_nights": 4, - "adults": 3, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "JPN", - "market_segment": "Online TA", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "E", - "assigned_room_type": "E", - "booking_changes": 1, - "deposit_type": "No Deposit", - "agent": 9.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 116.88, - "required_car_parking_spaces": 0, - "total_of_special_requests": 0, - "reservation_status": "Check-Out", - "reservation_status_date": "2017-03-04" - } - }, - { - "scenario_id": "488", - "features": { - "hotel": "Resort Hotel", - "is_canceled": 0, - "lead_time": 218, - "arrival_date_year": 2017, - "arrival_date_month": "August", - "arrival_date_week_number": 34, - "arrival_date_day_of_month": 22, - "stays_in_weekend_nights": 1, - "stays_in_week_nights": 5, - "adults": 2, - "children": 0.0, - "babies": 0, - "meal": "HB", - "country": "IRL", - "market_segment": "Direct", - "distribution_channel": "Direct", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "A", - "assigned_room_type": "A", - "booking_changes": 1, - "deposit_type": "No Deposit", - "agent": 250.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 170.38, - "required_car_parking_spaces": 0, - "total_of_special_requests": 1, - "reservation_status": "Check-Out", - "reservation_status_date": "2017-08-28" - } - }, - { - "scenario_id": "491", - "features": { - "hotel": "City Hotel", - "is_canceled": 0, - "lead_time": 4, - "arrival_date_year": 2016, - "arrival_date_month": "July", - "arrival_date_week_number": 29, - "arrival_date_day_of_month": 11, - "stays_in_weekend_nights": 1, - "stays_in_week_nights": 0, - "adults": 2, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "PRT", - "market_segment": "Online TA", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "A", - "assigned_room_type": "A", - "booking_changes": 0, - "deposit_type": "No Deposit", - "agent": 9.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 151.0, - "required_car_parking_spaces": 0, - "total_of_special_requests": 2, - "reservation_status": "Check-Out", - "reservation_status_date": "2016-07-12" - } - }, - { - "scenario_id": "493", - "features": { - "hotel": "Resort Hotel", - "is_canceled": 0, - "lead_time": 30, - "arrival_date_year": 2016, - "arrival_date_month": "October", - "arrival_date_week_number": 42, - "arrival_date_day_of_month": 15, - "stays_in_weekend_nights": 2, - "stays_in_week_nights": 2, - "adults": 2, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "ESP", - "market_segment": "Offline TA/TO", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "A", - "assigned_room_type": "D", - "booking_changes": 0, - "deposit_type": "No Deposit", - "agent": 6.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 42.0, - "required_car_parking_spaces": 1, - "total_of_special_requests": 0, - "reservation_status": "Check-Out", - "reservation_status_date": "2016-10-19" - } - }, - { - "scenario_id": "496", - "features": { - "hotel": "Resort Hotel", - "is_canceled": 0, - "lead_time": 55, - "arrival_date_year": 2015, - "arrival_date_month": "September", - "arrival_date_week_number": 40, - "arrival_date_day_of_month": 29, - "stays_in_weekend_nights": 2, - "stays_in_week_nights": 5, - "adults": 2, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "GBR", - "market_segment": "Offline TA/TO", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "A", - "assigned_room_type": "A", - "booking_changes": 0, - "deposit_type": "No Deposit", - "agent": 69.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Contract", - "adr": 44.5, - "required_car_parking_spaces": 0, - "total_of_special_requests": 1, - "reservation_status": "Check-Out", - "reservation_status_date": "2015-10-06" - } - }, - { - "scenario_id": "497", - "features": { - "hotel": "City Hotel", - "is_canceled": 0, - "lead_time": 9, - "arrival_date_year": 2016, - "arrival_date_month": "August", - "arrival_date_week_number": 36, - "arrival_date_day_of_month": 29, - "stays_in_weekend_nights": 1, - "stays_in_week_nights": 4, - "adults": 1, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "NLD", - "market_segment": "Online TA", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "A", - "assigned_room_type": "A", - "booking_changes": 0, - "deposit_type": "No Deposit", - "agent": 9.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 128.4, - "required_car_parking_spaces": 0, - "total_of_special_requests": 2, - "reservation_status": "Check-Out", - "reservation_status_date": "2016-09-03" - } - }, - { - "scenario_id": "498", - "features": { - "hotel": "City Hotel", - "is_canceled": 0, - "lead_time": 66, - "arrival_date_year": 2016, - "arrival_date_month": "April", - "arrival_date_week_number": 17, - "arrival_date_day_of_month": 19, - "stays_in_weekend_nights": 0, - "stays_in_week_nights": 4, - "adults": 2, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "FRA", - "market_segment": "Online TA", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "D", - "assigned_room_type": "D", - "booking_changes": 0, - "deposit_type": "No Deposit", - "agent": 9.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 112.2, - "required_car_parking_spaces": 0, - "total_of_special_requests": 0, - "reservation_status": "Check-Out", - "reservation_status_date": "2016-04-23" - } - }, - { - "scenario_id": "500", - "features": { - "hotel": "City Hotel", - "is_canceled": 1, - "lead_time": 288, - "arrival_date_year": 2016, - "arrival_date_month": "April", - "arrival_date_week_number": 17, - "arrival_date_day_of_month": 21, - "stays_in_weekend_nights": 0, - "stays_in_week_nights": 2, - "adults": 2, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "PRT", - "market_segment": "Groups", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "A", - "assigned_room_type": "A", - "booking_changes": 0, - "deposit_type": "Non Refund", - "agent": 1.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 62.0, - "required_car_parking_spaces": 0, - "total_of_special_requests": 0, - "reservation_status": "Canceled", - "reservation_status_date": "2015-10-21" - } - } - ], - "target_column": "reservation_status", - "task_sub_type": "classification", - "ranking_ground_truth": { - "top_k_ids": [ - "057", - "098", - "148", - "207", - "279", - "290", - "297", - "307", - "330", - "351", - "395", - "402", - "415", - "429", - "500" - ] - }, - "active_filters": [], - "target_class_value": "canceled" - }, - "response": "", - "evaluation_score": {} -} \ No newline at end of file + "active_filters": [], + "target_class_value": "canceled" + }, + "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_004/current.csv b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_004/current.csv index bbda055e0f9bcd1d4c9103603df6988cb516de34..c26d42914c56068d52044f96d6ffd786dc269980 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_004/current.csv +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_004/current.csv @@ -1,201 +1,200 @@ -hotel,is_canceled,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests,reservation_status_date -City Hotel,0,108,2016,March,13,24,2,3,0,2.0,0,BB,PRT,Online TA,TA/TO,0,0,0,B,B,0,No Deposit,8.0,,0,Transient-Party,120.0,0,1,2016-03-29 -Resort Hotel,0,6,2017,February,6,9,0,1,1,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,56.0,0,2,2017-02-10 -Resort Hotel,0,15,2015,July,31,31,0,2,2,0.0,0,BB,CHN,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,173.0,1,2,2015-08-02 -City Hotel,0,2,2016,October,41,8,1,1,1,0.0,0,BB,BEL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,160.0,0,2,2016-10-10 -City Hotel,0,462,2017,July,26,1,2,1,2,0.0,0,BB,GBR,Groups,TA/TO,0,0,0,A,A,0,No Deposit,229.0,,0,Transient-Party,112.67,0,2,2017-07-04 -Resort Hotel,0,111,2016,June,23,2,0,3,2,0.0,0,BB,IRL,Online TA,TA/TO,0,0,0,A,C,0,No Deposit,240.0,,0,Transient,80.0,0,1,2016-06-05 -City Hotel,0,155,2017,April,17,28,2,2,1,0.0,0,BB,DEU,Direct,Direct,0,0,0,A,A,1,No Deposit,14.0,,0,Transient,99.0,0,1,2017-05-02 -City Hotel,0,87,2016,March,12,14,1,4,2,0.0,0,BB,FRA,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,72.25,0,2,2016-03-19 -Resort Hotel,0,213,2016,May,21,16,1,1,2,0.0,0,HB,DEU,Groups,TA/TO,0,0,0,A,A,0,No Deposit,298.0,,0,Transient-Party,85.0,0,0,2016-05-18 -Resort Hotel,0,92,2015,September,36,3,2,7,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,240.0,,0,Transient,123.0,1,0,2015-09-12 -Resort Hotel,0,0,2016,April,16,11,1,3,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,3,A,A,0,No Deposit,,47.0,0,Transient,39.0,0,0,2016-04-15 -City Hotel,0,1,2016,December,52,24,1,1,2,0.0,0,BB,DZA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,71.69,0,1,2016-12-26 -City Hotel,0,192,2016,June,26,24,0,2,1,0.0,0,BB,SRB,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,34.0,,0,Transient-Party,95.0,0,0,2016-06-26 -City Hotel,0,177,2017,April,15,15,2,1,2,0.0,0,HB,CN,Groups,TA/TO,0,0,0,A,A,1,No Deposit,2.0,,0,Transient-Party,105.33,0,1,2017-04-18 -City Hotel,0,106,2017,June,25,19,1,3,1,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,117.0,0,1,2017-06-23 -Resort Hotel,0,18,2016,December,51,17,4,10,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,241.0,,0,Transient,46.22,0,1,2016-12-31 -City Hotel,0,35,2015,November,46,14,1,1,1,0.0,0,BB,KOR,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,81.0,0,1,2015-11-16 -City Hotel,0,8,2017,June,23,6,0,1,1,0.0,0,BB,JPN,Online TA,TA/TO,0,0,0,A,E,0,No Deposit,8.0,,0,Transient,150.0,0,1,2017-06-07 -Resort Hotel,0,217,2016,August,35,22,1,2,2,2.0,0,BB,USA,Online TA,TA/TO,0,0,0,C,C,0,No Deposit,242.0,,0,Transient,174.0,1,1,2016-08-25 -City Hotel,1,23,2017,August,35,30,0,2,2,2.0,0,BB,IDN,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,9.0,,0,Transient,250.0,0,0,2017-08-08 -City Hotel,0,10,2015,December,50,10,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,6,A,A,0,No Deposit,,40.0,0,Transient,74.0,0,0,2015-12-11 -City Hotel,0,12,2016,February,9,24,0,4,2,1.0,0,HB,FRA,Online TA,TA/TO,0,0,0,A,D,1,No Deposit,9.0,,0,Transient-Party,158.5,0,3,2016-02-28 -Resort Hotel,0,72,2016,May,20,12,1,3,3,0.0,0,BB,GBR,Direct,Direct,0,0,0,H,H,1,No Deposit,250.0,,0,Transient,160.0,0,0,2016-05-16 -City Hotel,0,66,2015,September,39,20,2,3,2,0.0,0,BB,CHE,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,89.25,0,1,2015-09-25 -City Hotel,0,179,2017,May,18,3,0,3,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,9.0,,0,Transient,117.0,0,2,2017-05-06 -City Hotel,0,164,2015,October,40,2,0,2,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,19.0,,0,Transient-Party,100.0,0,0,2015-10-04 -City Hotel,0,151,2017,March,12,20,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,37.0,,0,Transient-Party,95.0,0,1,2017-03-22 -Resort Hotel,0,102,2017,July,28,10,1,2,2,0.0,0,HB,ESP,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,211.0,0,2,2017-07-13 -City Hotel,0,8,2017,January,2,10,0,2,2,0.0,0,BB,PER,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,75.08,0,1,2017-01-12 -Resort Hotel,0,6,2016,April,15,7,2,6,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient-Party,75.0,0,0,2016-04-15 -Resort Hotel,0,126,2016,July,31,25,2,5,2,2.0,0,HB,PRT,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,240.0,,0,Transient,302.86,1,0,2016-08-01 -Resort Hotel,0,0,2015,December,51,16,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,31.0,0,Transient,35.0,1,0,2015-12-17 -Resort Hotel,0,29,2015,November,47,19,1,3,2,0.0,0,BB,IRL,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,115.0,,0,Transient,26.1,0,0,2015-11-23 -City Hotel,0,99,2016,May,22,26,0,3,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,140.4,0,0,2016-05-29 -City Hotel,0,6,2015,August,34,19,0,3,2,0.0,0,BB,BEL,Direct,Direct,0,0,0,D,D,0,No Deposit,14.0,,0,Transient,79.0,0,0,2015-08-22 -City Hotel,0,165,2017,June,24,14,0,4,2,0.0,0,BB,NLD,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,144.0,0,0,2017-06-18 -City Hotel,0,21,2016,February,8,17,0,3,2,0.0,0,SC,BRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,79.0,0,1,2016-02-20 -City Hotel,0,1,2015,December,50,6,2,1,1,0.0,0,SC,ESP,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,83.0,,0,Group,66.0,1,1,2015-12-09 -City Hotel,0,148,2016,May,19,5,0,3,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient-Party,105.3,0,0,2016-05-08 -City Hotel,1,69,2016,June,25,12,2,4,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,236.0,,0,Transient,120.0,0,0,2016-04-27 -City Hotel,0,30,2016,June,25,18,2,1,2,2.0,0,BB,ITA,Online TA,TA/TO,0,0,0,F,F,1,No Deposit,9.0,,0,Transient,198.9,0,3,2016-06-21 -City Hotel,0,39,2017,June,24,11,2,2,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,112.0,0,3,2017-06-15 -Resort Hotel,0,90,2016,December,52,19,1,3,2,0.0,0,HB,NLD,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,64.8,0,2,2016-12-23 -City Hotel,0,20,2016,October,41,4,0,2,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,28.0,,0,Transient,85.0,0,0,2016-10-06 -City Hotel,0,197,2016,August,34,18,0,3,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,28.0,,0,Transient-Party,112.0,0,1,2016-08-21 -City Hotel,0,21,2016,July,28,5,0,5,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,139.0,0,1,2016-07-10 -City Hotel,0,479,2017,August,32,9,0,3,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,229.0,,0,Transient-Party,112.67,0,1,2017-08-12 -Resort Hotel,0,236,2016,May,20,12,0,3,2,0.0,0,BB,GBR,Groups,TA/TO,0,0,0,A,C,0,No Deposit,315.0,,0,Transient-Party,48.0,0,0,2016-05-15 -City Hotel,0,28,2016,September,36,1,0,3,1,0.0,0,BB,GBR,Direct,Direct,0,0,0,E,E,1,No Deposit,14.0,,0,Transient,173.2,0,0,2016-09-04 -Resort Hotel,0,15,2017,March,13,30,2,6,0,0.0,0,SC,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,523.0,0,Transient-Party,0.0,0,0,2017-04-07 -Resort Hotel,0,0,2017,February,6,7,0,0,1,0.0,0,BB,PRT,Online TA,TA/TO,1,0,0,A,A,0,No Deposit,314.0,,0,Transient,0.0,0,0,2017-02-07 -Resort Hotel,0,325,2017,June,25,23,2,4,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,240.0,,0,Transient,85.21,0,1,2017-06-29 -City Hotel,0,5,2016,September,38,17,1,1,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,154.0,1,4,2016-09-19 -Resort Hotel,0,36,2016,October,43,20,1,3,2,0.0,0,BB,GBR,Groups,Corporate,0,0,0,A,A,3,No Deposit,,,0,Transient-Party,49.0,0,0,2016-10-24 -Resort Hotel,0,4,2017,June,22,1,0,2,2,0.0,0,HB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,122.0,0,1,2017-06-03 -City Hotel,0,11,2017,February,6,10,2,2,2,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,54.71,0,0,2017-02-14 -City Hotel,0,73,2016,December,49,1,0,3,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,28.0,,0,Transient,75.0,0,0,2016-12-04 -Resort Hotel,0,31,2016,December,53,30,0,2,2,0.0,0,HB,PRT,Groups,Direct,1,0,1,A,A,0,No Deposit,,,0,Transient-Party,305.0,0,1,2017-01-01 -City Hotel,0,75,2017,July,27,8,2,1,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,120.0,0,1,2017-07-11 -City Hotel,0,130,2017,July,30,24,2,5,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,27.0,,0,Transient,80.1,0,1,2017-07-31 -City Hotel,0,5,2017,May,21,23,0,3,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,2,A,A,0,No Deposit,,40.0,0,Transient,65.0,0,1,2017-05-26 -City Hotel,1,74,2015,September,38,18,0,2,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,6.0,,0,Transient-Party,101.5,0,0,2015-07-06 -City Hotel,0,3,2015,November,45,2,1,1,1,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,77.77,0,0,2015-11-04 -Resort Hotel,0,268,2016,June,25,15,2,5,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,241.0,,0,Transient,57.15,1,1,2016-06-22 -City Hotel,0,42,2015,September,38,17,0,3,2,0.0,0,BB,BEL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Contract,111.6,0,3,2015-09-20 -City Hotel,0,152,2016,August,35,23,2,5,2,0.0,0,BB,BEL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,98.87,0,0,2016-08-30 -City Hotel,0,59,2016,January,5,28,0,1,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,87.0,,0,Transient-Party,80.0,0,0,2016-01-29 -City Hotel,0,55,2016,April,15,6,0,1,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,56.0,,0,Transient-Party,104.0,0,0,2016-04-07 -Resort Hotel,0,32,2016,April,17,19,2,5,1,0.0,0,HB,GBR,Groups,Direct,0,0,0,A,C,0,No Deposit,273.0,,0,Transient-Party,44.36,0,0,2016-04-26 -City Hotel,0,143,2016,August,34,14,2,2,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,114.3,0,1,2016-08-18 -Resort Hotel,0,83,2016,April,18,26,0,3,2,0.0,0,HB,GBR,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,171.0,,0,Transient,75.6,0,1,2016-04-29 -Resort Hotel,0,28,2016,October,43,18,0,2,2,0.0,0,BB,USA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,241.0,,0,Transient,43.89,0,1,2016-10-20 -City Hotel,0,6,2016,November,47,13,1,0,2,0.0,0,BB,PRT,Complementary,Direct,0,0,0,F,F,0,No Deposit,45.0,,0,Transient,0.0,0,0,2016-11-14 -City Hotel,0,121,2017,May,21,27,2,4,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,A,1,No Deposit,168.0,,0,Transient,89.1,0,2,2017-06-02 -City Hotel,0,12,2015,August,32,5,0,3,2,0.0,0,BB,RUS,Corporate,Corporate,0,0,0,A,D,0,No Deposit,,38.0,0,Transient-Party,65.0,0,0,2015-08-08 -City Hotel,1,56,2017,May,20,17,0,1,1,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,7.0,,0,Transient,108.0,0,0,2017-05-17 -City Hotel,0,6,2017,June,24,12,1,0,1,0.0,0,BB,FIN,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,144.0,0,0,2017-06-13 -City Hotel,0,7,2017,March,12,22,2,9,2,0.0,1,BB,PRT,Online TA,TA/TO,0,0,0,F,F,2,No Deposit,9.0,,0,Transient,212.73,0,2,2017-04-02 -City Hotel,0,17,2016,September,37,8,0,1,2,0.0,0,BB,CHE,Online TA,TA/TO,0,0,0,D,E,1,No Deposit,7.0,,0,Transient,118.58,0,1,2016-09-09 -City Hotel,0,58,2016,June,24,10,1,2,2,0.0,0,SC,ESP,Online TA,TA/TO,0,0,0,A,D,2,No Deposit,9.0,,0,Transient,116.1,0,1,2016-06-13 -City Hotel,0,30,2017,May,18,1,1,3,2,0.0,0,BB,LUX,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,179.0,0,0,2017-05-05 -City Hotel,1,258,2015,July,27,2,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,1,2,0,A,A,0,No Deposit,1.0,,0,Contract,62.0,0,0,2015-01-01 -City Hotel,0,66,2016,April,14,1,1,2,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,92.7,0,1,2016-04-04 -Resort Hotel,0,156,2017,May,19,8,2,5,2,0.0,0,HB,IRL,Groups,TA/TO,0,0,0,E,E,0,No Deposit,434.0,,0,Transient-Party,102.0,0,0,2017-05-15 -Resort Hotel,0,235,2017,July,27,3,4,10,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,243.0,,0,Contract,106.85,0,0,2017-07-17 -Resort Hotel,0,63,2017,February,5,4,4,10,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,250.0,,0,Transient,39.84,0,1,2017-02-18 -City Hotel,1,49,2016,November,47,14,2,5,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,42.0,,0,Transient,80.0,0,0,2016-11-14 -City Hotel,0,12,2017,January,3,16,1,1,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,90.0,0,1,2017-01-18 -City Hotel,0,16,2016,November,46,6,2,3,1,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,169.6,0,1,2016-11-11 -City Hotel,0,66,2016,July,31,29,0,1,3,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,137.7,0,0,2016-07-30 -City Hotel,0,4,2016,February,7,12,2,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,28.0,,0,Transient,85.0,0,0,2016-02-16 -City Hotel,1,117,2016,August,35,24,1,4,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,28.0,,0,Transient,72.25,0,0,2016-08-24 -City Hotel,0,5,2015,August,33,10,1,5,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Contract,90.0,0,1,2015-08-16 -City Hotel,0,194,2016,September,38,12,1,3,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,,,0,Transient-Party,101.4,0,1,2016-09-16 -City Hotel,0,97,2016,April,18,25,1,3,2,0.0,0,SC,USA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Transient,80.75,0,1,2016-04-29 -City Hotel,0,128,2017,May,21,26,0,2,2,0.0,0,BB,NLD,Online TA,TA/TO,0,0,0,D,D,1,No Deposit,9.0,,0,Transient-Party,135.0,0,2,2017-05-28 -Resort Hotel,1,37,2017,February,8,25,2,2,2,0.0,0,BB,GBR,Direct,Direct,0,0,0,F,F,0,No Deposit,250.0,,0,Transient,77.0,0,1,2017-02-25 -City Hotel,0,65,2017,January,1,1,2,4,2,2.0,0,BB,CHE,Direct,Direct,0,0,0,F,F,1,No Deposit,14.0,,0,Transient,152.5,0,0,2017-01-07 -Resort Hotel,0,166,2017,June,26,25,4,10,2,0.0,0,HB,GBR,Offline TA/TO,TA/TO,0,0,0,D,D,1,No Deposit,243.0,,0,Transient,110.0,0,0,2017-07-09 -City Hotel,0,98,2016,October,45,30,2,2,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,90.45,0,1,2016-11-03 -Resort Hotel,0,29,2017,March,13,29,0,4,2,0.0,0,BB,ESP,Direct,Direct,0,0,0,A,A,1,No Deposit,250.0,,0,Transient,67.5,0,2,2017-04-02 -Resort Hotel,0,43,2017,May,18,4,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,169.0,0,Transient,50.0,0,0,2017-05-05 -Resort Hotel,1,92,2016,July,29,16,4,6,2,0.0,0,HB,PRT,Direct,Direct,0,1,1,E,E,0,No Deposit,,,0,Transient,195.0,0,0,2016-06-15 -Resort Hotel,0,194,2016,August,33,7,2,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,96.0,,0,Transient,97.4,0,1,2016-08-11 -City Hotel,0,4,2016,January,2,4,1,0,1,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,76.0,0,1,2016-01-05 -City Hotel,0,1,2017,May,19,9,0,1,1,0.0,0,BB,ESP,Online TA,GDS,0,0,0,D,D,0,No Deposit,195.0,,0,Transient,110.0,0,0,2017-05-10 -Resort Hotel,1,103,2017,March,13,27,1,1,2,0.0,0,BB,PRT,Groups,Corporate,0,0,0,D,D,0,Non Refund,495.0,,0,Transient,65.0,0,0,2016-12-23 -Resort Hotel,0,29,2017,February,8,25,1,1,2,0.0,0,BB,PRT,Online TA,Direct,1,0,0,D,D,0,No Deposit,240.0,,0,Transient-Party,58.0,0,2,2017-02-27 -City Hotel,0,11,2016,February,7,13,0,1,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,D,D,1,No Deposit,,,0,Transient,95.0,0,2,2016-02-14 -City Hotel,0,41,2016,December,49,1,0,3,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,89.0,,0,Transient,66.88,0,0,2016-12-04 -City Hotel,1,76,2017,July,27,2,1,0,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,403.0,,0,Transient,80.1,0,0,2017-04-17 -City Hotel,0,20,2016,May,22,28,1,1,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,143.0,0,1,2016-05-30 -Resort Hotel,0,8,2017,May,20,14,2,2,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,314.0,,0,Transient-Party,91.18,0,1,2017-05-18 -City Hotel,0,81,2016,November,46,6,2,2,1,0.0,0,BB,DNK,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,8.0,,0,Transient,146.6,0,1,2016-11-10 -City Hotel,0,151,2017,March,12,20,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,37.0,,0,Transient-Party,95.0,0,1,2017-03-22 -City Hotel,0,111,2017,April,14,3,1,1,2,0.0,0,BB,ISR,Groups,TA/TO,0,0,0,A,A,0,No Deposit,37.0,,0,Transient-Party,95.0,0,1,2017-04-05 -Resort Hotel,1,25,2017,February,8,24,2,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient,34.0,0,0,2017-02-14 -City Hotel,0,100,2016,August,35,26,2,2,3,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,150.3,0,1,2016-08-30 -Resort Hotel,0,10,2017,January,2,12,0,3,2,0.0,0,BB,CHE,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,48.0,0,1,2017-01-15 -City Hotel,0,9,2016,April,16,12,0,1,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,131.0,0,1,2016-04-13 -City Hotel,0,24,2016,March,13,26,2,1,2,0.0,0,BB,AUT,Groups,TA/TO,0,0,0,A,D,0,No Deposit,1.0,,0,Transient-Party,100.0,0,1,2016-03-29 -City Hotel,0,92,2016,March,12,18,0,2,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,87.3,0,0,2016-03-20 -City Hotel,0,15,2016,December,50,10,1,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,107.5,0,0,2016-12-12 -City Hotel,0,29,2017,May,19,9,0,2,1,0.0,0,BB,POL,Groups,TA/TO,0,0,0,A,A,4,No Deposit,39.0,,0,Transient-Party,130.0,0,0,2017-05-11 -City Hotel,1,67,2016,October,43,19,0,4,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,7.0,,0,Transient,117.6,0,0,2016-08-13 -Resort Hotel,0,125,2015,August,34,19,1,4,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,240.0,,0,Transient,134.0,1,0,2015-08-24 -City Hotel,0,169,2017,August,32,8,0,3,3,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,2,No Deposit,9.0,,0,Transient-Party,175.5,0,1,2017-08-11 -Resort Hotel,0,5,2017,May,20,15,1,0,1,0.0,0,BB,GBR,Complementary,TA/TO,1,0,1,E,E,0,No Deposit,40.0,,0,Transient,0.0,1,2,2017-05-16 -City Hotel,0,102,2015,October,42,16,0,2,2,0.0,0,HB,AUT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,109.0,0,0,2015-10-18 -Resort Hotel,0,183,2016,November,47,18,1,2,2,0.0,0,HB,GBR,Groups,TA/TO,0,0,0,A,I,0,No Deposit,411.0,,116,Transient-Party,52.0,0,0,2016-11-21 -Resort Hotel,0,1,2016,December,49,3,0,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,240.0,,0,Transient,65.0,0,0,2016-12-04 -City Hotel,0,149,2016,June,26,25,2,1,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,85.0,,0,Transient,80.75,0,1,2016-06-28 -Resort Hotel,0,121,2016,September,40,25,2,3,2,0.0,0,BB,NLD,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,241.0,,0,Transient,98.75,0,1,2016-09-30 -City Hotel,0,45,2016,July,28,9,2,2,3,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,E,0,No Deposit,9.0,,0,Transient,145.35,0,2,2016-07-13 -City Hotel,1,156,2017,April,17,26,0,3,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,37.0,,0,Transient,100.0,0,0,2016-11-21 -Resort Hotel,0,0,2016,April,17,19,0,1,2,0.0,0,BB,IRL,Online TA,TA/TO,0,0,0,A,G,0,No Deposit,240.0,,0,Transient,77.0,0,0,2016-04-20 -City Hotel,0,247,2016,June,24,6,1,1,2,0.0,0,HB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,115.0,0,1,2016-06-08 -City Hotel,0,36,2016,January,4,19,0,2,1,0.0,0,BB,USA,Offline TA/TO,TA/TO,0,0,0,A,D,2,No Deposit,19.0,,0,Transient-Party,71.0,0,0,2016-01-21 -Resort Hotel,0,36,2017,May,22,28,2,1,2,0.0,0,BB,FIN,Direct,Direct,0,0,0,F,F,0,No Deposit,250.0,,0,Transient,142.0,1,2,2017-05-31 -City Hotel,0,80,2017,July,27,6,2,4,1,0.0,0,SC,BEL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient-Party,120.0,0,1,2017-07-12 -City Hotel,0,32,2016,February,6,5,0,2,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,D,D,2,No Deposit,9.0,,0,Transient,89.3,0,1,2016-02-07 -Resort Hotel,0,19,2015,October,41,10,1,1,2,2.0,0,BB,ESP,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,240.0,,0,Transient,117.0,0,3,2015-10-12 -City Hotel,0,82,2017,January,1,7,6,16,2,0.0,0,SC,BRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,74.8,0,4,2017-01-29 -Resort Hotel,0,5,2015,November,48,22,2,0,1,0.0,0,BB,USA,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,240.0,,0,Transient,40.0,0,1,2015-11-24 -City Hotel,0,120,2016,August,32,5,4,10,2,2.0,0,BB,FRA,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,7.0,,0,Transient,220.0,1,1,2016-08-19 -Resort Hotel,0,13,2016,August,33,8,2,5,3,0.0,0,HB,DEU,Direct,Direct,0,0,0,A,A,1,No Deposit,,,0,Transient-Party,161.1,0,2,2016-08-15 -City Hotel,0,74,2016,April,14,1,2,2,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,11.0,,0,Transient,59.37,0,0,2016-04-05 -City Hotel,0,115,2016,February,9,27,2,2,1,0.0,0,BB,MOZ,Offline TA/TO,TA/TO,0,0,0,A,E,1,No Deposit,134.0,,0,Transient-Party,76.0,0,1,2016-03-02 -City Hotel,0,184,2017,August,34,20,2,2,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,126.0,0,0,2017-08-24 -Resort Hotel,0,1,2017,May,22,31,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,C,0,No Deposit,,195.0,0,Transient,75.0,0,0,2017-06-01 -Resort Hotel,1,58,2016,March,13,24,0,3,2,2.0,0,BB,PRT,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,240.0,,0,Transient,115.0,0,0,2016-01-27 -Resort Hotel,0,102,2016,April,18,30,2,4,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,241.0,,0,Transient,45.66,0,1,2016-05-06 -City Hotel,0,38,2016,June,27,27,1,3,2,0.0,0,SC,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,28.0,,0,Transient,63.75,0,0,2016-07-01 -City Hotel,1,203,2016,December,53,30,2,2,3,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,D,D,2,No Deposit,9.0,,0,Transient,120.7,0,0,2016-08-12 -City Hotel,0,34,2016,October,41,2,2,0,3,0.0,0,BB,CHE,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,7.0,,0,Transient,122.45,0,1,2016-10-04 -Resort Hotel,0,4,2017,April,17,26,0,2,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,1,A,A,0,No Deposit,,112.0,0,Transient,45.0,0,0,2017-04-28 -Resort Hotel,0,28,2016,February,7,10,2,6,2,0.0,0,HB,FRA,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,171.0,,0,Transient,55.0,0,0,2016-02-18 -Resort Hotel,0,168,2017,March,12,20,1,2,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,177.0,,0,Transient,30.6,0,0,2017-03-23 -City Hotel,0,320,2016,August,34,18,0,2,2,0.0,0,HB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,115.0,0,1,2016-08-20 -Resort Hotel,0,187,2016,April,16,11,3,7,2,0.0,0,HB,GBR,Online TA,TA/TO,0,0,0,E,E,1,No Deposit,240.0,,0,Transient,108.0,0,2,2016-04-21 -Resort Hotel,0,57,2015,July,29,15,0,2,2,0.0,0,FB,PRT,Groups,Direct,0,0,0,A,A,0,No Deposit,305.0,,0,Transient,107.0,0,0,2015-07-17 -City Hotel,0,21,2017,March,11,14,0,1,1,0.0,0,BB,ESP,Groups,TA/TO,0,0,0,A,A,2,No Deposit,254.0,,0,Transient-Party,251.0,0,0,2017-03-15 -City Hotel,1,97,2016,March,11,12,1,1,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,78.3,0,0,2016-02-22 -City Hotel,0,243,2016,September,40,27,2,6,2,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,28.0,,0,Transient,80.75,0,0,2016-10-05 -City Hotel,0,24,2016,March,13,26,2,1,2,0.0,0,BB,AUT,Groups,TA/TO,0,0,0,A,D,0,No Deposit,1.0,,0,Transient-Party,100.0,0,0,2016-03-29 -City Hotel,0,335,2016,October,42,13,0,3,1,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,56.0,,0,Transient-Party,80.0,0,0,2016-10-16 -City Hotel,0,56,2017,April,15,10,1,2,2,1.0,0,HB,ESP,Direct,Direct,0,0,0,A,D,1,No Deposit,14.0,,0,Transient,195.0,1,3,2017-04-13 -Resort Hotel,0,40,2017,January,3,16,1,1,1,0.0,0,Undefined,PRT,Groups,Direct,0,0,0,A,C,0,No Deposit,,,0,Transient-Party,55.0,0,0,2017-01-18 -Resort Hotel,1,208,2016,August,32,2,4,12,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,E,E,0,No Deposit,156.0,,0,Transient,141.1,0,0,2016-01-11 -City Hotel,0,349,2016,October,41,4,1,5,2,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,58.0,,162,Transient-Party,123.33,0,0,2016-10-10 -City Hotel,0,65,2016,December,52,21,0,4,3,0.0,0,HB,FRA,Offline TA/TO,TA/TO,0,0,0,A,D,1,No Deposit,22.0,,0,Transient,122.65,0,0,2016-12-25 -City Hotel,0,1,2015,August,33,11,0,1,1,0.0,0,BB,GRC,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,38.0,0,Transient-Party,70.0,0,0,2015-08-12 -Resort Hotel,0,131,2017,April,16,16,2,5,2,0.0,0,HB,CHE,Groups,TA/TO,0,0,0,A,A,0,No Deposit,493.0,,0,Contract,80.0,0,1,2017-04-23 -Resort Hotel,0,195,2016,September,39,22,2,5,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,243.0,,0,Contract,53.85,0,0,2016-09-29 -City Hotel,0,115,2016,February,9,27,2,2,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,1,No Deposit,134.0,,0,Transient-Party,76.0,0,0,2016-03-02 -Resort Hotel,0,22,2017,July,31,31,1,4,2,0.0,0,BB,SWE,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,225.0,0,2,2017-08-05 -Resort Hotel,0,218,2017,May,19,8,2,5,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,82.0,1,1,2017-05-15 -City Hotel,0,51,2016,March,13,21,1,3,2,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,B,B,0,No Deposit,9.0,,0,Transient-Party,83.3,0,1,2016-03-25 -Resort Hotel,0,6,2016,March,13,23,0,2,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,240.0,,0,Transient,66.5,1,1,2016-03-25 -City Hotel,0,34,2015,September,38,13,2,4,2,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Contract,94.95,0,1,2015-09-19 -City Hotel,0,48,2017,April,17,26,0,3,2,1.0,0,BB,IND,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient-Party,144.0,0,2,2017-04-29 -Resort Hotel,0,262,2015,September,39,21,1,3,2,0.0,0,HB,BEL,Groups,Corporate,0,0,0,A,A,0,No Deposit,183.0,,0,Transient-Party,91.2,0,0,2015-09-25 -City Hotel,0,180,2016,May,19,2,1,1,2,0.0,0,BB,ITA,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,3.0,,0,Transient-Party,100.0,0,1,2016-05-04 -City Hotel,0,386,2016,October,44,23,2,0,2,0.0,0,HB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,115.0,0,1,2016-10-25 -City Hotel,0,4,2017,July,29,17,1,1,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,19,A,C,0,No Deposit,,40.0,0,Transient,65.0,0,2,2017-07-19 -City Hotel,0,46,2016,June,25,17,0,2,2,0.0,0,BB,PRT,Complementary,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,0.0,0,0,2016-06-19 -City Hotel,0,0,2017,February,6,5,2,0,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,2,No Deposit,,,0,Transient,79.0,0,0,2017-02-07 -City Hotel,0,168,2015,September,37,11,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,0,Transient-Party,60.0,0,0,2015-09-13 -City Hotel,0,212,2016,May,21,20,2,3,2,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,21.0,,0,Transient-Party,78.0,0,0,2016-05-25 -Resort Hotel,0,92,2017,April,13,1,1,1,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,C,0,No Deposit,240.0,,0,Transient,48.0,0,1,2017-04-03 -City Hotel,0,35,2015,October,43,20,0,4,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Contract,96.3,0,2,2015-10-24 -Resort Hotel,0,360,2016,August,34,18,4,10,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,40.0,,0,Contract,59.8,0,0,2016-09-01 -City Hotel,0,54,2017,February,9,28,0,4,3,0.0,0,BB,JPN,Online TA,TA/TO,0,0,0,E,E,1,No Deposit,9.0,,0,Transient,116.88,0,0,2017-03-04 -Resort Hotel,0,218,2017,August,34,22,1,5,2,0.0,0,HB,IRL,Direct,Direct,0,0,0,A,A,1,No Deposit,250.0,,0,Transient,170.38,0,1,2017-08-28 -City Hotel,0,4,2016,July,29,11,1,0,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,151.0,0,2,2016-07-12 -Resort Hotel,0,30,2016,October,42,15,2,2,2,0.0,0,BB,ESP,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,6.0,,0,Transient,42.0,1,0,2016-10-19 -Resort Hotel,0,55,2015,September,40,29,2,5,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,69.0,,0,Contract,44.5,0,1,2015-10-06 -City Hotel,0,9,2016,August,36,29,1,4,1,0.0,0,BB,NLD,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,128.4,0,2,2016-09-03 -City Hotel,0,66,2016,April,17,19,0,4,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,112.2,0,0,2016-04-23 -City Hotel,1,288,2016,April,17,21,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0,2015-10-21 +hotel,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests +City Hotel,108,2016,March,13,24,2,3,0,2.0,0,BB,PRT,Online TA,TA/TO,0,0,0,B,B,0,No Deposit,8.0,,0,Transient-Party,120.0,0,1 +Resort Hotel,6,2017,February,6,9,0,1,1,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,56.0,0,2 +Resort Hotel,15,2015,July,31,31,0,2,2,0.0,0,BB,CHN,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,173.0,1,2 +City Hotel,2,2016,October,41,8,1,1,1,0.0,0,BB,BEL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,160.0,0,2 +City Hotel,462,2017,July,26,1,2,1,2,0.0,0,BB,GBR,Groups,TA/TO,0,0,0,A,A,0,No Deposit,229.0,,0,Transient-Party,112.67,0,2 +Resort Hotel,111,2016,June,23,2,0,3,2,0.0,0,BB,IRL,Online TA,TA/TO,0,0,0,A,C,0,No Deposit,240.0,,0,Transient,80.0,0,1 +City Hotel,155,2017,April,17,28,2,2,1,0.0,0,BB,DEU,Direct,Direct,0,0,0,A,A,1,No Deposit,14.0,,0,Transient,99.0,0,1 +City Hotel,87,2016,March,12,14,1,4,2,0.0,0,BB,FRA,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,72.25,0,2 +Resort Hotel,213,2016,May,21,16,1,1,2,0.0,0,HB,DEU,Groups,TA/TO,0,0,0,A,A,0,No Deposit,298.0,,0,Transient-Party,85.0,0,0 +Resort Hotel,92,2015,September,36,3,2,7,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,240.0,,0,Transient,123.0,1,0 +Resort Hotel,0,2016,April,16,11,1,3,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,3,A,A,0,No Deposit,,47.0,0,Transient,39.0,0,0 +City Hotel,1,2016,December,52,24,1,1,2,0.0,0,BB,DZA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,71.69,0,1 +City Hotel,192,2016,June,26,24,0,2,1,0.0,0,BB,SRB,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,34.0,,0,Transient-Party,95.0,0,0 +City Hotel,177,2017,April,15,15,2,1,2,0.0,0,HB,CN,Groups,TA/TO,0,0,0,A,A,1,No Deposit,2.0,,0,Transient-Party,105.33,0,1 +City Hotel,106,2017,June,25,19,1,3,1,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,117.0,0,1 +Resort Hotel,18,2016,December,51,17,4,10,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,241.0,,0,Transient,46.22,0,1 +City Hotel,35,2015,November,46,14,1,1,1,0.0,0,BB,KOR,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,81.0,0,1 +City Hotel,8,2017,June,23,6,0,1,1,0.0,0,BB,JPN,Online TA,TA/TO,0,0,0,A,E,0,No Deposit,8.0,,0,Transient,150.0,0,1 +Resort Hotel,217,2016,August,35,22,1,2,2,2.0,0,BB,USA,Online TA,TA/TO,0,0,0,C,C,0,No Deposit,242.0,,0,Transient,174.0,1,1 +City Hotel,23,2017,August,35,30,0,2,2,2.0,0,BB,IDN,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,9.0,,0,Transient,250.0,0,0 +City Hotel,10,2015,December,50,10,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,6,A,A,0,No Deposit,,40.0,0,Transient,74.0,0,0 +City Hotel,12,2016,February,9,24,0,4,2,1.0,0,HB,FRA,Online TA,TA/TO,0,0,0,A,D,1,No Deposit,9.0,,0,Transient-Party,158.5,0,3 +Resort Hotel,72,2016,May,20,12,1,3,3,0.0,0,BB,GBR,Direct,Direct,0,0,0,H,H,1,No Deposit,250.0,,0,Transient,160.0,0,0 +City Hotel,66,2015,September,39,20,2,3,2,0.0,0,BB,CHE,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,89.25,0,1 +City Hotel,179,2017,May,18,3,0,3,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,9.0,,0,Transient,117.0,0,2 +City Hotel,164,2015,October,40,2,0,2,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,19.0,,0,Transient-Party,100.0,0,0 +City Hotel,151,2017,March,12,20,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,37.0,,0,Transient-Party,95.0,0,1 +Resort Hotel,102,2017,July,28,10,1,2,2,0.0,0,HB,ESP,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,211.0,0,2 +City Hotel,8,2017,January,2,10,0,2,2,0.0,0,BB,PER,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,75.08,0,1 +Resort Hotel,6,2016,April,15,7,2,6,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient-Party,75.0,0,0 +Resort Hotel,126,2016,July,31,25,2,5,2,2.0,0,HB,PRT,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,240.0,,0,Transient,302.86,1,0 +Resort Hotel,0,2015,December,51,16,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,31.0,0,Transient,35.0,1,0 +Resort Hotel,29,2015,November,47,19,1,3,2,0.0,0,BB,IRL,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,115.0,,0,Transient,26.1,0,0 +City Hotel,99,2016,May,22,26,0,3,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,140.4,0,0 +City Hotel,6,2015,August,34,19,0,3,2,0.0,0,BB,BEL,Direct,Direct,0,0,0,D,D,0,No Deposit,14.0,,0,Transient,79.0,0,0 +City Hotel,165,2017,June,24,14,0,4,2,0.0,0,BB,NLD,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,144.0,0,0 +City Hotel,21,2016,February,8,17,0,3,2,0.0,0,SC,BRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,79.0,0,1 +City Hotel,1,2015,December,50,6,2,1,1,0.0,0,SC,ESP,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,83.0,,0,Group,66.0,1,1 +City Hotel,148,2016,May,19,5,0,3,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient-Party,105.3,0,0 +City Hotel,69,2016,June,25,12,2,4,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,236.0,,0,Transient,120.0,0,0 +City Hotel,30,2016,June,25,18,2,1,2,2.0,0,BB,ITA,Online TA,TA/TO,0,0,0,F,F,1,No Deposit,9.0,,0,Transient,198.9,0,3 +City Hotel,39,2017,June,24,11,2,2,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,112.0,0,3 +Resort Hotel,90,2016,December,52,19,1,3,2,0.0,0,HB,NLD,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,64.8,0,2 +City Hotel,20,2016,October,41,4,0,2,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,28.0,,0,Transient,85.0,0,0 +City Hotel,197,2016,August,34,18,0,3,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,28.0,,0,Transient-Party,112.0,0,1 +City Hotel,21,2016,July,28,5,0,5,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,139.0,0,1 +City Hotel,479,2017,August,32,9,0,3,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,229.0,,0,Transient-Party,112.67,0,1 +Resort Hotel,236,2016,May,20,12,0,3,2,0.0,0,BB,GBR,Groups,TA/TO,0,0,0,A,C,0,No Deposit,315.0,,0,Transient-Party,48.0,0,0 +City Hotel,28,2016,September,36,1,0,3,1,0.0,0,BB,GBR,Direct,Direct,0,0,0,E,E,1,No Deposit,14.0,,0,Transient,173.2,0,0 +Resort Hotel,15,2017,March,13,30,2,6,0,0.0,0,SC,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,523.0,0,Transient-Party,0.0,0,0 +Resort Hotel,0,2017,February,6,7,0,0,1,0.0,0,BB,PRT,Online TA,TA/TO,1,0,0,A,A,0,No Deposit,314.0,,0,Transient,0.0,0,0 +Resort Hotel,325,2017,June,25,23,2,4,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,240.0,,0,Transient,85.21,0,1 +City Hotel,5,2016,September,38,17,1,1,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,154.0,1,4 +Resort Hotel,36,2016,October,43,20,1,3,2,0.0,0,BB,GBR,Groups,Corporate,0,0,0,A,A,3,No Deposit,,,0,Transient-Party,49.0,0,0 +Resort Hotel,4,2017,June,22,1,0,2,2,0.0,0,HB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,122.0,0,1 +City Hotel,11,2017,February,6,10,2,2,2,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,54.71,0,0 +City Hotel,73,2016,December,49,1,0,3,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,28.0,,0,Transient,75.0,0,0 +Resort Hotel,31,2016,December,53,30,0,2,2,0.0,0,HB,PRT,Groups,Direct,1,0,1,A,A,0,No Deposit,,,0,Transient-Party,305.0,0,1 +City Hotel,75,2017,July,27,8,2,1,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,120.0,0,1 +City Hotel,130,2017,July,30,24,2,5,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,27.0,,0,Transient,80.1,0,1 +City Hotel,5,2017,May,21,23,0,3,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,2,A,A,0,No Deposit,,40.0,0,Transient,65.0,0,1 +City Hotel,74,2015,September,38,18,0,2,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,6.0,,0,Transient-Party,101.5,0,0 +City Hotel,3,2015,November,45,2,1,1,1,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,77.77,0,0 +Resort Hotel,268,2016,June,25,15,2,5,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,241.0,,0,Transient,57.15,1,1 +City Hotel,42,2015,September,38,17,0,3,2,0.0,0,BB,BEL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Contract,111.6,0,3 +City Hotel,152,2016,August,35,23,2,5,2,0.0,0,BB,BEL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,98.87,0,0 +City Hotel,59,2016,January,5,28,0,1,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,87.0,,0,Transient-Party,80.0,0,0 +City Hotel,55,2016,April,15,6,0,1,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,56.0,,0,Transient-Party,104.0,0,0 +Resort Hotel,32,2016,April,17,19,2,5,1,0.0,0,HB,GBR,Groups,Direct,0,0,0,A,C,0,No Deposit,273.0,,0,Transient-Party,44.36,0,0 +City Hotel,143,2016,August,34,14,2,2,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,114.3,0,1 +Resort Hotel,83,2016,April,18,26,0,3,2,0.0,0,HB,GBR,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,171.0,,0,Transient,75.6,0,1 +Resort Hotel,28,2016,October,43,18,0,2,2,0.0,0,BB,USA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,241.0,,0,Transient,43.89,0,1 +City Hotel,6,2016,November,47,13,1,0,2,0.0,0,BB,PRT,Complementary,Direct,0,0,0,F,F,0,No Deposit,45.0,,0,Transient,0.0,0,0 +City Hotel,121,2017,May,21,27,2,4,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,A,1,No Deposit,168.0,,0,Transient,89.1,0,2 +City Hotel,12,2015,August,32,5,0,3,2,0.0,0,BB,RUS,Corporate,Corporate,0,0,0,A,D,0,No Deposit,,38.0,0,Transient-Party,65.0,0,0 +City Hotel,56,2017,May,20,17,0,1,1,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,7.0,,0,Transient,108.0,0,0 +City Hotel,6,2017,June,24,12,1,0,1,0.0,0,BB,FIN,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,144.0,0,0 +City Hotel,7,2017,March,12,22,2,9,2,0.0,1,BB,PRT,Online TA,TA/TO,0,0,0,F,F,2,No Deposit,9.0,,0,Transient,212.73,0,2 +City Hotel,17,2016,September,37,8,0,1,2,0.0,0,BB,CHE,Online TA,TA/TO,0,0,0,D,E,1,No Deposit,7.0,,0,Transient,118.58,0,1 +City Hotel,58,2016,June,24,10,1,2,2,0.0,0,SC,ESP,Online TA,TA/TO,0,0,0,A,D,2,No Deposit,9.0,,0,Transient,116.1,0,1 +City Hotel,30,2017,May,18,1,1,3,2,0.0,0,BB,LUX,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,179.0,0,0 +City Hotel,258,2015,July,27,2,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,1,2,0,A,A,0,No Deposit,1.0,,0,Contract,62.0,0,0 +City Hotel,66,2016,April,14,1,1,2,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,92.7,0,1 +Resort Hotel,156,2017,May,19,8,2,5,2,0.0,0,HB,IRL,Groups,TA/TO,0,0,0,E,E,0,No Deposit,434.0,,0,Transient-Party,102.0,0,0 +Resort Hotel,235,2017,July,27,3,4,10,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,243.0,,0,Contract,106.85,0,0 +Resort Hotel,63,2017,February,5,4,4,10,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,250.0,,0,Transient,39.84,0,1 +City Hotel,49,2016,November,47,14,2,5,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,42.0,,0,Transient,80.0,0,0 +City Hotel,12,2017,January,3,16,1,1,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,90.0,0,1 +City Hotel,16,2016,November,46,6,2,3,1,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,169.6,0,1 +City Hotel,66,2016,July,31,29,0,1,3,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,137.7,0,0 +City Hotel,4,2016,February,7,12,2,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,28.0,,0,Transient,85.0,0,0 +City Hotel,117,2016,August,35,24,1,4,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,28.0,,0,Transient,72.25,0,0 +City Hotel,5,2015,August,33,10,1,5,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Contract,90.0,0,1 +City Hotel,194,2016,September,38,12,1,3,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,,,0,Transient-Party,101.4,0,1 +City Hotel,97,2016,April,18,25,1,3,2,0.0,0,SC,USA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Transient,80.75,0,1 +City Hotel,128,2017,May,21,26,0,2,2,0.0,0,BB,NLD,Online TA,TA/TO,0,0,0,D,D,1,No Deposit,9.0,,0,Transient-Party,135.0,0,2 +Resort Hotel,37,2017,February,8,25,2,2,2,0.0,0,BB,GBR,Direct,Direct,0,0,0,F,F,0,No Deposit,250.0,,0,Transient,77.0,0,1 +City Hotel,65,2017,January,1,1,2,4,2,2.0,0,BB,CHE,Direct,Direct,0,0,0,F,F,1,No Deposit,14.0,,0,Transient,152.5,0,0 +Resort Hotel,166,2017,June,26,25,4,10,2,0.0,0,HB,GBR,Offline TA/TO,TA/TO,0,0,0,D,D,1,No Deposit,243.0,,0,Transient,110.0,0,0 +City Hotel,98,2016,October,45,30,2,2,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,90.45,0,1 +Resort Hotel,29,2017,March,13,29,0,4,2,0.0,0,BB,ESP,Direct,Direct,0,0,0,A,A,1,No Deposit,250.0,,0,Transient,67.5,0,2 +Resort Hotel,43,2017,May,18,4,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,169.0,0,Transient,50.0,0,0 +Resort Hotel,92,2016,July,29,16,4,6,2,0.0,0,HB,PRT,Direct,Direct,0,1,1,E,E,0,No Deposit,,,0,Transient,195.0,0,0 +Resort Hotel,194,2016,August,33,7,2,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,96.0,,0,Transient,97.4,0,1 +City Hotel,4,2016,January,2,4,1,0,1,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,76.0,0,1 +City Hotel,1,2017,May,19,9,0,1,1,0.0,0,BB,ESP,Online TA,GDS,0,0,0,D,D,0,No Deposit,195.0,,0,Transient,110.0,0,0 +Resort Hotel,103,2017,March,13,27,1,1,2,0.0,0,BB,PRT,Groups,Corporate,0,0,0,D,D,0,Non Refund,495.0,,0,Transient,65.0,0,0 +Resort Hotel,29,2017,February,8,25,1,1,2,0.0,0,BB,PRT,Online TA,Direct,1,0,0,D,D,0,No Deposit,240.0,,0,Transient-Party,58.0,0,2 +City Hotel,11,2016,February,7,13,0,1,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,D,D,1,No Deposit,,,0,Transient,95.0,0,2 +City Hotel,41,2016,December,49,1,0,3,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,89.0,,0,Transient,66.88,0,0 +City Hotel,76,2017,July,27,2,1,0,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,403.0,,0,Transient,80.1,0,0 +City Hotel,20,2016,May,22,28,1,1,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,143.0,0,1 +Resort Hotel,8,2017,May,20,14,2,2,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,314.0,,0,Transient-Party,91.18,0,1 +City Hotel,81,2016,November,46,6,2,2,1,0.0,0,BB,DNK,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,8.0,,0,Transient,146.6,0,1 +City Hotel,111,2017,April,14,3,1,1,2,0.0,0,BB,ISR,Groups,TA/TO,0,0,0,A,A,0,No Deposit,37.0,,0,Transient-Party,95.0,0,1 +Resort Hotel,25,2017,February,8,24,2,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient,34.0,0,0 +City Hotel,100,2016,August,35,26,2,2,3,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,150.3,0,1 +Resort Hotel,10,2017,January,2,12,0,3,2,0.0,0,BB,CHE,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,48.0,0,1 +City Hotel,9,2016,April,16,12,0,1,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,131.0,0,1 +City Hotel,24,2016,March,13,26,2,1,2,0.0,0,BB,AUT,Groups,TA/TO,0,0,0,A,D,0,No Deposit,1.0,,0,Transient-Party,100.0,0,1 +City Hotel,92,2016,March,12,18,0,2,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,87.3,0,0 +City Hotel,15,2016,December,50,10,1,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,107.5,0,0 +City Hotel,29,2017,May,19,9,0,2,1,0.0,0,BB,POL,Groups,TA/TO,0,0,0,A,A,4,No Deposit,39.0,,0,Transient-Party,130.0,0,0 +City Hotel,67,2016,October,43,19,0,4,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,7.0,,0,Transient,117.6,0,0 +Resort Hotel,125,2015,August,34,19,1,4,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,240.0,,0,Transient,134.0,1,0 +City Hotel,169,2017,August,32,8,0,3,3,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,2,No Deposit,9.0,,0,Transient-Party,175.5,0,1 +Resort Hotel,5,2017,May,20,15,1,0,1,0.0,0,BB,GBR,Complementary,TA/TO,1,0,1,E,E,0,No Deposit,40.0,,0,Transient,0.0,1,2 +City Hotel,102,2015,October,42,16,0,2,2,0.0,0,HB,AUT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,109.0,0,0 +Resort Hotel,183,2016,November,47,18,1,2,2,0.0,0,HB,GBR,Groups,TA/TO,0,0,0,A,I,0,No Deposit,411.0,,116,Transient-Party,52.0,0,0 +Resort Hotel,1,2016,December,49,3,0,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,240.0,,0,Transient,65.0,0,0 +City Hotel,149,2016,June,26,25,2,1,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,85.0,,0,Transient,80.75,0,1 +Resort Hotel,121,2016,September,40,25,2,3,2,0.0,0,BB,NLD,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,241.0,,0,Transient,98.75,0,1 +City Hotel,45,2016,July,28,9,2,2,3,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,E,0,No Deposit,9.0,,0,Transient,145.35,0,2 +City Hotel,156,2017,April,17,26,0,3,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,37.0,,0,Transient,100.0,0,0 +Resort Hotel,0,2016,April,17,19,0,1,2,0.0,0,BB,IRL,Online TA,TA/TO,0,0,0,A,G,0,No Deposit,240.0,,0,Transient,77.0,0,0 +City Hotel,247,2016,June,24,6,1,1,2,0.0,0,HB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,115.0,0,1 +City Hotel,36,2016,January,4,19,0,2,1,0.0,0,BB,USA,Offline TA/TO,TA/TO,0,0,0,A,D,2,No Deposit,19.0,,0,Transient-Party,71.0,0,0 +Resort Hotel,36,2017,May,22,28,2,1,2,0.0,0,BB,FIN,Direct,Direct,0,0,0,F,F,0,No Deposit,250.0,,0,Transient,142.0,1,2 +City Hotel,80,2017,July,27,6,2,4,1,0.0,0,SC,BEL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient-Party,120.0,0,1 +City Hotel,32,2016,February,6,5,0,2,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,D,D,2,No Deposit,9.0,,0,Transient,89.3,0,1 +Resort Hotel,19,2015,October,41,10,1,1,2,2.0,0,BB,ESP,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,240.0,,0,Transient,117.0,0,3 +City Hotel,82,2017,January,1,7,6,16,2,0.0,0,SC,BRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,74.8,0,4 +Resort Hotel,5,2015,November,48,22,2,0,1,0.0,0,BB,USA,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,240.0,,0,Transient,40.0,0,1 +City Hotel,120,2016,August,32,5,4,10,2,2.0,0,BB,FRA,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,7.0,,0,Transient,220.0,1,1 +Resort Hotel,13,2016,August,33,8,2,5,3,0.0,0,HB,DEU,Direct,Direct,0,0,0,A,A,1,No Deposit,,,0,Transient-Party,161.1,0,2 +City Hotel,74,2016,April,14,1,2,2,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,11.0,,0,Transient,59.37,0,0 +City Hotel,115,2016,February,9,27,2,2,1,0.0,0,BB,MOZ,Offline TA/TO,TA/TO,0,0,0,A,E,1,No Deposit,134.0,,0,Transient-Party,76.0,0,1 +City Hotel,184,2017,August,34,20,2,2,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,126.0,0,0 +Resort Hotel,1,2017,May,22,31,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,C,0,No Deposit,,195.0,0,Transient,75.0,0,0 +Resort Hotel,58,2016,March,13,24,0,3,2,2.0,0,BB,PRT,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,240.0,,0,Transient,115.0,0,0 +Resort Hotel,102,2016,April,18,30,2,4,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,241.0,,0,Transient,45.66,0,1 +City Hotel,38,2016,June,27,27,1,3,2,0.0,0,SC,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,28.0,,0,Transient,63.75,0,0 +City Hotel,203,2016,December,53,30,2,2,3,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,D,D,2,No Deposit,9.0,,0,Transient,120.7,0,0 +City Hotel,34,2016,October,41,2,2,0,3,0.0,0,BB,CHE,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,7.0,,0,Transient,122.45,0,1 +Resort Hotel,4,2017,April,17,26,0,2,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,1,A,A,0,No Deposit,,112.0,0,Transient,45.0,0,0 +Resort Hotel,28,2016,February,7,10,2,6,2,0.0,0,HB,FRA,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,171.0,,0,Transient,55.0,0,0 +Resort Hotel,168,2017,March,12,20,1,2,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,177.0,,0,Transient,30.6,0,0 +City Hotel,320,2016,August,34,18,0,2,2,0.0,0,HB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,115.0,0,1 +Resort Hotel,187,2016,April,16,11,3,7,2,0.0,0,HB,GBR,Online TA,TA/TO,0,0,0,E,E,1,No Deposit,240.0,,0,Transient,108.0,0,2 +Resort Hotel,57,2015,July,29,15,0,2,2,0.0,0,FB,PRT,Groups,Direct,0,0,0,A,A,0,No Deposit,305.0,,0,Transient,107.0,0,0 +City Hotel,21,2017,March,11,14,0,1,1,0.0,0,BB,ESP,Groups,TA/TO,0,0,0,A,A,2,No Deposit,254.0,,0,Transient-Party,251.0,0,0 +City Hotel,97,2016,March,11,12,1,1,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,78.3,0,0 +City Hotel,243,2016,September,40,27,2,6,2,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,28.0,,0,Transient,80.75,0,0 +City Hotel,24,2016,March,13,26,2,1,2,0.0,0,BB,AUT,Groups,TA/TO,0,0,0,A,D,0,No Deposit,1.0,,0,Transient-Party,100.0,0,0 +City Hotel,335,2016,October,42,13,0,3,1,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,56.0,,0,Transient-Party,80.0,0,0 +City Hotel,56,2017,April,15,10,1,2,2,1.0,0,HB,ESP,Direct,Direct,0,0,0,A,D,1,No Deposit,14.0,,0,Transient,195.0,1,3 +Resort Hotel,40,2017,January,3,16,1,1,1,0.0,0,Undefined,PRT,Groups,Direct,0,0,0,A,C,0,No Deposit,,,0,Transient-Party,55.0,0,0 +Resort Hotel,208,2016,August,32,2,4,12,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,E,E,0,No Deposit,156.0,,0,Transient,141.1,0,0 +City Hotel,349,2016,October,41,4,1,5,2,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,58.0,,162,Transient-Party,123.33,0,0 +City Hotel,65,2016,December,52,21,0,4,3,0.0,0,HB,FRA,Offline TA/TO,TA/TO,0,0,0,A,D,1,No Deposit,22.0,,0,Transient,122.65,0,0 +City Hotel,1,2015,August,33,11,0,1,1,0.0,0,BB,GRC,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,38.0,0,Transient-Party,70.0,0,0 +Resort Hotel,131,2017,April,16,16,2,5,2,0.0,0,HB,CHE,Groups,TA/TO,0,0,0,A,A,0,No Deposit,493.0,,0,Contract,80.0,0,1 +Resort Hotel,195,2016,September,39,22,2,5,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,243.0,,0,Contract,53.85,0,0 +City Hotel,115,2016,February,9,27,2,2,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,1,No Deposit,134.0,,0,Transient-Party,76.0,0,0 +Resort Hotel,22,2017,July,31,31,1,4,2,0.0,0,BB,SWE,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,225.0,0,2 +Resort Hotel,218,2017,May,19,8,2,5,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,82.0,1,1 +City Hotel,51,2016,March,13,21,1,3,2,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,B,B,0,No Deposit,9.0,,0,Transient-Party,83.3,0,1 +Resort Hotel,6,2016,March,13,23,0,2,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,240.0,,0,Transient,66.5,1,1 +City Hotel,34,2015,September,38,13,2,4,2,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Contract,94.95,0,1 +City Hotel,48,2017,April,17,26,0,3,2,1.0,0,BB,IND,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient-Party,144.0,0,2 +Resort Hotel,262,2015,September,39,21,1,3,2,0.0,0,HB,BEL,Groups,Corporate,0,0,0,A,A,0,No Deposit,183.0,,0,Transient-Party,91.2,0,0 +City Hotel,180,2016,May,19,2,1,1,2,0.0,0,BB,ITA,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,3.0,,0,Transient-Party,100.0,0,1 +City Hotel,386,2016,October,44,23,2,0,2,0.0,0,HB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,115.0,0,1 +City Hotel,4,2017,July,29,17,1,1,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,19,A,C,0,No Deposit,,40.0,0,Transient,65.0,0,2 +City Hotel,46,2016,June,25,17,0,2,2,0.0,0,BB,PRT,Complementary,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,0.0,0,0 +City Hotel,0,2017,February,6,5,2,0,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,2,No Deposit,,,0,Transient,79.0,0,0 +City Hotel,168,2015,September,37,11,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,0,Transient-Party,60.0,0,0 +City Hotel,212,2016,May,21,20,2,3,2,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,21.0,,0,Transient-Party,78.0,0,0 +Resort Hotel,92,2017,April,13,1,1,1,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,C,0,No Deposit,240.0,,0,Transient,48.0,0,1 +City Hotel,35,2015,October,43,20,0,4,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Contract,96.3,0,2 +Resort Hotel,360,2016,August,34,18,4,10,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,40.0,,0,Contract,59.8,0,0 +City Hotel,54,2017,February,9,28,0,4,3,0.0,0,BB,JPN,Online TA,TA/TO,0,0,0,E,E,1,No Deposit,9.0,,0,Transient,116.88,0,0 +Resort Hotel,218,2017,August,34,22,1,5,2,0.0,0,HB,IRL,Direct,Direct,0,0,0,A,A,1,No Deposit,250.0,,0,Transient,170.38,0,1 +City Hotel,4,2016,July,29,11,1,0,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,151.0,0,2 +Resort Hotel,30,2016,October,42,15,2,2,2,0.0,0,BB,ESP,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,6.0,,0,Transient,42.0,1,0 +Resort Hotel,55,2015,September,40,29,2,5,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,69.0,,0,Contract,44.5,0,1 +City Hotel,9,2016,August,36,29,1,4,1,0.0,0,BB,NLD,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,128.4,0,2 +City Hotel,66,2016,April,17,19,0,4,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,112.2,0,0 +City Hotel,288,2016,April,17,21,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0 diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_004/history.csv b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_004/history.csv index b0a010d708c54817d343e9f562af1f21aac1b001..d57faea698700c12e7ff0fb6caf36a7d858f86b3 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_004/history.csv +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_004/history.csv @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:ce022d55149597d83ba49a74f887c9063633a177428691058aad8f5e827b5f60 -size 16812723 +oid sha256:8e3b33e87ff7d6ddc87407836b414833938941c617297952b07272caa1972a7a +size 15266648 diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_004/info.json b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_004/info.json index 4816c05993309461836de8622438e342f2e503ff..20956c93d07801fe3772275e08832605a78593ed 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_004/info.json +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_004/info.json @@ -1,398 +1,391 @@ -{ - "name": "Hotel Booking Demand", - "source": "https://www.kaggle.com/datasets/jessemostipak/hotel-booking-demand/data", - "data_intro": "This dataset contains hotel booking data for a city hotel and a resort hotel, providing details on booking dates, stay duration, guest count, cancellation trends, and pricing. It is useful for demand forecasting, revenue management, and customer behavior analysis.", - "is_splited": false, - "overall_size": 119390, - "train_size": 0, - "test_size": 0, - "c_classes": 12, - "n_classes": 20, - "cat_feature_intro": { - "hotel": "Type of hotel (City Hotel or Resort Hotel).", - "arrival_date_month": "Month of arrival for the booking.", - "meal": "Type of meal plan booked (e.g., Breakfast included, Half board, Full board).", - "country": "Country of origin of the guest.", - "market_segment": "Booking distribution channel (e.g., direct, corporate, travel agent).", - "distribution_channel": "Channel through which the booking was made (e.g., online travel agents, direct).", - "reserved_room_type": "Type of room reserved by the guest before any changes.", - "assigned_room_type": "Final room type assigned to the guest.", - "deposit_type": "Indicates if a deposit was made and its type (e.g., No Deposit, Refundable, Non-Refundable).", - "customer_type": "Type of customer (e.g., transient, group, contract).", - "reservation_status": "Final status of the booking (e.g., Canceled, Checked-out, No-show).", - "reservation_status_date": "Date when the reservation status was last updated." - }, - "num_feature_intro": { - "is_canceled": "Indicates whether the booking was canceled (1) or not (0).", - "lead_time": "Number of days between booking and arrival.", - "arrival_date_year": "Year of arrival for the booking.", - "arrival_date_week_number": "Week number of the arrival date.", - "arrival_date_day_of_month": "Day of the month of arrival.", - "stays_in_weekend_nights": "Number of weekend nights (Saturday or Sunday) the guest stayed.", - "stays_in_week_nights": "Number of weekday nights (Monday to Friday) the guest stayed.", - "adults": "Number of adults included in the booking.", - "children": "Number of children included in the booking.", - "babies": "Number of babies included in the booking.", - "is_repeated_guest": "Indicates whether the guest is a returning customer (1) or not (0).", - "previous_cancellations": "Number of times the guest has canceled previous bookings.", - "previous_bookings_not_canceled": "Number of previous bookings not canceled by the guest.", - "booking_changes": "Number of changes made to the booking.", - "agent": "ID of the booking agent (if applicable).", - "company": "ID of the company making the booking (if applicable).", - "days_in_waiting_list": "Number of days the booking was on the waiting list before confirmation.", - "adr": "Average daily rate (price per night per room).", - "required_car_parking_spaces": "Number of car parking spaces required by the guest.", - "total_of_special_requests": "Number of special requests made by the guest (e.g., high floor, twin beds)." - }, - "evaluation_metric": null, - "task_type": "classification", - "target": "reservation_status", - "num_feature_value": { - "adr": [ - -6.38, - 5400.0 - ], - "adults": [ - 0.0, - 55.0 - ], - "agent": [ - 1.0, - 535.0 - ], - "arrival_date_day_of_month": [ - 1.0, - 31.0 - ], - "arrival_date_week_number": [ - 1.0, - 53.0 - ], - "arrival_date_year": [ - 2015.0, - 2017.0 - ], - "babies": [ - 0.0, - 10.0 - ], - "booking_changes": [ - 0.0, - 21.0 - ], - "children": [ - 0.0, - 10.0 - ], - "company": [ - 6.0, - 543.0 - ], - "days_in_waiting_list": [ - 0.0, - 391.0 - ], - "is_canceled": [ - 0.0, - 1.0 - ], - "is_repeated_guest": [ - 0.0, - 1.0 - ], - "lead_time": [ - 0.0, - 737.0 - ], - "previous_bookings_not_canceled": [ - 0.0, - 72.0 - ], - "previous_cancellations": [ - 0.0, - 26.0 - ], - "required_car_parking_spaces": [ - 0.0, - 8.0 - ], - "stays_in_week_nights": [ - 0.0, - 50.0 - ], - "stays_in_weekend_nights": [ - 0.0, - 19.0 - ], - "total_of_special_requests": [ - 0.0, - 5.0 - ] - }, - "cat_feature_value": { - "arrival_date_month": [ - "April", - "August", - "December", - "February", - "January", - "July", - "June", - "March", - "May", - "November", - "October", - "September" - ], - "assigned_room_type": [ - "A", - "B", - "C", - "D", - "E", - "F", - "G", - "H", - "I", - "K", - "L", - "P" - ], - "country": [ - "ABW", - "AGO", - "AIA", - "ALB", - "AND", - "ARE", - "ARG", - "ARM", - "ASM", - "ATA", - "ATF", - "AUS", - "AUT", - "AZE", - "BDI", - "BEL", - "BEN", - "BFA", - "BGD", - "BGR", - "BHR", - "BHS", - "BIH", - "BLR", - "BOL", - "BRA", - "BRB", - "BWA", - "CAF", - "CHE", - "CHL", - "CHN", - "CIV", - "CMR", - "CN", - "COL", - "COM", - "CPV", - "CRI", - "CUB", - "CYM", - "CYP", - "CZE", - "DEU", - "DJI", - "DMA", - "DNK", - "DOM", - "DZA", - "ECU", - "EGY", - "ESP", - "EST", - "ETH", - "FIN", - "FJI", - "FRA", - "FRO", - "GAB", - "GBR", - "GEO", - "GGY", - "GHA", - "GIB", - "GLP", - "GNB", - "GRC", - "GTM", - "GUY", - "HKG", - "HND", - "HRV", - "HUN", - "IDN", - "IMN", - "IND", - "IRL", - "IRN", - "IRQ", - "ISL", - "ISR", - "ITA", - "JAM", - "JEY", - "JOR", - "JPN", - "KAZ", - "KEN", - "KHM", - "KIR", - "KNA", - "KOR", - "KWT", - "LAO", - "LBN", - "LBY", - "LCA", - "LIE", - "LKA", - "LTU", - "LUX", - "LVA", - "MAC", - "MAR", - "MCO", - "MDG", - "MDV", - "MEX", - "MKD", - "MLI", - "MLT", - "MMR", - "MNE", - "MOZ", - "MRT", - "MUS", - "MWI", - "MYS", - "MYT", - "NAM", - "NCL", - "NGA", - "NIC", - "NLD", - "NOR", - "NPL", - "NZL", - "OMN", - "PAK", - "PAN", - "PER", - "PHL", - "PLW", - "POL", - "PRI", - "PRT", - "PRY", - "PYF", - "QAT", - "ROU", - "RUS", - "RWA", - "SAU", - "SDN", - "SEN", - "SGP", - "SLE", - "SLV", - "SMR", - "SRB", - "STP", - "SUR", - "SVK", - "SVN", - "SWE", - "SYC", - "SYR", - "TGO", - "THA", - "TJK", - "TMP", - "TUN", - "TUR", - "TWN", - "TZA", - "UGA", - "UKR", - "UMI", - "URY", - "USA", - "UZB", - "VEN", - "VGB", - "VNM", - "ZAF", - "ZMB", - "ZWE" - ], - "customer_type": [ - "Contract", - "Group", - "Transient", - "Transient-Party" - ], - "deposit_type": [ - "No Deposit", - "Non Refund", - "Refundable" - ], - "distribution_channel": [ - "Corporate", - "Direct", - "GDS", - "TA/TO", - "Undefined" - ], - "hotel": [ - "City Hotel", - "Resort Hotel" - ], - "market_segment": [ - "Aviation", - "Complementary", - "Corporate", - "Direct", - "Groups", - "Offline TA/TO", - "Online TA", - "Undefined" - ], - "meal": [ - "BB", - "FB", - "HB", - "SC", - "Undefined" - ], - "reservation_status": [ - "Canceled", - "Check-Out", - "No-Show" - ], - "reservation_status_date": "string", - "reserved_room_type": [ - "A", - "B", - "C", - "D", - "E", - "F", - "G", - "H", - "L", - "P" - ] - } -} \ No newline at end of file +{ + "name": "Hotel Booking Demand", + "source": "https://www.kaggle.com/datasets/jessemostipak/hotel-booking-demand/data", + "data_intro": "This dataset contains hotel booking data for a city hotel and a resort hotel, providing details on booking dates, stay duration, guest count, cancellation trends, and pricing. It is useful for demand forecasting, revenue management, and customer behavior analysis.", + "is_splited": false, + "overall_size": 119390, + "train_size": 0, + "test_size": 0, + "c_classes": 11, + "n_classes": 19, + "cat_feature_intro": { + "hotel": "Type of hotel (City Hotel or Resort Hotel).", + "arrival_date_month": "Month of arrival for the booking.", + "meal": "Type of meal plan booked (e.g., Breakfast included, Half board, Full board).", + "country": "Country of origin of the guest.", + "market_segment": "Booking distribution channel (e.g., direct, corporate, travel agent).", + "distribution_channel": "Channel through which the booking was made (e.g., online travel agents, direct).", + "reserved_room_type": "Type of room reserved by the guest before any changes.", + "assigned_room_type": "Final room type assigned to the guest.", + "deposit_type": "Indicates if a deposit was made and its type (e.g., No Deposit, Refundable, Non-Refundable).", + "customer_type": "Type of customer (e.g., transient, group, contract).", + "reservation_status": "Final status of the booking (e.g., Canceled, Checked-out, No-show)." + }, + "num_feature_intro": { + "lead_time": "Number of days between booking and arrival.", + "arrival_date_year": "Year of arrival for the booking.", + "arrival_date_week_number": "Week number of the arrival date.", + "arrival_date_day_of_month": "Day of the month of arrival.", + "stays_in_weekend_nights": "Number of weekend nights (Saturday or Sunday) the guest stayed.", + "stays_in_week_nights": "Number of weekday nights (Monday to Friday) the guest stayed.", + "adults": "Number of adults included in the booking.", + "children": "Number of children included in the booking.", + "babies": "Number of babies included in the booking.", + "is_repeated_guest": "Indicates whether the guest is a returning customer (1) or not (0).", + "previous_cancellations": "Number of times the guest has canceled previous bookings.", + "previous_bookings_not_canceled": "Number of previous bookings not canceled by the guest.", + "booking_changes": "Number of changes made to the booking.", + "agent": "ID of the booking agent (if applicable).", + "company": "ID of the company making the booking (if applicable).", + "days_in_waiting_list": "Number of days the booking was on the waiting list before confirmation.", + "adr": "Average daily rate (price per night per room).", + "required_car_parking_spaces": "Number of car parking spaces required by the guest.", + "total_of_special_requests": "Number of special requests made by the guest (e.g., high floor, twin beds)." + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "reservation_status", + "num_feature_value": { + "adr": [ + -6.38, + 5400.0 + ], + "adults": [ + 0.0, + 55.0 + ], + "agent": [ + 1.0, + 535.0 + ], + "arrival_date_day_of_month": [ + 1.0, + 31.0 + ], + "arrival_date_week_number": [ + 1.0, + 53.0 + ], + "arrival_date_year": [ + 2015.0, + 2017.0 + ], + "babies": [ + 0.0, + 10.0 + ], + "booking_changes": [ + 0.0, + 21.0 + ], + "children": [ + 0.0, + 10.0 + ], + "company": [ + 6.0, + 543.0 + ], + "days_in_waiting_list": [ + 0.0, + 391.0 + ], + "is_repeated_guest": [ + 0.0, + 1.0 + ], + "lead_time": [ + 0.0, + 737.0 + ], + "previous_bookings_not_canceled": [ + 0.0, + 72.0 + ], + "previous_cancellations": [ + 0.0, + 26.0 + ], + "required_car_parking_spaces": [ + 0.0, + 8.0 + ], + "stays_in_week_nights": [ + 0.0, + 50.0 + ], + "stays_in_weekend_nights": [ + 0.0, + 19.0 + ], + "total_of_special_requests": [ + 0.0, + 5.0 + ] + }, + "cat_feature_value": { + "arrival_date_month": [ + "April", + "August", + "December", + "February", + "January", + "July", + "June", + "March", + "May", + "November", + "October", + "September" + ], + "assigned_room_type": [ + "A", + "B", + "C", + "D", + "E", + "F", + "G", + "H", + "I", + "K", + "L", + "P" + ], + "country": [ + "ABW", + "AGO", + "AIA", + "ALB", + "AND", + "ARE", + "ARG", + "ARM", + "ASM", + "ATA", + "ATF", + "AUS", + "AUT", + "AZE", + "BDI", + "BEL", + "BEN", + "BFA", + "BGD", + "BGR", + "BHR", + "BHS", + "BIH", + "BLR", + "BOL", + "BRA", + "BRB", + "BWA", + "CAF", + "CHE", + "CHL", + "CHN", + "CIV", + "CMR", + "CN", + "COL", + "COM", + "CPV", + "CRI", + "CUB", + "CYM", + "CYP", + "CZE", + "DEU", + "DJI", + "DMA", + "DNK", + "DOM", + "DZA", + "ECU", + "EGY", + "ESP", + "EST", + "ETH", + "FIN", + "FJI", + "FRA", + "FRO", + "GAB", + "GBR", + "GEO", + "GGY", + "GHA", + "GIB", + "GLP", + "GNB", + "GRC", + "GTM", + "GUY", + "HKG", + "HND", + "HRV", + "HUN", + "IDN", + "IMN", + "IND", + "IRL", + "IRN", + "IRQ", + "ISL", + "ISR", + "ITA", + "JAM", + "JEY", + "JOR", + "JPN", + "KAZ", + "KEN", + "KHM", + "KIR", + "KNA", + "KOR", + "KWT", + "LAO", + "LBN", + "LBY", + "LCA", + "LIE", + "LKA", + "LTU", + "LUX", + "LVA", + "MAC", + "MAR", + "MCO", + "MDG", + "MDV", + "MEX", + "MKD", + "MLI", + "MLT", + "MMR", + "MNE", + "MOZ", + "MRT", + "MUS", + "MWI", + "MYS", + "MYT", + "NAM", + "NCL", + "NGA", + "NIC", + "NLD", + "NOR", + "NPL", + "NZL", + "OMN", + "PAK", + "PAN", + "PER", + "PHL", + "PLW", + "POL", + "PRI", + "PRT", + "PRY", + "PYF", + "QAT", + "ROU", + "RUS", + "RWA", + "SAU", + "SDN", + "SEN", + "SGP", + "SLE", + "SLV", + "SMR", + "SRB", + "STP", + "SUR", + "SVK", + "SVN", + "SWE", + "SYC", + "SYR", + "TGO", + "THA", + "TJK", + "TMP", + "TUN", + "TUR", + "TWN", + "TZA", + "UGA", + "UKR", + "UMI", + "URY", + "USA", + "UZB", + "VEN", + "VGB", + "VNM", + "ZAF", + "ZMB", + "ZWE" + ], + "customer_type": [ + "Contract", + "Group", + "Transient", + "Transient-Party" + ], + "deposit_type": [ + "No Deposit", + "Non Refund", + "Refundable" + ], + "distribution_channel": [ + "Corporate", + "Direct", + "GDS", + "TA/TO", + "Undefined" + ], + "hotel": [ + "City Hotel", + "Resort Hotel" + ], + "market_segment": [ + "Aviation", + "Complementary", + "Corporate", + "Direct", + "Groups", + "Offline TA/TO", + "Online TA", + "Undefined" + ], + "meal": [ + "BB", + "FB", + "HB", + "SC", + "Undefined" + ], + "reservation_status": [ + "Canceled", + "Check-Out", + "No-Show" + ], + "reserved_room_type": [ + "A", + "B", + "C", + "D", + "E", + "F", + "G", + "H", + "L", + "P" + ] + } +} diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_004/test_current.csv b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_004/test_current.csv index f299328cc7d3dd922051f698521de1d23aea6b85..5cab1b4e1984f8fa935d21e5b54596cf331ab27b 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_004/test_current.csv +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_004/test_current.csv @@ -1,16 +1,16 @@ -hotel,is_canceled,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests,reservation_status,reservation_status_date -City Hotel,1,23,2017,August,35,30,0,2,2,2.0,0,BB,IDN,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,9.0,,0,Transient,250.0,0,0,Canceled,2017-08-08 -City Hotel,1,69,2016,June,25,12,2,4,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,236.0,,0,Transient,120.0,0,0,Canceled,2016-04-27 -City Hotel,1,74,2015,September,38,18,0,2,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,6.0,,0,Transient-Party,101.5,0,0,Canceled,2015-07-06 -City Hotel,1,258,2015,July,27,2,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,1,2,0,A,A,0,No Deposit,1.0,,0,Contract,62.0,0,0,Canceled,2015-01-01 -Resort Hotel,1,92,2016,July,29,16,4,6,2,0.0,0,HB,PRT,Direct,Direct,0,1,1,E,E,0,No Deposit,,,0,Transient,195.0,0,0,Canceled,2016-06-15 -Resort Hotel,1,103,2017,March,13,27,1,1,2,0.0,0,BB,PRT,Groups,Corporate,0,0,0,D,D,0,Non Refund,495.0,,0,Transient,65.0,0,0,Canceled,2016-12-23 -City Hotel,1,76,2017,July,27,2,1,0,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,403.0,,0,Transient,80.1,0,0,Canceled,2017-04-17 -Resort Hotel,1,25,2017,February,8,24,2,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient,34.0,0,0,Canceled,2017-02-14 -City Hotel,1,67,2016,October,43,19,0,4,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,7.0,,0,Transient,117.6,0,0,Canceled,2016-08-13 -City Hotel,1,156,2017,April,17,26,0,3,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,37.0,,0,Transient,100.0,0,0,Canceled,2016-11-21 -Resort Hotel,1,58,2016,March,13,24,0,3,2,2.0,0,BB,PRT,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,240.0,,0,Transient,115.0,0,0,Canceled,2016-01-27 -City Hotel,1,203,2016,December,53,30,2,2,3,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,D,D,2,No Deposit,9.0,,0,Transient,120.7,0,0,Canceled,2016-08-12 -City Hotel,1,97,2016,March,11,12,1,1,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,78.3,0,0,Canceled,2016-02-22 -Resort Hotel,1,208,2016,August,32,2,4,12,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,E,E,0,No Deposit,156.0,,0,Transient,141.1,0,0,Canceled,2016-01-11 -City Hotel,1,288,2016,April,17,21,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0,Canceled,2015-10-21 +hotel,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests,reservation_status +City Hotel,23,2017,August,35,30,0,2,2,2.0,0,BB,IDN,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,9.0,,0,Transient,250.0,0,0,Canceled +City Hotel,69,2016,June,25,12,2,4,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,236.0,,0,Transient,120.0,0,0,Canceled +City Hotel,74,2015,September,38,18,0,2,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,6.0,,0,Transient-Party,101.5,0,0,Canceled +City Hotel,258,2015,July,27,2,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,1,2,0,A,A,0,No Deposit,1.0,,0,Contract,62.0,0,0,Canceled +Resort Hotel,92,2016,July,29,16,4,6,2,0.0,0,HB,PRT,Direct,Direct,0,1,1,E,E,0,No Deposit,,,0,Transient,195.0,0,0,Canceled +Resort Hotel,103,2017,March,13,27,1,1,2,0.0,0,BB,PRT,Groups,Corporate,0,0,0,D,D,0,Non Refund,495.0,,0,Transient,65.0,0,0,Canceled +City Hotel,76,2017,July,27,2,1,0,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,403.0,,0,Transient,80.1,0,0,Canceled +Resort Hotel,25,2017,February,8,24,2,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient,34.0,0,0,Canceled +City Hotel,67,2016,October,43,19,0,4,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,7.0,,0,Transient,117.6,0,0,Canceled +City Hotel,156,2017,April,17,26,0,3,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,37.0,,0,Transient,100.0,0,0,Canceled +Resort Hotel,58,2016,March,13,24,0,3,2,2.0,0,BB,PRT,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,240.0,,0,Transient,115.0,0,0,Canceled +City Hotel,203,2016,December,53,30,2,2,3,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,D,D,2,No Deposit,9.0,,0,Transient,120.7,0,0,Canceled +City Hotel,97,2016,March,11,12,1,1,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,78.3,0,0,Canceled +Resort Hotel,208,2016,August,32,2,4,12,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,E,E,0,No Deposit,156.0,,0,Transient,141.1,0,0,Canceled +City Hotel,288,2016,April,17,21,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0,Canceled diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_005/Hotel_booking_demand_B4_005_current.json b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_005/Hotel_booking_demand_B4_005_current.json index d5c405645ca4c9b05bceba22bd711cec8641b6f7..23c6c74650247efaba99bc0689423b553010d7f0 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_005/Hotel_booking_demand_B4_005_current.json +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_005/Hotel_booking_demand_B4_005_current.json @@ -1,7375 +1,6979 @@ { - "id": "005", - "task_type": "B4", - "subtask_type": "filter", - "perspective": "data_holder", - "dataset_name": "Hotel booking demand", - "table_path": "output/daily/classification/Hotel booking demand_B4/Hotel booking demand_B4_005/test.csv", - "query": "We are planning a special promotion to try and win back some of our corporate clients who had bookings that didn't materialize. From our booking history I've provided, can you shortlist all the bookings that were made through the 'Corporate' market segment in the current data? Within that group, I need to identify the ones that ultimately resulted in a 'No-Show', as these are the relationships we should prioritize mending.", - "meta_info": { - "domain": "daily" + "id": "005", + "task_type": "B4", + "subtask_type": "filter", + "perspective": "data_holder", + "dataset_name": "Hotel booking demand", + "table_path": "output/daily/classification/Hotel booking demand_B4/Hotel booking demand_B4_005/test.csv", + "query": "We are planning a special promotion to try and win back some of our corporate clients who had bookings that didn't materialize. From our booking history I've provided, can you shortlist all the bookings that were made through the 'Corporate' market segment in the current data? Within that group, I need to identify the ones that ultimately resulted in a 'No-Show', as these are the relationships we should prioritize mending.", + "meta_info": { + "domain": "daily" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "006", + "features": { + "hotel": "City Hotel", + "lead_time": 0, + "arrival_date_year": 2015, + "arrival_date_month": "September", + "arrival_date_week_number": 37, + "arrival_date_day_of_month": 9, + "stays_in_weekend_nights": 0, + "stays_in_week_nights": 0, + "adults": 1, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "PRT", + "market_segment": "Corporate", + "distribution_channel": "Corporate", + "is_repeated_guest": 1, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": null, + "company": 40.0, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 0.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "007", + "features": { + "hotel": "Resort Hotel", + "lead_time": 130, + "arrival_date_year": 2015, + "arrival_date_month": "August", + "arrival_date_week_number": 32, + "arrival_date_day_of_month": 8, + "stays_in_weekend_nights": 2, + "stays_in_week_nights": 5, + "adults": 2, + "children": 0.0, + "babies": 1, + "meal": "BB", + "country": "PRT", + "market_segment": "Direct", + "distribution_channel": "Direct", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 250.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 113.9, + "required_car_parking_spaces": 0, + "total_of_special_requests": 1, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "009", + "features": { + "hotel": "Resort Hotel", + "lead_time": 9, + "arrival_date_year": 2017, + "arrival_date_month": "April", + "arrival_date_week_number": 16, + "arrival_date_day_of_month": 19, + "stays_in_weekend_nights": 0, + "stays_in_week_nights": 1, + "adults": 1, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "PRT", + "market_segment": "Direct", + "distribution_channel": "Direct", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 1, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": null, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 83.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "012", + "features": { + "hotel": "City Hotel", + "lead_time": 193, + "arrival_date_year": 2016, + "arrival_date_month": "July", + "arrival_date_week_number": 28, + "arrival_date_day_of_month": 4, + "stays_in_weekend_nights": 3, + "stays_in_week_nights": 10, + "adults": 0, + "children": 2.0, + "babies": 0, + "meal": "BB", + "country": "DEU", + "market_segment": "Online TA", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "B", + "assigned_room_type": "B", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 9.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 81.82, + "required_car_parking_spaces": 0, + "total_of_special_requests": 2, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "013", + "features": { + "hotel": "City Hotel", + "lead_time": 69, + "arrival_date_year": 2016, + "arrival_date_month": "March", + "arrival_date_week_number": 12, + "arrival_date_day_of_month": 16, + "stays_in_weekend_nights": 1, + "stays_in_week_nights": 4, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "PRT", + "market_segment": "Online TA", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 6, + "deposit_type": "No Deposit", + "agent": 8.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 79.05, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Canceled" + } + }, + { + "scenario_id": "014", + "features": { + "hotel": "City Hotel", + "lead_time": 51, + "arrival_date_year": 2016, + "arrival_date_month": "August", + "arrival_date_week_number": 36, + "arrival_date_day_of_month": 31, + "stays_in_weekend_nights": 1, + "stays_in_week_nights": 4, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "GBR", + "market_segment": "Online TA", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "D", + "assigned_room_type": "D", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 7.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 90.2, + "required_car_parking_spaces": 0, + "total_of_special_requests": 1, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "016", + "features": { + "hotel": "City Hotel", + "lead_time": 179, + "arrival_date_year": 2015, + "arrival_date_month": "August", + "arrival_date_week_number": 34, + "arrival_date_day_of_month": 22, + "stays_in_weekend_nights": 2, + "stays_in_week_nights": 1, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "HB", + "country": "PRT", + "market_segment": "Groups", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 1, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 0, + "deposit_type": "Non Refund", + "agent": 1.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient-Party", + "adr": 86.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Canceled" + } + }, + { + "scenario_id": "017", + "features": { + "hotel": "Resort Hotel", + "lead_time": 64, + "arrival_date_year": 2017, + "arrival_date_month": "August", + "arrival_date_week_number": 35, + "arrival_date_day_of_month": 31, + "stays_in_weekend_nights": 2, + "stays_in_week_nights": 3, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "GBR", + "market_segment": "Online TA", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "F", + "assigned_room_type": "F", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 241.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 187.19, + "required_car_parking_spaces": 0, + "total_of_special_requests": 1, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "019", + "features": { + "hotel": "City Hotel", + "lead_time": 181, + "arrival_date_year": 2017, + "arrival_date_month": "July", + "arrival_date_week_number": 27, + "arrival_date_day_of_month": 7, + "stays_in_weekend_nights": 2, + "stays_in_week_nights": 3, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "GBR", + "market_segment": "Direct", + "distribution_channel": "Direct", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 2, + "deposit_type": "No Deposit", + "agent": 14.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 86.63, + "required_car_parking_spaces": 0, + "total_of_special_requests": 2, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "020", + "features": { + "hotel": "Resort Hotel", + "lead_time": 150, + "arrival_date_year": 2017, + "arrival_date_month": "March", + "arrival_date_week_number": 12, + "arrival_date_day_of_month": 24, + "stays_in_weekend_nights": 2, + "stays_in_week_nights": 2, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "ESP", + "market_segment": "Offline TA/TO", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "D", + "assigned_room_type": "E", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 6.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 37.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "022", + "features": { + "hotel": "City Hotel", + "lead_time": 62, + "arrival_date_year": 2015, + "arrival_date_month": "September", + "arrival_date_week_number": 39, + "arrival_date_day_of_month": 20, + "stays_in_weekend_nights": 2, + "stays_in_week_nights": 3, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "ESP", + "market_segment": "Online TA", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 7.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 68.72, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "025", + "features": { + "hotel": "City Hotel", + "lead_time": 12, + "arrival_date_year": 2017, + "arrival_date_month": "July", + "arrival_date_week_number": 30, + "arrival_date_day_of_month": 24, + "stays_in_weekend_nights": 3, + "stays_in_week_nights": 5, + "adults": 1, + "children": 0.0, + "babies": 0, + "meal": "SC", + "country": "RUS", + "market_segment": "Online TA", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 2, + "deposit_type": "No Deposit", + "agent": 9.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 141.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 1, + "reservation_status": "Canceled" + } + }, + { + "scenario_id": "028", + "features": { + "hotel": "City Hotel", + "lead_time": 78, + "arrival_date_year": 2017, + "arrival_date_month": "July", + "arrival_date_week_number": 28, + "arrival_date_day_of_month": 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"stays_in_week_nights": 2, - "adults": 2, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "NLD", - "market_segment": "Online TA", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "D", - "assigned_room_type": "D", - "booking_changes": 0, - "deposit_type": "No Deposit", - "agent": 85.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 98.1, - "required_car_parking_spaces": 1, - "total_of_special_requests": 0, - "reservation_status": "Check-Out", - "reservation_status_date": "2017-08-18" - } - }, - { - "scenario_id": "476", - "features": { - "hotel": "Resort Hotel", - "is_canceled": 0, - "lead_time": 2, - "arrival_date_year": 2016, - "arrival_date_month": "February", - "arrival_date_week_number": 6, - "arrival_date_day_of_month": 3, - "stays_in_weekend_nights": 2, - "stays_in_week_nights": 5, - "adults": 1, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "GBR", - "market_segment": "Online TA", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "D", - "assigned_room_type": "D", - "booking_changes": 0, - "deposit_type": "No Deposit", - "agent": 240.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 49.0, - "required_car_parking_spaces": 0, - "total_of_special_requests": 1, - "reservation_status": "Check-Out", - "reservation_status_date": "2016-02-10" - } - }, - { - "scenario_id": "479", - "features": { - "hotel": "City Hotel", - "is_canceled": 0, - "lead_time": 58, - "arrival_date_year": 2015, - "arrival_date_month": "November", - "arrival_date_week_number": 47, - "arrival_date_day_of_month": 18, - "stays_in_weekend_nights": 0, - "stays_in_week_nights": 4, - "adults": 2, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "FRA", - "market_segment": "Offline TA/TO", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "D", - "assigned_room_type": "D", - "booking_changes": 0, - "deposit_type": "No Deposit", - "agent": 28.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 60.0, - "required_car_parking_spaces": 0, - "total_of_special_requests": 0, - "reservation_status": "Check-Out", - "reservation_status_date": "2015-11-22" - } - }, - { - "scenario_id": "480", - "features": { - "hotel": "City Hotel", - "is_canceled": 1, - "lead_time": 56, - "arrival_date_year": 2017, - "arrival_date_month": "March", - "arrival_date_week_number": 10, - "arrival_date_day_of_month": 10, - "stays_in_weekend_nights": 1, - "stays_in_week_nights": 2, - "adults": 2, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "NLD", - "market_segment": "Online TA", - "distribution_channel": 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- "previous_bookings_not_canceled": 0, - "reserved_room_type": "A", - "assigned_room_type": "D", - "booking_changes": 0, - "deposit_type": "No Deposit", - "agent": 335.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient-Party", - "adr": 75.57, - "required_car_parking_spaces": 0, - "total_of_special_requests": 1, - "reservation_status": "Check-Out", - "reservation_status_date": "2015-12-29" - } - }, - { - "scenario_id": "485", - "features": { - "hotel": "Resort Hotel", - "is_canceled": 0, - "lead_time": 5, - "arrival_date_year": 2015, - "arrival_date_month": "October", - "arrival_date_week_number": 41, - "arrival_date_day_of_month": 10, - "stays_in_weekend_nights": 1, - "stays_in_week_nights": 1, - "adults": 2, - "children": 0.0, - "babies": 0, - "meal": "HB", - "country": "ESP", - "market_segment": "Online TA", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "D", - "assigned_room_type": "D", - "booking_changes": 1, - "deposit_type": "No Deposit", - "agent": 240.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 101.5, - "required_car_parking_spaces": 0, - "total_of_special_requests": 2, - "reservation_status": "Check-Out", - "reservation_status_date": "2015-10-12" - } - }, - { - "scenario_id": "486", - "features": { - "hotel": "City Hotel", - "is_canceled": 0, - "lead_time": 120, - "arrival_date_year": 2016, - "arrival_date_month": "May", - "arrival_date_week_number": 19, - "arrival_date_day_of_month": 2, - "stays_in_weekend_nights": 1, - "stays_in_week_nights": 1, - "adults": 2, - "children": 0.0, - "babies": 0, - "meal": "SC", - "country": "FRA", - "market_segment": "Online TA", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "A", - "assigned_room_type": "A", - "booking_changes": 0, - "deposit_type": "No Deposit", - "agent": 9.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient-Party", - "adr": 94.5, - "required_car_parking_spaces": 0, - "total_of_special_requests": 1, - "reservation_status": "Check-Out", - "reservation_status_date": "2016-05-04" - } - }, - { - "scenario_id": "488", - "features": { - "hotel": "Resort Hotel", - "is_canceled": 1, - "lead_time": 54, - "arrival_date_year": 2017, - "arrival_date_month": "May", - "arrival_date_week_number": 19, - "arrival_date_day_of_month": 9, - "stays_in_weekend_nights": 0, - "stays_in_week_nights": 3, - "adults": 2, - "children": 2.0, - "babies": 0, - "meal": "BB", - "country": "GBR", - "market_segment": "Online TA", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "G", - "assigned_room_type": "G", - "booking_changes": 0, - "deposit_type": "No Deposit", - "agent": 240.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 165.0, - "required_car_parking_spaces": 0, - "total_of_special_requests": 0, - "reservation_status": "Canceled", - "reservation_status_date": "2017-04-18" - } - }, - { - "scenario_id": "489", - "features": { - "hotel": "City Hotel", - "is_canceled": 1, - "lead_time": 328, - "arrival_date_year": 2017, - "arrival_date_month": "January", - "arrival_date_week_number": 1, - "arrival_date_day_of_month": 3, - "stays_in_weekend_nights": 2, - "stays_in_week_nights": 5, - "adults": 3, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "BRA", - "market_segment": "Online TA", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "D", - "assigned_room_type": "D", - "booking_changes": 0, - "deposit_type": "No Deposit", - "agent": 9.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 107.95, - "required_car_parking_spaces": 0, - "total_of_special_requests": 1, - "reservation_status": "Canceled", - "reservation_status_date": "2016-12-13" - } - }, - { - "scenario_id": "492", - "features": { - "hotel": "Resort Hotel", - "is_canceled": 0, - "lead_time": 152, - "arrival_date_year": 2016, - "arrival_date_month": "June", - "arrival_date_week_number": 25, - "arrival_date_day_of_month": 14, - "stays_in_weekend_nights": 2, - "stays_in_week_nights": 5, - "adults": 2, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "PRT", - "market_segment": "Direct", - "distribution_channel": "Direct", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "D", - "assigned_room_type": "E", - "booking_changes": 0, - "deposit_type": "No Deposit", - "agent": 250.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 100.6, - "required_car_parking_spaces": 0, - "total_of_special_requests": 1, - "reservation_status": "Check-Out", - "reservation_status_date": "2016-06-21" - } - }, - { - "scenario_id": "493", - "features": { - "hotel": "City Hotel", - "is_canceled": 0, - "lead_time": 100, - "arrival_date_year": 2016, - "arrival_date_month": "September", - "arrival_date_week_number": 40, - "arrival_date_day_of_month": 28, - "stays_in_weekend_nights": 0, - "stays_in_week_nights": 4, - "adults": 1, - "children": 0.0, - "babies": 0, - "meal": "HB", - "country": "ITA", - "market_segment": "Offline TA/TO", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "A", - "assigned_room_type": "A", - "booking_changes": 1, - "deposit_type": "No Deposit", - "agent": 26.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient-Party", - "adr": 110.0, - "required_car_parking_spaces": 0, - "total_of_special_requests": 0, - "reservation_status": "Check-Out", - "reservation_status_date": "2016-10-02" - } - }, - { - "scenario_id": "496", - "features": { - "hotel": "City Hotel", - "is_canceled": 1, - "lead_time": 204, - "arrival_date_year": 2017, - "arrival_date_month": "August", - "arrival_date_week_number": 34, - "arrival_date_day_of_month": 25, - "stays_in_weekend_nights": 1, - "stays_in_week_nights": 2, - "adults": 2, - "children": 0.0, - "babies": 0, - "meal": "SC", - "country": "DEU", - "market_segment": "Online TA", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "A", - "assigned_room_type": "A", - "booking_changes": 0, - "deposit_type": "No Deposit", - "agent": 9.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 99.0, - "required_car_parking_spaces": 0, - "total_of_special_requests": 0, - "reservation_status": "Canceled", - "reservation_status_date": "2017-08-03" - } - }, - { - "scenario_id": "499", - "features": { - "hotel": "Resort Hotel", - "is_canceled": 1, - "lead_time": 24, - "arrival_date_year": 2017, - "arrival_date_month": "August", - "arrival_date_week_number": 34, - "arrival_date_day_of_month": 23, - "stays_in_weekend_nights": 2, - "stays_in_week_nights": 4, - "adults": 2, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "AGO", - "market_segment": "Online TA", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "A", - "assigned_room_type": "A", - "booking_changes": 0, - "deposit_type": "No Deposit", - "agent": 240.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 216.67, - "required_car_parking_spaces": 0, - "total_of_special_requests": 1, - "reservation_status": "Canceled", - "reservation_status_date": "2017-08-13" - } - } - ], - "target_column": "reservation_status", - "task_sub_type": "classification", - "ranking_ground_truth": { - "top_k_ids": [ - "035", - "043", - "066", - "071", - "073", - "079", - "083", - "203", - "216", - "299", - "307", - "316", - "411", - "440", - "461" - ] - }, - "active_filters": [ - { - "col": "market_segment", - "op": "==", - "val": "Corporate" - } - ], - "target_class_value": "no-show" - }, - "response": "", - "evaluation_score": {} -} \ No newline at end of file + "active_filters": [ + { + "col": "market_segment", + "op": "==", + "val": "Corporate" + } + ], + "target_class_value": "no-show" + }, + "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_005/current.csv b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_005/current.csv index 2484da03396f7cf6e9b576989bb10ff4f18eeb44..ca3b1eacedc5e95641d7f78b3561a2f9b16e7ba8 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_005/current.csv +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_005/current.csv @@ -1,201 +1,199 @@ -hotel,is_canceled,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests,reservation_status_date -City Hotel,0,0,2015,September,37,9,0,0,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,0,A,A,0,No Deposit,,40.0,0,Transient,0.0,0,0,2015-09-09 -Resort Hotel,0,130,2015,August,32,8,2,5,2,0.0,1,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,250.0,,0,Transient,113.9,0,1,2015-08-15 -Resort Hotel,0,9,2017,April,16,19,0,1,1,0.0,0,BB,PRT,Direct,Direct,0,0,1,A,A,0,No Deposit,,,0,Transient,83.0,0,0,2017-04-20 -City Hotel,0,193,2016,July,28,4,3,10,0,2.0,0,BB,DEU,Online TA,TA/TO,0,0,0,B,B,0,No Deposit,9.0,,0,Transient,81.82,0,2,2016-07-17 -City Hotel,1,69,2016,March,12,16,1,4,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,6,No Deposit,8.0,,0,Transient,79.05,0,0,2016-02-13 -City Hotel,0,51,2016,August,36,31,1,4,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,7.0,,0,Transient,90.2,0,1,2016-09-05 -City Hotel,1,179,2015,August,34,22,2,1,2,0.0,0,HB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient-Party,86.0,0,0,2015-07-09 -Resort Hotel,0,64,2017,August,35,31,2,3,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,241.0,,0,Transient,187.19,0,1,2017-09-05 -City Hotel,0,181,2017,July,27,7,2,3,2,0.0,0,BB,GBR,Direct,Direct,0,0,0,A,A,2,No Deposit,14.0,,0,Transient,86.63,0,2,2017-07-12 -Resort Hotel,0,150,2017,March,12,24,2,2,2,0.0,0,BB,ESP,Offline TA/TO,TA/TO,0,0,0,D,E,0,No Deposit,6.0,,0,Transient,37.0,0,0,2017-03-28 -City Hotel,0,62,2015,September,39,20,2,3,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,68.72,0,0,2015-09-25 -City Hotel,1,12,2017,July,30,24,3,5,1,0.0,0,SC,RUS,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,9.0,,0,Transient,141.0,0,1,2017-07-15 -City Hotel,0,78,2017,July,28,15,1,1,3,0.0,0,BB,PRT,Direct,Direct,0,0,0,D,D,0,No Deposit,,,0,Transient,170.5,0,1,2017-07-17 -City Hotel,1,13,2016,June,25,12,2,0,1,0.0,0,BB,RUS,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,115.0,0,0,2016-06-08 -Resort Hotel,1,83,2016,April,17,22,2,5,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,D,D,0,Non Refund,71.0,,0,Transient,58.0,0,0,2016-02-22 -City Hotel,1,26,2016,November,45,2,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,10,A,E,0,No Deposit,,219.0,0,Transient,88.0,0,1,2016-11-02 -City Hotel,0,108,2017,May,20,19,1,2,1,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,E,E,1,No Deposit,9.0,,0,Transient,180.0,0,1,2017-05-22 -City Hotel,1,156,2017,April,17,26,0,3,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,37.0,,0,Transient,100.0,0,0,2016-11-21 -City Hotel,1,18,2016,May,19,7,1,1,2,0.0,0,BB,BEL,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,141.0,0,0,2016-04-20 -City Hotel,1,0,2016,May,21,18,0,1,1,0.0,0,BB,PRT,Corporate,Direct,0,0,0,A,E,2,No Deposit,,38.0,0,Transient-Party,163.18,0,0,2016-05-18 -City Hotel,0,35,2015,October,41,7,0,2,2,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,66.53,0,1,2015-10-09 -Resort Hotel,1,41,2016,August,33,12,2,2,2,1.0,0,BB,RUS,Direct,Direct,0,0,0,F,F,0,No Deposit,250.0,,0,Transient,234.6,0,0,2016-08-08 -City Hotel,0,86,2015,September,37,9,0,2,2,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,D,0,No Deposit,,68.0,0,Transient-Party,100.0,0,0,2015-09-11 -City Hotel,0,28,2017,April,16,21,0,1,1,0.0,0,BB,USA,Direct,Direct,0,0,0,A,D,1,No Deposit,14.0,,0,Transient,93.5,0,0,2017-04-22 -Resort Hotel,0,38,2016,January,2,3,2,2,2,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,6.0,,0,Transient,28.0,0,0,2016-01-07 -Resort Hotel,0,0,2016,April,16,13,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,2,A,E,0,No Deposit,,399.0,0,Transient,56.0,0,1,2016-04-14 -Resort Hotel,1,33,2015,November,45,1,1,0,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,1,A,A,0,No Deposit,,94.0,0,Transient,25.0,0,0,2015-11-01 -Resort Hotel,0,113,2017,June,22,2,2,6,2,0.0,0,BB,GBR,Direct,Direct,0,0,0,F,F,0,No Deposit,250.0,,0,Transient,145.0,0,1,2017-06-10 -City Hotel,0,15,2016,June,27,26,2,1,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,D,A,0,No Deposit,9.0,,0,Transient,146.0,0,1,2016-06-29 -City Hotel,1,5,2015,August,35,26,0,2,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,D,0,No Deposit,,40.0,0,Transient,65.0,0,0,2015-08-26 -Resort Hotel,1,0,2016,May,23,30,1,4,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,195.0,0,Transient,57.0,0,0,2016-05-30 -Resort Hotel,0,1,2017,July,31,31,1,4,1,2.0,0,HB,ESP,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,242.0,,0,Transient,339.4,1,2,2017-08-05 -City Hotel,0,14,2017,March,11,15,0,1,1,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,1,No Deposit,,,0,Transient,65.0,1,1,2017-03-16 -City Hotel,1,8,2016,February,8,19,0,1,1,0.0,0,BB,PRT,Corporate,TA/TO,0,0,0,A,A,0,No Deposit,,67.0,0,Transient-Party,81.0,0,0,2016-02-19 -Resort Hotel,0,135,2016,July,30,23,2,5,3,1.0,0,BB,PRT,Direct,Direct,0,0,0,C,C,0,No Deposit,250.0,,0,Transient,222.0,0,1,2016-07-30 -City Hotel,1,105,2017,May,19,13,1,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,135.0,0,0,2017-05-03 -Resort Hotel,1,4,2017,April,17,24,1,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,E,0,No Deposit,,515.0,0,Transient,52.5,0,0,2017-04-24 -City Hotel,1,333,2016,September,39,20,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,Non Refund,58.0,,19,Transient,90.0,0,0,2015-11-11 -Resort Hotel,0,374,2016,August,35,27,4,10,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,E,E,0,No Deposit,40.0,,0,Contract,85.3,0,0,2016-09-10 -City Hotel,0,92,2017,August,31,3,0,3,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,100.0,0,1,2017-08-06 -City Hotel,0,1,2017,May,19,9,0,1,1,0.0,0,SC,NLD,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,9.0,,0,Transient,130.0,0,0,2017-05-10 -City Hotel,1,188,2016,June,25,15,0,2,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,119.0,,39,Transient,130.0,0,0,2016-01-18 -Resort Hotel,0,17,2016,December,53,26,1,3,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,D,0,No Deposit,,154.0,0,Transient,27.0,0,0,2016-12-30 -City Hotel,0,12,2015,October,43,20,0,4,2,0.0,0,BB,GBR,Direct,Direct,0,0,0,E,E,0,No Deposit,14.0,,0,Group,146.0,0,1,2015-10-24 -City Hotel,0,75,2017,January,4,22,2,3,3,0.0,0,BB,PAN,Online TA,TA/TO,0,0,0,D,E,0,No Deposit,9.0,,0,Transient,138.6,0,1,2017-01-27 -City Hotel,1,144,2016,July,27,1,1,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,28.0,,0,Transient,75.0,0,0,2016-07-01 -City Hotel,1,297,2015,August,33,10,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Contract,62.0,0,0,2015-01-01 -Resort Hotel,1,72,2017,January,3,20,1,2,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,62.0,0,0,2016-12-22 -Resort Hotel,0,41,2016,June,25,14,0,2,2,0.0,0,BB,PRT,Complementary,Direct,0,0,0,E,E,0,No Deposit,,,0,Transient,0.0,0,0,2016-06-16 -City Hotel,1,10,2017,February,9,27,1,3,2,0.0,0,SC,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,74.8,0,0,2017-02-17 -City Hotel,0,38,2016,October,42,15,2,1,2,0.0,0,BB,DEU,Direct,Direct,0,0,0,D,D,0,No Deposit,14.0,,0,Transient,157.33,0,0,2016-10-18 -City Hotel,1,310,2017,April,17,23,2,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,21.0,,0,Transient-Party,75.0,0,0,2017-03-31 -City Hotel,0,62,2017,March,11,18,1,1,2,0.0,0,SC,BEL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,79.2,0,1,2017-03-20 -Resort Hotel,1,20,2016,October,45,30,2,2,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,241.0,,0,Transient,64.1,0,1,2016-10-13 -City Hotel,0,23,2017,June,25,21,0,1,2,0.0,0,HB,ESP,Online TA,TA/TO,0,0,0,D,E,0,No Deposit,9.0,,0,Transient,188.0,0,2,2017-06-22 -City Hotel,0,2,2015,December,49,3,0,1,1,0.0,0,BB,DEU,Direct,Direct,0,0,0,E,E,0,No Deposit,,,0,Transient,115.0,0,0,2015-12-04 -City Hotel,0,63,2017,August,34,23,0,2,2,0.0,0,BB,USA,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,8.0,,0,Transient,139.0,0,1,2017-08-25 -City Hotel,1,6,2017,March,12,22,0,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,C,0,No Deposit,86.0,,0,Transient,94.4,0,0,2017-03-22 -City Hotel,0,0,2015,August,35,28,0,2,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,106.0,0,1,2015-08-30 -City Hotel,0,54,2016,November,45,4,0,2,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,88.0,0,1,2016-11-06 -Resort Hotel,1,80,2017,May,22,28,2,2,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,15.0,,0,Transient,80.0,0,0,2017-05-28 -City Hotel,0,175,2017,June,25,22,0,3,2,0.0,0,HB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,166.5,0,0,2017-06-25 -City Hotel,0,0,2017,April,15,9,1,0,2,0.0,0,SC,AGO,Online TA,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,135.0,0,2,2017-04-10 -Resort Hotel,0,120,2015,October,41,6,2,5,2,0.0,0,BB,NOR,Online TA,TA/TO,0,0,0,E,E,1,No Deposit,240.0,,0,Transient,76.0,0,0,2015-10-13 -City Hotel,0,0,2017,March,10,9,0,0,0,0.0,0,BB,PRT,Offline TA/TO,TA/TO,1,0,0,A,K,0,No Deposit,15.0,,0,Transient,0.0,0,0,2017-03-09 -City Hotel,1,82,2017,April,16,17,1,3,3,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,171.0,0,1,2017-01-30 -City Hotel,0,18,2016,April,16,14,0,2,2,0.0,0,BB,SAU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,127.0,0,1,2016-04-16 -City Hotel,1,179,2015,August,34,22,2,1,2,0.0,0,HB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient-Party,86.0,0,0,2015-07-09 -City Hotel,0,41,2016,October,44,24,1,3,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,28.0,,0,Transient,85.0,0,0,2016-10-28 -Resort Hotel,1,220,2017,August,33,13,2,3,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,162.3,0,1,2017-07-25 -City Hotel,0,346,2016,September,38,13,0,2,2,0.0,0,HB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,115.0,0,1,2016-09-15 -City Hotel,0,28,2016,March,10,4,2,2,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,9.0,,0,Transient,73.88,0,0,2016-03-08 -City Hotel,1,177,2017,April,16,21,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,286.0,,47,Transient,95.0,0,0,2016-12-12 -Resort Hotel,0,3,2017,April,16,21,0,2,2,0.0,0,BB,ESP,Direct,Direct,0,0,0,F,F,0,No Deposit,250.0,,0,Transient,157.0,1,0,2017-04-23 -Resort Hotel,1,289,2015,November,47,20,1,2,2,0.0,0,FB,PRT,Groups,TA/TO,0,1,0,A,A,0,No Deposit,134.0,,0,Transient-Party,69.0,0,0,2015-10-29 -City Hotel,1,19,2016,February,7,12,1,2,2,0.0,0,HB,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,138.0,0,1,2016-01-28 -City Hotel,1,195,2015,September,38,17,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,No Deposit,20.0,,0,Transient,48.0,0,0,2015-07-23 -Resort Hotel,0,0,2015,November,49,30,1,3,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,146.0,,0,Transient-Party,25.0,0,0,2015-12-04 -Resort Hotel,0,164,2016,October,44,29,0,0,1,0.0,0,BB,PRT,Direct,Direct,1,0,1,H,H,0,No Deposit,250.0,,0,Transient,0.0,0,2,2016-10-29 -City Hotel,0,36,2016,December,52,18,2,2,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,88.4,0,1,2016-12-22 -City Hotel,0,8,2016,September,39,21,0,2,1,0.0,0,BB,ITA,Corporate,Corporate,0,0,0,D,D,0,No Deposit,,304.0,0,Transient-Party,169.38,0,0,2016-09-23 -Resort Hotel,1,13,2017,August,33,17,0,2,3,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,250.0,,0,Transient,241.0,0,0,2017-08-17 -Resort Hotel,1,9,2016,December,52,24,1,1,2,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,D,D,0,No Deposit,,270.0,0,Transient,45.0,0,1,2016-12-24 -Resort Hotel,0,204,2015,July,31,27,1,4,3,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,175.0,,0,Transient,131.4,0,1,2015-08-01 -City Hotel,0,20,2016,July,31,28,0,2,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,10.0,,0,Transient,122.6,0,0,2016-07-30 -City Hotel,1,12,2015,September,38,14,1,3,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,40.0,0,Transient,65.0,0,0,2015-09-14 -City Hotel,1,16,2016,September,40,27,0,2,2,0.0,0,SC,VEN,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,129.0,0,1,2016-09-22 -City Hotel,1,134,2017,February,8,25,0,1,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,30.0,,0,Transient-Party,75.0,0,0,2017-02-25 -City Hotel,1,398,2016,August,33,11,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0,2015-10-21 -City Hotel,1,51,2016,September,36,3,2,2,2,0.0,0,BB,CN,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,139.28,0,0,2016-07-15 -City Hotel,1,14,2015,October,42,15,0,1,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,No Deposit,99.0,,0,Transient-Party,100.0,0,0,2015-10-07 -Resort Hotel,0,0,2016,November,47,14,1,0,2,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,79.0,1,1,2016-11-15 -City Hotel,0,99,2016,March,13,23,0,2,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient-Party,89.3,0,1,2016-03-25 -Resort Hotel,0,171,2016,April,15,4,1,1,2,0.0,0,HB,DEU,Groups,TA/TO,0,0,0,A,A,0,Non Refund,298.0,,0,Transient-Party,85.0,0,0,2016-04-06 -Resort Hotel,0,301,2017,July,30,25,4,13,2,0.0,0,HB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient-Party,169.06,0,2,2017-08-11 -Resort Hotel,0,6,2017,March,10,8,0,1,2,0.0,0,BB,RUS,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,177.0,,0,Transient,34.0,0,0,2017-03-09 -City Hotel,1,11,2017,April,17,23,1,0,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,Non Refund,,,0,Transient,102.6,0,0,2017-04-13 -City Hotel,0,27,2017,March,12,19,2,3,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,7.0,,0,Transient,76.23,0,0,2017-03-24 -City Hotel,1,135,2015,July,29,16,0,3,1,0.0,0,BB,PRT,Online TA,TA/TO,0,1,0,A,A,0,No Deposit,9.0,,0,Transient-Party,68.0,0,2,2015-07-09 -City Hotel,1,37,2016,October,42,13,0,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,56.0,,0,Transient-Party,105.0,0,0,2016-09-06 -City Hotel,0,0,2015,August,34,19,0,1,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Contract,106.0,0,2,2015-08-20 -Resort Hotel,0,82,2017,May,19,8,1,3,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,240.0,,0,Transient-Party,87.0,0,0,2017-05-12 -City Hotel,0,110,2015,December,50,9,0,1,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,D,1,No Deposit,56.0,,69,Transient-Party,70.0,0,0,2015-12-10 -City Hotel,1,302,2015,August,33,15,2,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.0,0,0,2015-07-06 -Resort Hotel,0,60,2016,January,3,16,2,5,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,E,1,No Deposit,6.0,,0,Transient,26.0,0,0,2016-01-23 -City Hotel,0,60,2015,September,39,21,1,1,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,1,No Deposit,20.0,,0,Transient-Party,49.5,0,0,2015-09-23 -City Hotel,0,1,2017,February,9,28,0,2,1,0.0,0,BB,DEU,Online TA,TA/TO,1,0,1,A,A,0,No Deposit,10.0,,0,Transient,85.0,0,0,2017-03-02 -City Hotel,0,4,2016,February,8,17,0,2,1,0.0,0,BB,DEU,Direct,Direct,0,0,0,A,A,1,No Deposit,,,0,Transient,80.0,0,0,2016-02-19 -City Hotel,1,125,2016,July,30,22,2,5,2,0.0,0,BB,POL,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,113.41,0,0,2016-06-23 -City Hotel,0,115,2017,March,11,16,0,3,2,0.0,0,SC,LVA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,79.2,0,0,2017-03-19 -City Hotel,0,0,2015,August,34,22,2,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,9.0,,0,Transient-Party,124.0,0,1,2015-08-25 -City Hotel,0,1,2017,May,21,21,2,2,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,A,1,No Deposit,20.0,,0,Transient,99.0,0,0,2017-05-25 -City Hotel,1,4,2017,April,15,15,1,1,2,0.0,0,SC,USA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,155.0,0,0,2017-04-14 -City Hotel,1,7,2016,August,35,22,1,1,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,140.0,0,1,2016-08-16 -Resort Hotel,0,118,2017,May,19,10,2,5,1,0.0,0,BB,IRL,Groups,TA/TO,0,0,0,A,A,0,No Deposit,96.0,,0,Transient-Party,42.0,0,0,2017-05-17 -City Hotel,1,12,2016,October,42,12,0,2,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,4,A,A,0,No Deposit,,40.0,0,Transient,65.0,0,1,2016-10-12 -Resort Hotel,0,101,2016,May,21,17,2,5,2,0.0,0,BB,IRL,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,314.0,,0,Transient,52.56,0,0,2016-05-24 -City Hotel,1,92,2015,December,49,1,4,15,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Contract,63.75,0,1,2015-11-19 -City Hotel,1,68,2015,December,49,2,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,40.0,0,Transient,65.0,0,1,2015-12-02 -Resort Hotel,1,139,2016,June,23,2,2,7,2,2.0,0,BB,PRT,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,240.0,,0,Transient,137.89,0,0,2016-01-29 -Resort Hotel,0,66,2015,August,34,16,2,2,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,E,1,No Deposit,250.0,,0,Transient,134.0,0,0,2015-08-20 -City Hotel,0,3,2016,May,22,26,0,3,3,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,K,1,No Deposit,9.0,,0,Transient,137.33,0,1,2016-05-29 -City Hotel,1,0,2016,April,16,12,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,40.0,0,Transient,65.0,0,0,2016-04-12 -City Hotel,0,0,2017,May,19,9,0,1,1,0.0,0,SC,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,15.0,,0,Transient,104.0,0,0,2017-05-10 -City Hotel,0,25,2016,July,28,8,1,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,138.0,,0,Transient,85.0,0,0,2016-07-11 -City Hotel,1,276,2016,May,20,13,2,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,21.0,,160,Transient,61.0,0,0,2016-01-18 -City Hotel,1,48,2017,July,27,7,0,1,1,0.0,0,BB,TUR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,170.0,0,1,2017-05-25 -Resort Hotel,0,9,2016,February,6,5,0,2,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,D,1,No Deposit,240.0,,0,Transient,58.0,0,1,2016-02-07 -Resort Hotel,0,7,2017,April,16,17,1,0,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,184.0,,0,Transient,95.0,1,0,2017-04-18 -Resort Hotel,1,3,2016,February,6,1,1,0,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,1,A,D,0,No Deposit,,286.0,0,Transient-Party,39.0,0,0,2016-02-01 -City Hotel,1,204,2017,June,25,20,0,3,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,130.5,0,0,2016-11-29 -City Hotel,1,26,2017,April,14,7,0,1,2,0.0,0,BB,CHN,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,126.0,0,0,2017-03-28 -City Hotel,0,81,2016,September,36,3,2,1,2,0.0,0,SC,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,125.1,0,2,2016-09-06 -City Hotel,0,88,2016,August,34,19,0,1,3,0.0,0,BB,BEL,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,150.3,0,1,2016-08-20 -City Hotel,1,111,2017,March,12,23,1,3,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,100.3,0,0,2017-02-06 -Resort Hotel,0,163,2017,July,29,18,0,3,2,0.0,0,HB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,241.0,,0,Transient,147.84,0,0,2017-07-21 -Resort Hotel,0,39,2015,October,43,23,2,2,2,0.0,0,BB,IRL,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,58.0,0,0,2015-10-27 -City Hotel,0,241,2015,October,43,19,1,2,2,0.0,0,BB,USA,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,0,Transient-Party,60.0,0,0,2015-10-22 -Resort Hotel,1,274,2015,October,40,3,2,5,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,96.0,,0,Transient,36.05,0,0,2015-06-17 -Resort Hotel,0,51,2015,October,43,23,2,5,2,0.0,0,HB,GBR,Direct,Direct,0,0,0,F,F,0,No Deposit,250.0,,0,Transient,119.43,1,0,2015-10-30 -City Hotel,1,48,2017,June,23,6,0,4,1,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,150.0,0,1,2017-05-16 -City Hotel,0,73,2017,March,12,24,0,2,1,0.0,0,BB,FRA,Groups,TA/TO,0,0,0,A,A,1,No Deposit,171.0,,0,Transient-Party,90.0,0,0,2017-03-26 -City Hotel,0,149,2017,June,26,27,0,4,2,0.0,0,HB,NLD,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,162.0,0,0,2017-07-01 -City Hotel,0,28,2017,March,10,7,0,3,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,A,1,No Deposit,28.0,,0,Contract,68.0,0,0,2017-03-10 -Resort Hotel,1,1,2016,February,9,25,0,2,2,1.0,0,BB,PRT,Direct,Direct,0,0,0,A,C,0,No Deposit,,,0,Transient,60.0,0,1,2016-02-25 -Resort Hotel,1,462,2017,May,21,25,0,3,2,0.0,0,HB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,78.0,0,0,2016-06-07 -Resort Hotel,0,68,2015,August,33,15,2,2,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,146.0,,0,Transient,157.0,0,1,2015-08-19 -Resort Hotel,1,21,2017,July,29,20,1,3,2,0.0,0,HB,POL,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,249.5,0,0,2017-07-06 -Resort Hotel,0,45,2015,August,35,25,0,5,2,0.0,0,BB,ESP,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Contract,103.28,0,2,2015-08-30 -Resort Hotel,0,163,2016,April,14,1,2,5,2,0.0,0,BB,CHE,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,240.0,,0,Transient,60.2,1,2,2016-04-08 -City Hotel,1,233,2017,June,25,23,1,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,20.0,,0,Transient,105.0,0,0,2017-04-21 -City Hotel,1,41,2017,August,31,3,0,1,1,0.0,0,SC,USA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,126.0,0,0,2017-06-27 -City Hotel,0,1,2016,December,53,31,1,1,1,0.0,0,BB,USA,Direct,Direct,0,0,0,D,D,1,No Deposit,14.0,,0,Transient,172.8,0,0,2017-01-02 -City Hotel,0,175,2016,July,29,15,2,2,2,0.0,0,BB,ROU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,90.95,0,1,2016-07-19 -Resort Hotel,1,138,2016,July,27,2,4,6,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,134.0,0,0,2016-02-19 -City Hotel,1,175,2017,May,20,19,0,1,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,44.0,,0,Transient,90.0,0,0,2016-11-25 -City Hotel,1,38,2017,July,27,2,2,0,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,119.0,0,1,2017-05-28 -Resort Hotel,1,50,2016,October,44,24,1,2,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,90.0,0,1,2016-09-11 -City Hotel,1,20,2017,June,26,29,0,1,2,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,465.0,0,Transient,95.0,0,2,2017-06-29 -City Hotel,0,279,2016,October,42,10,1,2,2,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,16.0,,0,Transient-Party,80.75,0,1,2016-10-13 -City Hotel,1,87,2017,April,15,15,2,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,95.0,0,0,2017-01-18 -City Hotel,0,1,2017,February,8,21,0,1,2,0.0,0,SC,ESP,Online TA,TA/TO,1,0,1,A,A,0,No Deposit,9.0,,0,Group,88.0,0,1,2017-02-22 -Resort Hotel,0,59,2015,October,41,8,0,3,2,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,281.0,,50,Contract,45.0,0,0,2015-10-11 -Resort Hotel,0,3,2015,July,30,23,0,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,E,0,No Deposit,240.0,,0,Transient,154.0,0,2,2015-07-24 -City Hotel,0,329,2017,May,19,10,0,4,2,0.0,0,BB,NOR,Online TA,TA/TO,0,0,0,A,A,14,No Deposit,9.0,,0,Transient,104.4,0,2,2017-05-14 -City Hotel,1,101,2016,March,12,15,0,3,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,78.3,0,0,2015-12-18 -City Hotel,1,105,2017,March,13,31,2,2,2,0.0,0,BB,POL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,110.7,0,1,2016-12-27 -City Hotel,1,120,2016,September,36,2,1,2,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,37.0,,0,Transient,115.0,0,0,2016-06-14 -Resort Hotel,0,95,2017,June,24,15,0,3,2,0.0,0,HB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,166.0,1,1,2017-06-18 -City Hotel,0,9,2016,March,14,29,0,2,2,0.0,0,SC,IRL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,89.0,0,1,2016-03-31 -Resort Hotel,1,3,2017,January,3,19,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,2,A,A,0,No Deposit,,470.0,0,Transient,35.0,0,1,2017-01-19 -City Hotel,1,55,2017,March,11,17,2,2,2,0.0,0,BB,AZE,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,91.8,0,0,2017-03-09 -City Hotel,1,150,2017,June,24,17,2,3,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,120.0,0,0,2017-01-18 -City Hotel,0,37,2016,November,46,12,0,1,1,0.0,0,BB,IRL,Offline TA/TO,TA/TO,0,0,0,A,A,2,No Deposit,22.0,,0,Transient,67.5,0,0,2016-11-13 -Resort Hotel,0,5,2017,March,12,22,0,1,1,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,C,0,No Deposit,,,0,Transient,60.0,0,0,2017-03-23 -City Hotel,0,154,2017,April,14,4,0,1,2,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,9.0,,0,Transient,117.0,0,0,2017-04-05 -Resort Hotel,0,112,2015,December,53,30,0,3,2,0.0,0,Undefined,PRT,Groups,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient-Party,224.67,0,0,2016-01-02 -City Hotel,0,4,2015,August,35,25,0,4,2,0.0,0,BB,ESP,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.0,0,0,2015-08-29 -Resort Hotel,0,175,2016,April,14,2,2,2,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,240.0,,0,Transient,52.2,0,1,2016-04-06 -City Hotel,0,101,2016,April,18,26,0,1,2,0.0,0,HB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,35.0,,0,Transient-Party,98.0,0,0,2016-04-27 -City Hotel,1,1,2015,November,47,19,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,40.0,0,Transient,65.0,0,0,2015-11-19 -Resort Hotel,0,1,2016,February,7,13,1,1,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,15.0,,0,Transient,60.0,0,0,2016-02-15 -City Hotel,0,20,2017,May,20,20,1,1,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,145.0,0,1,2017-05-22 -City Hotel,0,29,2017,January,3,18,0,2,1,0.0,0,BB,AUT,Groups,TA/TO,0,0,0,A,A,1,No Deposit,171.0,,0,Transient-Party,65.0,0,0,2017-01-20 -Resort Hotel,0,156,2017,May,19,8,2,5,2,0.0,0,HB,IRL,Groups,TA/TO,0,0,0,D,D,0,No Deposit,434.0,,0,Transient-Party,90.0,0,0,2017-05-15 -City Hotel,0,41,2015,December,52,21,1,2,2,0.0,0,BB,UKR,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,9.0,,0,Transient,87.0,0,2,2015-12-24 -City Hotel,0,120,2017,August,33,16,0,2,2,0.0,0,BB,NLD,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,85.0,,0,Transient,98.1,1,0,2017-08-18 -Resort Hotel,0,2,2016,February,6,3,2,5,1,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,49.0,0,1,2016-02-10 -City Hotel,0,58,2015,November,47,18,0,4,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,28.0,,0,Transient,60.0,0,0,2015-11-22 -City Hotel,1,56,2017,March,10,10,1,2,2,0.0,0,BB,NLD,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,97.2,0,0,2017-01-13 -City Hotel,1,14,2015,October,42,15,0,1,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,No Deposit,99.0,,0,Transient-Party,100.0,0,0,2015-10-07 -Resort Hotel,0,131,2015,December,52,22,2,5,2,0.0,0,HB,IRL,Groups,TA/TO,0,0,0,A,D,0,No Deposit,335.0,,0,Transient-Party,75.57,0,1,2015-12-29 -Resort Hotel,0,5,2015,October,41,10,1,1,2,0.0,0,HB,ESP,Online TA,TA/TO,0,0,0,D,D,1,No Deposit,240.0,,0,Transient,101.5,0,2,2015-10-12 -City Hotel,0,120,2016,May,19,2,1,1,2,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient-Party,94.5,0,1,2016-05-04 -Resort Hotel,1,54,2017,May,19,9,0,3,2,2.0,0,BB,GBR,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,240.0,,0,Transient,165.0,0,0,2017-04-18 -City Hotel,1,328,2017,January,1,3,2,5,3,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,107.95,0,1,2016-12-13 -Resort Hotel,0,152,2016,June,25,14,2,5,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,D,E,0,No Deposit,250.0,,0,Transient,100.6,0,1,2016-06-21 -City Hotel,0,100,2016,September,40,28,0,4,1,0.0,0,HB,ITA,Offline TA/TO,TA/TO,0,0,0,A,A,1,No Deposit,26.0,,0,Transient-Party,110.0,0,0,2016-10-02 -City Hotel,1,204,2017,August,34,25,1,2,2,0.0,0,SC,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,99.0,0,0,2017-08-03 -Resort Hotel,1,24,2017,August,34,23,2,4,2,0.0,0,BB,AGO,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,216.67,0,1,2017-08-13 +hotel,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests +City Hotel,0,2015,September,37,9,0,0,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,0,A,A,0,No Deposit,,40.0,0,Transient,0.0,0,0 +Resort Hotel,130,2015,August,32,8,2,5,2,0.0,1,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,250.0,,0,Transient,113.9,0,1 +Resort Hotel,9,2017,April,16,19,0,1,1,0.0,0,BB,PRT,Direct,Direct,0,0,1,A,A,0,No Deposit,,,0,Transient,83.0,0,0 +City Hotel,193,2016,July,28,4,3,10,0,2.0,0,BB,DEU,Online TA,TA/TO,0,0,0,B,B,0,No Deposit,9.0,,0,Transient,81.82,0,2 +City Hotel,69,2016,March,12,16,1,4,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,6,No Deposit,8.0,,0,Transient,79.05,0,0 +City Hotel,51,2016,August,36,31,1,4,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,7.0,,0,Transient,90.2,0,1 +City Hotel,179,2015,August,34,22,2,1,2,0.0,0,HB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient-Party,86.0,0,0 +Resort Hotel,64,2017,August,35,31,2,3,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,241.0,,0,Transient,187.19,0,1 +City Hotel,181,2017,July,27,7,2,3,2,0.0,0,BB,GBR,Direct,Direct,0,0,0,A,A,2,No Deposit,14.0,,0,Transient,86.63,0,2 +Resort Hotel,150,2017,March,12,24,2,2,2,0.0,0,BB,ESP,Offline TA/TO,TA/TO,0,0,0,D,E,0,No Deposit,6.0,,0,Transient,37.0,0,0 +City Hotel,62,2015,September,39,20,2,3,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,68.72,0,0 +City Hotel,12,2017,July,30,24,3,5,1,0.0,0,SC,RUS,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,9.0,,0,Transient,141.0,0,1 +City Hotel,78,2017,July,28,15,1,1,3,0.0,0,BB,PRT,Direct,Direct,0,0,0,D,D,0,No Deposit,,,0,Transient,170.5,0,1 +City Hotel,13,2016,June,25,12,2,0,1,0.0,0,BB,RUS,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,115.0,0,0 +Resort Hotel,83,2016,April,17,22,2,5,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,D,D,0,Non Refund,71.0,,0,Transient,58.0,0,0 +City Hotel,26,2016,November,45,2,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,10,A,E,0,No Deposit,,219.0,0,Transient,88.0,0,1 +City Hotel,108,2017,May,20,19,1,2,1,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,E,E,1,No Deposit,9.0,,0,Transient,180.0,0,1 +City Hotel,156,2017,April,17,26,0,3,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,37.0,,0,Transient,100.0,0,0 +City Hotel,18,2016,May,19,7,1,1,2,0.0,0,BB,BEL,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,141.0,0,0 +City Hotel,0,2016,May,21,18,0,1,1,0.0,0,BB,PRT,Corporate,Direct,0,0,0,A,E,2,No Deposit,,38.0,0,Transient-Party,163.18,0,0 +City Hotel,35,2015,October,41,7,0,2,2,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,66.53,0,1 +Resort Hotel,41,2016,August,33,12,2,2,2,1.0,0,BB,RUS,Direct,Direct,0,0,0,F,F,0,No Deposit,250.0,,0,Transient,234.6,0,0 +City Hotel,86,2015,September,37,9,0,2,2,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,D,0,No Deposit,,68.0,0,Transient-Party,100.0,0,0 +City Hotel,28,2017,April,16,21,0,1,1,0.0,0,BB,USA,Direct,Direct,0,0,0,A,D,1,No Deposit,14.0,,0,Transient,93.5,0,0 +Resort Hotel,38,2016,January,2,3,2,2,2,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,6.0,,0,Transient,28.0,0,0 +Resort Hotel,0,2016,April,16,13,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,2,A,E,0,No Deposit,,399.0,0,Transient,56.0,0,1 +Resort Hotel,33,2015,November,45,1,1,0,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,1,A,A,0,No Deposit,,94.0,0,Transient,25.0,0,0 +Resort Hotel,113,2017,June,22,2,2,6,2,0.0,0,BB,GBR,Direct,Direct,0,0,0,F,F,0,No Deposit,250.0,,0,Transient,145.0,0,1 +City Hotel,15,2016,June,27,26,2,1,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,D,A,0,No Deposit,9.0,,0,Transient,146.0,0,1 +City Hotel,5,2015,August,35,26,0,2,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,D,0,No Deposit,,40.0,0,Transient,65.0,0,0 +Resort Hotel,0,2016,May,23,30,1,4,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,195.0,0,Transient,57.0,0,0 +Resort Hotel,1,2017,July,31,31,1,4,1,2.0,0,HB,ESP,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,242.0,,0,Transient,339.4,1,2 +City Hotel,14,2017,March,11,15,0,1,1,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,1,No Deposit,,,0,Transient,65.0,1,1 +City Hotel,8,2016,February,8,19,0,1,1,0.0,0,BB,PRT,Corporate,TA/TO,0,0,0,A,A,0,No Deposit,,67.0,0,Transient-Party,81.0,0,0 +Resort Hotel,135,2016,July,30,23,2,5,3,1.0,0,BB,PRT,Direct,Direct,0,0,0,C,C,0,No Deposit,250.0,,0,Transient,222.0,0,1 +City Hotel,105,2017,May,19,13,1,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,135.0,0,0 +Resort Hotel,4,2017,April,17,24,1,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,E,0,No Deposit,,515.0,0,Transient,52.5,0,0 +City Hotel,333,2016,September,39,20,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,Non Refund,58.0,,19,Transient,90.0,0,0 +Resort Hotel,374,2016,August,35,27,4,10,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,E,E,0,No Deposit,40.0,,0,Contract,85.3,0,0 +City Hotel,92,2017,August,31,3,0,3,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,100.0,0,1 +City Hotel,1,2017,May,19,9,0,1,1,0.0,0,SC,NLD,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,9.0,,0,Transient,130.0,0,0 +City Hotel,188,2016,June,25,15,0,2,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,119.0,,39,Transient,130.0,0,0 +Resort Hotel,17,2016,December,53,26,1,3,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,D,0,No Deposit,,154.0,0,Transient,27.0,0,0 +City Hotel,12,2015,October,43,20,0,4,2,0.0,0,BB,GBR,Direct,Direct,0,0,0,E,E,0,No Deposit,14.0,,0,Group,146.0,0,1 +City Hotel,75,2017,January,4,22,2,3,3,0.0,0,BB,PAN,Online TA,TA/TO,0,0,0,D,E,0,No Deposit,9.0,,0,Transient,138.6,0,1 +City Hotel,144,2016,July,27,1,1,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,28.0,,0,Transient,75.0,0,0 +City Hotel,297,2015,August,33,10,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Contract,62.0,0,0 +Resort Hotel,72,2017,January,3,20,1,2,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,62.0,0,0 +Resort Hotel,41,2016,June,25,14,0,2,2,0.0,0,BB,PRT,Complementary,Direct,0,0,0,E,E,0,No Deposit,,,0,Transient,0.0,0,0 +City Hotel,10,2017,February,9,27,1,3,2,0.0,0,SC,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,74.8,0,0 +City Hotel,38,2016,October,42,15,2,1,2,0.0,0,BB,DEU,Direct,Direct,0,0,0,D,D,0,No Deposit,14.0,,0,Transient,157.33,0,0 +City Hotel,310,2017,April,17,23,2,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,21.0,,0,Transient-Party,75.0,0,0 +City Hotel,62,2017,March,11,18,1,1,2,0.0,0,SC,BEL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,79.2,0,1 +Resort Hotel,20,2016,October,45,30,2,2,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,241.0,,0,Transient,64.1,0,1 +City Hotel,23,2017,June,25,21,0,1,2,0.0,0,HB,ESP,Online TA,TA/TO,0,0,0,D,E,0,No Deposit,9.0,,0,Transient,188.0,0,2 +City Hotel,2,2015,December,49,3,0,1,1,0.0,0,BB,DEU,Direct,Direct,0,0,0,E,E,0,No Deposit,,,0,Transient,115.0,0,0 +City Hotel,63,2017,August,34,23,0,2,2,0.0,0,BB,USA,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,8.0,,0,Transient,139.0,0,1 +City Hotel,6,2017,March,12,22,0,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,C,0,No Deposit,86.0,,0,Transient,94.4,0,0 +City Hotel,0,2015,August,35,28,0,2,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,106.0,0,1 +City Hotel,54,2016,November,45,4,0,2,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,88.0,0,1 +Resort Hotel,80,2017,May,22,28,2,2,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,15.0,,0,Transient,80.0,0,0 +City Hotel,175,2017,June,25,22,0,3,2,0.0,0,HB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,166.5,0,0 +City Hotel,0,2017,April,15,9,1,0,2,0.0,0,SC,AGO,Online TA,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,135.0,0,2 +Resort Hotel,120,2015,October,41,6,2,5,2,0.0,0,BB,NOR,Online TA,TA/TO,0,0,0,E,E,1,No Deposit,240.0,,0,Transient,76.0,0,0 +City Hotel,0,2017,March,10,9,0,0,0,0.0,0,BB,PRT,Offline TA/TO,TA/TO,1,0,0,A,K,0,No Deposit,15.0,,0,Transient,0.0,0,0 +City Hotel,82,2017,April,16,17,1,3,3,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,171.0,0,1 +City Hotel,18,2016,April,16,14,0,2,2,0.0,0,BB,SAU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,127.0,0,1 +City Hotel,41,2016,October,44,24,1,3,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,28.0,,0,Transient,85.0,0,0 +Resort Hotel,220,2017,August,33,13,2,3,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,162.3,0,1 +City Hotel,346,2016,September,38,13,0,2,2,0.0,0,HB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,115.0,0,1 +City Hotel,28,2016,March,10,4,2,2,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,9.0,,0,Transient,73.88,0,0 +City Hotel,177,2017,April,16,21,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,286.0,,47,Transient,95.0,0,0 +Resort Hotel,3,2017,April,16,21,0,2,2,0.0,0,BB,ESP,Direct,Direct,0,0,0,F,F,0,No Deposit,250.0,,0,Transient,157.0,1,0 +Resort Hotel,289,2015,November,47,20,1,2,2,0.0,0,FB,PRT,Groups,TA/TO,0,1,0,A,A,0,No Deposit,134.0,,0,Transient-Party,69.0,0,0 +City Hotel,19,2016,February,7,12,1,2,2,0.0,0,HB,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,138.0,0,1 +City Hotel,195,2015,September,38,17,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,No Deposit,20.0,,0,Transient,48.0,0,0 +Resort Hotel,0,2015,November,49,30,1,3,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,146.0,,0,Transient-Party,25.0,0,0 +Resort Hotel,164,2016,October,44,29,0,0,1,0.0,0,BB,PRT,Direct,Direct,1,0,1,H,H,0,No Deposit,250.0,,0,Transient,0.0,0,2 +City Hotel,36,2016,December,52,18,2,2,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,88.4,0,1 +City Hotel,8,2016,September,39,21,0,2,1,0.0,0,BB,ITA,Corporate,Corporate,0,0,0,D,D,0,No Deposit,,304.0,0,Transient-Party,169.38,0,0 +Resort Hotel,13,2017,August,33,17,0,2,3,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,250.0,,0,Transient,241.0,0,0 +Resort Hotel,9,2016,December,52,24,1,1,2,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,D,D,0,No Deposit,,270.0,0,Transient,45.0,0,1 +Resort Hotel,204,2015,July,31,27,1,4,3,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,175.0,,0,Transient,131.4,0,1 +City Hotel,20,2016,July,31,28,0,2,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,10.0,,0,Transient,122.6,0,0 +City Hotel,12,2015,September,38,14,1,3,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,40.0,0,Transient,65.0,0,0 +City Hotel,16,2016,September,40,27,0,2,2,0.0,0,SC,VEN,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,129.0,0,1 +City Hotel,134,2017,February,8,25,0,1,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,30.0,,0,Transient-Party,75.0,0,0 +City Hotel,398,2016,August,33,11,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0 +City Hotel,51,2016,September,36,3,2,2,2,0.0,0,BB,CN,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,139.28,0,0 +City Hotel,14,2015,October,42,15,0,1,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,No Deposit,99.0,,0,Transient-Party,100.0,0,0 +Resort Hotel,0,2016,November,47,14,1,0,2,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,79.0,1,1 +City Hotel,99,2016,March,13,23,0,2,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient-Party,89.3,0,1 +Resort Hotel,171,2016,April,15,4,1,1,2,0.0,0,HB,DEU,Groups,TA/TO,0,0,0,A,A,0,Non Refund,298.0,,0,Transient-Party,85.0,0,0 +Resort Hotel,301,2017,July,30,25,4,13,2,0.0,0,HB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient-Party,169.06,0,2 +Resort Hotel,6,2017,March,10,8,0,1,2,0.0,0,BB,RUS,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,177.0,,0,Transient,34.0,0,0 +City Hotel,11,2017,April,17,23,1,0,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,Non Refund,,,0,Transient,102.6,0,0 +City Hotel,27,2017,March,12,19,2,3,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,7.0,,0,Transient,76.23,0,0 +City Hotel,135,2015,July,29,16,0,3,1,0.0,0,BB,PRT,Online TA,TA/TO,0,1,0,A,A,0,No Deposit,9.0,,0,Transient-Party,68.0,0,2 +City Hotel,37,2016,October,42,13,0,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,56.0,,0,Transient-Party,105.0,0,0 +City Hotel,0,2015,August,34,19,0,1,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Contract,106.0,0,2 +Resort Hotel,82,2017,May,19,8,1,3,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,240.0,,0,Transient-Party,87.0,0,0 +City Hotel,110,2015,December,50,9,0,1,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,D,1,No Deposit,56.0,,69,Transient-Party,70.0,0,0 +City Hotel,302,2015,August,33,15,2,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.0,0,0 +Resort Hotel,60,2016,January,3,16,2,5,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,E,1,No Deposit,6.0,,0,Transient,26.0,0,0 +City Hotel,60,2015,September,39,21,1,1,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,1,No Deposit,20.0,,0,Transient-Party,49.5,0,0 +City Hotel,1,2017,February,9,28,0,2,1,0.0,0,BB,DEU,Online TA,TA/TO,1,0,1,A,A,0,No Deposit,10.0,,0,Transient,85.0,0,0 +City Hotel,4,2016,February,8,17,0,2,1,0.0,0,BB,DEU,Direct,Direct,0,0,0,A,A,1,No Deposit,,,0,Transient,80.0,0,0 +City Hotel,125,2016,July,30,22,2,5,2,0.0,0,BB,POL,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,113.41,0,0 +City Hotel,115,2017,March,11,16,0,3,2,0.0,0,SC,LVA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,79.2,0,0 +City Hotel,0,2015,August,34,22,2,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,9.0,,0,Transient-Party,124.0,0,1 +City Hotel,1,2017,May,21,21,2,2,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,A,1,No Deposit,20.0,,0,Transient,99.0,0,0 +City Hotel,4,2017,April,15,15,1,1,2,0.0,0,SC,USA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,155.0,0,0 +City Hotel,7,2016,August,35,22,1,1,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,140.0,0,1 +Resort Hotel,118,2017,May,19,10,2,5,1,0.0,0,BB,IRL,Groups,TA/TO,0,0,0,A,A,0,No Deposit,96.0,,0,Transient-Party,42.0,0,0 +City Hotel,12,2016,October,42,12,0,2,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,4,A,A,0,No Deposit,,40.0,0,Transient,65.0,0,1 +Resort Hotel,101,2016,May,21,17,2,5,2,0.0,0,BB,IRL,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,314.0,,0,Transient,52.56,0,0 +City Hotel,92,2015,December,49,1,4,15,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Contract,63.75,0,1 +City Hotel,68,2015,December,49,2,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,40.0,0,Transient,65.0,0,1 +Resort Hotel,139,2016,June,23,2,2,7,2,2.0,0,BB,PRT,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,240.0,,0,Transient,137.89,0,0 +Resort Hotel,66,2015,August,34,16,2,2,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,E,1,No Deposit,250.0,,0,Transient,134.0,0,0 +City Hotel,3,2016,May,22,26,0,3,3,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,K,1,No Deposit,9.0,,0,Transient,137.33,0,1 +City Hotel,0,2016,April,16,12,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,40.0,0,Transient,65.0,0,0 +City Hotel,0,2017,May,19,9,0,1,1,0.0,0,SC,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,15.0,,0,Transient,104.0,0,0 +City Hotel,25,2016,July,28,8,1,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,138.0,,0,Transient,85.0,0,0 +City Hotel,276,2016,May,20,13,2,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,21.0,,160,Transient,61.0,0,0 +City Hotel,48,2017,July,27,7,0,1,1,0.0,0,BB,TUR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,170.0,0,1 +Resort Hotel,9,2016,February,6,5,0,2,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,D,1,No Deposit,240.0,,0,Transient,58.0,0,1 +Resort Hotel,7,2017,April,16,17,1,0,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,184.0,,0,Transient,95.0,1,0 +Resort Hotel,3,2016,February,6,1,1,0,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,1,A,D,0,No Deposit,,286.0,0,Transient-Party,39.0,0,0 +City Hotel,204,2017,June,25,20,0,3,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,130.5,0,0 +City Hotel,26,2017,April,14,7,0,1,2,0.0,0,BB,CHN,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,126.0,0,0 +City Hotel,81,2016,September,36,3,2,1,2,0.0,0,SC,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,125.1,0,2 +City Hotel,88,2016,August,34,19,0,1,3,0.0,0,BB,BEL,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,150.3,0,1 +City Hotel,111,2017,March,12,23,1,3,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,100.3,0,0 +Resort Hotel,163,2017,July,29,18,0,3,2,0.0,0,HB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,241.0,,0,Transient,147.84,0,0 +Resort Hotel,39,2015,October,43,23,2,2,2,0.0,0,BB,IRL,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,58.0,0,0 +City Hotel,241,2015,October,43,19,1,2,2,0.0,0,BB,USA,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,0,Transient-Party,60.0,0,0 +Resort Hotel,274,2015,October,40,3,2,5,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,96.0,,0,Transient,36.05,0,0 +Resort Hotel,51,2015,October,43,23,2,5,2,0.0,0,HB,GBR,Direct,Direct,0,0,0,F,F,0,No Deposit,250.0,,0,Transient,119.43,1,0 +City Hotel,48,2017,June,23,6,0,4,1,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,150.0,0,1 +City Hotel,73,2017,March,12,24,0,2,1,0.0,0,BB,FRA,Groups,TA/TO,0,0,0,A,A,1,No Deposit,171.0,,0,Transient-Party,90.0,0,0 +City Hotel,149,2017,June,26,27,0,4,2,0.0,0,HB,NLD,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,162.0,0,0 +City Hotel,28,2017,March,10,7,0,3,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,A,1,No Deposit,28.0,,0,Contract,68.0,0,0 +Resort Hotel,1,2016,February,9,25,0,2,2,1.0,0,BB,PRT,Direct,Direct,0,0,0,A,C,0,No Deposit,,,0,Transient,60.0,0,1 +Resort Hotel,462,2017,May,21,25,0,3,2,0.0,0,HB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,78.0,0,0 +Resort Hotel,68,2015,August,33,15,2,2,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,146.0,,0,Transient,157.0,0,1 +Resort Hotel,21,2017,July,29,20,1,3,2,0.0,0,HB,POL,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,249.5,0,0 +Resort Hotel,45,2015,August,35,25,0,5,2,0.0,0,BB,ESP,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Contract,103.28,0,2 +Resort Hotel,163,2016,April,14,1,2,5,2,0.0,0,BB,CHE,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,240.0,,0,Transient,60.2,1,2 +City Hotel,233,2017,June,25,23,1,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,20.0,,0,Transient,105.0,0,0 +City Hotel,41,2017,August,31,3,0,1,1,0.0,0,SC,USA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,126.0,0,0 +City Hotel,1,2016,December,53,31,1,1,1,0.0,0,BB,USA,Direct,Direct,0,0,0,D,D,1,No Deposit,14.0,,0,Transient,172.8,0,0 +City Hotel,175,2016,July,29,15,2,2,2,0.0,0,BB,ROU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,90.95,0,1 +Resort Hotel,138,2016,July,27,2,4,6,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,134.0,0,0 +City Hotel,175,2017,May,20,19,0,1,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,44.0,,0,Transient,90.0,0,0 +City Hotel,38,2017,July,27,2,2,0,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,119.0,0,1 +Resort Hotel,50,2016,October,44,24,1,2,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,90.0,0,1 +City Hotel,20,2017,June,26,29,0,1,2,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,465.0,0,Transient,95.0,0,2 +City Hotel,279,2016,October,42,10,1,2,2,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,16.0,,0,Transient-Party,80.75,0,1 +City Hotel,87,2017,April,15,15,2,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,95.0,0,0 +City Hotel,1,2017,February,8,21,0,1,2,0.0,0,SC,ESP,Online TA,TA/TO,1,0,1,A,A,0,No Deposit,9.0,,0,Group,88.0,0,1 +Resort Hotel,59,2015,October,41,8,0,3,2,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,281.0,,50,Contract,45.0,0,0 +Resort Hotel,3,2015,July,30,23,0,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,E,0,No Deposit,240.0,,0,Transient,154.0,0,2 +City Hotel,329,2017,May,19,10,0,4,2,0.0,0,BB,NOR,Online TA,TA/TO,0,0,0,A,A,14,No Deposit,9.0,,0,Transient,104.4,0,2 +City Hotel,101,2016,March,12,15,0,3,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,78.3,0,0 +City Hotel,105,2017,March,13,31,2,2,2,0.0,0,BB,POL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,110.7,0,1 +City Hotel,120,2016,September,36,2,1,2,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,37.0,,0,Transient,115.0,0,0 +Resort Hotel,95,2017,June,24,15,0,3,2,0.0,0,HB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,166.0,1,1 +City Hotel,9,2016,March,14,29,0,2,2,0.0,0,SC,IRL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,89.0,0,1 +Resort Hotel,3,2017,January,3,19,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,2,A,A,0,No Deposit,,470.0,0,Transient,35.0,0,1 +City Hotel,55,2017,March,11,17,2,2,2,0.0,0,BB,AZE,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,91.8,0,0 +City Hotel,150,2017,June,24,17,2,3,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,120.0,0,0 +City Hotel,37,2016,November,46,12,0,1,1,0.0,0,BB,IRL,Offline TA/TO,TA/TO,0,0,0,A,A,2,No Deposit,22.0,,0,Transient,67.5,0,0 +Resort Hotel,5,2017,March,12,22,0,1,1,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,C,0,No Deposit,,,0,Transient,60.0,0,0 +City Hotel,154,2017,April,14,4,0,1,2,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,9.0,,0,Transient,117.0,0,0 +Resort Hotel,112,2015,December,53,30,0,3,2,0.0,0,Undefined,PRT,Groups,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient-Party,224.67,0,0 +City Hotel,4,2015,August,35,25,0,4,2,0.0,0,BB,ESP,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.0,0,0 +Resort Hotel,175,2016,April,14,2,2,2,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,240.0,,0,Transient,52.2,0,1 +City Hotel,101,2016,April,18,26,0,1,2,0.0,0,HB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,35.0,,0,Transient-Party,98.0,0,0 +City Hotel,1,2015,November,47,19,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,40.0,0,Transient,65.0,0,0 +Resort Hotel,1,2016,February,7,13,1,1,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,15.0,,0,Transient,60.0,0,0 +City Hotel,20,2017,May,20,20,1,1,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,145.0,0,1 +City Hotel,29,2017,January,3,18,0,2,1,0.0,0,BB,AUT,Groups,TA/TO,0,0,0,A,A,1,No Deposit,171.0,,0,Transient-Party,65.0,0,0 +Resort Hotel,156,2017,May,19,8,2,5,2,0.0,0,HB,IRL,Groups,TA/TO,0,0,0,D,D,0,No Deposit,434.0,,0,Transient-Party,90.0,0,0 +City Hotel,41,2015,December,52,21,1,2,2,0.0,0,BB,UKR,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,9.0,,0,Transient,87.0,0,2 +City Hotel,120,2017,August,33,16,0,2,2,0.0,0,BB,NLD,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,85.0,,0,Transient,98.1,1,0 +Resort Hotel,2,2016,February,6,3,2,5,1,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,49.0,0,1 +City Hotel,58,2015,November,47,18,0,4,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,28.0,,0,Transient,60.0,0,0 +City Hotel,56,2017,March,10,10,1,2,2,0.0,0,BB,NLD,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,97.2,0,0 +Resort Hotel,131,2015,December,52,22,2,5,2,0.0,0,HB,IRL,Groups,TA/TO,0,0,0,A,D,0,No Deposit,335.0,,0,Transient-Party,75.57,0,1 +Resort Hotel,5,2015,October,41,10,1,1,2,0.0,0,HB,ESP,Online TA,TA/TO,0,0,0,D,D,1,No Deposit,240.0,,0,Transient,101.5,0,2 +City Hotel,120,2016,May,19,2,1,1,2,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient-Party,94.5,0,1 +Resort Hotel,54,2017,May,19,9,0,3,2,2.0,0,BB,GBR,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,240.0,,0,Transient,165.0,0,0 +City Hotel,328,2017,January,1,3,2,5,3,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,107.95,0,1 +Resort Hotel,152,2016,June,25,14,2,5,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,D,E,0,No Deposit,250.0,,0,Transient,100.6,0,1 +City Hotel,100,2016,September,40,28,0,4,1,0.0,0,HB,ITA,Offline TA/TO,TA/TO,0,0,0,A,A,1,No Deposit,26.0,,0,Transient-Party,110.0,0,0 +City Hotel,204,2017,August,34,25,1,2,2,0.0,0,SC,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,99.0,0,0 +Resort Hotel,24,2017,August,34,23,2,4,2,0.0,0,BB,AGO,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,216.67,0,1 diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_005/history.csv b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_005/history.csv index d247fc5afb304e55a6b6c68fde195c8ac836e439..555c3058ec55a3e7da350854b90ebc3b15ef4d70 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_005/history.csv +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_005/history.csv @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:500cb52376fa81f916474f85570cc132e874f56127a160d1b84c47f976e5c1bc -size 16783109 +oid sha256:8863c6a2a8672f693b3cd0bad11dcc3fd36bfef96bb0e937a10d5cc136c867e1 +size 15232257 diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_005/info.json b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_005/info.json index 4816c05993309461836de8622438e342f2e503ff..20956c93d07801fe3772275e08832605a78593ed 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_005/info.json +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_005/info.json @@ -1,398 +1,391 @@ -{ - "name": "Hotel Booking Demand", - "source": "https://www.kaggle.com/datasets/jessemostipak/hotel-booking-demand/data", - "data_intro": "This dataset contains hotel booking data for a city hotel and a resort hotel, providing details on booking dates, stay duration, guest count, cancellation trends, and pricing. It is useful for demand forecasting, revenue management, and customer behavior analysis.", - "is_splited": false, - "overall_size": 119390, - "train_size": 0, - "test_size": 0, - "c_classes": 12, - "n_classes": 20, - "cat_feature_intro": { - "hotel": "Type of hotel (City Hotel or Resort Hotel).", - "arrival_date_month": "Month of arrival for the booking.", - "meal": "Type of meal plan booked (e.g., Breakfast included, Half board, Full board).", - "country": "Country of origin of the guest.", - "market_segment": "Booking distribution channel (e.g., direct, corporate, travel agent).", - "distribution_channel": "Channel through which the booking was made (e.g., online travel agents, direct).", - "reserved_room_type": "Type of room reserved by the guest before any changes.", - "assigned_room_type": "Final room type assigned to the guest.", - "deposit_type": "Indicates if a deposit was made and its type (e.g., No Deposit, Refundable, Non-Refundable).", - "customer_type": "Type of customer (e.g., transient, group, contract).", - "reservation_status": "Final status of the booking (e.g., Canceled, Checked-out, No-show).", - "reservation_status_date": "Date when the reservation status was last updated." - }, - "num_feature_intro": { - "is_canceled": "Indicates whether the booking was canceled (1) or not (0).", - "lead_time": "Number of days between booking and arrival.", - "arrival_date_year": "Year of arrival for the booking.", - "arrival_date_week_number": "Week number of the arrival date.", - "arrival_date_day_of_month": "Day of the month of arrival.", - "stays_in_weekend_nights": "Number of weekend nights (Saturday or Sunday) the guest stayed.", - "stays_in_week_nights": "Number of weekday nights (Monday to Friday) the guest stayed.", - "adults": "Number of adults included in the booking.", - "children": "Number of children included in the booking.", - "babies": "Number of babies included in the booking.", - "is_repeated_guest": "Indicates whether the guest is a returning customer (1) or not (0).", - "previous_cancellations": "Number of times the guest has canceled previous bookings.", - "previous_bookings_not_canceled": "Number of previous bookings not canceled by the guest.", - "booking_changes": "Number of changes made to the booking.", - "agent": "ID of the booking agent (if applicable).", - "company": "ID of the company making the booking (if applicable).", - "days_in_waiting_list": "Number of days the booking was on the waiting list before confirmation.", - "adr": "Average daily rate (price per night per room).", - "required_car_parking_spaces": "Number of car parking spaces required by the guest.", - "total_of_special_requests": "Number of special requests made by the guest (e.g., high floor, twin beds)." - }, - "evaluation_metric": null, - "task_type": "classification", - "target": "reservation_status", - "num_feature_value": { - "adr": [ - -6.38, - 5400.0 - ], - "adults": [ - 0.0, - 55.0 - ], - "agent": [ - 1.0, - 535.0 - ], - "arrival_date_day_of_month": [ - 1.0, - 31.0 - ], - "arrival_date_week_number": [ - 1.0, - 53.0 - ], - "arrival_date_year": [ - 2015.0, - 2017.0 - ], - "babies": [ - 0.0, - 10.0 - ], - "booking_changes": [ - 0.0, - 21.0 - ], - "children": [ - 0.0, - 10.0 - ], - "company": [ - 6.0, - 543.0 - ], - "days_in_waiting_list": [ - 0.0, - 391.0 - ], - "is_canceled": [ - 0.0, - 1.0 - ], - "is_repeated_guest": [ - 0.0, - 1.0 - ], - "lead_time": [ - 0.0, - 737.0 - ], - "previous_bookings_not_canceled": [ - 0.0, - 72.0 - ], - "previous_cancellations": [ - 0.0, - 26.0 - ], - "required_car_parking_spaces": [ - 0.0, - 8.0 - ], - "stays_in_week_nights": [ - 0.0, - 50.0 - ], - "stays_in_weekend_nights": [ - 0.0, - 19.0 - ], - "total_of_special_requests": [ - 0.0, - 5.0 - ] - }, - "cat_feature_value": { - "arrival_date_month": [ - "April", - "August", - "December", - "February", - "January", - "July", - "June", - "March", - "May", - "November", - "October", - "September" - ], - "assigned_room_type": [ - "A", - "B", - "C", - "D", - "E", - "F", - "G", - "H", - "I", - "K", - "L", - "P" - ], - "country": [ - "ABW", - "AGO", - "AIA", - "ALB", - "AND", - "ARE", - "ARG", - "ARM", - "ASM", - "ATA", - "ATF", - "AUS", - "AUT", - "AZE", - "BDI", - "BEL", - "BEN", - "BFA", - "BGD", - "BGR", - "BHR", - "BHS", - "BIH", - "BLR", - "BOL", - "BRA", - "BRB", - "BWA", - "CAF", - "CHE", - "CHL", - "CHN", - "CIV", - "CMR", - "CN", - "COL", - "COM", - "CPV", - "CRI", - "CUB", - "CYM", - "CYP", - "CZE", - "DEU", - "DJI", - "DMA", - "DNK", - "DOM", - "DZA", - "ECU", - "EGY", - "ESP", - "EST", - "ETH", - "FIN", - "FJI", - "FRA", - "FRO", - "GAB", - "GBR", - "GEO", - "GGY", - "GHA", - "GIB", - "GLP", - "GNB", - "GRC", - "GTM", - "GUY", - "HKG", - "HND", - "HRV", - "HUN", - "IDN", - "IMN", - "IND", - "IRL", - "IRN", - "IRQ", - "ISL", - "ISR", - "ITA", - "JAM", - "JEY", - "JOR", - "JPN", - "KAZ", - "KEN", - "KHM", - "KIR", - "KNA", - "KOR", - "KWT", - "LAO", - "LBN", - "LBY", - "LCA", - "LIE", - "LKA", - "LTU", - "LUX", - "LVA", - "MAC", - "MAR", - "MCO", - "MDG", - "MDV", - "MEX", - "MKD", - "MLI", - "MLT", - "MMR", - "MNE", - "MOZ", - "MRT", - "MUS", - "MWI", - "MYS", - "MYT", - "NAM", - "NCL", - "NGA", - "NIC", - "NLD", - "NOR", - "NPL", - "NZL", - "OMN", - "PAK", - "PAN", - "PER", - "PHL", - "PLW", - "POL", - "PRI", - "PRT", - "PRY", - "PYF", - "QAT", - "ROU", - "RUS", - "RWA", - "SAU", - "SDN", - "SEN", - "SGP", - "SLE", - "SLV", - "SMR", - "SRB", - "STP", - "SUR", - "SVK", - "SVN", - "SWE", - "SYC", - "SYR", - "TGO", - "THA", - "TJK", - "TMP", - "TUN", - "TUR", - "TWN", - "TZA", - "UGA", - "UKR", - "UMI", - "URY", - "USA", - "UZB", - "VEN", - "VGB", - "VNM", - "ZAF", - "ZMB", - "ZWE" - ], - "customer_type": [ - "Contract", - "Group", - "Transient", - "Transient-Party" - ], - "deposit_type": [ - "No Deposit", - "Non Refund", - "Refundable" - ], - "distribution_channel": [ - "Corporate", - "Direct", - "GDS", - "TA/TO", - "Undefined" - ], - "hotel": [ - "City Hotel", - "Resort Hotel" - ], - "market_segment": [ - "Aviation", - "Complementary", - "Corporate", - "Direct", - "Groups", - "Offline TA/TO", - "Online TA", - "Undefined" - ], - "meal": [ - "BB", - "FB", - "HB", - "SC", - "Undefined" - ], - "reservation_status": [ - "Canceled", - "Check-Out", - "No-Show" - ], - "reservation_status_date": "string", - "reserved_room_type": [ - "A", - "B", - "C", - "D", - "E", - "F", - "G", - "H", - "L", - "P" - ] - } -} \ No newline at end of file +{ + "name": "Hotel Booking Demand", + "source": "https://www.kaggle.com/datasets/jessemostipak/hotel-booking-demand/data", + "data_intro": "This dataset contains hotel booking data for a city hotel and a resort hotel, providing details on booking dates, stay duration, guest count, cancellation trends, and pricing. It is useful for demand forecasting, revenue management, and customer behavior analysis.", + "is_splited": false, + "overall_size": 119390, + "train_size": 0, + "test_size": 0, + "c_classes": 11, + "n_classes": 19, + "cat_feature_intro": { + "hotel": "Type of hotel (City Hotel or Resort Hotel).", + "arrival_date_month": "Month of arrival for the booking.", + "meal": "Type of meal plan booked (e.g., Breakfast included, Half board, Full board).", + "country": "Country of origin of the guest.", + "market_segment": "Booking distribution channel (e.g., direct, corporate, travel agent).", + "distribution_channel": "Channel through which the booking was made (e.g., online travel agents, direct).", + "reserved_room_type": "Type of room reserved by the guest before any changes.", + "assigned_room_type": "Final room type assigned to the guest.", + "deposit_type": "Indicates if a deposit was made and its type (e.g., No Deposit, Refundable, Non-Refundable).", + "customer_type": "Type of customer (e.g., transient, group, contract).", + "reservation_status": "Final status of the booking (e.g., Canceled, Checked-out, No-show)." + }, + "num_feature_intro": { + "lead_time": "Number of days between booking and arrival.", + "arrival_date_year": "Year of arrival for the booking.", + "arrival_date_week_number": "Week number of the arrival date.", + "arrival_date_day_of_month": "Day of the month of arrival.", + "stays_in_weekend_nights": "Number of weekend nights (Saturday or Sunday) the guest stayed.", + "stays_in_week_nights": "Number of weekday nights (Monday to Friday) the guest stayed.", + "adults": "Number of adults included in the booking.", + "children": "Number of children included in the booking.", + "babies": "Number of babies included in the booking.", + "is_repeated_guest": "Indicates whether the guest is a returning customer (1) or not (0).", + "previous_cancellations": "Number of times the guest has canceled previous bookings.", + "previous_bookings_not_canceled": "Number of previous bookings not canceled by the guest.", + "booking_changes": "Number of changes made to the booking.", + "agent": "ID of the booking agent (if applicable).", + "company": "ID of the company making the booking (if applicable).", + "days_in_waiting_list": "Number of days the booking was on the waiting list before confirmation.", + "adr": "Average daily rate (price per night per room).", + "required_car_parking_spaces": "Number of car parking spaces required by the guest.", + "total_of_special_requests": "Number of special requests made by the guest (e.g., high floor, twin beds)." + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "reservation_status", + "num_feature_value": { + "adr": [ + -6.38, + 5400.0 + ], + "adults": [ + 0.0, + 55.0 + ], + "agent": [ + 1.0, + 535.0 + ], + "arrival_date_day_of_month": [ + 1.0, + 31.0 + ], + "arrival_date_week_number": [ + 1.0, + 53.0 + ], + "arrival_date_year": [ + 2015.0, + 2017.0 + ], + "babies": [ + 0.0, + 10.0 + ], + "booking_changes": [ + 0.0, + 21.0 + ], + "children": [ + 0.0, + 10.0 + ], + "company": [ + 6.0, + 543.0 + ], + "days_in_waiting_list": [ + 0.0, + 391.0 + ], + "is_repeated_guest": [ + 0.0, + 1.0 + ], + "lead_time": [ + 0.0, + 737.0 + ], + "previous_bookings_not_canceled": [ + 0.0, + 72.0 + ], + "previous_cancellations": [ + 0.0, + 26.0 + ], + "required_car_parking_spaces": [ + 0.0, + 8.0 + ], + "stays_in_week_nights": [ + 0.0, + 50.0 + ], + "stays_in_weekend_nights": [ + 0.0, + 19.0 + ], + "total_of_special_requests": [ + 0.0, + 5.0 + ] + }, + "cat_feature_value": { + "arrival_date_month": [ + "April", + "August", + "December", + "February", + "January", + "July", + "June", + "March", + "May", + "November", + "October", + "September" + ], + "assigned_room_type": [ + "A", + "B", + "C", + "D", + "E", + "F", + "G", + "H", + "I", + "K", + "L", + "P" + ], + "country": [ + "ABW", + "AGO", + "AIA", + "ALB", + "AND", + "ARE", + "ARG", + "ARM", + "ASM", + "ATA", + "ATF", + "AUS", + "AUT", + "AZE", + "BDI", + "BEL", + "BEN", + "BFA", + "BGD", + "BGR", + "BHR", + "BHS", + "BIH", + "BLR", + "BOL", + "BRA", + "BRB", + "BWA", + "CAF", + "CHE", + "CHL", + "CHN", + "CIV", + "CMR", + "CN", + "COL", + "COM", + "CPV", + "CRI", + "CUB", + "CYM", + "CYP", + "CZE", + "DEU", + "DJI", + "DMA", + "DNK", + "DOM", + "DZA", + "ECU", + "EGY", + "ESP", + "EST", + "ETH", + "FIN", + "FJI", + "FRA", + "FRO", + "GAB", + "GBR", + "GEO", + "GGY", + "GHA", + "GIB", + "GLP", + "GNB", + "GRC", + "GTM", + "GUY", + "HKG", + "HND", + "HRV", + "HUN", + "IDN", + "IMN", + "IND", + "IRL", + "IRN", + "IRQ", + "ISL", + "ISR", + "ITA", + "JAM", + "JEY", + "JOR", + "JPN", + "KAZ", + "KEN", + "KHM", + "KIR", + "KNA", + "KOR", + "KWT", + "LAO", + "LBN", + "LBY", + "LCA", + "LIE", + "LKA", + "LTU", + "LUX", + "LVA", + "MAC", + "MAR", + "MCO", + "MDG", + "MDV", + "MEX", + "MKD", + "MLI", + "MLT", + "MMR", + "MNE", + "MOZ", + "MRT", + "MUS", + "MWI", + "MYS", + "MYT", + "NAM", + "NCL", + "NGA", + "NIC", + "NLD", + "NOR", + "NPL", + "NZL", + "OMN", + "PAK", + "PAN", + "PER", + "PHL", + "PLW", + "POL", + "PRI", + "PRT", + "PRY", + "PYF", + "QAT", + "ROU", + "RUS", + "RWA", + "SAU", + "SDN", + "SEN", + "SGP", + "SLE", + "SLV", + "SMR", + "SRB", + "STP", + "SUR", + "SVK", + "SVN", + "SWE", + "SYC", + "SYR", + "TGO", + "THA", + "TJK", + "TMP", + "TUN", + "TUR", + "TWN", + "TZA", + "UGA", + "UKR", + "UMI", + "URY", + "USA", + "UZB", + "VEN", + "VGB", + "VNM", + "ZAF", + "ZMB", + "ZWE" + ], + "customer_type": [ + "Contract", + "Group", + "Transient", + "Transient-Party" + ], + "deposit_type": [ + "No Deposit", + "Non Refund", + "Refundable" + ], + "distribution_channel": [ + "Corporate", + "Direct", + "GDS", + "TA/TO", + "Undefined" + ], + "hotel": [ + "City Hotel", + "Resort Hotel" + ], + "market_segment": [ + "Aviation", + "Complementary", + "Corporate", + "Direct", + "Groups", + "Offline TA/TO", + "Online TA", + "Undefined" + ], + "meal": [ + "BB", + "FB", + "HB", + "SC", + "Undefined" + ], + "reservation_status": [ + "Canceled", + "Check-Out", + "No-Show" + ], + "reserved_room_type": [ + "A", + "B", + "C", + "D", + "E", + "F", + "G", + "H", + "L", + "P" + ] + } +} diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_005/test_current.csv b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_005/test_current.csv index b2588234e8edc11b4c9bace82a1af5cce8cd557b..313fd5f6664c874b67b5a5a92f7e543ffc12db62 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_005/test_current.csv +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_005/test_current.csv @@ -1,16 +1,16 @@ -hotel,is_canceled,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests,reservation_status,reservation_status_date -City Hotel,1,26,2016,November,45,2,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,10,A,E,0,No Deposit,,219.0,0,Transient,88.0,0,1,No-Show,2016-11-02 -City Hotel,1,0,2016,May,21,18,0,1,1,0.0,0,BB,PRT,Corporate,Direct,0,0,0,A,E,2,No Deposit,,38.0,0,Transient-Party,163.18,0,0,No-Show,2016-05-18 -Resort Hotel,1,33,2015,November,45,1,1,0,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,1,A,A,0,No Deposit,,94.0,0,Transient,25.0,0,0,No-Show,2015-11-01 -City Hotel,1,5,2015,August,35,26,0,2,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,D,0,No Deposit,,40.0,0,Transient,65.0,0,0,No-Show,2015-08-26 -Resort Hotel,1,0,2016,May,23,30,1,4,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,195.0,0,Transient,57.0,0,0,No-Show,2016-05-30 -City Hotel,1,8,2016,February,8,19,0,1,1,0.0,0,BB,PRT,Corporate,TA/TO,0,0,0,A,A,0,No Deposit,,67.0,0,Transient-Party,81.0,0,0,No-Show,2016-02-19 -Resort Hotel,1,4,2017,April,17,24,1,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,E,0,No Deposit,,515.0,0,Transient,52.5,0,0,No-Show,2017-04-24 -Resort Hotel,1,9,2016,December,52,24,1,1,2,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,D,D,0,No Deposit,,270.0,0,Transient,45.0,0,1,No-Show,2016-12-24 -City Hotel,1,12,2015,September,38,14,1,3,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,40.0,0,Transient,65.0,0,0,No-Show,2015-09-14 -City Hotel,1,12,2016,October,42,12,0,2,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,4,A,A,0,No Deposit,,40.0,0,Transient,65.0,0,1,No-Show,2016-10-12 -City Hotel,1,68,2015,December,49,2,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,40.0,0,Transient,65.0,0,1,No-Show,2015-12-02 -City Hotel,1,0,2016,April,16,12,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,40.0,0,Transient,65.0,0,0,No-Show,2016-04-12 -City Hotel,1,20,2017,June,26,29,0,1,2,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,465.0,0,Transient,95.0,0,2,No-Show,2017-06-29 -Resort Hotel,1,3,2017,January,3,19,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,2,A,A,0,No Deposit,,470.0,0,Transient,35.0,0,1,No-Show,2017-01-19 -City Hotel,1,1,2015,November,47,19,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,40.0,0,Transient,65.0,0,0,No-Show,2015-11-19 +hotel,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests,reservation_status +City Hotel,26,2016,November,45,2,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,10,A,E,0,No Deposit,,219.0,0,Transient,88.0,0,1,No-Show +City Hotel,0,2016,May,21,18,0,1,1,0.0,0,BB,PRT,Corporate,Direct,0,0,0,A,E,2,No Deposit,,38.0,0,Transient-Party,163.18,0,0,No-Show +Resort Hotel,33,2015,November,45,1,1,0,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,1,A,A,0,No Deposit,,94.0,0,Transient,25.0,0,0,No-Show +City Hotel,5,2015,August,35,26,0,2,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,D,0,No Deposit,,40.0,0,Transient,65.0,0,0,No-Show +Resort Hotel,0,2016,May,23,30,1,4,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,195.0,0,Transient,57.0,0,0,No-Show +City Hotel,8,2016,February,8,19,0,1,1,0.0,0,BB,PRT,Corporate,TA/TO,0,0,0,A,A,0,No Deposit,,67.0,0,Transient-Party,81.0,0,0,No-Show +Resort Hotel,4,2017,April,17,24,1,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,E,0,No Deposit,,515.0,0,Transient,52.5,0,0,No-Show +Resort Hotel,9,2016,December,52,24,1,1,2,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,D,D,0,No Deposit,,270.0,0,Transient,45.0,0,1,No-Show +City Hotel,12,2015,September,38,14,1,3,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,40.0,0,Transient,65.0,0,0,No-Show +City Hotel,12,2016,October,42,12,0,2,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,4,A,A,0,No Deposit,,40.0,0,Transient,65.0,0,1,No-Show +City Hotel,68,2015,December,49,2,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,40.0,0,Transient,65.0,0,1,No-Show +City Hotel,0,2016,April,16,12,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,40.0,0,Transient,65.0,0,0,No-Show +City Hotel,20,2017,June,26,29,0,1,2,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,465.0,0,Transient,95.0,0,2,No-Show +Resort Hotel,3,2017,January,3,19,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,2,A,A,0,No Deposit,,470.0,0,Transient,35.0,0,1,No-Show +City Hotel,1,2015,November,47,19,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,40.0,0,Transient,65.0,0,0,No-Show diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_006/Hotel_booking_demand_B4_006_current.json b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_006/Hotel_booking_demand_B4_006_current.json index 53e36e377494a67d74c547cc1821d208f745e379..6cbbcaa55a252894238ff020015b5c2d6a1c0017 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_006/Hotel_booking_demand_B4_006_current.json +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_006/Hotel_booking_demand_B4_006_current.json @@ -1,7403 +1,7005 @@ { - "id": "006", - "task_type": "B4", - "subtask_type": "filter", - "perspective": "data_holder", - "dataset_name": "Hotel booking demand", - "table_path": "output/daily/classification/Hotel booking demand_B4/Hotel booking demand_B4_006/test.csv", - "query": "Our resort hotel is looking to create a premium package for our most loyal, repeat customers. I've shared our archives with you. Could you please flag all the bookings at the 'Resort Hotel' where the guest was marked as a 'repeated guest' in the NEW batch of data? From this list, I specifically want to isolate those who have a history of actually checking out, so we can reward our reliable regulars.", - "meta_info": { - "domain": "daily" + "id": "006", + "task_type": "B4", + "subtask_type": "filter", + "perspective": "data_holder", + "dataset_name": "Hotel booking demand", + "table_path": "output/daily/classification/Hotel booking demand_B4/Hotel booking demand_B4_006/test.csv", + "query": "Our resort hotel is looking to create a premium package for our most loyal, repeat customers. I've shared our archives with you. Could you please flag all the bookings at the 'Resort Hotel' where the guest was marked as a 'repeated guest' in the NEW batch of data? From this list, I specifically want to isolate those who have a history of actually checking out, so we can reward our reliable regulars.", + "meta_info": { + "domain": "daily" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "hotel": "Resort Hotel", + "lead_time": 169, + "arrival_date_year": 2017, + "arrival_date_month": "July", + "arrival_date_week_number": 31, + "arrival_date_day_of_month": 31, + "stays_in_weekend_nights": 1, + "stays_in_week_nights": 3, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "HB", + "country": "PRT", + "market_segment": "Online TA", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "D", + "assigned_room_type": "D", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 240.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 212.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 3, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "002", + "features": { + "hotel": "City Hotel", + "lead_time": 2, + "arrival_date_year": 2016, + "arrival_date_month": "March", + "arrival_date_week_number": 10, + "arrival_date_day_of_month": 2, + "stays_in_weekend_nights": 0, + "stays_in_week_nights": 1, + "adults": 1, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "PRT", + "market_segment": "Offline TA/TO", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 21.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient-Party", + "adr": 55.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "004", + "features": { + "hotel": "City Hotel", + "lead_time": 290, + "arrival_date_year": 2016, + "arrival_date_month": "June", + "arrival_date_week_number": 25, + "arrival_date_day_of_month": 17, + "stays_in_weekend_nights": 1, + "stays_in_week_nights": 2, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "PRT", + "market_segment": "Offline TA/TO", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 3.0, + "company": null, + "days_in_waiting_list": 108, + "customer_type": "Transient-Party", + "adr": 72.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Canceled" + } + }, + { + "scenario_id": "007", + "features": { + "hotel": "City Hotel", + "lead_time": 15, + "arrival_date_year": 2016, + "arrival_date_month": "September", + "arrival_date_week_number": 40, + "arrival_date_day_of_month": 26, + "stays_in_weekend_nights": 1, + "stays_in_week_nights": 2, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "PRT", + "market_segment": "Online TA", + "distribution_channel": "TA/TO", + "is_repeated_guest": 1, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 1, + "reserved_room_type": "D", + "assigned_room_type": "D", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 9.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 164.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "009", + "features": { + "hotel": "City Hotel", + "lead_time": 20, + "arrival_date_year": 2016, + "arrival_date_month": "February", + "arrival_date_week_number": 7, + "arrival_date_day_of_month": 8, + "stays_in_weekend_nights": 3, + "stays_in_week_nights": 5, + "adults": 1, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "AGO", + "market_segment": "Online TA", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 9.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 86.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 1, + "reservation_status": "No-Show" + } + }, + { + "scenario_id": "011", + "features": { + "hotel": "City Hotel", + "lead_time": 0, + "arrival_date_year": 2017, + "arrival_date_month": "August", + "arrival_date_week_number": 32, + "arrival_date_day_of_month": 6, + "stays_in_weekend_nights": 1, + "stays_in_week_nights": 0, + "adults": 2, + "children": 1.0, + "babies": 0, + "meal": "BB", + "country": "PRT", + "market_segment": "Direct", + "distribution_channel": "Direct", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": null, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 170.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "012", + "features": { + "hotel": "City Hotel", + "lead_time": 71, + "arrival_date_year": 2016, + "arrival_date_month": "June", + "arrival_date_week_number": 25, + "arrival_date_day_of_month": 14, + "stays_in_weekend_nights": 0, + "stays_in_week_nights": 3, + "adults": 1, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "PRT", + "market_segment": "Offline TA/TO", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 0, + "deposit_type": "Non Refund", + "agent": 236.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 120.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Canceled" + } + }, + { + "scenario_id": "013", + "features": { + "hotel": "City Hotel", + "lead_time": 552, + "arrival_date_year": 2017, + "arrival_date_month": "January", + "arrival_date_week_number": 2, + "arrival_date_day_of_month": 12, + "stays_in_weekend_nights": 0, + "stays_in_week_nights": 2, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "PRT", + "market_segment": "Groups", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 0, + "deposit_type": "Non Refund", + "agent": 1.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 62.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Canceled" + } + }, + { + "scenario_id": "014", + "features": { + "hotel": "City Hotel", + "lead_time": 329, + "arrival_date_year": 2015, + "arrival_date_month": "September", + "arrival_date_week_number": 37, + "arrival_date_day_of_month": 11, + "stays_in_weekend_nights": 2, + "stays_in_week_nights": 3, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "PRT", + "market_segment": "Offline TA/TO", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 1, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 0, + "deposit_type": "Non Refund", + "agent": 5.0, + "company": null, + "days_in_waiting_list": 0, + 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"is_canceled": 1, - "lead_time": 74, - "arrival_date_year": 2016, - "arrival_date_month": "February", - "arrival_date_week_number": 9, - "arrival_date_day_of_month": 26, - "stays_in_weekend_nights": 1, - "stays_in_week_nights": 2, - "adults": 2, - "children": 0.0, - "babies": 0, - "meal": "Undefined", - "country": "PRT", - "market_segment": "Groups", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "A", - "assigned_room_type": "A", - "booking_changes": 0, - "deposit_type": "Non Refund", - "agent": 68.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 70.0, - "required_car_parking_spaces": 0, - "total_of_special_requests": 0, - "reservation_status": "Canceled", - "reservation_status_date": "2016-01-21" - } - }, - { - "scenario_id": "492", - "features": { - "hotel": "City Hotel", - "is_canceled": 1, - "lead_time": 95, - "arrival_date_year": 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4, - "adults": 2, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "GBR", - "market_segment": "Online TA", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "D", - "assigned_room_type": "D", - "booking_changes": 2, - "deposit_type": "No Deposit", - "agent": 8.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 134.4, - "required_car_parking_spaces": 0, - "total_of_special_requests": 1, - "reservation_status": "Check-Out", - "reservation_status_date": "2016-10-04" - } - } - ], - "target_column": "reservation_status", - "task_sub_type": "classification", - "ranking_ground_truth": { - "top_k_ids": [ - "126" - ] - }, - "active_filters": [ - { - "col": "hotel", - "op": "==", - "val": "Resort Hotel" - }, - { - "col": "is_repeated_guest", - "op": "==", - "val": 1 - } - ], - "target_class_value": "check-out" - }, - "response": "", - "evaluation_score": {} -} \ No newline at end of file + "active_filters": [ + { + "col": "hotel", + "op": "==", + "val": "Resort Hotel" + }, + { + "col": "is_repeated_guest", + "op": "==", + "val": 1 + } + ], + "target_class_value": "check-out" + }, + "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_006/current.csv b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_006/current.csv index 531f90d9ad9ff31b93b68e65d62f9c2b65397826..84984c17a5858c379263521a26e7f21b6b8e38df 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_006/current.csv +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_006/current.csv @@ -1,201 +1,200 @@ -hotel,is_canceled,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests,reservation_status_date -Resort Hotel,0,169,2017,July,31,31,1,3,2,0.0,0,HB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,212.0,0,3,2017-08-04 -City Hotel,0,2,2016,March,10,2,0,1,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,21.0,,0,Transient-Party,55.0,0,0,2016-03-03 -City Hotel,1,290,2016,June,25,17,1,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,3.0,,108,Transient-Party,72.0,0,0,2016-06-15 -City Hotel,0,15,2016,September,40,26,1,2,2,0.0,0,BB,PRT,Online TA,TA/TO,1,0,1,D,D,0,No Deposit,9.0,,0,Transient,164.0,0,0,2016-09-29 -City Hotel,1,20,2016,February,7,8,3,5,1,0.0,0,BB,AGO,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,86.0,0,1,2016-02-08 -City Hotel,0,0,2017,August,32,6,1,0,2,1.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,170.0,0,0,2017-08-07 -City Hotel,1,71,2016,June,25,14,0,3,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,236.0,,0,Transient,120.0,0,0,2016-04-27 -City Hotel,1,552,2017,January,2,12,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0,2015-10-21 -City Hotel,1,329,2015,September,37,11,2,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,Non Refund,5.0,,0,Transient,90.0,0,0,2015-02-20 -City Hotel,1,37,2017,March,11,15,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,378.0,,0,Transient,110.0,0,0,2017-02-06 -Resort Hotel,1,107,2017,June,24,16,2,6,2,0.0,0,HB,PRT,Direct,Direct,0,0,0,C,C,1,No Deposit,250.0,,0,Transient,185.0,0,0,2017-03-03 -City Hotel,1,299,2015,August,33,12,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient-Party,62.0,0,0,2015-07-02 -City Hotel,1,213,2016,June,24,7,0,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,19.0,,62,Transient,130.0,0,0,2016-01-08 -City Hotel,0,93,2017,August,32,9,0,3,2,0.0,0,BB,IRL,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,155.0,0,2,2017-08-12 -City Hotel,0,242,2016,July,31,29,0,2,0,2.0,0,BB,BEL,Direct,Direct,0,0,0,B,B,0,No Deposit,14.0,,0,Transient-Party,6.0,0,1,2016-07-31 -City Hotel,0,155,2017,July,30,28,2,3,2,0.0,0,BB,AUT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,130.5,0,0,2017-08-02 -City Hotel,1,227,2016,October,42,10,1,2,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,E,E,0,Non Refund,,51.0,0,Transient-Party,106.0,0,0,2016-08-29 -City Hotel,0,117,2016,October,44,28,2,2,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,104.4,0,2,2016-11-01 -City Hotel,0,49,2017,March,10,10,1,2,2,0.0,0,BB,USA,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,83.0,,0,Transient,86.76,1,1,2017-03-13 -City Hotel,0,184,2016,October,43,17,1,3,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,128.7,0,2,2016-10-21 -City Hotel,0,52,2017,March,11,13,1,3,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,D,2,No Deposit,28.0,,0,Transient,75.0,0,0,2017-03-17 -City Hotel,0,5,2016,October,44,28,1,2,2,0.0,0,HB,FRA,Online TA,TA/TO,0,0,0,A,B,0,No Deposit,9.0,,0,Transient,200.33,0,2,2016-10-31 -City Hotel,0,257,2015,July,27,1,0,2,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient,101.5,0,0,2015-07-03 -City Hotel,0,133,2016,April,16,14,0,3,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,28.0,,0,Transient,80.0,0,0,2016-04-17 -City Hotel,0,13,2016,March,13,20,1,0,2,0.0,0,BB,BRA,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,220.0,,0,Transient-Party,85.0,0,1,2016-03-21 -City Hotel,1,127,2016,August,35,21,3,5,2,0.0,0,BB,ESP,Direct,Direct,0,0,0,D,D,0,No Deposit,14.0,,0,Transient,113.55,0,1,2016-04-23 -City Hotel,0,141,2017,August,34,23,0,2,2,0.0,0,BB,USA,Direct,Direct,0,0,0,D,D,0,No Deposit,14.0,,0,Transient,150.0,0,2,2017-08-25 -City Hotel,0,1,2016,June,25,14,0,4,1,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,121.5,0,1,2016-06-18 -City Hotel,1,4,2016,June,25,14,0,2,2,0.0,0,HB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,164.0,0,0,2016-06-14 -City Hotel,1,0,2015,August,33,14,0,2,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,109.0,0,0,2015-08-14 -City Hotel,0,5,2016,March,10,2,0,1,1,0.0,0,BB,FRA,Aviation,TA/TO,0,0,0,A,A,0,No Deposit,153.0,,0,Transient,91.0,0,0,2016-03-03 -City Hotel,1,166,2016,November,45,1,0,3,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,236.0,,0,Transient,110.0,0,0,2016-07-13 -City Hotel,0,15,2017,July,28,15,1,1,2,1.0,0,BB,CHN,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,9.0,,0,Transient,159.0,0,1,2017-07-17 -City Hotel,0,96,2015,July,28,11,2,1,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,17.0,,0,Transient-Party,94.5,0,0,2015-07-14 -City Hotel,0,0,2017,January,5,31,0,1,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,83.0,,0,Transient,78.4,0,0,2017-02-01 -City Hotel,0,1,2016,August,32,3,2,5,0,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,K,4,No Deposit,,,0,Transient,0.0,0,0,2016-08-10 -City Hotel,0,1,2017,February,8,23,0,2,1,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,71.0,0,1,2017-02-25 -City Hotel,1,346,2015,September,40,28,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Contract,62.0,0,0,2015-01-01 -City Hotel,0,70,2015,November,48,26,0,3,1,0.0,0,BB,USA,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,27.0,,0,Transient,46.33,0,0,2015-11-29 -Resort Hotel,1,228,2016,March,11,11,1,2,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,D,D,0,No Deposit,250.0,,0,Transient,39.1,0,1,2016-03-11 -City Hotel,1,74,2016,May,21,17,0,3,2,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,126.9,0,0,2016-03-04 -City Hotel,0,17,2016,November,45,3,1,3,1,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,61.52,0,1,2016-11-07 -City Hotel,0,145,2016,July,28,3,2,1,3,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,131.7,0,1,2016-07-06 -City Hotel,0,33,2016,December,49,1,1,3,2,0.0,0,SC,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,57.6,0,3,2016-12-05 -City Hotel,0,0,2016,December,53,28,0,1,2,2.0,0,BB,FRA,Direct,Direct,0,0,0,F,F,0,No Deposit,,,0,Transient,168.0,0,0,2016-12-29 -City Hotel,1,290,2015,August,32,3,1,3,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.0,0,0,2015-07-06 -Resort Hotel,1,259,2015,September,38,17,0,3,1,0.0,0,BB,GBR,Groups,Corporate,0,1,0,A,A,0,Non Refund,,223.0,0,Transient,33.3,0,0,2015-01-21 -Resort Hotel,0,165,2016,October,41,2,2,2,2,0.0,0,BB,CN,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,96.0,,0,Transient,46.0,0,0,2016-10-06 -Resort Hotel,1,17,2016,December,53,30,0,2,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,240.0,,0,Transient,120.0,0,1,2016-12-15 -City Hotel,0,26,2017,February,9,27,1,0,1,0.0,0,SC,AUT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,88.0,0,1,2017-02-28 -City Hotel,0,91,2016,October,42,12,0,2,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,9.0,,0,Transient,113.4,0,0,2016-10-14 -Resort Hotel,1,179,2016,October,43,21,1,2,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,240.0,,0,Transient,75.33,0,3,2016-10-05 -Resort Hotel,0,0,2015,November,48,24,0,1,1,0.0,0,BB,PRT,Online TA,TA/TO,1,0,0,A,A,0,No Deposit,241.0,,0,Transient,36.96,0,0,2015-11-25 -City Hotel,0,26,2016,February,7,7,2,2,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,11.0,,0,Transient,64.64,0,0,2016-02-11 -City Hotel,1,1,2016,November,49,28,1,3,1,0.0,0,BB,BRA,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,78.53,0,0,2016-11-27 -City Hotel,0,76,2016,November,47,17,0,3,2,1.0,0,BB,GBR,Direct,Direct,0,0,0,A,D,1,No Deposit,14.0,,0,Transient,92.2,0,2,2016-11-20 -City Hotel,0,36,2016,February,9,27,1,1,2,1.0,0,BB,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient-Party,107.3,1,1,2016-02-29 -City Hotel,0,7,2016,December,50,4,2,3,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,88.4,0,1,2016-12-09 -City Hotel,1,49,2016,July,29,11,1,0,2,0.0,0,SC,CHN,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,93.15,0,0,2016-06-20 -City Hotel,1,163,2016,August,35,23,0,5,2,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,84.15,0,0,2016-03-24 -City Hotel,1,42,2016,August,36,29,1,3,3,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,144.23,0,0,2016-07-19 -City Hotel,0,159,2016,April,15,9,1,1,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,3.0,,0,Transient-Party,70.0,0,1,2016-04-11 -City Hotel,0,98,2015,November,48,23,1,3,1,0.0,0,BB,NOR,Groups,TA/TO,0,0,0,A,D,1,No Deposit,37.0,,0,Transient-Party,70.0,0,0,2015-11-27 -City Hotel,1,60,2016,October,44,27,0,1,2,0.0,0,SC,CN,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,80.1,0,2,2016-08-29 -Resort Hotel,1,128,2016,June,26,22,2,5,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,121.0,0,1,2016-02-15 -City Hotel,0,154,2016,September,38,12,1,4,2,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,168.0,,0,Transient,80.75,0,0,2016-09-17 -City Hotel,1,130,2016,April,16,13,0,4,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,29.0,,10,Transient,100.0,0,0,2015-12-15 -City Hotel,1,91,2016,March,12,18,0,2,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,29.0,,20,Transient,75.0,0,0,2016-02-03 -City Hotel,0,1,2017,May,20,17,0,1,1,0.0,0,SC,ESP,Direct,Direct,0,0,0,A,C,0,No Deposit,,,0,Transient,160.0,0,0,2017-05-18 -City Hotel,0,16,2015,September,39,25,2,3,2,0.0,0,BB,ITA,Groups,TA/TO,0,0,0,A,B,1,No Deposit,1.0,,0,Transient-Party,170.0,0,0,2015-09-30 -City Hotel,0,7,2015,December,50,8,0,4,1,0.0,0,BB,GBR,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,40.0,0,Transient,65.0,0,0,2015-12-12 -City Hotel,1,164,2017,May,20,15,1,2,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,160.0,0,0,2017-01-31 -City Hotel,1,75,2016,March,12,17,1,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,6.0,,0,Transient,80.0,0,0,2016-02-03 -City Hotel,1,112,2017,April,17,27,0,3,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,9.0,,0,Transient,153.0,0,0,2017-01-15 -Resort Hotel,1,53,2016,February,9,26,2,7,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,48.0,0,1,2016-01-05 -City Hotel,1,59,2017,July,29,17,1,2,2,0.0,0,SC,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,130.0,0,0,2017-05-22 -City Hotel,1,216,2015,October,43,19,1,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,0,Transient-Party,12.0,0,0,2015-09-11 -City Hotel,1,37,2017,March,11,15,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,378.0,,0,Transient,110.0,0,0,2017-02-06 -City Hotel,0,103,2016,April,18,29,0,2,2,0.0,0,BB,BEL,Direct,Direct,0,0,0,D,D,0,No Deposit,14.0,,0,Transient,108.9,0,0,2016-05-01 -City Hotel,0,305,2017,May,21,25,0,2,3,0.0,0,BB,CN,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient-Party,142.2,0,2,2017-05-27 -City Hotel,1,111,2017,July,28,15,2,1,3,0.0,0,BB,CHE,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,177.33,0,2,2017-07-15 -Resort Hotel,1,105,2016,December,50,8,1,3,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,C,1,No Deposit,240.0,,0,Transient,37.8,0,3,2016-11-24 -City Hotel,1,75,2017,April,14,5,0,4,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,132.0,,0,Transient-Party,85.0,0,0,2017-03-25 -City Hotel,1,159,2017,May,21,22,1,2,1,0.0,0,BB,CYP,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,9.0,,0,Transient,126.0,0,0,2017-02-22 -City Hotel,1,322,2016,May,21,19,1,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,31.0,,120,Transient,80.0,0,0,2016-02-10 -City Hotel,1,163,2017,July,28,13,0,3,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,116.1,0,0,2017-01-31 -City Hotel,1,94,2016,November,45,4,1,2,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,90.0,0,0,2016-08-18 -City Hotel,0,5,2015,August,34,17,3,10,1,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Contract,92.31,0,2,2015-08-30 -City Hotel,0,24,2016,September,38,16,0,2,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,149.0,0,1,2016-09-18 -City Hotel,1,163,2017,July,27,3,1,5,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,116.1,0,1,2017-05-22 -City Hotel,0,2,2017,February,5,3,0,1,1,0.0,0,BB,GBR,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,80.0,0,0,2017-02-04 -City Hotel,0,19,2015,October,41,6,0,3,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,1,0,0,A,D,0,No Deposit,82.0,,0,Transient-Party,80.0,0,0,2015-10-09 -City Hotel,1,184,2017,May,18,1,1,2,2,0.0,0,SC,RUS,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,99.0,0,0,2016-10-29 -Resort Hotel,0,51,2016,March,12,17,0,1,2,0.0,0,BB,CHN,Groups,TA/TO,0,0,0,A,D,0,No Deposit,66.0,,0,Transient-Party,30.0,0,0,2016-03-18 -City Hotel,0,3,2016,May,20,13,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,14,E,E,0,Non Refund,,51.0,0,Transient-Party,106.0,0,0,2016-05-14 -City Hotel,0,192,2016,June,26,24,0,2,1,0.0,0,BB,SRB,Offline TA/TO,TA/TO,0,0,0,A,B,0,No Deposit,34.0,,0,Transient-Party,95.0,0,0,2016-06-26 -City Hotel,0,214,2016,August,33,7,2,2,2,2.0,0,BB,NLD,Online TA,TA/TO,0,0,0,F,F,2,No Deposit,9.0,,0,Transient-Party,158.95,0,1,2016-08-11 -City Hotel,0,22,2015,November,45,6,2,2,2,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,27.0,,0,Transient,43.5,0,0,2015-11-10 -City Hotel,1,196,2016,August,35,22,1,5,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,99.45,0,0,2016-02-09 -City Hotel,1,292,2015,August,32,5,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient-Party,62.0,0,0,2015-07-02 -Resort Hotel,0,79,2016,May,23,30,1,3,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,99.5,1,1,2016-06-03 -City Hotel,0,11,2015,December,50,11,0,2,2,0.0,0,BB,PRT,Complementary,Direct,0,0,0,B,B,0,No Deposit,,,0,Transient,0.0,0,1,2015-12-13 -City Hotel,0,7,2017,March,13,28,0,2,1,0.0,0,BB,ESP,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,441.0,,0,Transient,103.0,0,0,2017-03-30 -City Hotel,1,40,2017,May,22,30,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,130.0,0,0,2017-05-05 -City Hotel,0,98,2015,November,48,23,1,2,1,0.0,0,BB,MYS,Groups,TA/TO,0,0,0,A,A,0,No Deposit,37.0,,0,Transient-Party,70.0,0,1,2015-11-26 -City Hotel,0,73,2016,September,37,6,0,2,3,0.0,0,BB,CHE,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,168.3,0,2,2016-09-08 -City Hotel,0,38,2017,June,24,15,0,1,2,0.0,0,SC,POL,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,9.0,,0,Transient,120.0,0,2,2017-06-16 -City Hotel,1,337,2016,June,24,9,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0,2015-10-21 -City Hotel,1,212,2017,February,8,22,0,4,2,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,85.0,0,0,2016-07-29 -City Hotel,0,5,2015,September,38,19,0,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,E,0,No Deposit,9.0,,0,Contract,133.0,0,1,2015-09-20 -City Hotel,0,25,2016,October,43,16,2,1,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,28.0,,0,Transient,85.0,0,0,2016-10-19 -City Hotel,1,28,2016,August,35,24,0,2,2,0.0,0,BB,CHN,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,149.4,0,1,2016-08-24 -City Hotel,0,86,2015,September,39,24,0,2,1,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,D,1,No Deposit,12.0,,0,Transient-Party,87.5,0,0,2015-09-26 -City Hotel,0,12,2017,August,31,1,0,1,2,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,109.0,0,1,2017-08-02 -City Hotel,1,475,2016,October,44,27,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0,2015-10-21 -City Hotel,1,16,2016,July,30,19,0,2,3,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,162.0,0,1,2016-07-04 -City Hotel,1,216,2017,August,33,16,0,4,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,124.65,0,0,2017-01-14 -City Hotel,1,279,2015,July,30,23,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,No Deposit,1.0,,0,Contract,62.0,0,0,2015-01-01 -City Hotel,0,101,2016,May,21,17,0,3,2,0.0,0,BB,BEL,Offline TA/TO,TA/TO,0,0,0,A,C,0,No Deposit,173.0,,0,Transient,80.75,0,2,2016-05-20 -City Hotel,0,8,2016,April,15,8,0,1,1,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,95.0,0,1,2016-04-09 -City Hotel,0,37,2016,June,27,26,2,2,2,0.0,0,HB,FRA,Online TA,TA/TO,0,0,0,D,D,2,No Deposit,9.0,,0,Transient,156.6,0,2,2016-06-30 -City Hotel,0,327,2015,September,37,9,2,4,1,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,1,No Deposit,21.0,,0,Transient-Party,76.0,0,0,2015-09-15 -City Hotel,1,23,2017,June,23,7,0,2,2,0.0,0,HB,ESP,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,52.0,,0,Transient,166.5,0,0,2017-05-19 -City Hotel,1,288,2016,May,19,5,0,3,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,236,Transient-Party,65.0,0,0,2016-03-14 -City Hotel,1,159,2017,July,29,21,0,2,3,0.0,0,BB,BEL,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,163.8,0,0,2017-03-03 -City Hotel,1,23,2017,February,8,21,0,0,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,K,1,No Deposit,14.0,,0,Transient,0.0,0,0,2017-02-21 -City Hotel,0,29,2016,February,7,12,0,2,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient-Party,78.5,1,1,2016-02-14 -City Hotel,1,135,2016,May,22,28,1,1,2,0.0,0,HB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,96.0,0,0,2016-02-09 -City Hotel,0,82,2016,July,27,1,2,4,2,0.0,0,BB,BEL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,114.3,0,1,2016-07-07 -City Hotel,1,261,2015,October,42,16,0,1,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,Non Refund,6.0,,0,Transient-Party,120.0,0,0,2015-07-08 -Resort Hotel,1,27,2017,August,35,28,1,4,2,2.0,0,HB,ESP,Direct,Direct,0,0,0,G,G,0,No Deposit,250.0,,0,Transient,341.0,0,0,2017-08-02 -Resort Hotel,1,62,2017,April,15,13,1,3,2,2.0,0,BB,ESP,Online TA,TA/TO,0,0,0,C,C,0,No Deposit,240.0,,0,Transient,116.75,0,0,2017-03-09 -City Hotel,1,56,2017,March,12,21,0,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,86.0,,0,Transient,85.0,0,0,2017-01-24 -Resort Hotel,0,26,2017,April,15,10,1,3,2,0.0,0,BB,NLD,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,113.25,0,1,2017-04-14 -City Hotel,1,147,2016,July,28,7,1,3,3,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,138.55,0,1,2016-07-07 -City Hotel,0,5,2016,November,47,15,0,3,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,11,A,A,0,No Deposit,,40.0,0,Transient,65.0,0,1,2016-11-18 -Resort Hotel,0,57,2017,April,14,5,0,3,2,0.0,0,BB,PRT,Direct,Corporate,0,0,0,A,E,4,No Deposit,,,0,Transient,76.67,0,0,2017-04-08 -City Hotel,1,98,2015,November,48,23,1,2,1,0.0,0,FB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,37.0,,0,Transient-Party,84.0,0,0,2015-09-14 -City Hotel,0,19,2017,April,16,19,0,2,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,D,0,No Deposit,,,0,Transient,95.0,0,2,2017-04-21 -City Hotel,1,79,2017,April,15,9,2,2,2,0.0,0,SC,IRL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,99.0,0,2,2017-04-08 -Resort Hotel,1,296,2017,May,19,10,0,1,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,67.5,0,2,2016-07-20 -City Hotel,0,55,2015,December,53,31,0,3,1,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,104.0,,0,Transient,62.67,0,0,2016-01-03 -City Hotel,0,80,2017,July,27,7,2,2,3,2.0,0,BB,ESP,Direct,Direct,0,0,0,G,G,0,No Deposit,14.0,,0,Transient,297.25,0,0,2017-07-11 -City Hotel,0,176,2016,July,28,8,1,2,2,0.0,0,BB,BEL,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient-Party,85.5,0,0,2016-07-11 -City Hotel,0,109,2017,March,10,7,0,2,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,79.0,,0,Transient,63.36,0,0,2017-03-09 -City Hotel,0,68,2017,March,10,7,1,5,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,22.0,,0,Transient,68.0,0,1,2017-03-13 -City Hotel,0,35,2016,June,27,26,2,3,2,0.0,0,BB,BEL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,110.7,0,1,2016-07-01 -City Hotel,1,173,2017,June,24,17,1,1,2,1.0,0,BB,PRT,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,83.0,,0,Transient,168.0,0,0,2016-12-26 -City Hotel,1,321,2015,September,36,3,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,1,2,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.8,0,0,2014-10-17 -City Hotel,1,26,2016,December,51,16,0,2,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,104.0,0,0,2016-11-25 -City Hotel,0,11,2015,August,34,18,0,3,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,9.0,,0,Contract,95.33,0,1,2015-08-21 -City Hotel,0,46,2015,December,52,23,0,2,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,9.0,,0,Transient,87.0,0,1,2015-12-25 -City Hotel,1,47,2016,March,12,16,0,2,1,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,76.5,0,0,2016-02-20 -City Hotel,1,50,2016,November,47,13,2,1,2,0.0,0,BB,BEL,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,114.0,0,2,2016-11-07 -City Hotel,1,20,2017,July,30,25,0,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Transient,160.0,0,1,2017-07-25 -City Hotel,1,32,2016,May,21,21,0,1,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,D,D,0,No Deposit,14.0,,0,Transient,129.2,0,0,2016-04-20 -City Hotel,0,243,2015,September,36,1,0,3,2,0.0,0,BB,AUT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,65.29,0,0,2015-09-04 -City Hotel,1,61,2017,March,10,10,2,5,1,0.0,0,BB,DZA,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,9.0,,0,Transient,74.8,0,0,2017-01-09 -City Hotel,0,4,2015,November,48,27,0,2,1,0.0,0,BB,ESP,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.0,0,0,2015-11-29 -City Hotel,0,0,2016,August,32,2,0,1,2,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,152.0,,0,Transient,73.81,0,0,2016-08-03 -City Hotel,1,323,2015,September,36,5,2,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.0,0,0,2015-07-06 -City Hotel,1,123,2017,August,34,26,2,3,3,0.0,0,HB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,252.0,0,1,2017-04-28 -City Hotel,1,307,2015,August,34,20,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Contract,62.0,0,0,2015-01-01 -City Hotel,1,283,2015,July,31,27,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient-Party,62.0,0,0,2015-06-29 -City Hotel,0,44,2015,October,44,31,0,1,2,1.0,0,BB,ROU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Contract,113.8,0,3,2015-11-01 -City Hotel,0,273,2015,July,29,17,0,2,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,109.0,0,0,2015-07-19 -City Hotel,1,152,2017,May,19,12,0,2,2,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,144.0,0,1,2016-12-12 -City Hotel,0,101,2016,May,22,25,0,4,2,0.0,0,BB,AUT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient-Party,132.6,0,0,2016-05-29 -City Hotel,0,6,2017,May,20,14,2,1,1,0.0,0,BB,BRA,Groups,Corporate,0,0,0,A,A,0,No Deposit,,169.0,0,Transient-Party,110.0,0,0,2017-05-17 -City Hotel,1,112,2016,September,39,23,1,2,2,0.0,0,SC,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,134.1,0,1,2016-09-09 -City Hotel,1,91,2017,April,15,12,0,3,2,0.0,0,SC,ARG,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,105.0,0,0,2017-01-24 -City Hotel,0,1,2015,October,41,6,0,1,2,0.0,0,BB,USA,Direct,Direct,0,0,0,E,A,1,No Deposit,14.0,,0,Transient,146.0,0,0,2015-10-07 -City Hotel,0,48,2017,May,18,4,1,3,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,115.6,0,0,2017-05-08 -City Hotel,0,95,2016,April,17,20,0,2,1,0.0,0,HB,AUT,Groups,TA/TO,0,0,0,A,B,0,No Deposit,35.0,,0,Transient-Party,74.0,0,0,2016-04-22 -Resort Hotel,1,253,2015,September,37,12,2,5,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,Non Refund,96.0,,0,Transient,58.95,0,0,2015-06-17 -City Hotel,1,162,2016,October,42,14,2,2,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,34.0,,0,Transient,115.0,0,0,2016-08-11 -City Hotel,0,163,2016,October,44,23,2,3,1,1.0,0,BB,NLD,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,74.65,0,2,2016-10-28 -City Hotel,0,336,2016,September,39,21,2,6,2,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,1,No Deposit,21.0,,0,Transient-Party,84.13,0,0,2016-09-29 -City Hotel,0,101,2017,April,15,9,2,3,2,1.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,9.0,,0,Transient,136.8,0,4,2017-04-14 -City Hotel,0,120,2017,January,4,25,1,4,2,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,74.8,0,2,2017-01-30 -City Hotel,0,138,2015,August,34,22,2,1,1,0.0,0,HB,ITA,Offline TA/TO,TA/TO,0,0,0,A,A,1,No Deposit,17.0,,0,Transient-Party,72.5,0,0,2015-08-25 -City Hotel,0,2,2016,June,25,16,0,1,2,1.0,0,BB,ITA,Online TA,TA/TO,0,0,0,A,E,0,No Deposit,9.0,,0,Transient,168.0,0,2,2016-06-17 -City Hotel,1,356,2015,October,41,8,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Contract,62.0,0,0,2015-01-01 -City Hotel,0,43,2017,May,19,8,1,2,2,0.0,0,SC,CN,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,119.2,0,0,2017-05-11 -City Hotel,1,207,2017,August,31,1,0,4,2,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,130.5,0,0,2017-04-30 -City Hotel,0,1,2016,March,10,4,1,2,3,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,9.0,,0,Transient,137.0,0,2,2016-03-07 -City Hotel,1,182,2016,July,31,26,0,4,2,0.0,0,BB,FRA,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient-Party,85.25,0,0,2016-05-09 -City Hotel,0,282,2015,October,41,10,2,1,2,0.0,0,BB,SWE,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,21.0,,0,Transient-Party,76.67,0,0,2015-10-13 -City Hotel,1,52,2017,March,11,13,1,1,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,56.0,,0,Transient,80.0,0,0,2017-01-20 -City Hotel,0,135,2017,March,13,31,2,3,2,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,86.49,0,1,2017-04-05 -City Hotel,1,113,2016,February,9,25,0,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,Non Refund,19.0,,44,Transient,75.0,0,0,2015-12-18 -City Hotel,0,33,2015,October,43,21,0,3,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,B,B,0,No Deposit,83.0,,0,Transient,83.16,0,0,2015-10-24 -City Hotel,0,46,2016,August,34,14,2,1,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,135.9,0,2,2016-08-17 -City Hotel,1,158,2017,June,25,23,2,2,2,2.0,0,BB,SVK,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,9.0,,0,Transient,198.0,0,1,2017-05-05 -City Hotel,0,123,2016,March,14,28,1,3,3,0.0,0,BB,DEU,Direct,Direct,0,0,0,A,B,1,No Deposit,,,0,Transient,93.0,0,3,2016-04-01 -Resort Hotel,1,74,2016,February,9,26,1,2,2,0.0,0,Undefined,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,68.0,,0,Transient,70.0,0,0,2016-01-21 -City Hotel,1,95,2016,April,18,27,0,4,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,7.0,,0,Transient,82.07,0,1,2016-04-27 -City Hotel,0,32,2016,November,47,15,0,2,2,0.0,0,BB,DEU,Complementary,Direct,0,0,0,E,E,0,No Deposit,,,0,Transient,75.0,0,1,2016-11-17 -City Hotel,0,5,2017,January,3,17,0,3,1,0.0,0,BB,ESP,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,99.67,0,0,2017-01-20 -City Hotel,0,188,2016,September,40,28,2,4,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,2,No Deposit,8.0,,0,Transient,134.4,0,1,2016-10-04 +hotel,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests +Resort Hotel,169,2017,July,31,31,1,3,2,0.0,0,HB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,212.0,0,3 +City Hotel,2,2016,March,10,2,0,1,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,21.0,,0,Transient-Party,55.0,0,0 +City Hotel,290,2016,June,25,17,1,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,3.0,,108,Transient-Party,72.0,0,0 +City Hotel,15,2016,September,40,26,1,2,2,0.0,0,BB,PRT,Online TA,TA/TO,1,0,1,D,D,0,No Deposit,9.0,,0,Transient,164.0,0,0 +City Hotel,20,2016,February,7,8,3,5,1,0.0,0,BB,AGO,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,86.0,0,1 +City Hotel,0,2017,August,32,6,1,0,2,1.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,170.0,0,0 +City Hotel,71,2016,June,25,14,0,3,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,236.0,,0,Transient,120.0,0,0 +City Hotel,552,2017,January,2,12,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0 +City Hotel,329,2015,September,37,11,2,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,Non Refund,5.0,,0,Transient,90.0,0,0 +City Hotel,37,2017,March,11,15,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,378.0,,0,Transient,110.0,0,0 +Resort Hotel,107,2017,June,24,16,2,6,2,0.0,0,HB,PRT,Direct,Direct,0,0,0,C,C,1,No Deposit,250.0,,0,Transient,185.0,0,0 +City Hotel,299,2015,August,33,12,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient-Party,62.0,0,0 +City Hotel,213,2016,June,24,7,0,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,19.0,,62,Transient,130.0,0,0 +City Hotel,93,2017,August,32,9,0,3,2,0.0,0,BB,IRL,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,155.0,0,2 +City Hotel,242,2016,July,31,29,0,2,0,2.0,0,BB,BEL,Direct,Direct,0,0,0,B,B,0,No Deposit,14.0,,0,Transient-Party,6.0,0,1 +City Hotel,155,2017,July,30,28,2,3,2,0.0,0,BB,AUT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,130.5,0,0 +City Hotel,227,2016,October,42,10,1,2,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,E,E,0,Non Refund,,51.0,0,Transient-Party,106.0,0,0 +City Hotel,117,2016,October,44,28,2,2,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,104.4,0,2 +City Hotel,49,2017,March,10,10,1,2,2,0.0,0,BB,USA,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,83.0,,0,Transient,86.76,1,1 +City Hotel,184,2016,October,43,17,1,3,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,128.7,0,2 +City Hotel,52,2017,March,11,13,1,3,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,D,2,No Deposit,28.0,,0,Transient,75.0,0,0 +City Hotel,5,2016,October,44,28,1,2,2,0.0,0,HB,FRA,Online TA,TA/TO,0,0,0,A,B,0,No Deposit,9.0,,0,Transient,200.33,0,2 +City Hotel,257,2015,July,27,1,0,2,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient,101.5,0,0 +City Hotel,133,2016,April,16,14,0,3,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,28.0,,0,Transient,80.0,0,0 +City Hotel,13,2016,March,13,20,1,0,2,0.0,0,BB,BRA,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,220.0,,0,Transient-Party,85.0,0,1 +City Hotel,127,2016,August,35,21,3,5,2,0.0,0,BB,ESP,Direct,Direct,0,0,0,D,D,0,No Deposit,14.0,,0,Transient,113.55,0,1 +City Hotel,141,2017,August,34,23,0,2,2,0.0,0,BB,USA,Direct,Direct,0,0,0,D,D,0,No Deposit,14.0,,0,Transient,150.0,0,2 +City Hotel,1,2016,June,25,14,0,4,1,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,121.5,0,1 +City Hotel,4,2016,June,25,14,0,2,2,0.0,0,HB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,164.0,0,0 +City Hotel,0,2015,August,33,14,0,2,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,109.0,0,0 +City Hotel,5,2016,March,10,2,0,1,1,0.0,0,BB,FRA,Aviation,TA/TO,0,0,0,A,A,0,No Deposit,153.0,,0,Transient,91.0,0,0 +City Hotel,166,2016,November,45,1,0,3,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,236.0,,0,Transient,110.0,0,0 +City Hotel,15,2017,July,28,15,1,1,2,1.0,0,BB,CHN,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,9.0,,0,Transient,159.0,0,1 +City Hotel,96,2015,July,28,11,2,1,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,17.0,,0,Transient-Party,94.5,0,0 +City Hotel,0,2017,January,5,31,0,1,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,83.0,,0,Transient,78.4,0,0 +City Hotel,1,2016,August,32,3,2,5,0,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,K,4,No Deposit,,,0,Transient,0.0,0,0 +City Hotel,1,2017,February,8,23,0,2,1,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,71.0,0,1 +City Hotel,346,2015,September,40,28,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Contract,62.0,0,0 +City Hotel,70,2015,November,48,26,0,3,1,0.0,0,BB,USA,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,27.0,,0,Transient,46.33,0,0 +Resort Hotel,228,2016,March,11,11,1,2,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,D,D,0,No Deposit,250.0,,0,Transient,39.1,0,1 +City Hotel,74,2016,May,21,17,0,3,2,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,126.9,0,0 +City Hotel,17,2016,November,45,3,1,3,1,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,61.52,0,1 +City Hotel,145,2016,July,28,3,2,1,3,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,131.7,0,1 +City Hotel,33,2016,December,49,1,1,3,2,0.0,0,SC,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,57.6,0,3 +City Hotel,0,2016,December,53,28,0,1,2,2.0,0,BB,FRA,Direct,Direct,0,0,0,F,F,0,No Deposit,,,0,Transient,168.0,0,0 +City Hotel,290,2015,August,32,3,1,3,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.0,0,0 +Resort Hotel,259,2015,September,38,17,0,3,1,0.0,0,BB,GBR,Groups,Corporate,0,1,0,A,A,0,Non Refund,,223.0,0,Transient,33.3,0,0 +Resort Hotel,165,2016,October,41,2,2,2,2,0.0,0,BB,CN,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,96.0,,0,Transient,46.0,0,0 +Resort Hotel,17,2016,December,53,30,0,2,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,240.0,,0,Transient,120.0,0,1 +City Hotel,26,2017,February,9,27,1,0,1,0.0,0,SC,AUT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,88.0,0,1 +City Hotel,91,2016,October,42,12,0,2,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,9.0,,0,Transient,113.4,0,0 +Resort Hotel,179,2016,October,43,21,1,2,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,240.0,,0,Transient,75.33,0,3 +Resort Hotel,0,2015,November,48,24,0,1,1,0.0,0,BB,PRT,Online TA,TA/TO,1,0,0,A,A,0,No Deposit,241.0,,0,Transient,36.96,0,0 +City Hotel,26,2016,February,7,7,2,2,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,11.0,,0,Transient,64.64,0,0 +City Hotel,1,2016,November,49,28,1,3,1,0.0,0,BB,BRA,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,78.53,0,0 +City Hotel,76,2016,November,47,17,0,3,2,1.0,0,BB,GBR,Direct,Direct,0,0,0,A,D,1,No Deposit,14.0,,0,Transient,92.2,0,2 +City Hotel,36,2016,February,9,27,1,1,2,1.0,0,BB,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient-Party,107.3,1,1 +City Hotel,7,2016,December,50,4,2,3,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,88.4,0,1 +City Hotel,49,2016,July,29,11,1,0,2,0.0,0,SC,CHN,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,93.15,0,0 +City Hotel,163,2016,August,35,23,0,5,2,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,84.15,0,0 +City Hotel,42,2016,August,36,29,1,3,3,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,144.23,0,0 +City Hotel,159,2016,April,15,9,1,1,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,3.0,,0,Transient-Party,70.0,0,1 +City Hotel,98,2015,November,48,23,1,3,1,0.0,0,BB,NOR,Groups,TA/TO,0,0,0,A,D,1,No Deposit,37.0,,0,Transient-Party,70.0,0,0 +City Hotel,60,2016,October,44,27,0,1,2,0.0,0,SC,CN,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,80.1,0,2 +Resort Hotel,128,2016,June,26,22,2,5,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,121.0,0,1 +City Hotel,154,2016,September,38,12,1,4,2,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,168.0,,0,Transient,80.75,0,0 +City Hotel,130,2016,April,16,13,0,4,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,29.0,,10,Transient,100.0,0,0 +City Hotel,91,2016,March,12,18,0,2,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,29.0,,20,Transient,75.0,0,0 +City Hotel,1,2017,May,20,17,0,1,1,0.0,0,SC,ESP,Direct,Direct,0,0,0,A,C,0,No Deposit,,,0,Transient,160.0,0,0 +City Hotel,16,2015,September,39,25,2,3,2,0.0,0,BB,ITA,Groups,TA/TO,0,0,0,A,B,1,No Deposit,1.0,,0,Transient-Party,170.0,0,0 +City Hotel,7,2015,December,50,8,0,4,1,0.0,0,BB,GBR,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,40.0,0,Transient,65.0,0,0 +City Hotel,164,2017,May,20,15,1,2,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,160.0,0,0 +City Hotel,75,2016,March,12,17,1,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,6.0,,0,Transient,80.0,0,0 +City Hotel,112,2017,April,17,27,0,3,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,9.0,,0,Transient,153.0,0,0 +Resort Hotel,53,2016,February,9,26,2,7,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,48.0,0,1 +City Hotel,59,2017,July,29,17,1,2,2,0.0,0,SC,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,130.0,0,0 +City Hotel,216,2015,October,43,19,1,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,0,Transient-Party,12.0,0,0 +City Hotel,103,2016,April,18,29,0,2,2,0.0,0,BB,BEL,Direct,Direct,0,0,0,D,D,0,No Deposit,14.0,,0,Transient,108.9,0,0 +City Hotel,305,2017,May,21,25,0,2,3,0.0,0,BB,CN,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient-Party,142.2,0,2 +City Hotel,111,2017,July,28,15,2,1,3,0.0,0,BB,CHE,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,177.33,0,2 +Resort Hotel,105,2016,December,50,8,1,3,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,C,1,No Deposit,240.0,,0,Transient,37.8,0,3 +City Hotel,75,2017,April,14,5,0,4,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,132.0,,0,Transient-Party,85.0,0,0 +City Hotel,159,2017,May,21,22,1,2,1,0.0,0,BB,CYP,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,9.0,,0,Transient,126.0,0,0 +City Hotel,322,2016,May,21,19,1,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,31.0,,120,Transient,80.0,0,0 +City Hotel,163,2017,July,28,13,0,3,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,116.1,0,0 +City Hotel,94,2016,November,45,4,1,2,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,90.0,0,0 +City Hotel,5,2015,August,34,17,3,10,1,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Contract,92.31,0,2 +City Hotel,24,2016,September,38,16,0,2,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,149.0,0,1 +City Hotel,163,2017,July,27,3,1,5,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,116.1,0,1 +City Hotel,2,2017,February,5,3,0,1,1,0.0,0,BB,GBR,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,80.0,0,0 +City Hotel,19,2015,October,41,6,0,3,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,1,0,0,A,D,0,No Deposit,82.0,,0,Transient-Party,80.0,0,0 +City Hotel,184,2017,May,18,1,1,2,2,0.0,0,SC,RUS,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,99.0,0,0 +Resort Hotel,51,2016,March,12,17,0,1,2,0.0,0,BB,CHN,Groups,TA/TO,0,0,0,A,D,0,No Deposit,66.0,,0,Transient-Party,30.0,0,0 +City Hotel,3,2016,May,20,13,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,14,E,E,0,Non Refund,,51.0,0,Transient-Party,106.0,0,0 +City Hotel,192,2016,June,26,24,0,2,1,0.0,0,BB,SRB,Offline TA/TO,TA/TO,0,0,0,A,B,0,No Deposit,34.0,,0,Transient-Party,95.0,0,0 +City Hotel,214,2016,August,33,7,2,2,2,2.0,0,BB,NLD,Online TA,TA/TO,0,0,0,F,F,2,No Deposit,9.0,,0,Transient-Party,158.95,0,1 +City Hotel,22,2015,November,45,6,2,2,2,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,27.0,,0,Transient,43.5,0,0 +City Hotel,196,2016,August,35,22,1,5,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,99.45,0,0 +City Hotel,292,2015,August,32,5,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient-Party,62.0,0,0 +Resort Hotel,79,2016,May,23,30,1,3,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,99.5,1,1 +City Hotel,11,2015,December,50,11,0,2,2,0.0,0,BB,PRT,Complementary,Direct,0,0,0,B,B,0,No Deposit,,,0,Transient,0.0,0,1 +City Hotel,7,2017,March,13,28,0,2,1,0.0,0,BB,ESP,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,441.0,,0,Transient,103.0,0,0 +City Hotel,40,2017,May,22,30,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,130.0,0,0 +City Hotel,98,2015,November,48,23,1,2,1,0.0,0,BB,MYS,Groups,TA/TO,0,0,0,A,A,0,No Deposit,37.0,,0,Transient-Party,70.0,0,1 +City Hotel,73,2016,September,37,6,0,2,3,0.0,0,BB,CHE,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,168.3,0,2 +City Hotel,38,2017,June,24,15,0,1,2,0.0,0,SC,POL,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,9.0,,0,Transient,120.0,0,2 +City Hotel,337,2016,June,24,9,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0 +City Hotel,212,2017,February,8,22,0,4,2,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,85.0,0,0 +City Hotel,5,2015,September,38,19,0,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,E,0,No Deposit,9.0,,0,Contract,133.0,0,1 +City Hotel,25,2016,October,43,16,2,1,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,28.0,,0,Transient,85.0,0,0 +City Hotel,28,2016,August,35,24,0,2,2,0.0,0,BB,CHN,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,149.4,0,1 +City Hotel,86,2015,September,39,24,0,2,1,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,D,1,No Deposit,12.0,,0,Transient-Party,87.5,0,0 +City Hotel,12,2017,August,31,1,0,1,2,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,109.0,0,1 +City Hotel,475,2016,October,44,27,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0 +City Hotel,16,2016,July,30,19,0,2,3,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,162.0,0,1 +City Hotel,216,2017,August,33,16,0,4,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,124.65,0,0 +City Hotel,279,2015,July,30,23,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,No Deposit,1.0,,0,Contract,62.0,0,0 +City Hotel,101,2016,May,21,17,0,3,2,0.0,0,BB,BEL,Offline TA/TO,TA/TO,0,0,0,A,C,0,No Deposit,173.0,,0,Transient,80.75,0,2 +City Hotel,8,2016,April,15,8,0,1,1,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,95.0,0,1 +City Hotel,37,2016,June,27,26,2,2,2,0.0,0,HB,FRA,Online TA,TA/TO,0,0,0,D,D,2,No Deposit,9.0,,0,Transient,156.6,0,2 +City Hotel,327,2015,September,37,9,2,4,1,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,1,No Deposit,21.0,,0,Transient-Party,76.0,0,0 +City Hotel,23,2017,June,23,7,0,2,2,0.0,0,HB,ESP,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,52.0,,0,Transient,166.5,0,0 +City Hotel,288,2016,May,19,5,0,3,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,236,Transient-Party,65.0,0,0 +City Hotel,159,2017,July,29,21,0,2,3,0.0,0,BB,BEL,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,163.8,0,0 +City Hotel,23,2017,February,8,21,0,0,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,K,1,No Deposit,14.0,,0,Transient,0.0,0,0 +City Hotel,29,2016,February,7,12,0,2,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient-Party,78.5,1,1 +City Hotel,135,2016,May,22,28,1,1,2,0.0,0,HB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,96.0,0,0 +City Hotel,82,2016,July,27,1,2,4,2,0.0,0,BB,BEL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,114.3,0,1 +City Hotel,261,2015,October,42,16,0,1,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,Non Refund,6.0,,0,Transient-Party,120.0,0,0 +Resort Hotel,27,2017,August,35,28,1,4,2,2.0,0,HB,ESP,Direct,Direct,0,0,0,G,G,0,No Deposit,250.0,,0,Transient,341.0,0,0 +Resort Hotel,62,2017,April,15,13,1,3,2,2.0,0,BB,ESP,Online TA,TA/TO,0,0,0,C,C,0,No Deposit,240.0,,0,Transient,116.75,0,0 +City Hotel,56,2017,March,12,21,0,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,86.0,,0,Transient,85.0,0,0 +Resort Hotel,26,2017,April,15,10,1,3,2,0.0,0,BB,NLD,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,113.25,0,1 +City Hotel,147,2016,July,28,7,1,3,3,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,138.55,0,1 +City Hotel,5,2016,November,47,15,0,3,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,11,A,A,0,No Deposit,,40.0,0,Transient,65.0,0,1 +Resort Hotel,57,2017,April,14,5,0,3,2,0.0,0,BB,PRT,Direct,Corporate,0,0,0,A,E,4,No Deposit,,,0,Transient,76.67,0,0 +City Hotel,98,2015,November,48,23,1,2,1,0.0,0,FB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,37.0,,0,Transient-Party,84.0,0,0 +City Hotel,19,2017,April,16,19,0,2,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,D,0,No Deposit,,,0,Transient,95.0,0,2 +City Hotel,79,2017,April,15,9,2,2,2,0.0,0,SC,IRL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,99.0,0,2 +Resort Hotel,296,2017,May,19,10,0,1,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,67.5,0,2 +City Hotel,55,2015,December,53,31,0,3,1,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,104.0,,0,Transient,62.67,0,0 +City Hotel,80,2017,July,27,7,2,2,3,2.0,0,BB,ESP,Direct,Direct,0,0,0,G,G,0,No Deposit,14.0,,0,Transient,297.25,0,0 +City Hotel,176,2016,July,28,8,1,2,2,0.0,0,BB,BEL,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient-Party,85.5,0,0 +City Hotel,109,2017,March,10,7,0,2,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,79.0,,0,Transient,63.36,0,0 +City Hotel,68,2017,March,10,7,1,5,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,22.0,,0,Transient,68.0,0,1 +City Hotel,35,2016,June,27,26,2,3,2,0.0,0,BB,BEL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,110.7,0,1 +City Hotel,173,2017,June,24,17,1,1,2,1.0,0,BB,PRT,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,83.0,,0,Transient,168.0,0,0 +City Hotel,321,2015,September,36,3,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,1,2,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.8,0,0 +City Hotel,26,2016,December,51,16,0,2,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,104.0,0,0 +City Hotel,11,2015,August,34,18,0,3,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,9.0,,0,Contract,95.33,0,1 +City Hotel,46,2015,December,52,23,0,2,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,9.0,,0,Transient,87.0,0,1 +City Hotel,47,2016,March,12,16,0,2,1,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,76.5,0,0 +City Hotel,50,2016,November,47,13,2,1,2,0.0,0,BB,BEL,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,114.0,0,2 +City Hotel,20,2017,July,30,25,0,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Transient,160.0,0,1 +City Hotel,32,2016,May,21,21,0,1,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,D,D,0,No Deposit,14.0,,0,Transient,129.2,0,0 +City Hotel,243,2015,September,36,1,0,3,2,0.0,0,BB,AUT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,65.29,0,0 +City Hotel,61,2017,March,10,10,2,5,1,0.0,0,BB,DZA,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,9.0,,0,Transient,74.8,0,0 +City Hotel,4,2015,November,48,27,0,2,1,0.0,0,BB,ESP,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.0,0,0 +City Hotel,0,2016,August,32,2,0,1,2,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,152.0,,0,Transient,73.81,0,0 +City Hotel,323,2015,September,36,5,2,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.0,0,0 +City Hotel,123,2017,August,34,26,2,3,3,0.0,0,HB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,252.0,0,1 +City Hotel,307,2015,August,34,20,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Contract,62.0,0,0 +City Hotel,283,2015,July,31,27,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient-Party,62.0,0,0 +City Hotel,44,2015,October,44,31,0,1,2,1.0,0,BB,ROU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Contract,113.8,0,3 +City Hotel,273,2015,July,29,17,0,2,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,109.0,0,0 +City Hotel,152,2017,May,19,12,0,2,2,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,144.0,0,1 +City Hotel,101,2016,May,22,25,0,4,2,0.0,0,BB,AUT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient-Party,132.6,0,0 +City Hotel,6,2017,May,20,14,2,1,1,0.0,0,BB,BRA,Groups,Corporate,0,0,0,A,A,0,No Deposit,,169.0,0,Transient-Party,110.0,0,0 +City Hotel,112,2016,September,39,23,1,2,2,0.0,0,SC,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,134.1,0,1 +City Hotel,91,2017,April,15,12,0,3,2,0.0,0,SC,ARG,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,105.0,0,0 +City Hotel,1,2015,October,41,6,0,1,2,0.0,0,BB,USA,Direct,Direct,0,0,0,E,A,1,No Deposit,14.0,,0,Transient,146.0,0,0 +City Hotel,48,2017,May,18,4,1,3,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,115.6,0,0 +City Hotel,95,2016,April,17,20,0,2,1,0.0,0,HB,AUT,Groups,TA/TO,0,0,0,A,B,0,No Deposit,35.0,,0,Transient-Party,74.0,0,0 +Resort Hotel,253,2015,September,37,12,2,5,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,Non Refund,96.0,,0,Transient,58.95,0,0 +City Hotel,162,2016,October,42,14,2,2,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,34.0,,0,Transient,115.0,0,0 +City Hotel,163,2016,October,44,23,2,3,1,1.0,0,BB,NLD,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,74.65,0,2 +City Hotel,336,2016,September,39,21,2,6,2,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,1,No Deposit,21.0,,0,Transient-Party,84.13,0,0 +City Hotel,101,2017,April,15,9,2,3,2,1.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,9.0,,0,Transient,136.8,0,4 +City Hotel,120,2017,January,4,25,1,4,2,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,74.8,0,2 +City Hotel,138,2015,August,34,22,2,1,1,0.0,0,HB,ITA,Offline TA/TO,TA/TO,0,0,0,A,A,1,No Deposit,17.0,,0,Transient-Party,72.5,0,0 +City Hotel,2,2016,June,25,16,0,1,2,1.0,0,BB,ITA,Online TA,TA/TO,0,0,0,A,E,0,No Deposit,9.0,,0,Transient,168.0,0,2 +City Hotel,356,2015,October,41,8,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Contract,62.0,0,0 +City Hotel,43,2017,May,19,8,1,2,2,0.0,0,SC,CN,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,119.2,0,0 +City Hotel,207,2017,August,31,1,0,4,2,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,130.5,0,0 +City Hotel,1,2016,March,10,4,1,2,3,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,9.0,,0,Transient,137.0,0,2 +City Hotel,182,2016,July,31,26,0,4,2,0.0,0,BB,FRA,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient-Party,85.25,0,0 +City Hotel,282,2015,October,41,10,2,1,2,0.0,0,BB,SWE,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,21.0,,0,Transient-Party,76.67,0,0 +City Hotel,52,2017,March,11,13,1,1,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,56.0,,0,Transient,80.0,0,0 +City Hotel,135,2017,March,13,31,2,3,2,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,86.49,0,1 +City Hotel,113,2016,February,9,25,0,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,Non Refund,19.0,,44,Transient,75.0,0,0 +City Hotel,33,2015,October,43,21,0,3,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,B,B,0,No Deposit,83.0,,0,Transient,83.16,0,0 +City Hotel,46,2016,August,34,14,2,1,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,135.9,0,2 +City Hotel,158,2017,June,25,23,2,2,2,2.0,0,BB,SVK,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,9.0,,0,Transient,198.0,0,1 +City Hotel,123,2016,March,14,28,1,3,3,0.0,0,BB,DEU,Direct,Direct,0,0,0,A,B,1,No Deposit,,,0,Transient,93.0,0,3 +Resort Hotel,74,2016,February,9,26,1,2,2,0.0,0,Undefined,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,68.0,,0,Transient,70.0,0,0 +City Hotel,95,2016,April,18,27,0,4,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,7.0,,0,Transient,82.07,0,1 +City Hotel,32,2016,November,47,15,0,2,2,0.0,0,BB,DEU,Complementary,Direct,0,0,0,E,E,0,No Deposit,,,0,Transient,75.0,0,1 +City Hotel,5,2017,January,3,17,0,3,1,0.0,0,BB,ESP,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,99.67,0,0 +City Hotel,188,2016,September,40,28,2,4,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,2,No Deposit,8.0,,0,Transient,134.4,0,1 diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_006/history.csv b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_006/history.csv index 8ad803c82081fda0117c64618e29848ad2dd4644..4276b895f31a2523bb22598fdf6e586dcde1e940 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_006/history.csv +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_006/history.csv @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:2e468708de0cac14edfaf23f52e9c1e8d7a5dee8802447e6be2525453ce6b654 -size 16736298 +oid sha256:03900f3d545f3b42741181bc9a8b7f159a8d4d2d25409ca175577f58fbdaaf0c +size 15199659 diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_006/info.json b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_006/info.json index 4816c05993309461836de8622438e342f2e503ff..20956c93d07801fe3772275e08832605a78593ed 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_006/info.json +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_006/info.json @@ -1,398 +1,391 @@ -{ - "name": "Hotel Booking Demand", - "source": "https://www.kaggle.com/datasets/jessemostipak/hotel-booking-demand/data", - "data_intro": "This dataset contains hotel booking data for a city hotel and a resort hotel, providing details on booking dates, stay duration, guest count, cancellation trends, and pricing. It is useful for demand forecasting, revenue management, and customer behavior analysis.", - "is_splited": false, - "overall_size": 119390, - "train_size": 0, - "test_size": 0, - "c_classes": 12, - "n_classes": 20, - "cat_feature_intro": { - "hotel": "Type of hotel (City Hotel or Resort Hotel).", - "arrival_date_month": "Month of arrival for the booking.", - "meal": "Type of meal plan booked (e.g., Breakfast included, Half board, Full board).", - "country": "Country of origin of the guest.", - "market_segment": "Booking distribution channel (e.g., direct, corporate, travel agent).", - "distribution_channel": "Channel through which the booking was made (e.g., online travel agents, direct).", - "reserved_room_type": "Type of room reserved by the guest before any changes.", - "assigned_room_type": "Final room type assigned to the guest.", - "deposit_type": "Indicates if a deposit was made and its type (e.g., No Deposit, Refundable, Non-Refundable).", - "customer_type": "Type of customer (e.g., transient, group, contract).", - "reservation_status": "Final status of the booking (e.g., Canceled, Checked-out, No-show).", - "reservation_status_date": "Date when the reservation status was last updated." - }, - "num_feature_intro": { - "is_canceled": "Indicates whether the booking was canceled (1) or not (0).", - "lead_time": "Number of days between booking and arrival.", - "arrival_date_year": "Year of arrival for the booking.", - "arrival_date_week_number": "Week number of the arrival date.", - "arrival_date_day_of_month": "Day of the month of arrival.", - "stays_in_weekend_nights": "Number of weekend nights (Saturday or Sunday) the guest stayed.", - "stays_in_week_nights": "Number of weekday nights (Monday to Friday) the guest stayed.", - "adults": "Number of adults included in the booking.", - "children": "Number of children included in the booking.", - "babies": "Number of babies included in the booking.", - "is_repeated_guest": "Indicates whether the guest is a returning customer (1) or not (0).", - "previous_cancellations": "Number of times the guest has canceled previous bookings.", - "previous_bookings_not_canceled": "Number of previous bookings not canceled by the guest.", - "booking_changes": "Number of changes made to the booking.", - "agent": "ID of the booking agent (if applicable).", - "company": "ID of the company making the booking (if applicable).", - "days_in_waiting_list": "Number of days the booking was on the waiting list before confirmation.", - "adr": "Average daily rate (price per night per room).", - "required_car_parking_spaces": "Number of car parking spaces required by the guest.", - "total_of_special_requests": "Number of special requests made by the guest (e.g., high floor, twin beds)." - }, - "evaluation_metric": null, - "task_type": "classification", - "target": "reservation_status", - "num_feature_value": { - "adr": [ - -6.38, - 5400.0 - ], - "adults": [ - 0.0, - 55.0 - ], - "agent": [ - 1.0, - 535.0 - ], - "arrival_date_day_of_month": [ - 1.0, - 31.0 - ], - "arrival_date_week_number": [ - 1.0, - 53.0 - ], - "arrival_date_year": [ - 2015.0, - 2017.0 - ], - "babies": [ - 0.0, - 10.0 - ], - "booking_changes": [ - 0.0, - 21.0 - ], - "children": [ - 0.0, - 10.0 - ], - "company": [ - 6.0, - 543.0 - ], - "days_in_waiting_list": [ - 0.0, - 391.0 - ], - "is_canceled": [ - 0.0, - 1.0 - ], - "is_repeated_guest": [ - 0.0, - 1.0 - ], - "lead_time": [ - 0.0, - 737.0 - ], - "previous_bookings_not_canceled": [ - 0.0, - 72.0 - ], - "previous_cancellations": [ - 0.0, - 26.0 - ], - "required_car_parking_spaces": [ - 0.0, - 8.0 - ], - "stays_in_week_nights": [ - 0.0, - 50.0 - ], - "stays_in_weekend_nights": [ - 0.0, - 19.0 - ], - "total_of_special_requests": [ - 0.0, - 5.0 - ] - }, - "cat_feature_value": { - "arrival_date_month": [ - "April", - "August", - "December", - "February", - "January", - "July", - "June", - "March", - "May", - "November", - "October", - "September" - ], - "assigned_room_type": [ - "A", - "B", - "C", - "D", - "E", - "F", - "G", - "H", - "I", - "K", - "L", - "P" - ], - "country": [ - "ABW", - "AGO", - "AIA", - "ALB", - "AND", - "ARE", - "ARG", - "ARM", - "ASM", - "ATA", - "ATF", - "AUS", - "AUT", - "AZE", - "BDI", - "BEL", - "BEN", - "BFA", - "BGD", - "BGR", - "BHR", - "BHS", - "BIH", - "BLR", - "BOL", - "BRA", - "BRB", - "BWA", - "CAF", - "CHE", - "CHL", - "CHN", - "CIV", - "CMR", - "CN", - "COL", - "COM", - "CPV", - "CRI", - "CUB", - "CYM", - "CYP", - "CZE", - "DEU", - "DJI", - "DMA", - "DNK", - "DOM", - "DZA", - "ECU", - "EGY", - "ESP", - "EST", - "ETH", - "FIN", - "FJI", - "FRA", - "FRO", - "GAB", - "GBR", - "GEO", - "GGY", - "GHA", - "GIB", - "GLP", - "GNB", - "GRC", - "GTM", - "GUY", - "HKG", - "HND", - "HRV", - "HUN", - "IDN", - "IMN", - "IND", - "IRL", - "IRN", - "IRQ", - "ISL", - "ISR", - "ITA", - "JAM", - "JEY", - "JOR", - "JPN", - "KAZ", - "KEN", - "KHM", - "KIR", - "KNA", - "KOR", - "KWT", - "LAO", - "LBN", - "LBY", - "LCA", - "LIE", - "LKA", - "LTU", - "LUX", - "LVA", - "MAC", - "MAR", - "MCO", - "MDG", - "MDV", - "MEX", - "MKD", - "MLI", - "MLT", - "MMR", - "MNE", - "MOZ", - "MRT", - "MUS", - "MWI", - "MYS", - "MYT", - "NAM", - "NCL", - "NGA", - "NIC", - "NLD", - "NOR", - "NPL", - "NZL", - "OMN", - "PAK", - "PAN", - "PER", - "PHL", - "PLW", - "POL", - "PRI", - "PRT", - "PRY", - "PYF", - "QAT", - "ROU", - "RUS", - "RWA", - "SAU", - "SDN", - "SEN", - "SGP", - "SLE", - "SLV", - "SMR", - "SRB", - "STP", - "SUR", - "SVK", - "SVN", - "SWE", - "SYC", - "SYR", - "TGO", - "THA", - "TJK", - "TMP", - "TUN", - "TUR", - "TWN", - "TZA", - "UGA", - "UKR", - "UMI", - "URY", - "USA", - "UZB", - "VEN", - "VGB", - "VNM", - "ZAF", - "ZMB", - "ZWE" - ], - "customer_type": [ - "Contract", - "Group", - "Transient", - "Transient-Party" - ], - "deposit_type": [ - "No Deposit", - "Non Refund", - "Refundable" - ], - "distribution_channel": [ - "Corporate", - "Direct", - "GDS", - "TA/TO", - "Undefined" - ], - "hotel": [ - "City Hotel", - "Resort Hotel" - ], - "market_segment": [ - "Aviation", - "Complementary", - "Corporate", - "Direct", - "Groups", - "Offline TA/TO", - "Online TA", - "Undefined" - ], - "meal": [ - "BB", - "FB", - "HB", - "SC", - "Undefined" - ], - "reservation_status": [ - "Canceled", - "Check-Out", - "No-Show" - ], - "reservation_status_date": "string", - "reserved_room_type": [ - "A", - "B", - "C", - "D", - "E", - "F", - "G", - "H", - "L", - "P" - ] - } -} \ No newline at end of file +{ + "name": "Hotel Booking Demand", + "source": "https://www.kaggle.com/datasets/jessemostipak/hotel-booking-demand/data", + "data_intro": "This dataset contains hotel booking data for a city hotel and a resort hotel, providing details on booking dates, stay duration, guest count, cancellation trends, and pricing. It is useful for demand forecasting, revenue management, and customer behavior analysis.", + "is_splited": false, + "overall_size": 119390, + "train_size": 0, + "test_size": 0, + "c_classes": 11, + "n_classes": 19, + "cat_feature_intro": { + "hotel": "Type of hotel (City Hotel or Resort Hotel).", + "arrival_date_month": "Month of arrival for the booking.", + "meal": "Type of meal plan booked (e.g., Breakfast included, Half board, Full board).", + "country": "Country of origin of the guest.", + "market_segment": "Booking distribution channel (e.g., direct, corporate, travel agent).", + "distribution_channel": "Channel through which the booking was made (e.g., online travel agents, direct).", + "reserved_room_type": "Type of room reserved by the guest before any changes.", + "assigned_room_type": "Final room type assigned to the guest.", + "deposit_type": "Indicates if a deposit was made and its type (e.g., No Deposit, Refundable, Non-Refundable).", + "customer_type": "Type of customer (e.g., transient, group, contract).", + "reservation_status": "Final status of the booking (e.g., Canceled, Checked-out, No-show)." + }, + "num_feature_intro": { + "lead_time": "Number of days between booking and arrival.", + "arrival_date_year": "Year of arrival for the booking.", + "arrival_date_week_number": "Week number of the arrival date.", + "arrival_date_day_of_month": "Day of the month of arrival.", + "stays_in_weekend_nights": "Number of weekend nights (Saturday or Sunday) the guest stayed.", + "stays_in_week_nights": "Number of weekday nights (Monday to Friday) the guest stayed.", + "adults": "Number of adults included in the booking.", + "children": "Number of children included in the booking.", + "babies": "Number of babies included in the booking.", + "is_repeated_guest": "Indicates whether the guest is a returning customer (1) or not (0).", + "previous_cancellations": "Number of times the guest has canceled previous bookings.", + "previous_bookings_not_canceled": "Number of previous bookings not canceled by the guest.", + "booking_changes": "Number of changes made to the booking.", + "agent": "ID of the booking agent (if applicable).", + "company": "ID of the company making the booking (if applicable).", + "days_in_waiting_list": "Number of days the booking was on the waiting list before confirmation.", + "adr": "Average daily rate (price per night per room).", + "required_car_parking_spaces": "Number of car parking spaces required by the guest.", + "total_of_special_requests": "Number of special requests made by the guest (e.g., high floor, twin beds)." + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "reservation_status", + "num_feature_value": { + "adr": [ + -6.38, + 5400.0 + ], + "adults": [ + 0.0, + 55.0 + ], + "agent": [ + 1.0, + 535.0 + ], + "arrival_date_day_of_month": [ + 1.0, + 31.0 + ], + "arrival_date_week_number": [ + 1.0, + 53.0 + ], + "arrival_date_year": [ + 2015.0, + 2017.0 + ], + "babies": [ + 0.0, + 10.0 + ], + "booking_changes": [ + 0.0, + 21.0 + ], + "children": [ + 0.0, + 10.0 + ], + "company": [ + 6.0, + 543.0 + ], + "days_in_waiting_list": [ + 0.0, + 391.0 + ], + "is_repeated_guest": [ + 0.0, + 1.0 + ], + "lead_time": [ + 0.0, + 737.0 + ], + "previous_bookings_not_canceled": [ + 0.0, + 72.0 + ], + "previous_cancellations": [ + 0.0, + 26.0 + ], + "required_car_parking_spaces": [ + 0.0, + 8.0 + ], + "stays_in_week_nights": [ + 0.0, + 50.0 + ], + "stays_in_weekend_nights": [ + 0.0, + 19.0 + ], + "total_of_special_requests": [ + 0.0, + 5.0 + ] + }, + "cat_feature_value": { + "arrival_date_month": [ + "April", + "August", + "December", + "February", + "January", + "July", + "June", + "March", + "May", + "November", + "October", + "September" + ], + "assigned_room_type": [ + "A", + "B", + "C", + "D", + "E", + "F", + "G", + "H", + "I", + "K", + "L", + "P" + ], + "country": [ + "ABW", + "AGO", + "AIA", + "ALB", + "AND", + "ARE", + "ARG", + "ARM", + "ASM", + "ATA", + "ATF", + "AUS", + "AUT", + "AZE", + "BDI", + "BEL", + "BEN", + "BFA", + "BGD", + "BGR", + "BHR", + "BHS", + "BIH", + "BLR", + "BOL", + "BRA", + "BRB", + "BWA", + "CAF", + "CHE", + "CHL", + "CHN", + "CIV", + "CMR", + "CN", + "COL", + "COM", + "CPV", + "CRI", + "CUB", + "CYM", + "CYP", + "CZE", + "DEU", + "DJI", + "DMA", + "DNK", + "DOM", + "DZA", + "ECU", + "EGY", + "ESP", + "EST", + "ETH", + "FIN", + "FJI", + "FRA", + "FRO", + "GAB", + "GBR", + "GEO", + "GGY", + "GHA", + "GIB", + "GLP", + "GNB", + "GRC", + "GTM", + "GUY", + "HKG", + "HND", + "HRV", + "HUN", + "IDN", + "IMN", + "IND", + "IRL", + "IRN", + "IRQ", + "ISL", + "ISR", + "ITA", + "JAM", + "JEY", + "JOR", + "JPN", + "KAZ", + "KEN", + "KHM", + "KIR", + "KNA", + "KOR", + "KWT", + "LAO", + "LBN", + "LBY", + "LCA", + "LIE", + "LKA", + "LTU", + "LUX", + "LVA", + "MAC", + "MAR", + "MCO", + "MDG", + "MDV", + "MEX", + "MKD", + "MLI", + "MLT", + "MMR", + "MNE", + "MOZ", + "MRT", + "MUS", + "MWI", + "MYS", + "MYT", + "NAM", + "NCL", + "NGA", + "NIC", + "NLD", + "NOR", + "NPL", + "NZL", + "OMN", + "PAK", + "PAN", + "PER", + "PHL", + "PLW", + "POL", + "PRI", + "PRT", + "PRY", + "PYF", + "QAT", + "ROU", + "RUS", + "RWA", + "SAU", + "SDN", + "SEN", + "SGP", + "SLE", + "SLV", + "SMR", + "SRB", + "STP", + "SUR", + "SVK", + "SVN", + "SWE", + "SYC", + "SYR", + "TGO", + "THA", + "TJK", + "TMP", + "TUN", + "TUR", + "TWN", + "TZA", + "UGA", + "UKR", + "UMI", + "URY", + "USA", + "UZB", + "VEN", + "VGB", + "VNM", + "ZAF", + "ZMB", + "ZWE" + ], + "customer_type": [ + "Contract", + "Group", + "Transient", + "Transient-Party" + ], + "deposit_type": [ + "No Deposit", + "Non Refund", + "Refundable" + ], + "distribution_channel": [ + "Corporate", + "Direct", + "GDS", + "TA/TO", + "Undefined" + ], + "hotel": [ + "City Hotel", + "Resort Hotel" + ], + "market_segment": [ + "Aviation", + "Complementary", + "Corporate", + "Direct", + "Groups", + "Offline TA/TO", + "Online TA", + "Undefined" + ], + "meal": [ + "BB", + "FB", + "HB", + "SC", + "Undefined" + ], + "reservation_status": [ + "Canceled", + "Check-Out", + "No-Show" + ], + "reserved_room_type": [ + "A", + "B", + "C", + "D", + "E", + "F", + "G", + "H", + "L", + "P" + ] + } +} diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_006/test_current.csv b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_006/test_current.csv index 4accbefbbf9f34ee2c27c0b553f8fc832ae43620..29d88e798014659e1f93b1a98d0cd29ef2257792 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_006/test_current.csv +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_006/test_current.csv @@ -1,2 +1,2 @@ -hotel,is_canceled,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests,reservation_status,reservation_status_date -Resort Hotel,0,0,2015,November,48,24,0,1,1,0.0,0,BB,PRT,Online TA,TA/TO,1,0,0,A,A,0,No Deposit,241.0,,0,Transient,36.96,0,0,Check-Out,2015-11-25 +hotel,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests,reservation_status +Resort Hotel,0,2015,November,48,24,0,1,1,0.0,0,BB,PRT,Online TA,TA/TO,1,0,0,A,A,0,No Deposit,241.0,,0,Transient,36.96,0,0,Check-Out diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_007/Hotel_booking_demand_B4_007_current.json b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_007/Hotel_booking_demand_B4_007_current.json index dd3dca603c68899ef53f4e5df908b00f24a10bbf..1198b811d324fa3194107ab6932dc1ebd5bd1931 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_007/Hotel_booking_demand_B4_007_current.json +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_007/Hotel_booking_demand_B4_007_current.json @@ -1,7453 +1,7053 @@ { - "id": "007", - "task_type": "B4", - "subtask_type": "filter", - "perspective": "data_holder", - "dataset_name": "Hotel booking demand", - "table_path": "output/daily/classification/Hotel booking demand_B4/Hotel booking demand_B4_007/test.csv", - "query": "To better manage our high-season inventory, we need to understand which long-lead-time bookings are most stable. Looking at the trends in the new batch of data , pull out all reservations for the 'City Hotel' with a 'lead time' greater than 300 days. Among these, highlight the ones that were eventually canceled, as these pose the biggest risk to our revenue planning.", - "meta_info": { - "domain": "daily" + "id": "007", + "task_type": "B4", + "subtask_type": "filter", + "perspective": "data_holder", + "dataset_name": "Hotel booking demand", + "table_path": "output/daily/classification/Hotel booking demand_B4/Hotel booking demand_B4_007/test.csv", + "query": "To better manage our high-season inventory, we need to understand which long-lead-time bookings are most stable. Looking at the trends in the new batch of data , pull out all reservations for the 'City Hotel' with a 'lead time' greater than 300 days. Among these, highlight the ones that were eventually canceled, as these pose the biggest risk to our revenue planning.", + "meta_info": { + "domain": "daily" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "002", + "features": { + "hotel": "City Hotel", + "lead_time": 20, + "arrival_date_year": 2017, + "arrival_date_month": "March", + "arrival_date_week_number": 11, + "arrival_date_day_of_month": 13, + "stays_in_weekend_nights": 1, + "stays_in_week_nights": 2, + "adults": 1, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "FRA", + "market_segment": "Groups", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 1, + "deposit_type": "No Deposit", + "agent": 254.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient-Party", + "adr": 153.67, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "004", + "features": { + "hotel": "Resort Hotel", + "lead_time": 131, + "arrival_date_year": 2017, + "arrival_date_month": "May", + "arrival_date_week_number": 21, + "arrival_date_day_of_month": 23, + "stays_in_weekend_nights": 2, + "stays_in_week_nights": 5, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "HB", + "country": "GBR", + "market_segment": "Direct", + "distribution_channel": "Direct", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "C", + "assigned_room_type": "C", + "booking_changes": 1, + "deposit_type": "No Deposit", + "agent": 250.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 131.63, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "010", + "features": { + "hotel": "City Hotel", + "lead_time": 55, + "arrival_date_year": 2016, + "arrival_date_month": "July", + "arrival_date_week_number": 28, + "arrival_date_day_of_month": 7, + "stays_in_weekend_nights": 0, + "stays_in_week_nights": 1, + "adults": 1, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "PRT", + "market_segment": "Corporate", + "distribution_channel": "Corporate", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": null, + "company": 219.0, + "days_in_waiting_list": 0, + "customer_type": "Transient-Party", + "adr": 79.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "014", + "features": { + "hotel": "City Hotel", + "lead_time": 140, + "arrival_date_year": 2016, + "arrival_date_month": "July", + "arrival_date_week_number": 32, + "arrival_date_day_of_month": 31, + "stays_in_weekend_nights": 2, + "stays_in_week_nights": 3, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "FRA", + "market_segment": "Online TA", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "D", + "assigned_room_type": "D", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 9.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 113.9, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Canceled" + } + }, + { + "scenario_id": "017", + "features": { + "hotel": "City Hotel", + "lead_time": 1, + "arrival_date_year": 2015, + "arrival_date_month": "September", + "arrival_date_week_number": 37, + "arrival_date_day_of_month": 6, + "stays_in_weekend_nights": 2, + "stays_in_week_nights": 0, + "adults": 1, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "PRT", + "market_segment": "Groups", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 1.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient-Party", + "adr": 6.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Canceled" + } + }, + { + "scenario_id": "024", + "features": { + "hotel": "City Hotel", + "lead_time": 167, + "arrival_date_year": 2017, + "arrival_date_month": "May", + "arrival_date_week_number": 18, + "arrival_date_day_of_month": 6, + "stays_in_weekend_nights": 2, + "stays_in_week_nights": 5, + "adults": 3, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "BRA", + "market_segment": "Online TA", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "D", + "assigned_room_type": "D", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 9.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 174.21, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Canceled" + } + }, + { + "scenario_id": "028", + "features": { + "hotel": "Resort Hotel", + "lead_time": 15, + "arrival_date_year": 2017, + "arrival_date_month": "August", + "arrival_date_week_number": 31, + "arrival_date_day_of_month": 2, + "stays_in_weekend_nights": 0, + "stays_in_week_nights": 4, + "adults": 2, + "children": 2.0, + "babies": 0, + "meal": "BB", + "country": "PRT", + "market_segment": "Online TA", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "C", + "assigned_room_type": "C", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 240.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 264.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 1, + "reservation_status": "Canceled" + } + }, + { + "scenario_id": "030", + "features": { + "hotel": "City Hotel", + "lead_time": 68, + "arrival_date_year": 2016, + "arrival_date_month": "July", + "arrival_date_week_number": 28, + "arrival_date_day_of_month": 4, + "stays_in_weekend_nights": 1, + "stays_in_week_nights": 1, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "HB", + "country": "PRT", + "market_segment": "Offline TA/TO", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "D", + "assigned_room_type": "D", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 6.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 107.95, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "031", + "features": { + "hotel": "Resort Hotel", + "lead_time": 99, + "arrival_date_year": 2016, + "arrival_date_month": "January", + "arrival_date_week_number": 3, + "arrival_date_day_of_month": 12, + "stays_in_weekend_nights": 2, + "stays_in_week_nights": 10, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "PRT", + "market_segment": "Online TA", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "D", + "assigned_room_type": "D", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 242.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 41.4, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Canceled" + } + }, + { + "scenario_id": "034", + "features": { + "hotel": "Resort Hotel", + "lead_time": 132, + "arrival_date_year": 2017, + "arrival_date_month": "June", + "arrival_date_week_number": 24, + "arrival_date_day_of_month": 15, + "stays_in_weekend_nights": 0, + "stays_in_week_nights": 3, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "PRT", + "market_segment": "Online TA", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 240.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 110.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 1, + "reservation_status": "Canceled" + } + }, + { + "scenario_id": "038", + "features": { + "hotel": "City Hotel", + "lead_time": 192, + "arrival_date_year": 2016, + "arrival_date_month": "October", + "arrival_date_week_number": 43, + "arrival_date_day_of_month": 21, + "stays_in_weekend_nights": 0, + "stays_in_week_nights": 2, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "PRT", + "market_segment": "Groups", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 29.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient-Party", + "adr": 95.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Canceled" + } + }, + { + "scenario_id": "042", + "features": { + "hotel": "City Hotel", + "lead_time": 53, + 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- "adr": 55.04, - "required_car_parking_spaces": 0, - "total_of_special_requests": 0, - "reservation_status": "Check-Out", - "reservation_status_date": "2015-11-06" - } - }, - { - "scenario_id": "472", - "features": { - "hotel": "City Hotel", - "is_canceled": 1, - "lead_time": 433, - "arrival_date_year": 2016, - "arrival_date_month": "September", - "arrival_date_week_number": 37, - "arrival_date_day_of_month": 8, - "stays_in_weekend_nights": 0, - "stays_in_week_nights": 3, - "adults": 2, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "PRT", - "market_segment": "Offline TA/TO", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "A", - "assigned_room_type": "A", - "booking_changes": 0, - "deposit_type": "Non Refund", - "agent": 3.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 70.0, - "required_car_parking_spaces": 0, - 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"Check-Out", - "reservation_status_date": "2016-06-25" - } - }, - { - "scenario_id": "483", - "features": { - "hotel": "City Hotel", - "is_canceled": 0, - "lead_time": 12, - "arrival_date_year": 2016, - "arrival_date_month": "November", - "arrival_date_week_number": 48, - "arrival_date_day_of_month": 26, - "stays_in_weekend_nights": 0, - "stays_in_week_nights": 1, - "adults": 2, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "PRT", - "market_segment": "Direct", - "distribution_channel": "Direct", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "A", - "assigned_room_type": "A", - "booking_changes": 0, - "deposit_type": "No Deposit", - "agent": 14.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 83.13, - "required_car_parking_spaces": 0, - "total_of_special_requests": 0, - "reservation_status": "Check-Out", - "reservation_status_date": "2016-11-27" - } - }, - { - "scenario_id": "484", - "features": { - "hotel": "Resort Hotel", - "is_canceled": 0, - "lead_time": 134, - "arrival_date_year": 2015, - "arrival_date_month": "July", - "arrival_date_week_number": 28, - "arrival_date_day_of_month": 8, - "stays_in_weekend_nights": 0, - "stays_in_week_nights": 4, - "adults": 2, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "PRT", - "market_segment": "Direct", - "distribution_channel": "Direct", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "D", - "assigned_room_type": "D", - "booking_changes": 0, - "deposit_type": "No Deposit", - "agent": 250.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 84.15, - "required_car_parking_spaces": 0, - "total_of_special_requests": 0, - "reservation_status": "Check-Out", - "reservation_status_date": "2015-07-12" - } - }, - { - "scenario_id": "487", - "features": { - "hotel": "Resort Hotel", - "is_canceled": 0, - "lead_time": 107, - "arrival_date_year": 2015, - "arrival_date_month": "July", - "arrival_date_week_number": 27, - "arrival_date_day_of_month": 2, - "stays_in_weekend_nights": 2, - "stays_in_week_nights": 5, - "adults": 2, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "PRT", - "market_segment": "Online TA", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "A", - "assigned_room_type": "A", - "booking_changes": 0, - "deposit_type": "No Deposit", - "agent": 240.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 110.7, - "required_car_parking_spaces": 0, - "total_of_special_requests": 2, - "reservation_status": "Check-Out", - "reservation_status_date": "2015-07-09" - } - }, - { - "scenario_id": "489", - "features": { - "hotel": "City Hotel", - "is_canceled": 0, - "lead_time": 183, - "arrival_date_year": 2016, - "arrival_date_month": "September", - "arrival_date_week_number": 37, - "arrival_date_day_of_month": 8, - "stays_in_weekend_nights": 0, - "stays_in_week_nights": 3, - "adults": 1, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "FRA", - "market_segment": "Offline TA/TO", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "A", - "assigned_room_type": "A", - "booking_changes": 1, - "deposit_type": "No Deposit", - "agent": 44.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient-Party", - "adr": 100.0, - "required_car_parking_spaces": 0, - "total_of_special_requests": 0, - "reservation_status": "Check-Out", - "reservation_status_date": "2016-09-11" - } - }, - { - "scenario_id": "491", - "features": { - "hotel": "Resort Hotel", - "is_canceled": 0, - "lead_time": 243, - "arrival_date_year": 2016, - "arrival_date_month": "October", - "arrival_date_week_number": 41, - "arrival_date_day_of_month": 6, - "stays_in_weekend_nights": 2, - "stays_in_week_nights": 3, - "adults": 1, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "IRL", - "market_segment": "Groups", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "D", - "assigned_room_type": "D", - "booking_changes": 0, - "deposit_type": "No Deposit", - "agent": 91.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient-Party", - "adr": 55.1, - "required_car_parking_spaces": 0, - "total_of_special_requests": 0, - "reservation_status": "Check-Out", - "reservation_status_date": "2016-10-11" - } - }, - { - "scenario_id": "499", - "features": { - "hotel": "Resort Hotel", - "is_canceled": 0, - "lead_time": 33, - "arrival_date_year": 2016, - "arrival_date_month": "November", - "arrival_date_week_number": 47, - "arrival_date_day_of_month": 15, - "stays_in_weekend_nights": 0, - "stays_in_week_nights": 4, - "adults": 1, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "GBR", - "market_segment": "Online TA", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "A", - "assigned_room_type": "A", - "booking_changes": 0, - "deposit_type": "No Deposit", - "agent": 241.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 26.99, - "required_car_parking_spaces": 0, - "total_of_special_requests": 1, - "reservation_status": "Check-Out", - "reservation_status_date": "2016-11-19" - } - } - ], - "target_column": "reservation_status", - "task_sub_type": "classification", - "ranking_ground_truth": { - "top_k_ids": [ - "072", - "143", - "197", - "214", - "248", - "280", - "310", - "324", - "333", - "361", - "378", - "433", - "458", - "472" - ] - }, - "active_filters": [ - { - "col": "hotel", - "op": "==", - "val": "City Hotel" - }, - { - "col": "lead_time", - "op": ">", - "val": 300 - } - ], - "target_class_value": "canceled" - }, - "response": "", - "evaluation_score": {} -} \ No newline at end of file + "active_filters": [ + { + "col": "hotel", + "op": "==", + "val": "City Hotel" + }, + { + "col": "lead_time", + "op": ">", + "val": 300 + } + ], + "target_class_value": "canceled" + }, + "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_007/current.csv b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_007/current.csv index 6aadceb3a3362e67818e0d8936eec9bf53a36820..089ea9bd55aa2f7251db51c38c31594b22acfba3 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_007/current.csv +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_007/current.csv @@ -1,201 +1,201 @@ -hotel,is_canceled,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests,reservation_status_date -Resort Hotel,0,131,2017,May,21,23,2,5,2,0.0,0,HB,GBR,Direct,Direct,0,0,0,C,C,1,No Deposit,250.0,,0,Transient,131.63,0,0,2017-05-30 -City Hotel,0,55,2016,July,28,7,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,219.0,0,Transient-Party,79.0,0,0,2016-07-08 -City Hotel,1,140,2016,July,32,31,2,3,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,113.9,0,0,2016-03-16 -City Hotel,1,1,2015,September,37,6,2,0,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,0,Transient-Party,6.0,0,0,2015-09-05 -City Hotel,1,167,2017,May,18,6,2,5,3,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,174.21,0,0,2017-04-04 -Resort Hotel,1,15,2017,August,31,2,0,4,2,2.0,0,BB,PRT,Online TA,TA/TO,0,0,0,C,C,0,No Deposit,240.0,,0,Transient,264.0,0,1,2017-07-19 -City Hotel,0,68,2016,July,28,4,1,1,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,6.0,,0,Transient,107.95,0,0,2016-07-06 -Resort Hotel,1,99,2016,January,3,12,2,10,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,242.0,,0,Transient,41.4,0,0,2015-10-28 -Resort Hotel,1,132,2017,June,24,15,0,3,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,110.0,0,1,2017-02-08 -City Hotel,1,192,2016,October,43,21,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,29.0,,0,Transient-Party,95.0,0,0,2016-09-06 -City Hotel,0,53,2016,March,12,17,1,3,1,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,10.0,,0,Transient,81.7,0,1,2016-03-21 -Resort Hotel,0,0,2016,January,4,22,0,1,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,45.0,0,0,2016-01-23 -City Hotel,0,2,2016,November,49,27,2,2,2,0.0,0,BB,TUR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,89.89,0,1,2016-12-01 -City Hotel,0,282,2015,October,41,10,2,1,2,0.0,0,BB,SWE,Offline TA/TO,TA/TO,0,0,0,A,F,0,No Deposit,21.0,,0,Transient-Party,76.67,0,0,2015-10-13 -City Hotel,1,249,2017,May,21,24,0,4,3,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,157.5,0,2,2016-09-25 -City Hotel,0,80,2017,July,27,7,0,1,2,0.0,0,BB,DNK,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient-Party,108.0,0,1,2017-07-08 -City Hotel,1,265,2015,July,28,9,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,1,1,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.8,0,0,2014-10-17 -City Hotel,1,155,2016,June,27,27,1,2,2,0.0,0,BB,BEL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,96.3,0,0,2016-01-24 -Resort Hotel,0,145,2017,February,9,26,4,7,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,40.0,,0,Contract,36.72,0,0,2017-03-09 -City Hotel,0,167,2017,August,31,4,2,2,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,9.0,,0,Transient,166.5,0,0,2017-08-08 -Resort Hotel,0,143,2016,June,23,2,1,3,2,0.0,0,BB,IRL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,72.0,0,1,2016-06-06 -City Hotel,1,173,2016,July,28,8,0,2,2,0.0,0,SC,ITA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,85.5,0,0,2016-06-26 -City Hotel,0,181,2016,October,42,11,0,2,1,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,234.0,,0,Transient-Party,120.0,0,0,2016-10-13 -Resort Hotel,0,303,2016,September,36,1,4,10,2,0.0,0,BB,IRL,Offline TA/TO,TA/TO,0,0,0,E,E,0,No Deposit,171.0,,0,Transient,90.06,0,0,2016-09-15 -City Hotel,1,175,2017,July,28,15,2,1,2,0.0,0,BB,CN,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,107.1,0,1,2017-01-28 -City Hotel,0,2,2017,May,21,21,2,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,27.0,,0,Transient,157.25,0,0,2017-05-25 -City Hotel,1,303,2016,August,34,19,2,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,21.0,,0,Transient-Party,78.0,0,0,2016-07-19 -Resort Hotel,0,9,2017,April,14,7,0,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,240.0,,0,Transient,115.0,0,2,2017-04-08 -City Hotel,1,122,2016,April,18,29,2,2,2,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,95.2,0,0,2015-12-29 -Resort Hotel,0,2,2016,October,45,30,2,0,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,E,1,No Deposit,240.0,,0,Transient,66.0,0,1,2016-11-01 -Resort Hotel,0,66,2015,December,49,5,1,1,2,0.0,0,FB,ESP,Groups,TA/TO,0,0,0,A,A,0,No Deposit,155.0,,0,Transient-Party,64.0,0,0,2015-12-07 -City Hotel,0,28,2016,November,45,2,0,1,1,0.0,0,SC,POL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,120.0,0,0,2016-11-03 -Resort Hotel,0,1,2015,December,50,12,0,1,2,0.0,0,HB,PRT,Direct,Direct,0,0,0,A,D,0,No Deposit,,,0,Transient,79.0,1,1,2015-12-13 -Resort Hotel,0,197,2016,April,18,30,1,1,2,0.0,0,HB,AUT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,298.0,,0,Transient-Party,85.0,0,0,2016-05-02 -City Hotel,0,63,2017,August,34,23,0,3,1,0.0,0,BB,CHE,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,119.4,0,0,2017-08-26 -Resort Hotel,0,203,2017,May,21,25,2,5,2,0.0,0,BB,GBR,Groups,TA/TO,0,0,0,A,A,1,No Deposit,,223.0,0,Transient-Party,59.71,1,0,2017-06-01 -City Hotel,1,139,2017,July,28,13,1,3,3,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,152.1,0,0,2017-02-25 -City Hotel,1,135,2017,June,26,30,0,1,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,30.0,,0,Transient,120.0,0,0,2017-02-15 -Resort Hotel,0,71,2015,October,41,8,0,3,1,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,250.0,,0,Contract,48.0,1,1,2015-10-11 -City Hotel,0,44,2017,June,25,19,2,5,2,0.0,0,BB,NOR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,112.91,0,1,2017-06-26 -City Hotel,1,212,2016,July,31,27,4,10,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,90.95,0,2,2016-04-17 -City Hotel,0,29,2016,August,36,30,0,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,122.0,0,2,2016-08-31 -Resort Hotel,1,53,2017,March,10,5,2,5,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,298.0,,0,Transient-Party,30.0,0,0,2017-01-11 -City Hotel,1,35,2017,August,32,8,0,1,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,229.0,,0,Transient-Party,90.0,0,0,2017-07-07 -City Hotel,0,10,2015,October,40,1,2,5,2,0.0,0,HB,FRA,Direct,Direct,0,0,0,A,A,2,No Deposit,14.0,,0,Transient,171.71,0,0,2015-10-08 -City Hotel,0,1,2016,August,33,7,1,0,2,0.0,0,SC,NLD,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,9.0,,0,Transient,98.0,0,0,2016-08-08 -City Hotel,0,22,2016,March,13,20,1,0,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,91.0,0,1,2016-03-21 -City Hotel,0,0,2016,December,50,7,0,1,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,9.0,,0,Transient,98.0,0,2,2016-12-08 -City Hotel,1,181,2017,May,21,25,0,2,2,0.0,0,BB,PRT,Groups,Direct,0,0,0,A,A,0,Non Refund,,,0,Transient,130.0,0,0,2016-11-25 -City Hotel,1,46,2017,January,1,1,2,0,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,,,0,Transient-Party,65.0,0,0,2016-12-21 -City Hotel,0,44,2017,April,16,20,0,3,1,0.0,0,BB,ITA,Groups,TA/TO,0,0,0,A,A,1,No Deposit,1.0,,0,Transient-Party,106.67,0,0,2017-04-23 -City Hotel,0,16,2016,April,16,12,0,3,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,144.67,0,2,2016-04-15 -City Hotel,0,9,2016,December,51,16,0,1,1,0.0,0,BB,PRT,Online TA,GDS,0,0,0,A,A,0,No Deposit,195.0,,0,Transient,86.0,0,0,2016-12-17 -Resort Hotel,1,277,2016,July,29,12,2,5,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,92.57,0,1,2015-10-16 -City Hotel,1,372,2016,July,29,14,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0,2015-10-21 -City Hotel,1,77,2017,July,28,11,0,1,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,120.0,0,2,2017-04-26 -Resort Hotel,0,104,2016,April,18,28,2,7,2,2.0,0,BB,CHE,Direct,Direct,0,0,0,G,G,0,No Deposit,250.0,,0,Transient,99.22,1,1,2016-05-07 -Resort Hotel,0,26,2016,February,7,9,1,5,2,0.0,0,SC,PRT,Direct,Direct,0,0,0,A,I,5,No Deposit,,364.0,0,Transient-Party,0.0,0,1,2016-02-15 -City Hotel,0,12,2016,October,43,21,0,2,2,1.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,180.0,0,1,2016-10-23 -City Hotel,0,28,2017,April,16,21,0,1,1,0.0,0,BB,USA,Direct,Direct,0,0,0,A,D,1,No Deposit,14.0,,0,Transient,93.5,0,0,2017-04-22 -City Hotel,1,23,2016,October,43,20,0,2,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,119.0,0,1,2016-10-10 -City Hotel,1,2,2017,June,24,11,1,0,2,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,10.0,,0,Transient,100.0,0,0,2017-06-09 -City Hotel,0,377,2016,October,42,14,0,2,2,0.0,0,HB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,115.0,0,1,2016-10-16 -City Hotel,0,164,2016,September,40,30,2,2,2,0.0,0,BB,BEL,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,126.9,0,1,2016-10-04 -Resort Hotel,0,60,2016,January,3,16,2,5,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,E,1,No Deposit,6.0,,0,Transient,26.0,0,0,2016-01-23 -Resort Hotel,0,2,2016,November,48,26,1,1,1,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,D,0,No Deposit,250.0,,0,Transient,38.0,0,0,2016-11-28 -City Hotel,0,38,2016,November,46,10,0,3,2,0.0,0,BB,FIN,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,145.0,0,0,2016-11-13 -Resort Hotel,0,134,2016,August,34,18,0,3,2,0.0,0,BB,CN,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,179.0,0,1,2016-08-21 -City Hotel,0,55,2016,June,27,26,2,0,3,0.0,0,BB,PRT,Direct,Direct,0,0,0,D,D,0,No Deposit,14.0,,0,Transient,148.8,1,2,2016-06-28 -Resort Hotel,0,222,2016,June,25,13,2,5,1,0.0,0,BB,GBR,Direct,Direct,0,0,0,E,F,3,No Deposit,250.0,,0,Transient,93.5,0,0,2016-06-20 -City Hotel,0,217,2016,December,50,5,3,7,2,0.0,0,SC,BRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,46.63,0,1,2016-12-15 -City Hotel,0,130,2016,June,27,28,1,5,3,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,A,D,4,No Deposit,7.0,,0,Transient,125.51,0,2,2016-07-04 -Resort Hotel,0,2,2016,May,23,30,1,2,1,0.0,0,HB,ESP,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,175.0,,0,Transient,68.8,0,0,2016-06-02 -City Hotel,1,102,2016,June,24,5,1,0,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,0,Transient-Party,65.0,0,0,2016-06-02 -Resort Hotel,1,178,2015,August,33,10,1,4,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,E,E,0,No Deposit,5.0,,0,Transient,119.25,0,0,2015-06-15 -City Hotel,1,305,2016,October,40,1,1,1,2,0.0,0,HB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,35.0,,17,Transient,100.0,0,0,2015-12-18 -City Hotel,1,81,2017,July,28,12,0,2,2,0.0,0,BB,NOR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,150.0,0,2,2017-04-23 -City Hotel,0,81,2016,June,27,30,1,3,2,0.0,0,SC,CHL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,103.5,0,1,2016-07-04 -Resort Hotel,0,40,2015,July,28,11,2,5,2,0.0,0,HB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,242.0,,0,Transient,166.0,0,0,2015-07-18 -Resort Hotel,0,129,2015,July,28,10,2,4,2,0.0,0,FB,ESP,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,5.0,,0,Transient,157.5,1,0,2015-07-16 -City Hotel,0,332,2016,September,39,22,1,3,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,77.23,0,2,2016-09-26 -City Hotel,1,343,2015,September,39,25,0,1,1,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient,170.0,0,0,2015-09-22 -City Hotel,1,255,2016,November,46,12,2,5,0,2.0,0,BB,TUR,Online TA,TA/TO,0,0,0,B,B,0,No Deposit,9.0,,0,Transient-Party,72.0,0,0,2016-03-10 -Resort Hotel,1,166,2015,July,29,15,0,2,2,0.0,0,FB,PRT,Groups,TA/TO,0,24,0,A,A,0,Non Refund,,,0,Transient,121.5,0,0,2015-04-28 -Resort Hotel,0,4,2016,January,5,26,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,D,0,No Deposit,,,0,Transient,38.0,0,0,2016-01-27 -City Hotel,0,30,2016,March,10,4,0,2,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,9.0,,0,Transient-Party,88.4,0,0,2016-03-06 -City Hotel,0,133,2017,April,16,19,0,4,2,0.0,0,BB,ISL,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,390.0,,0,Transient-Party,124.0,0,0,2017-04-23 -City Hotel,0,4,2016,July,28,6,0,3,2,0.0,0,BB,USA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,151.0,0,1,2016-07-09 -City Hotel,0,55,2016,April,15,6,0,1,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,56.0,,0,Transient-Party,104.0,0,0,2016-04-07 -Resort Hotel,1,12,2016,March,11,8,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,82.0,0,Transient,30.0,0,0,2016-02-29 -City Hotel,0,1,2015,September,37,6,2,0,1,0.0,0,BB,FRA,Groups,TA/TO,0,0,0,A,B,0,No Deposit,1.0,,0,Transient-Party,75.0,0,0,2015-09-08 -City Hotel,1,104,2017,May,20,16,0,3,1,0.0,0,SC,USA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,117.0,0,0,2017-03-02 -City Hotel,0,140,2015,November,47,18,0,3,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,D,1,No Deposit,29.0,,87,Transient-Party,65.0,0,0,2015-11-21 -City Hotel,1,393,2017,April,16,22,2,1,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,229.0,,0,Transient,90.0,0,0,2016-04-17 -City Hotel,0,3,2017,June,23,5,1,5,1,0.0,0,BB,AUT,Aviation,Corporate,0,0,0,A,A,0,No Deposit,,153.0,0,Transient,95.0,0,0,2017-06-11 -Resort Hotel,0,0,2016,June,25,13,1,1,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,121.0,1,0,2016-06-15 -Resort Hotel,0,0,2016,April,18,30,0,1,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,F,F,0,No Deposit,250.0,,0,Transient-Party,101.0,1,0,2016-05-01 -City Hotel,1,46,2017,June,25,20,0,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,229.0,,0,Transient-Party,90.0,0,0,2017-06-06 -City Hotel,0,13,2016,June,24,8,0,2,2,0.0,0,BB,SGP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,121.0,0,1,2016-06-10 -City Hotel,0,192,2016,June,26,24,0,2,1,0.0,0,BB,SRB,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,34.0,,0,Transient-Party,95.0,0,0,2016-06-26 -City Hotel,0,233,2017,June,26,29,2,3,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient-Party,111.06,0,2,2017-07-04 -Resort Hotel,0,140,2017,June,24,12,1,3,2,1.0,0,BB,CHN,Online TA,TA/TO,0,0,0,D,D,1,No Deposit,240.0,,0,Transient-Party,164.0,1,1,2017-06-16 -Resort Hotel,0,77,2015,September,40,28,3,7,2,0.0,0,HB,GBR,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,40.0,,0,Contract,72.1,0,0,2015-10-08 -City Hotel,0,80,2017,June,22,2,1,2,2,2.0,0,BB,PRT,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,7.0,,0,Transient,250.96,0,1,2017-06-05 -City Hotel,0,44,2016,May,22,23,1,1,2,0.0,0,BB,PRT,Complementary,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,0.0,0,1,2016-05-25 -Resort Hotel,0,320,2017,June,22,3,4,10,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,40.0,,0,Contract,68.4,0,0,2017-06-17 -City Hotel,1,626,2016,November,46,7,1,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.8,0,0,2016-09-26 -City Hotel,0,59,2016,July,27,1,1,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,138.0,,0,Transient,85.0,0,0,2016-07-04 -City Hotel,1,212,2017,May,21,22,2,5,2,0.0,0,HB,BRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,114.56,0,1,2017-05-10 -City Hotel,0,48,2016,March,14,28,1,1,2,0.0,0,BB,RUS,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,80.1,0,1,2016-03-30 -City Hotel,0,11,2016,June,25,14,0,1,1,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,10.0,,0,Transient,78.8,0,0,2016-06-15 -Resort Hotel,0,6,2016,March,12,17,0,1,1,0.0,0,BB,ESP,Groups,TA/TO,0,0,0,A,D,1,No Deposit,66.0,,0,Transient-Party,26.0,0,0,2016-03-18 -City Hotel,0,23,2015,October,42,11,1,0,1,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Contract,114.0,0,1,2015-10-12 -City Hotel,0,24,2017,June,23,4,2,1,2,0.0,0,BB,ISR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,170.0,0,1,2017-06-07 -City Hotel,1,0,2016,January,4,19,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,,0,Transient,65.0,0,0,2016-01-19 -City Hotel,1,223,2017,August,34,24,0,3,3,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,172.8,0,0,2017-05-10 -City Hotel,0,209,2016,August,33,7,2,1,3,0.0,0,BB,DNK,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Transient,127.8,0,2,2016-08-10 -City Hotel,0,4,2016,May,23,31,0,1,1,0.0,0,BB,FRA,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,260.0,0,Transient-Party,121.0,0,0,2016-06-01 -Resort Hotel,0,6,2017,March,9,1,0,4,2,0.0,0,BB,BEL,Groups,Corporate,0,0,0,A,A,3,No Deposit,,223.0,0,Transient-Party,37.0,0,0,2017-03-05 -Resort Hotel,0,11,2015,July,29,17,0,2,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,240.0,,0,Transient,155.5,1,0,2015-07-19 -City Hotel,0,7,2015,August,34,19,1,4,2,0.0,0,BB,ESP,Direct,Direct,0,0,0,A,B,0,No Deposit,,,0,Transient,67.0,0,0,2015-08-24 -City Hotel,0,145,2016,July,31,30,2,2,3,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,A,D,1,No Deposit,9.0,,0,Transient-Party,134.3,0,2,2016-08-03 -City Hotel,0,5,2015,September,39,20,1,0,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,E,E,1,No Deposit,14.0,,0,Transient,146.0,1,2,2015-09-21 -City Hotel,1,333,2016,September,39,20,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,Non Refund,58.0,,19,Transient,90.0,0,0,2015-11-11 -City Hotel,0,81,2016,May,19,1,2,2,2,0.0,0,BB,NLD,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,119.85,0,2,2016-05-05 -Resort Hotel,0,4,2016,May,22,28,0,1,1,0.0,0,HB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,408.0,0,Transient,65.0,0,0,2016-05-29 -Resort Hotel,1,23,2016,July,29,13,0,4,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,161.1,0,0,2016-06-21 -City Hotel,1,89,2016,July,28,5,0,3,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Transient,114.3,0,0,2016-04-24 -City Hotel,0,78,2016,April,15,7,0,2,2,2.0,0,BB,RUS,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,9.0,,0,Transient,177.3,1,2,2016-04-09 -City Hotel,1,365,2015,October,42,17,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient,62.8,0,0,2015-01-30 -Resort Hotel,0,138,2016,August,33,10,0,0,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,E,I,0,No Deposit,240.0,,0,Transient,0.0,0,1,2016-08-10 -Resort Hotel,0,2,2016,March,11,10,0,1,1,0.0,0,BB,,Corporate,Corporate,0,0,1,A,A,0,No Deposit,146.0,,0,Transient,42.0,1,0,2016-03-11 -City Hotel,0,53,2016,September,39,18,2,0,2,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,27.0,,0,Transient,94.5,1,0,2016-09-20 -City Hotel,0,111,2017,August,34,24,0,2,3,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,1,No Deposit,9.0,,0,Transient,195.0,0,2,2017-08-26 -City Hotel,1,315,2017,July,30,28,1,2,2,0.0,0,SC,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,89.1,0,0,2016-11-09 -City Hotel,0,20,2017,March,11,13,1,2,1,0.0,0,BB,FRA,Groups,TA/TO,0,0,0,A,A,1,No Deposit,254.0,,0,Transient-Party,153.67,0,0,2017-03-16 -Resort Hotel,0,8,2016,March,10,3,0,3,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,240.0,,0,Transient,48.0,0,1,2016-03-06 -City Hotel,1,40,2016,June,27,30,4,10,1,0.0,0,BB,AGO,Direct,Direct,0,0,0,G,G,0,No Deposit,14.0,,0,Transient,244.43,0,0,2016-06-30 -Resort Hotel,0,10,2016,March,12,14,1,2,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,1,A,A,2,No Deposit,139.0,99.0,0,Transient,37.0,0,0,2016-03-17 -City Hotel,0,256,2016,June,25,15,0,2,2,0.0,0,HB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,115.0,0,1,2016-06-17 -Resort Hotel,0,161,2017,July,30,28,1,2,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,177.0,,0,Transient,141.0,0,0,2017-07-31 -Resort Hotel,0,36,2015,November,47,20,0,2,2,0.0,0,FB,ESP,Groups,TA/TO,0,0,0,A,C,0,No Deposit,38.0,,0,Transient-Party,64.0,0,0,2015-11-22 -City Hotel,0,336,2017,May,19,13,0,1,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,108.0,0,1,2017-05-14 -City Hotel,0,12,2016,September,39,18,2,1,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,171.33,0,1,2016-09-21 -City Hotel,1,447,2016,September,40,29,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0,2015-10-21 -City Hotel,1,42,2017,June,24,17,2,4,2,0.0,0,SC,NOR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,133.33,0,1,2017-06-05 -City Hotel,1,34,2015,August,32,5,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.0,0,0,2015-07-29 -Resort Hotel,1,140,2016,February,7,12,0,2,2,0.0,0,HB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,50.0,0,0,2015-12-11 -City Hotel,1,17,2016,July,31,29,0,1,2,1.0,0,BB,ISR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,129.0,0,2,2016-07-15 -Resort Hotel,0,163,2015,September,38,13,4,7,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,243.0,,0,Contract,62.48,0,0,2015-09-24 -City Hotel,0,7,2016,March,10,2,0,2,1,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,A,1,No Deposit,94.0,,0,Transient-Party,65.0,0,0,2016-03-04 -City Hotel,0,27,2016,March,11,6,2,1,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,28.0,,0,Transient,66.0,0,0,2016-03-09 -City Hotel,1,328,2015,September,37,10,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Contract,62.0,0,0,2015-01-01 -City Hotel,0,26,2016,March,11,7,1,4,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,28.0,,0,Transient,87.0,0,0,2016-03-12 -Resort Hotel,1,17,2016,October,43,17,1,2,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,60.0,0,1,2016-10-04 -Resort Hotel,0,144,2016,May,21,18,4,9,1,0.0,0,BB,GBR,Direct,Direct,0,0,0,F,F,1,No Deposit,250.0,,0,Transient,79.05,0,0,2016-05-31 -City Hotel,0,141,2016,August,36,29,1,2,2,0.0,0,SC,ITA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,85.5,0,2,2016-09-01 -Resort Hotel,0,14,2017,February,6,9,0,1,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,467.0,,0,Transient,38.4,0,0,2017-02-10 -Resort Hotel,0,134,2017,March,10,9,2,3,1,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,241.0,,0,Transient,48.43,0,2,2017-03-14 -Resort Hotel,0,1,2017,February,9,27,1,0,1,0.0,0,BB,PRT,Direct,Direct,1,0,2,A,A,0,No Deposit,,,0,Transient-Party,55.0,0,1,2017-02-28 -City Hotel,0,264,2015,July,28,8,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.0,0,0,2015-07-10 -Resort Hotel,0,89,2015,August,33,15,2,5,2,1.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,67.0,,0,Transient,145.75,1,1,2015-08-22 -City Hotel,0,99,2016,February,8,19,0,1,1,0.0,0,BB,PRT,Corporate,TA/TO,0,0,0,A,E,0,No Deposit,,67.0,0,Transient-Party,81.0,0,0,2016-02-20 -City Hotel,0,78,2016,April,15,7,0,1,1,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,86.0,,0,Transient,72.72,0,0,2016-04-08 -City Hotel,0,1,2017,August,32,10,0,1,2,0.0,0,SC,USA,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,190.0,0,0,2017-08-11 -Resort Hotel,0,103,2016,May,19,1,2,3,2,0.0,0,BB,NLD,Groups,TA/TO,0,0,0,A,A,7,No Deposit,95.0,,0,Transient-Party,47.0,0,0,2016-05-06 -Resort Hotel,0,2,2017,April,16,21,0,1,3,1.0,0,BB,PRT,Online TA,TA/TO,0,0,0,H,H,0,No Deposit,240.0,,0,Transient,185.0,0,0,2017-04-22 -City Hotel,0,106,2016,April,18,29,1,2,2,0.0,0,BB,ITA,Direct,Direct,0,0,0,A,B,1,No Deposit,14.0,,0,Transient-Party,94.5,0,0,2016-05-02 -City Hotel,0,122,2017,July,29,19,0,4,3,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,9.0,,0,Transient,199.13,0,1,2017-07-23 -Resort Hotel,0,7,2015,December,49,2,0,1,2,0.0,0,BB,ESP,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,6.0,,0,Transient-Party,8.0,0,1,2015-12-03 -City Hotel,0,75,2016,June,23,4,1,1,2,0.0,0,SC,IRL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,11.0,,0,Transient,89.28,0,0,2016-06-06 -City Hotel,1,278,2015,July,30,22,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient-Party,62.0,0,0,2015-06-29 -Resort Hotel,0,110,2017,May,19,9,2,5,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,40.0,,0,Contract,40.95,0,0,2017-05-16 -Resort Hotel,0,69,2015,December,53,31,0,3,2,1.0,0,BB,PRT,Direct,Direct,0,0,0,D,D,0,No Deposit,,,0,Transient,199.73,0,3,2016-01-03 -City Hotel,1,351,2016,June,26,23,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0,2015-10-21 -City Hotel,0,2,2016,June,25,12,2,0,2,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,108.5,0,2,2016-06-14 -Resort Hotel,0,3,2017,June,24,12,1,1,1,0.0,0,BB,PRT,Complementary,Direct,0,0,0,A,C,0,No Deposit,,,0,Transient,4.0,0,1,2017-06-14 -City Hotel,1,131,2017,June,24,16,1,2,2,0.0,0,BB,CZE,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,117.0,0,0,2017-02-05 -City Hotel,1,86,2016,January,4,22,0,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,29.0,,35,Transient,85.0,0,0,2015-12-02 -City Hotel,0,4,2016,April,16,14,0,3,2,0.0,0,BB,ARG,Direct,Direct,0,0,0,A,A,2,No Deposit,14.0,,0,Transient,110.15,1,0,2016-04-17 -Resort Hotel,0,5,2016,April,18,29,0,1,2,0.0,0,BB,PRT,Complementary,Direct,0,0,0,D,D,0,No Deposit,,,0,Transient,0.0,1,3,2016-04-30 -Resort Hotel,0,9,2017,July,27,7,1,2,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,160.0,0,2,2017-07-10 -Resort Hotel,0,4,2016,August,34,18,0,1,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,E,E,1,No Deposit,240.0,,0,Transient,20.0,1,1,2016-08-19 -City Hotel,1,84,2017,June,26,27,0,3,2,0.0,0,SC,IRL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,120.0,0,1,2017-04-06 -City Hotel,1,140,2017,April,17,28,2,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,119.0,,0,Transient-Party,130.0,0,0,2017-03-20 -City Hotel,1,414,2015,December,49,5,2,1,1,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient,60.0,0,0,2015-07-23 -Resort Hotel,1,262,2016,May,22,22,2,3,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,52.2,0,1,2016-05-06 -Resort Hotel,0,142,2017,April,15,9,2,4,2,0.0,0,HB,FRA,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,241.0,,0,Transient,84.49,0,2,2017-04-15 -Resort Hotel,0,294,2016,June,26,21,0,5,2,0.0,0,BB,SWE,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,64.8,0,1,2016-06-26 -Resort Hotel,0,17,2016,October,44,28,0,2,2,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,2,No Deposit,,317.0,0,Transient-Party,63.0,0,0,2016-10-30 -Resort Hotel,0,3,2017,February,5,4,0,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,240.0,,0,Transient,48.0,0,1,2017-02-05 -Resort Hotel,0,76,2015,November,45,1,2,3,2,0.0,0,HB,GBR,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,156.0,,0,Contract,55.04,0,0,2015-11-06 -City Hotel,1,433,2016,September,37,8,0,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,3.0,,0,Transient,70.0,0,0,2015-10-20 -Resort Hotel,0,322,2016,June,26,22,0,3,2,0.0,0,HB,POL,Groups,Direct,0,0,0,A,A,0,No Deposit,,,75,Transient-Party,92.0,0,0,2016-06-25 -City Hotel,0,12,2016,November,48,26,0,1,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,83.13,0,0,2016-11-27 -Resort Hotel,0,134,2015,July,28,8,0,4,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,D,D,0,No Deposit,250.0,,0,Transient,84.15,0,0,2015-07-12 -Resort Hotel,0,107,2015,July,27,2,2,5,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,110.7,0,2,2015-07-09 -City Hotel,0,183,2016,September,37,8,0,3,1,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,A,1,No Deposit,44.0,,0,Transient-Party,100.0,0,0,2016-09-11 -Resort Hotel,0,243,2016,October,41,6,2,3,1,0.0,0,BB,IRL,Groups,TA/TO,0,0,0,D,D,0,No Deposit,91.0,,0,Transient-Party,55.1,0,0,2016-10-11 -Resort Hotel,0,33,2016,November,47,15,0,4,1,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,241.0,,0,Transient,26.99,0,1,2016-11-19 +hotel,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests +Resort Hotel,131,2017,May,21,23,2,5,2,0.0,0,HB,GBR,Direct,Direct,0,0,0,C,C,1,No Deposit,250.0,,0,Transient,131.63,0,0 +City Hotel,55,2016,July,28,7,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,219.0,0,Transient-Party,79.0,0,0 +City Hotel,140,2016,July,32,31,2,3,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,113.9,0,0 +City Hotel,1,2015,September,37,6,2,0,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,0,Transient-Party,6.0,0,0 +City Hotel,167,2017,May,18,6,2,5,3,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,174.21,0,0 +Resort Hotel,15,2017,August,31,2,0,4,2,2.0,0,BB,PRT,Online TA,TA/TO,0,0,0,C,C,0,No Deposit,240.0,,0,Transient,264.0,0,1 +City Hotel,68,2016,July,28,4,1,1,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,6.0,,0,Transient,107.95,0,0 +Resort Hotel,99,2016,January,3,12,2,10,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,242.0,,0,Transient,41.4,0,0 +Resort Hotel,132,2017,June,24,15,0,3,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,110.0,0,1 +City Hotel,192,2016,October,43,21,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,29.0,,0,Transient-Party,95.0,0,0 +City Hotel,53,2016,March,12,17,1,3,1,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,10.0,,0,Transient,81.7,0,1 +Resort Hotel,0,2016,January,4,22,0,1,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,45.0,0,0 +City Hotel,2,2016,November,49,27,2,2,2,0.0,0,BB,TUR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,89.89,0,1 +City Hotel,282,2015,October,41,10,2,1,2,0.0,0,BB,SWE,Offline TA/TO,TA/TO,0,0,0,A,F,0,No Deposit,21.0,,0,Transient-Party,76.67,0,0 +City Hotel,249,2017,May,21,24,0,4,3,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,157.5,0,2 +City Hotel,80,2017,July,27,7,0,1,2,0.0,0,BB,DNK,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient-Party,108.0,0,1 +City Hotel,265,2015,July,28,9,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,1,1,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.8,0,0 +City Hotel,155,2016,June,27,27,1,2,2,0.0,0,BB,BEL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,96.3,0,0 +Resort Hotel,145,2017,February,9,26,4,7,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,40.0,,0,Contract,36.72,0,0 +City Hotel,167,2017,August,31,4,2,2,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,9.0,,0,Transient,166.5,0,0 +Resort Hotel,143,2016,June,23,2,1,3,2,0.0,0,BB,IRL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,72.0,0,1 +City Hotel,173,2016,July,28,8,0,2,2,0.0,0,SC,ITA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,85.5,0,0 +City Hotel,181,2016,October,42,11,0,2,1,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,234.0,,0,Transient-Party,120.0,0,0 +Resort Hotel,303,2016,September,36,1,4,10,2,0.0,0,BB,IRL,Offline TA/TO,TA/TO,0,0,0,E,E,0,No Deposit,171.0,,0,Transient,90.06,0,0 +City Hotel,175,2017,July,28,15,2,1,2,0.0,0,BB,CN,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,107.1,0,1 +City Hotel,2,2017,May,21,21,2,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,27.0,,0,Transient,157.25,0,0 +City Hotel,303,2016,August,34,19,2,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,21.0,,0,Transient-Party,78.0,0,0 +Resort Hotel,9,2017,April,14,7,0,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,240.0,,0,Transient,115.0,0,2 +City Hotel,122,2016,April,18,29,2,2,2,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,95.2,0,0 +Resort Hotel,2,2016,October,45,30,2,0,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,E,1,No Deposit,240.0,,0,Transient,66.0,0,1 +Resort Hotel,66,2015,December,49,5,1,1,2,0.0,0,FB,ESP,Groups,TA/TO,0,0,0,A,A,0,No Deposit,155.0,,0,Transient-Party,64.0,0,0 +City Hotel,28,2016,November,45,2,0,1,1,0.0,0,SC,POL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,120.0,0,0 +Resort Hotel,1,2015,December,50,12,0,1,2,0.0,0,HB,PRT,Direct,Direct,0,0,0,A,D,0,No Deposit,,,0,Transient,79.0,1,1 +Resort Hotel,197,2016,April,18,30,1,1,2,0.0,0,HB,AUT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,298.0,,0,Transient-Party,85.0,0,0 +City Hotel,63,2017,August,34,23,0,3,1,0.0,0,BB,CHE,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,119.4,0,0 +Resort Hotel,203,2017,May,21,25,2,5,2,0.0,0,BB,GBR,Groups,TA/TO,0,0,0,A,A,1,No Deposit,,223.0,0,Transient-Party,59.71,1,0 +City Hotel,139,2017,July,28,13,1,3,3,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,152.1,0,0 +City Hotel,135,2017,June,26,30,0,1,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,30.0,,0,Transient,120.0,0,0 +Resort Hotel,71,2015,October,41,8,0,3,1,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,250.0,,0,Contract,48.0,1,1 +City Hotel,44,2017,June,25,19,2,5,2,0.0,0,BB,NOR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,112.91,0,1 +City Hotel,212,2016,July,31,27,4,10,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,90.95,0,2 +City Hotel,29,2016,August,36,30,0,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,122.0,0,2 +Resort Hotel,53,2017,March,10,5,2,5,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,298.0,,0,Transient-Party,30.0,0,0 +City Hotel,35,2017,August,32,8,0,1,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,229.0,,0,Transient-Party,90.0,0,0 +City Hotel,10,2015,October,40,1,2,5,2,0.0,0,HB,FRA,Direct,Direct,0,0,0,A,A,2,No Deposit,14.0,,0,Transient,171.71,0,0 +City Hotel,1,2016,August,33,7,1,0,2,0.0,0,SC,NLD,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,9.0,,0,Transient,98.0,0,0 +City Hotel,22,2016,March,13,20,1,0,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,91.0,0,1 +City Hotel,0,2016,December,50,7,0,1,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,9.0,,0,Transient,98.0,0,2 +City Hotel,181,2017,May,21,25,0,2,2,0.0,0,BB,PRT,Groups,Direct,0,0,0,A,A,0,Non Refund,,,0,Transient,130.0,0,0 +City Hotel,46,2017,January,1,1,2,0,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,,,0,Transient-Party,65.0,0,0 +City Hotel,44,2017,April,16,20,0,3,1,0.0,0,BB,ITA,Groups,TA/TO,0,0,0,A,A,1,No Deposit,1.0,,0,Transient-Party,106.67,0,0 +City Hotel,16,2016,April,16,12,0,3,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,144.67,0,2 +City Hotel,9,2016,December,51,16,0,1,1,0.0,0,BB,PRT,Online TA,GDS,0,0,0,A,A,0,No Deposit,195.0,,0,Transient,86.0,0,0 +Resort Hotel,277,2016,July,29,12,2,5,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,92.57,0,1 +City Hotel,372,2016,July,29,14,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0 +City Hotel,77,2017,July,28,11,0,1,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,120.0,0,2 +Resort Hotel,104,2016,April,18,28,2,7,2,2.0,0,BB,CHE,Direct,Direct,0,0,0,G,G,0,No Deposit,250.0,,0,Transient,99.22,1,1 +Resort Hotel,26,2016,February,7,9,1,5,2,0.0,0,SC,PRT,Direct,Direct,0,0,0,A,I,5,No Deposit,,364.0,0,Transient-Party,0.0,0,1 +City Hotel,12,2016,October,43,21,0,2,2,1.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,180.0,0,1 +City Hotel,28,2017,April,16,21,0,1,1,0.0,0,BB,USA,Direct,Direct,0,0,0,A,D,1,No Deposit,14.0,,0,Transient,93.5,0,0 +City Hotel,23,2016,October,43,20,0,2,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,119.0,0,1 +City Hotel,2,2017,June,24,11,1,0,2,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,10.0,,0,Transient,100.0,0,0 +City Hotel,377,2016,October,42,14,0,2,2,0.0,0,HB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,115.0,0,1 +City Hotel,164,2016,September,40,30,2,2,2,0.0,0,BB,BEL,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,126.9,0,1 +Resort Hotel,60,2016,January,3,16,2,5,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,E,1,No Deposit,6.0,,0,Transient,26.0,0,0 +Resort Hotel,2,2016,November,48,26,1,1,1,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,D,0,No Deposit,250.0,,0,Transient,38.0,0,0 +City Hotel,38,2016,November,46,10,0,3,2,0.0,0,BB,FIN,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,145.0,0,0 +Resort Hotel,134,2016,August,34,18,0,3,2,0.0,0,BB,CN,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,179.0,0,1 +City Hotel,55,2016,June,27,26,2,0,3,0.0,0,BB,PRT,Direct,Direct,0,0,0,D,D,0,No Deposit,14.0,,0,Transient,148.8,1,2 +Resort Hotel,222,2016,June,25,13,2,5,1,0.0,0,BB,GBR,Direct,Direct,0,0,0,E,F,3,No Deposit,250.0,,0,Transient,93.5,0,0 +City Hotel,217,2016,December,50,5,3,7,2,0.0,0,SC,BRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,46.63,0,1 +City Hotel,130,2016,June,27,28,1,5,3,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,A,D,4,No Deposit,7.0,,0,Transient,125.51,0,2 +Resort Hotel,2,2016,May,23,30,1,2,1,0.0,0,HB,ESP,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,175.0,,0,Transient,68.8,0,0 +City Hotel,102,2016,June,24,5,1,0,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,0,Transient-Party,65.0,0,0 +Resort Hotel,178,2015,August,33,10,1,4,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,E,E,0,No Deposit,5.0,,0,Transient,119.25,0,0 +City Hotel,305,2016,October,40,1,1,1,2,0.0,0,HB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,35.0,,17,Transient,100.0,0,0 +City Hotel,81,2017,July,28,12,0,2,2,0.0,0,BB,NOR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,150.0,0,2 +City Hotel,81,2016,June,27,30,1,3,2,0.0,0,SC,CHL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,103.5,0,1 +Resort Hotel,40,2015,July,28,11,2,5,2,0.0,0,HB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,242.0,,0,Transient,166.0,0,0 +Resort Hotel,129,2015,July,28,10,2,4,2,0.0,0,FB,ESP,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,5.0,,0,Transient,157.5,1,0 +City Hotel,332,2016,September,39,22,1,3,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,77.23,0,2 +City Hotel,343,2015,September,39,25,0,1,1,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient,170.0,0,0 +City Hotel,255,2016,November,46,12,2,5,0,2.0,0,BB,TUR,Online TA,TA/TO,0,0,0,B,B,0,No Deposit,9.0,,0,Transient-Party,72.0,0,0 +Resort Hotel,166,2015,July,29,15,0,2,2,0.0,0,FB,PRT,Groups,TA/TO,0,24,0,A,A,0,Non Refund,,,0,Transient,121.5,0,0 +Resort Hotel,4,2016,January,5,26,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,D,0,No Deposit,,,0,Transient,38.0,0,0 +City Hotel,30,2016,March,10,4,0,2,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,9.0,,0,Transient-Party,88.4,0,0 +City Hotel,133,2017,April,16,19,0,4,2,0.0,0,BB,ISL,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,390.0,,0,Transient-Party,124.0,0,0 +City Hotel,4,2016,July,28,6,0,3,2,0.0,0,BB,USA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,151.0,0,1 +City Hotel,55,2016,April,15,6,0,1,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,56.0,,0,Transient-Party,104.0,0,0 +Resort Hotel,12,2016,March,11,8,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,82.0,0,Transient,30.0,0,0 +City Hotel,1,2015,September,37,6,2,0,1,0.0,0,BB,FRA,Groups,TA/TO,0,0,0,A,B,0,No Deposit,1.0,,0,Transient-Party,75.0,0,0 +City Hotel,104,2017,May,20,16,0,3,1,0.0,0,SC,USA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,117.0,0,0 +City Hotel,140,2015,November,47,18,0,3,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,D,1,No Deposit,29.0,,87,Transient-Party,65.0,0,0 +City Hotel,393,2017,April,16,22,2,1,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,229.0,,0,Transient,90.0,0,0 +City Hotel,3,2017,June,23,5,1,5,1,0.0,0,BB,AUT,Aviation,Corporate,0,0,0,A,A,0,No Deposit,,153.0,0,Transient,95.0,0,0 +Resort Hotel,0,2016,June,25,13,1,1,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,121.0,1,0 +Resort Hotel,0,2016,April,18,30,0,1,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,F,F,0,No Deposit,250.0,,0,Transient-Party,101.0,1,0 +City Hotel,46,2017,June,25,20,0,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,229.0,,0,Transient-Party,90.0,0,0 +City Hotel,13,2016,June,24,8,0,2,2,0.0,0,BB,SGP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,121.0,0,1 +City Hotel,192,2016,June,26,24,0,2,1,0.0,0,BB,SRB,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,34.0,,0,Transient-Party,95.0,0,0 +City Hotel,233,2017,June,26,29,2,3,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient-Party,111.06,0,2 +Resort Hotel,140,2017,June,24,12,1,3,2,1.0,0,BB,CHN,Online TA,TA/TO,0,0,0,D,D,1,No Deposit,240.0,,0,Transient-Party,164.0,1,1 +Resort Hotel,77,2015,September,40,28,3,7,2,0.0,0,HB,GBR,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,40.0,,0,Contract,72.1,0,0 +City Hotel,80,2017,June,22,2,1,2,2,2.0,0,BB,PRT,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,7.0,,0,Transient,250.96,0,1 +City Hotel,44,2016,May,22,23,1,1,2,0.0,0,BB,PRT,Complementary,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,0.0,0,1 +Resort Hotel,320,2017,June,22,3,4,10,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,40.0,,0,Contract,68.4,0,0 +City Hotel,626,2016,November,46,7,1,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.8,0,0 +City Hotel,59,2016,July,27,1,1,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,138.0,,0,Transient,85.0,0,0 +City Hotel,212,2017,May,21,22,2,5,2,0.0,0,HB,BRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,114.56,0,1 +City Hotel,48,2016,March,14,28,1,1,2,0.0,0,BB,RUS,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,80.1,0,1 +City Hotel,11,2016,June,25,14,0,1,1,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,10.0,,0,Transient,78.8,0,0 +Resort Hotel,6,2016,March,12,17,0,1,1,0.0,0,BB,ESP,Groups,TA/TO,0,0,0,A,D,1,No Deposit,66.0,,0,Transient-Party,26.0,0,0 +City Hotel,23,2015,October,42,11,1,0,1,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Contract,114.0,0,1 +City Hotel,24,2017,June,23,4,2,1,2,0.0,0,BB,ISR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,170.0,0,1 +City Hotel,0,2016,January,4,19,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,,0,Transient,65.0,0,0 +City Hotel,223,2017,August,34,24,0,3,3,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,172.8,0,0 +City Hotel,209,2016,August,33,7,2,1,3,0.0,0,BB,DNK,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Transient,127.8,0,2 +City Hotel,4,2016,May,23,31,0,1,1,0.0,0,BB,FRA,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,260.0,0,Transient-Party,121.0,0,0 +Resort Hotel,6,2017,March,9,1,0,4,2,0.0,0,BB,BEL,Groups,Corporate,0,0,0,A,A,3,No Deposit,,223.0,0,Transient-Party,37.0,0,0 +Resort Hotel,11,2015,July,29,17,0,2,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,240.0,,0,Transient,155.5,1,0 +City Hotel,7,2015,August,34,19,1,4,2,0.0,0,BB,ESP,Direct,Direct,0,0,0,A,B,0,No Deposit,,,0,Transient,67.0,0,0 +City Hotel,145,2016,July,31,30,2,2,3,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,A,D,1,No Deposit,9.0,,0,Transient-Party,134.3,0,2 +City Hotel,5,2015,September,39,20,1,0,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,E,E,1,No Deposit,14.0,,0,Transient,146.0,1,2 +City Hotel,333,2016,September,39,20,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,Non Refund,58.0,,19,Transient,90.0,0,0 +City Hotel,81,2016,May,19,1,2,2,2,0.0,0,BB,NLD,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,119.85,0,2 +Resort Hotel,4,2016,May,22,28,0,1,1,0.0,0,HB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,408.0,0,Transient,65.0,0,0 +Resort Hotel,23,2016,July,29,13,0,4,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,161.1,0,0 +City Hotel,89,2016,July,28,5,0,3,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Transient,114.3,0,0 +City Hotel,78,2016,April,15,7,0,2,2,2.0,0,BB,RUS,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,9.0,,0,Transient,177.3,1,2 +City Hotel,365,2015,October,42,17,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient,62.8,0,0 +Resort Hotel,138,2016,August,33,10,0,0,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,E,I,0,No Deposit,240.0,,0,Transient,0.0,0,1 +Resort Hotel,2,2016,March,11,10,0,1,1,0.0,0,BB,,Corporate,Corporate,0,0,1,A,A,0,No Deposit,146.0,,0,Transient,42.0,1,0 +City Hotel,53,2016,September,39,18,2,0,2,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,27.0,,0,Transient,94.5,1,0 +City Hotel,111,2017,August,34,24,0,2,3,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,1,No Deposit,9.0,,0,Transient,195.0,0,2 +City Hotel,315,2017,July,30,28,1,2,2,0.0,0,SC,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,89.1,0,0 +City Hotel,20,2017,March,11,13,1,2,1,0.0,0,BB,FRA,Groups,TA/TO,0,0,0,A,A,1,No Deposit,254.0,,0,Transient-Party,153.67,0,0 +Resort Hotel,8,2016,March,10,3,0,3,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,240.0,,0,Transient,48.0,0,1 +City Hotel,40,2016,June,27,30,4,10,1,0.0,0,BB,AGO,Direct,Direct,0,0,0,G,G,0,No Deposit,14.0,,0,Transient,244.43,0,0 +Resort Hotel,10,2016,March,12,14,1,2,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,1,A,A,2,No Deposit,139.0,99.0,0,Transient,37.0,0,0 +City Hotel,256,2016,June,25,15,0,2,2,0.0,0,HB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,115.0,0,1 +Resort Hotel,161,2017,July,30,28,1,2,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,177.0,,0,Transient,141.0,0,0 +Resort Hotel,36,2015,November,47,20,0,2,2,0.0,0,FB,ESP,Groups,TA/TO,0,0,0,A,C,0,No Deposit,38.0,,0,Transient-Party,64.0,0,0 +City Hotel,336,2017,May,19,13,0,1,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,108.0,0,1 +City Hotel,12,2016,September,39,18,2,1,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,171.33,0,1 +City Hotel,447,2016,September,40,29,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0 +City Hotel,42,2017,June,24,17,2,4,2,0.0,0,SC,NOR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,133.33,0,1 +City Hotel,34,2015,August,32,5,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.0,0,0 +Resort Hotel,140,2016,February,7,12,0,2,2,0.0,0,HB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,50.0,0,0 +City Hotel,17,2016,July,31,29,0,1,2,1.0,0,BB,ISR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,129.0,0,2 +Resort Hotel,163,2015,September,38,13,4,7,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,243.0,,0,Contract,62.48,0,0 +City Hotel,7,2016,March,10,2,0,2,1,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,A,1,No Deposit,94.0,,0,Transient-Party,65.0,0,0 +City Hotel,27,2016,March,11,6,2,1,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,28.0,,0,Transient,66.0,0,0 +City Hotel,328,2015,September,37,10,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Contract,62.0,0,0 +City Hotel,26,2016,March,11,7,1,4,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,28.0,,0,Transient,87.0,0,0 +Resort Hotel,17,2016,October,43,17,1,2,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,60.0,0,1 +Resort Hotel,144,2016,May,21,18,4,9,1,0.0,0,BB,GBR,Direct,Direct,0,0,0,F,F,1,No Deposit,250.0,,0,Transient,79.05,0,0 +City Hotel,141,2016,August,36,29,1,2,2,0.0,0,SC,ITA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,85.5,0,2 +Resort Hotel,14,2017,February,6,9,0,1,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,467.0,,0,Transient,38.4,0,0 +Resort Hotel,134,2017,March,10,9,2,3,1,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,241.0,,0,Transient,48.43,0,2 +Resort Hotel,1,2017,February,9,27,1,0,1,0.0,0,BB,PRT,Direct,Direct,1,0,2,A,A,0,No Deposit,,,0,Transient-Party,55.0,0,1 +City Hotel,264,2015,July,28,8,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.0,0,0 +Resort Hotel,89,2015,August,33,15,2,5,2,1.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,67.0,,0,Transient,145.75,1,1 +City Hotel,99,2016,February,8,19,0,1,1,0.0,0,BB,PRT,Corporate,TA/TO,0,0,0,A,E,0,No Deposit,,67.0,0,Transient-Party,81.0,0,0 +City Hotel,78,2016,April,15,7,0,1,1,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,86.0,,0,Transient,72.72,0,0 +City Hotel,1,2017,August,32,10,0,1,2,0.0,0,SC,USA,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,190.0,0,0 +Resort Hotel,103,2016,May,19,1,2,3,2,0.0,0,BB,NLD,Groups,TA/TO,0,0,0,A,A,7,No Deposit,95.0,,0,Transient-Party,47.0,0,0 +Resort Hotel,2,2017,April,16,21,0,1,3,1.0,0,BB,PRT,Online TA,TA/TO,0,0,0,H,H,0,No Deposit,240.0,,0,Transient,185.0,0,0 +City Hotel,106,2016,April,18,29,1,2,2,0.0,0,BB,ITA,Direct,Direct,0,0,0,A,B,1,No Deposit,14.0,,0,Transient-Party,94.5,0,0 +City Hotel,122,2017,July,29,19,0,4,3,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,9.0,,0,Transient,199.13,0,1 +Resort Hotel,7,2015,December,49,2,0,1,2,0.0,0,BB,ESP,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,6.0,,0,Transient-Party,8.0,0,1 +City Hotel,75,2016,June,23,4,1,1,2,0.0,0,SC,IRL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,11.0,,0,Transient,89.28,0,0 +City Hotel,278,2015,July,30,22,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient-Party,62.0,0,0 +Resort Hotel,110,2017,May,19,9,2,5,2,0.0,0,BB,GBR,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,40.0,,0,Contract,40.95,0,0 +Resort Hotel,69,2015,December,53,31,0,3,2,1.0,0,BB,PRT,Direct,Direct,0,0,0,D,D,0,No Deposit,,,0,Transient,199.73,0,3 +City Hotel,351,2016,June,26,23,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0 +City Hotel,2,2016,June,25,12,2,0,2,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,108.5,0,2 +Resort Hotel,3,2017,June,24,12,1,1,1,0.0,0,BB,PRT,Complementary,Direct,0,0,0,A,C,0,No Deposit,,,0,Transient,4.0,0,1 +City Hotel,131,2017,June,24,16,1,2,2,0.0,0,BB,CZE,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,117.0,0,0 +City Hotel,86,2016,January,4,22,0,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,29.0,,35,Transient,85.0,0,0 +City Hotel,4,2016,April,16,14,0,3,2,0.0,0,BB,ARG,Direct,Direct,0,0,0,A,A,2,No Deposit,14.0,,0,Transient,110.15,1,0 +Resort Hotel,5,2016,April,18,29,0,1,2,0.0,0,BB,PRT,Complementary,Direct,0,0,0,D,D,0,No Deposit,,,0,Transient,0.0,1,3 +Resort Hotel,9,2017,July,27,7,1,2,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,160.0,0,2 +Resort Hotel,4,2016,August,34,18,0,1,2,0.0,0,BB,ESP,Online TA,TA/TO,0,0,0,E,E,1,No Deposit,240.0,,0,Transient,20.0,1,1 +City Hotel,84,2017,June,26,27,0,3,2,0.0,0,SC,IRL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,120.0,0,1 +City Hotel,140,2017,April,17,28,2,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,119.0,,0,Transient-Party,130.0,0,0 +City Hotel,414,2015,December,49,5,2,1,1,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient,60.0,0,0 +Resort Hotel,262,2016,May,22,22,2,3,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,52.2,0,1 +Resort Hotel,142,2017,April,15,9,2,4,2,0.0,0,HB,FRA,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,241.0,,0,Transient,84.49,0,2 +Resort Hotel,294,2016,June,26,21,0,5,2,0.0,0,BB,SWE,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,64.8,0,1 +Resort Hotel,17,2016,October,44,28,0,2,2,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,2,No Deposit,,317.0,0,Transient-Party,63.0,0,0 +Resort Hotel,3,2017,February,5,4,0,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,240.0,,0,Transient,48.0,0,1 +Resort Hotel,76,2015,November,45,1,2,3,2,0.0,0,HB,GBR,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,156.0,,0,Contract,55.04,0,0 +City Hotel,433,2016,September,37,8,0,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,3.0,,0,Transient,70.0,0,0 +Resort Hotel,322,2016,June,26,22,0,3,2,0.0,0,HB,POL,Groups,Direct,0,0,0,A,A,0,No Deposit,,,75,Transient-Party,92.0,0,0 +City Hotel,12,2016,November,48,26,0,1,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,83.13,0,0 +Resort Hotel,134,2015,July,28,8,0,4,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,D,D,0,No Deposit,250.0,,0,Transient,84.15,0,0 +Resort Hotel,107,2015,July,27,2,2,5,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,110.7,0,2 +City Hotel,183,2016,September,37,8,0,3,1,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,A,1,No Deposit,44.0,,0,Transient-Party,100.0,0,0 +Resort Hotel,243,2016,October,41,6,2,3,1,0.0,0,BB,IRL,Groups,TA/TO,0,0,0,D,D,0,No Deposit,91.0,,0,Transient-Party,55.1,0,0 +Resort Hotel,33,2016,November,47,15,0,4,1,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,241.0,,0,Transient,26.99,0,1 diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_007/history.csv b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_007/history.csv index d11caaac0f6037f2164f96f3a6b374cd8cf520e4..53b22c3317f657f6e4502ec83adf68c0859e8cd3 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_007/history.csv +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_007/history.csv @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:34ad6f46cde90b84c983adc29a70ef362d3be0e23b40dcf629486c0ae180b835 -size 16800441 +oid sha256:9a6de453ec7fc7abbe092178722fe13ceccbe8c83893ab1cc4dc3b7aacd0e36d +size 15256041 diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_007/info.json b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_007/info.json index 4816c05993309461836de8622438e342f2e503ff..20956c93d07801fe3772275e08832605a78593ed 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_007/info.json +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_007/info.json @@ -1,398 +1,391 @@ -{ - "name": "Hotel Booking Demand", - "source": "https://www.kaggle.com/datasets/jessemostipak/hotel-booking-demand/data", - "data_intro": "This dataset contains hotel booking data for a city hotel and a resort hotel, providing details on booking dates, stay duration, guest count, cancellation trends, and pricing. It is useful for demand forecasting, revenue management, and customer behavior analysis.", - "is_splited": false, - "overall_size": 119390, - "train_size": 0, - "test_size": 0, - "c_classes": 12, - "n_classes": 20, - "cat_feature_intro": { - "hotel": "Type of hotel (City Hotel or Resort Hotel).", - "arrival_date_month": "Month of arrival for the booking.", - "meal": "Type of meal plan booked (e.g., Breakfast included, Half board, Full board).", - "country": "Country of origin of the guest.", - "market_segment": "Booking distribution channel (e.g., direct, corporate, travel agent).", - "distribution_channel": "Channel through which the booking was made (e.g., online travel agents, direct).", - "reserved_room_type": "Type of room reserved by the guest before any changes.", - "assigned_room_type": "Final room type assigned to the guest.", - "deposit_type": "Indicates if a deposit was made and its type (e.g., No Deposit, Refundable, Non-Refundable).", - "customer_type": "Type of customer (e.g., transient, group, contract).", - "reservation_status": "Final status of the booking (e.g., Canceled, Checked-out, No-show).", - "reservation_status_date": "Date when the reservation status was last updated." - }, - "num_feature_intro": { - "is_canceled": "Indicates whether the booking was canceled (1) or not (0).", - "lead_time": "Number of days between booking and arrival.", - "arrival_date_year": "Year of arrival for the booking.", - "arrival_date_week_number": "Week number of the arrival date.", - "arrival_date_day_of_month": "Day of the month of arrival.", - "stays_in_weekend_nights": "Number of weekend nights (Saturday or Sunday) the guest stayed.", - "stays_in_week_nights": "Number of weekday nights (Monday to Friday) the guest stayed.", - "adults": "Number of adults included in the booking.", - "children": "Number of children included in the booking.", - "babies": "Number of babies included in the booking.", - "is_repeated_guest": "Indicates whether the guest is a returning customer (1) or not (0).", - "previous_cancellations": "Number of times the guest has canceled previous bookings.", - "previous_bookings_not_canceled": "Number of previous bookings not canceled by the guest.", - "booking_changes": "Number of changes made to the booking.", - "agent": "ID of the booking agent (if applicable).", - "company": "ID of the company making the booking (if applicable).", - "days_in_waiting_list": "Number of days the booking was on the waiting list before confirmation.", - "adr": "Average daily rate (price per night per room).", - "required_car_parking_spaces": "Number of car parking spaces required by the guest.", - "total_of_special_requests": "Number of special requests made by the guest (e.g., high floor, twin beds)." - }, - "evaluation_metric": null, - "task_type": "classification", - "target": "reservation_status", - "num_feature_value": { - "adr": [ - -6.38, - 5400.0 - ], - "adults": [ - 0.0, - 55.0 - ], - "agent": [ - 1.0, - 535.0 - ], - "arrival_date_day_of_month": [ - 1.0, - 31.0 - ], - "arrival_date_week_number": [ - 1.0, - 53.0 - ], - "arrival_date_year": [ - 2015.0, - 2017.0 - ], - "babies": [ - 0.0, - 10.0 - ], - "booking_changes": [ - 0.0, - 21.0 - ], - "children": [ - 0.0, - 10.0 - ], - "company": [ - 6.0, - 543.0 - ], - "days_in_waiting_list": [ - 0.0, - 391.0 - ], - "is_canceled": [ - 0.0, - 1.0 - ], - "is_repeated_guest": [ - 0.0, - 1.0 - ], - "lead_time": [ - 0.0, - 737.0 - ], - "previous_bookings_not_canceled": [ - 0.0, - 72.0 - ], - "previous_cancellations": [ - 0.0, - 26.0 - ], - "required_car_parking_spaces": [ - 0.0, - 8.0 - ], - "stays_in_week_nights": [ - 0.0, - 50.0 - ], - "stays_in_weekend_nights": [ - 0.0, - 19.0 - ], - "total_of_special_requests": [ - 0.0, - 5.0 - ] - }, - "cat_feature_value": { - "arrival_date_month": [ - "April", - "August", - "December", - "February", - "January", - "July", - "June", - "March", - "May", - "November", - "October", - "September" - ], - "assigned_room_type": [ - "A", - "B", - "C", - "D", - "E", - "F", - "G", - "H", - "I", - "K", - "L", - "P" - ], - "country": [ - "ABW", - "AGO", - "AIA", - "ALB", - "AND", - "ARE", - "ARG", - "ARM", - "ASM", - "ATA", - "ATF", - "AUS", - "AUT", - "AZE", - "BDI", - "BEL", - "BEN", - "BFA", - "BGD", - "BGR", - "BHR", - "BHS", - "BIH", - "BLR", - "BOL", - "BRA", - "BRB", - "BWA", - "CAF", - "CHE", - "CHL", - "CHN", - "CIV", - "CMR", - "CN", - "COL", - "COM", - "CPV", - "CRI", - "CUB", - "CYM", - "CYP", - "CZE", - "DEU", - "DJI", - "DMA", - "DNK", - "DOM", - "DZA", - "ECU", - "EGY", - "ESP", - "EST", - "ETH", - "FIN", - "FJI", - "FRA", - "FRO", - "GAB", - "GBR", - "GEO", - "GGY", - "GHA", - "GIB", - "GLP", - "GNB", - "GRC", - "GTM", - "GUY", - "HKG", - "HND", - "HRV", - "HUN", - "IDN", - "IMN", - "IND", - "IRL", - "IRN", - "IRQ", - "ISL", - "ISR", - "ITA", - "JAM", - "JEY", - "JOR", - "JPN", - "KAZ", - "KEN", - "KHM", - "KIR", - "KNA", - "KOR", - "KWT", - "LAO", - "LBN", - "LBY", - "LCA", - "LIE", - "LKA", - "LTU", - "LUX", - "LVA", - "MAC", - "MAR", - "MCO", - "MDG", - "MDV", - "MEX", - "MKD", - "MLI", - "MLT", - "MMR", - "MNE", - "MOZ", - "MRT", - "MUS", - "MWI", - "MYS", - "MYT", - "NAM", - "NCL", - "NGA", - "NIC", - "NLD", - "NOR", - "NPL", - "NZL", - "OMN", - "PAK", - "PAN", - "PER", - "PHL", - "PLW", - "POL", - "PRI", - "PRT", - "PRY", - "PYF", - "QAT", - "ROU", - "RUS", - "RWA", - "SAU", - "SDN", - "SEN", - "SGP", - "SLE", - "SLV", - "SMR", - "SRB", - "STP", - "SUR", - "SVK", - "SVN", - "SWE", - "SYC", - "SYR", - "TGO", - "THA", - "TJK", - "TMP", - "TUN", - "TUR", - "TWN", - "TZA", - "UGA", - "UKR", - "UMI", - "URY", - "USA", - "UZB", - "VEN", - "VGB", - "VNM", - "ZAF", - "ZMB", - "ZWE" - ], - "customer_type": [ - "Contract", - "Group", - "Transient", - "Transient-Party" - ], - "deposit_type": [ - "No Deposit", - "Non Refund", - "Refundable" - ], - "distribution_channel": [ - "Corporate", - "Direct", - "GDS", - "TA/TO", - "Undefined" - ], - "hotel": [ - "City Hotel", - "Resort Hotel" - ], - "market_segment": [ - "Aviation", - "Complementary", - "Corporate", - "Direct", - "Groups", - "Offline TA/TO", - "Online TA", - "Undefined" - ], - "meal": [ - "BB", - "FB", - "HB", - "SC", - "Undefined" - ], - "reservation_status": [ - "Canceled", - "Check-Out", - "No-Show" - ], - "reservation_status_date": "string", - "reserved_room_type": [ - "A", - "B", - "C", - "D", - "E", - "F", - "G", - "H", - "L", - "P" - ] - } -} \ No newline at end of file +{ + "name": "Hotel Booking Demand", + "source": "https://www.kaggle.com/datasets/jessemostipak/hotel-booking-demand/data", + "data_intro": "This dataset contains hotel booking data for a city hotel and a resort hotel, providing details on booking dates, stay duration, guest count, cancellation trends, and pricing. It is useful for demand forecasting, revenue management, and customer behavior analysis.", + "is_splited": false, + "overall_size": 119390, + "train_size": 0, + "test_size": 0, + "c_classes": 11, + "n_classes": 19, + "cat_feature_intro": { + "hotel": "Type of hotel (City Hotel or Resort Hotel).", + "arrival_date_month": "Month of arrival for the booking.", + "meal": "Type of meal plan booked (e.g., Breakfast included, Half board, Full board).", + "country": "Country of origin of the guest.", + "market_segment": "Booking distribution channel (e.g., direct, corporate, travel agent).", + "distribution_channel": "Channel through which the booking was made (e.g., online travel agents, direct).", + "reserved_room_type": "Type of room reserved by the guest before any changes.", + "assigned_room_type": "Final room type assigned to the guest.", + "deposit_type": "Indicates if a deposit was made and its type (e.g., No Deposit, Refundable, Non-Refundable).", + "customer_type": "Type of customer (e.g., transient, group, contract).", + "reservation_status": "Final status of the booking (e.g., Canceled, Checked-out, No-show)." + }, + "num_feature_intro": { + "lead_time": "Number of days between booking and arrival.", + "arrival_date_year": "Year of arrival for the booking.", + "arrival_date_week_number": "Week number of the arrival date.", + "arrival_date_day_of_month": "Day of the month of arrival.", + "stays_in_weekend_nights": "Number of weekend nights (Saturday or Sunday) the guest stayed.", + "stays_in_week_nights": "Number of weekday nights (Monday to Friday) the guest stayed.", + "adults": "Number of adults included in the booking.", + "children": "Number of children included in the booking.", + "babies": "Number of babies included in the booking.", + "is_repeated_guest": "Indicates whether the guest is a returning customer (1) or not (0).", + "previous_cancellations": "Number of times the guest has canceled previous bookings.", + "previous_bookings_not_canceled": "Number of previous bookings not canceled by the guest.", + "booking_changes": "Number of changes made to the booking.", + "agent": "ID of the booking agent (if applicable).", + "company": "ID of the company making the booking (if applicable).", + "days_in_waiting_list": "Number of days the booking was on the waiting list before confirmation.", + "adr": "Average daily rate (price per night per room).", + "required_car_parking_spaces": "Number of car parking spaces required by the guest.", + "total_of_special_requests": "Number of special requests made by the guest (e.g., high floor, twin beds)." + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "reservation_status", + "num_feature_value": { + "adr": [ + -6.38, + 5400.0 + ], + "adults": [ + 0.0, + 55.0 + ], + "agent": [ + 1.0, + 535.0 + ], + "arrival_date_day_of_month": [ + 1.0, + 31.0 + ], + "arrival_date_week_number": [ + 1.0, + 53.0 + ], + "arrival_date_year": [ + 2015.0, + 2017.0 + ], + "babies": [ + 0.0, + 10.0 + ], + "booking_changes": [ + 0.0, + 21.0 + ], + "children": [ + 0.0, + 10.0 + ], + "company": [ + 6.0, + 543.0 + ], + "days_in_waiting_list": [ + 0.0, + 391.0 + ], + "is_repeated_guest": [ + 0.0, + 1.0 + ], + "lead_time": [ + 0.0, + 737.0 + ], + "previous_bookings_not_canceled": [ + 0.0, + 72.0 + ], + "previous_cancellations": [ + 0.0, + 26.0 + ], + "required_car_parking_spaces": [ + 0.0, + 8.0 + ], + "stays_in_week_nights": [ + 0.0, + 50.0 + ], + "stays_in_weekend_nights": [ + 0.0, + 19.0 + ], + "total_of_special_requests": [ + 0.0, + 5.0 + ] + }, + "cat_feature_value": { + "arrival_date_month": [ + "April", + "August", + "December", + "February", + "January", + "July", + "June", + "March", + "May", + "November", + "October", + "September" + ], + "assigned_room_type": [ + "A", + "B", + "C", + "D", + "E", + "F", + "G", + "H", + "I", + "K", + "L", + "P" + ], + "country": [ + "ABW", + "AGO", + "AIA", + "ALB", + "AND", + "ARE", + "ARG", + "ARM", + "ASM", + "ATA", + "ATF", + "AUS", + "AUT", + "AZE", + "BDI", + "BEL", + "BEN", + "BFA", + "BGD", + "BGR", + "BHR", + "BHS", + "BIH", + "BLR", + "BOL", + "BRA", + "BRB", + "BWA", + "CAF", + "CHE", + "CHL", + "CHN", + "CIV", + "CMR", + "CN", + "COL", + "COM", + "CPV", + "CRI", + "CUB", + "CYM", + "CYP", + "CZE", + "DEU", + "DJI", + "DMA", + "DNK", + "DOM", + "DZA", + "ECU", + "EGY", + "ESP", + "EST", + "ETH", + "FIN", + "FJI", + "FRA", + "FRO", + "GAB", + "GBR", + "GEO", + "GGY", + "GHA", + "GIB", + "GLP", + "GNB", + "GRC", + "GTM", + "GUY", + "HKG", + "HND", + "HRV", + "HUN", + "IDN", + "IMN", + "IND", + "IRL", + "IRN", + "IRQ", + "ISL", + "ISR", + "ITA", + "JAM", + "JEY", + "JOR", + "JPN", + "KAZ", + "KEN", + "KHM", + "KIR", + "KNA", + "KOR", + "KWT", + "LAO", + "LBN", + "LBY", + "LCA", + "LIE", + "LKA", + "LTU", + "LUX", + "LVA", + "MAC", + "MAR", + "MCO", + "MDG", + "MDV", + "MEX", + "MKD", + "MLI", + "MLT", + "MMR", + "MNE", + "MOZ", + "MRT", + "MUS", + "MWI", + "MYS", + "MYT", + "NAM", + "NCL", + "NGA", + "NIC", + "NLD", + "NOR", + "NPL", + "NZL", + "OMN", + "PAK", + "PAN", + "PER", + "PHL", + "PLW", + "POL", + "PRI", + "PRT", + "PRY", + "PYF", + "QAT", + "ROU", + "RUS", + "RWA", + "SAU", + "SDN", + "SEN", + "SGP", + "SLE", + "SLV", + "SMR", + "SRB", + "STP", + "SUR", + "SVK", + "SVN", + "SWE", + "SYC", + "SYR", + "TGO", + "THA", + "TJK", + "TMP", + "TUN", + "TUR", + "TWN", + "TZA", + "UGA", + "UKR", + "UMI", + "URY", + "USA", + "UZB", + "VEN", + "VGB", + "VNM", + "ZAF", + "ZMB", + "ZWE" + ], + "customer_type": [ + "Contract", + "Group", + "Transient", + "Transient-Party" + ], + "deposit_type": [ + "No Deposit", + "Non Refund", + "Refundable" + ], + "distribution_channel": [ + "Corporate", + "Direct", + "GDS", + "TA/TO", + "Undefined" + ], + "hotel": [ + "City Hotel", + "Resort Hotel" + ], + "market_segment": [ + "Aviation", + "Complementary", + "Corporate", + "Direct", + "Groups", + "Offline TA/TO", + "Online TA", + "Undefined" + ], + "meal": [ + "BB", + "FB", + "HB", + "SC", + "Undefined" + ], + "reservation_status": [ + "Canceled", + "Check-Out", + "No-Show" + ], + "reserved_room_type": [ + "A", + "B", + "C", + "D", + "E", + "F", + "G", + "H", + "L", + "P" + ] + } +} diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_007/test_current.csv b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_007/test_current.csv index caae5f15d387ceb161bc9bdffaaec587b745fa08..dc66706d9896d0969e7706166ce7d08addf8d6e2 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_007/test_current.csv +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_007/test_current.csv @@ -1,15 +1,15 @@ -hotel,is_canceled,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests,reservation_status,reservation_status_date -City Hotel,1,303,2016,August,34,19,2,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,21.0,,0,Transient-Party,78.0,0,0,Canceled,2016-07-19 -City Hotel,1,372,2016,July,29,14,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0,Canceled,2015-10-21 -City Hotel,1,305,2016,October,40,1,1,1,2,0.0,0,HB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,35.0,,17,Transient,100.0,0,0,Canceled,2015-12-18 -City Hotel,1,343,2015,September,39,25,0,1,1,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient,170.0,0,0,Canceled,2015-09-22 -City Hotel,1,393,2017,April,16,22,2,1,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,229.0,,0,Transient,90.0,0,0,Canceled,2016-04-17 -City Hotel,1,626,2016,November,46,7,1,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.8,0,0,Canceled,2016-09-26 -City Hotel,1,333,2016,September,39,20,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,Non Refund,58.0,,19,Transient,90.0,0,0,Canceled,2015-11-11 -City Hotel,1,365,2015,October,42,17,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient,62.8,0,0,Canceled,2015-01-30 -City Hotel,1,315,2017,July,30,28,1,2,2,0.0,0,SC,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,89.1,0,0,Canceled,2016-11-09 -City Hotel,1,447,2016,September,40,29,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0,Canceled,2015-10-21 -City Hotel,1,328,2015,September,37,10,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Contract,62.0,0,0,Canceled,2015-01-01 -City Hotel,1,351,2016,June,26,23,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0,Canceled,2015-10-21 -City Hotel,1,414,2015,December,49,5,2,1,1,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient,60.0,0,0,Canceled,2015-07-23 -City Hotel,1,433,2016,September,37,8,0,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,3.0,,0,Transient,70.0,0,0,Canceled,2015-10-20 +hotel,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests,reservation_status +City Hotel,303,2016,August,34,19,2,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,21.0,,0,Transient-Party,78.0,0,0,Canceled +City Hotel,372,2016,July,29,14,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0,Canceled +City Hotel,305,2016,October,40,1,1,1,2,0.0,0,HB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,35.0,,17,Transient,100.0,0,0,Canceled +City Hotel,343,2015,September,39,25,0,1,1,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient,170.0,0,0,Canceled +City Hotel,393,2017,April,16,22,2,1,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,229.0,,0,Transient,90.0,0,0,Canceled +City Hotel,626,2016,November,46,7,1,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.8,0,0,Canceled +City Hotel,333,2016,September,39,20,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,Non Refund,58.0,,19,Transient,90.0,0,0,Canceled +City Hotel,365,2015,October,42,17,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient,62.8,0,0,Canceled +City Hotel,315,2017,July,30,28,1,2,2,0.0,0,SC,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,89.1,0,0,Canceled +City Hotel,447,2016,September,40,29,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0,Canceled +City Hotel,328,2015,September,37,10,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Contract,62.0,0,0,Canceled +City Hotel,351,2016,June,26,23,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0,Canceled +City Hotel,414,2015,December,49,5,2,1,1,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient,60.0,0,0,Canceled +City Hotel,433,2016,September,37,8,0,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,3.0,,0,Transient,70.0,0,0,Canceled diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_008/Hotel_booking_demand_B4_008_current.json b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_008/Hotel_booking_demand_B4_008_current.json index 764fda76de6b35c3bb3244b01662f54a787918f9..8060802ccc788bad4aca882026bf3265c260a491 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_008/Hotel_booking_demand_B4_008_current.json +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_008/Hotel_booking_demand_B4_008_current.json @@ -1,7449 +1,7049 @@ { - "id": "008", - "task_type": "B4", - "subtask_type": "filter", - "perspective": "data_holder", - "dataset_name": "Hotel booking demand", - "table_path": "output/daily/classification/Hotel booking demand_B4/Hotel booking demand_B4_008/test.csv", - "query": "Our goal is to improve the booking experience for families. Reviewing our past records, for the new batch of data, could you group all the bookings that included at least one \"child\"? For these family bookings, I want to know which ones successfully completed their stay and checked out, so we can analyze what made those reservations successful.", - "meta_info": { - "domain": "daily" + "id": "008", + "task_type": "B4", + "subtask_type": "filter", + "perspective": "data_holder", + "dataset_name": "Hotel booking demand", + "table_path": "output/daily/classification/Hotel booking demand_B4/Hotel booking demand_B4_008/test.csv", + "query": "Our goal is to improve the booking experience for families. Reviewing our past records, for the new batch of data, could you group all the bookings that included at least one \"child\"? For these family bookings, I want to know which ones successfully completed their stay and checked out, so we can analyze what made those reservations successful.", + "meta_info": { + "domain": "daily" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "hotel": "Resort Hotel", + "lead_time": 58, + "arrival_date_year": 2016, + "arrival_date_month": "May", + "arrival_date_week_number": 20, + "arrival_date_day_of_month": 8, + "stays_in_weekend_nights": 2, + "stays_in_week_nights": 0, + "adults": 3, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "PRT", + "market_segment": "Offline TA/TO", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "I", + "booking_changes": 3, + "deposit_type": "No Deposit", + "agent": 393.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient-Party", + "adr": 75.6, + "required_car_parking_spaces": 0, + "total_of_special_requests": 1, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "002", + "features": { + "hotel": "Resort Hotel", + "lead_time": 122, + "arrival_date_year": 2016, + "arrival_date_month": "August", + "arrival_date_week_number": 35, + "arrival_date_day_of_month": 23, + "stays_in_weekend_nights": 1, + "stays_in_week_nights": 5, + "adults": 3, + "children": 1.0, + "babies": 0, + "meal": "BB", + "country": "ESP", + "market_segment": "Online TA", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "H", + "assigned_room_type": "H", + "booking_changes": 2, + "deposit_type": "No Deposit", + "agent": 240.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 265.83, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "003", + "features": { + "hotel": "Resort Hotel", + "lead_time": 101, + "arrival_date_year": 2016, + "arrival_date_month": "August", + "arrival_date_week_number": 36, + "arrival_date_day_of_month": 30, + "stays_in_weekend_nights": 0, + "stays_in_week_nights": 3, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "CN", + "market_segment": "Direct", + "distribution_channel": "Direct", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "F", + "assigned_room_type": "F", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 250.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient", + "adr": 197.67, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Check-Out" + } + }, + { + "scenario_id": "004", + "features": { + "hotel": "City Hotel", + "lead_time": 356, + "arrival_date_year": 2015, + "arrival_date_month": "October", + "arrival_date_week_number": 41, + "arrival_date_day_of_month": 8, + "stays_in_weekend_nights": 0, + "stays_in_week_nights": 2, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "PRT", + "market_segment": "Groups", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 1, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 0, + "deposit_type": "Non Refund", + "agent": 1.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Contract", + "adr": 62.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Canceled" + } + }, + { + "scenario_id": "010", + "features": { + "hotel": "City Hotel", + "lead_time": 48, + "arrival_date_year": 2016, + "arrival_date_month": "September", + "arrival_date_week_number": 38, + "arrival_date_day_of_month": 14, + "stays_in_weekend_nights": 0, + "stays_in_week_nights": 2, + "adults": 2, + "children": 0.0, + "babies": 0, + "meal": "BB", + "country": "PRT", + "market_segment": "Offline TA/TO", + "distribution_channel": "TA/TO", + "is_repeated_guest": 0, + "previous_cancellations": 0, + "previous_bookings_not_canceled": 0, + "reserved_room_type": "A", + "assigned_room_type": "A", + "booking_changes": 0, + "deposit_type": "No Deposit", + "agent": 281.0, + "company": null, + "days_in_waiting_list": 0, + "customer_type": "Transient-Party", + "adr": 130.0, + "required_car_parking_spaces": 0, + "total_of_special_requests": 0, + "reservation_status": "Canceled" + } + }, + { + "scenario_id": "011", + "features": { + "hotel": "City Hotel", + "lead_time": 167, + "arrival_date_year": 2017, + "arrival_date_month": "April", + "arrival_date_week_number": 16, + "arrival_date_day_of_month": 17, + "stays_in_weekend_nights": 1, + 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"Canceled", - "reservation_status_date": "2016-02-09" - } - }, - { - "scenario_id": "489", - "features": { - "hotel": "City Hotel", - "is_canceled": 0, - "lead_time": 58, - "arrival_date_year": 2016, - "arrival_date_month": "November", - "arrival_date_week_number": 47, - "arrival_date_day_of_month": 16, - "stays_in_weekend_nights": 0, - "stays_in_week_nights": 3, - "adults": 2, - "children": 0.0, - "babies": 0, - "meal": "BB", - "country": "FRA", - "market_segment": "Offline TA/TO", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "A", - "assigned_room_type": "D", - "booking_changes": 0, - "deposit_type": "No Deposit", - "agent": 28.0, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 75.0, - "required_car_parking_spaces": 0, - "total_of_special_requests": 0, - "reservation_status": "Check-Out", - "reservation_status_date": "2016-11-19" - } - }, - { - "scenario_id": "495", - "features": { - "hotel": "Resort Hotel", - "is_canceled": 1, - "lead_time": 140, - "arrival_date_year": 2016, - "arrival_date_month": "February", - "arrival_date_week_number": 7, - "arrival_date_day_of_month": 12, - "stays_in_weekend_nights": 0, - "stays_in_week_nights": 2, - "adults": 2, - "children": 0.0, - "babies": 0, - "meal": "HB", - "country": "PRT", - "market_segment": "Groups", - "distribution_channel": "TA/TO", - "is_repeated_guest": 0, - "previous_cancellations": 0, - "previous_bookings_not_canceled": 0, - "reserved_room_type": "A", - "assigned_room_type": "A", - "booking_changes": 0, - "deposit_type": "Non Refund", - "agent": null, - "company": null, - "days_in_waiting_list": 0, - "customer_type": "Transient", - "adr": 50.0, - "required_car_parking_spaces": 0, - "total_of_special_requests": 0, - "reservation_status": "Canceled", - "reservation_status_date": "2015-12-11" - } - } - ], - "target_column": "reservation_status", - "task_sub_type": "classification", - "ranking_ground_truth": { - "top_k_ids": [ - "002", - "053", - "107", - "129", - "179", - "188", - "189", - "242", - "266", - "278", - "279", - "335", - "354", - "394", - "426" - ] - }, - "active_filters": [ - { - "col": "children", - "op": ">", - "val": 0 - } - ], - "target_class_value": "check-out" - }, - "response": "", - "evaluation_score": {} -} \ No newline at end of file + "active_filters": [ + { + "col": "children", + "op": ">", + "val": 0 + } + ], + "target_class_value": "check-out" + }, + "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_008/current.csv b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_008/current.csv index 7f5c06cf13d5d0b7fca2a408b63094c6aefa6572..c16f0a8a91344c909e41f0d7dc8b5853c2c3fc76 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_008/current.csv +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_008/current.csv @@ -1,201 +1,201 @@ -hotel,is_canceled,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests,reservation_status_date -Resort Hotel,0,58,2016,May,20,8,2,0,3,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,I,3,No Deposit,393.0,,0,Transient-Party,75.6,0,1,2016-05-10 -Resort Hotel,0,122,2016,August,35,23,1,5,3,1.0,0,BB,ESP,Online TA,TA/TO,0,0,0,H,H,2,No Deposit,240.0,,0,Transient,265.83,0,0,2016-08-29 -Resort Hotel,0,101,2016,August,36,30,0,3,2,0.0,0,BB,CN,Direct,Direct,0,0,0,F,F,0,No Deposit,250.0,,0,Transient,197.67,0,0,2016-09-02 -City Hotel,1,356,2015,October,41,8,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Contract,62.0,0,0,2015-01-01 -City Hotel,1,48,2016,September,38,14,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,281.0,,0,Transient-Party,130.0,0,0,2016-09-06 -City Hotel,0,167,2017,April,16,17,1,3,2,0.0,0,BB,CHE,Offline TA/TO,TA/TO,0,0,0,A,A,1,No Deposit,16.0,,0,Transient-Party,89.1,0,0,2017-04-21 -City Hotel,0,3,2015,September,37,9,0,2,1,0.0,0,BB,FRA,Corporate,Corporate,0,0,0,A,B,0,No Deposit,,68.0,0,Transient-Party,90.0,0,0,2015-09-11 -City Hotel,0,123,2017,May,19,8,1,4,2,0.0,0,BB,NOR,Online TA,TA/TO,0,0,0,A,A,3,No Deposit,9.0,,0,Transient,111.6,0,1,2017-05-13 -City Hotel,1,0,2015,August,33,14,0,2,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,109.0,0,0,2015-08-14 -City Hotel,0,158,2017,March,13,27,1,1,1,0.0,0,BB,ISR,Groups,TA/TO,0,0,0,A,A,1,No Deposit,37.0,,0,Transient-Party,95.0,0,0,2017-03-29 -City Hotel,1,130,2016,April,16,13,0,4,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,29.0,,10,Transient,100.0,0,0,2015-12-15 -City Hotel,0,138,2016,July,28,4,1,2,2,0.0,0,BB,IND,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,7.0,,0,Transient,91.48,0,1,2016-07-07 -Resort Hotel,0,69,2017,February,8,25,2,5,2,0.0,0,BB,BEL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,53.14,0,2,2017-03-04 -Resort Hotel,0,2,2016,October,43,19,0,1,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,81.0,1,2,2016-10-20 -City Hotel,0,45,2016,March,13,21,1,2,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,28.0,,0,Transient,85.0,0,0,2016-03-24 -City Hotel,0,26,2016,November,47,17,0,3,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,83.0,,0,Transient,91.2,0,0,2016-11-20 -Resort Hotel,0,364,2016,October,42,12,0,4,2,0.0,0,BB,GBR,Groups,Corporate,0,0,0,A,D,0,No Deposit,,223.0,0,Transient-Party,69.0,1,0,2016-10-16 -Resort Hotel,0,254,2015,September,37,12,2,2,2,0.0,0,BB,CN,Groups,TA/TO,0,0,0,A,A,0,No Deposit,15.0,,0,Transient-Party,0.0,0,0,2015-09-16 -City Hotel,1,11,2016,December,50,6,0,1,2,0.0,0,SC,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,88.0,0,0,2016-11-29 -Resort Hotel,0,0,2017,January,1,2,1,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,D,0,No Deposit,165.0,,0,Transient,41.0,1,0,2017-01-04 -Resort Hotel,0,3,2016,April,16,14,0,1,1,0.0,0,HB,PRT,Direct,Direct,0,0,0,E,E,1,No Deposit,250.0,,0,Transient-Party,116.0,1,0,2016-04-15 -Resort Hotel,0,1,2016,March,12,15,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,165.0,0,Transient,30.0,0,1,2016-03-16 -Resort Hotel,0,10,2016,December,53,31,0,1,2,2.0,0,BB,USA,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,240.0,,0,Transient,180.0,0,0,2017-01-01 -City Hotel,0,0,2015,December,52,22,0,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,96.0,1,1,2015-12-23 -City Hotel,0,179,2016,August,35,26,2,2,2,0.0,0,BB,AUT,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,7.0,,0,Transient,82.18,0,0,2016-08-30 -City Hotel,0,65,2016,July,31,27,0,2,2,0.0,0,BB,AUS,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,9.0,,0,Transient,109.9,0,1,2016-07-29 -City Hotel,0,73,2016,October,44,28,0,1,1,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,1,A,A,1,No Deposit,21.0,,0,Group,62.5,0,0,2016-10-29 -City Hotel,1,152,2015,September,38,16,2,4,3,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,13.0,,0,Transient,136.68,0,0,2015-05-25 -City Hotel,0,39,2015,August,33,14,0,2,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,6.0,,0,Transient-Party,109.0,0,0,2015-08-16 -City Hotel,0,11,2016,March,11,7,1,3,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,28.0,,0,Transient,70.0,0,0,2016-03-11 -City Hotel,1,267,2016,March,14,31,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0,2015-10-21 -City Hotel,0,3,2016,January,3,15,2,3,2,0.0,0,BB,BEL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,11.0,,0,Transient,69.84,0,0,2016-01-20 -City Hotel,1,81,2017,May,18,1,1,2,3,0.0,0,HB,ITA,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,9.0,,0,Transient,196.2,0,0,2017-02-09 -City Hotel,0,99,2016,February,8,19,0,2,1,0.0,0,BB,PRT,Corporate,TA/TO,0,0,0,A,D,1,No Deposit,,67.0,0,Transient-Party,80.5,0,0,2016-02-21 -City Hotel,1,10,2016,November,45,1,0,4,2,0.0,0,SC,COL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,102.0,0,0,2016-10-23 -Resort Hotel,1,1,2015,December,52,22,2,5,1,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,1,No Deposit,8.0,,0,Transient,51.0,0,0,2015-12-22 -Resort Hotel,1,274,2015,October,40,3,2,5,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,96.0,,0,Transient,36.05,0,0,2015-08-12 -City Hotel,1,54,2017,January,2,14,0,1,3,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,138.6,0,0,2016-12-27 -City Hotel,0,133,2017,July,28,9,2,1,2,0.0,0,BB,BEL,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient-Party,89.1,0,2,2017-07-12 -City Hotel,1,396,2017,June,26,25,2,0,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,B,1,No Deposit,6.0,,0,Transient-Party,80.0,0,0,2017-05-24 -City Hotel,0,69,2016,April,17,18,1,3,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient-Party,110.08,0,1,2016-04-22 -City Hotel,1,44,2016,June,27,28,0,2,2,1.0,0,BB,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,134.1,0,0,2016-06-22 -City Hotel,1,218,2017,July,27,5,0,4,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,107.1,0,0,2016-12-08 -City Hotel,0,71,2016,August,35,26,2,2,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,131.4,0,1,2016-08-30 -City Hotel,0,340,2017,April,16,21,2,2,1,1.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,98.1,0,2,2017-04-25 -City Hotel,0,114,2016,July,29,13,0,4,2,0.0,0,SC,IRL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Transient,89.25,0,0,2016-07-17 -Resort Hotel,1,232,2016,November,48,26,2,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,11.0,,0,Transient,30.0,0,0,2016-10-18 -City Hotel,1,311,2015,August,35,24,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient-Party,62.0,0,0,2015-07-02 -Resort Hotel,0,138,2016,July,30,23,2,4,3,0.0,0,Undefined,ESP,Direct,Direct,0,0,0,F,F,0,No Deposit,,,0,Transient,318.0,0,1,2016-07-29 -City Hotel,1,301,2016,November,47,18,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,20.0,,0,Transient,52.0,0,0,2016-03-15 -Resort Hotel,0,110,2016,August,33,10,1,4,1,0.0,0,BB,IRL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,189.0,0,2,2016-08-15 -Resort Hotel,0,0,2017,February,6,8,0,1,2,0.0,0,BB,,Direct,Direct,0,0,0,F,F,0,No Deposit,,,0,Transient,73.0,0,0,2017-02-09 -City Hotel,1,11,2016,November,45,5,0,1,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,130.0,0,1,2016-10-28 -Resort Hotel,0,0,2017,May,20,15,1,0,1,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,79.0,1,0,2017-05-16 -City Hotel,0,2,2016,November,48,23,0,1,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,103.0,0,1,2016-11-24 -City Hotel,0,23,2017,January,2,8,2,3,2,1.0,0,HB,ITA,Direct,Direct,0,0,0,E,E,0,No Deposit,14.0,,0,Transient,168.0,1,1,2017-01-13 -City Hotel,0,78,2016,December,50,8,1,3,2,0.0,0,BB,DZA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,88.4,0,1,2016-12-12 -City Hotel,1,44,2016,October,41,2,2,1,2,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Transient,108.0,0,1,2016-08-22 -Resort Hotel,0,0,2017,January,1,3,0,2,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,D,0,No Deposit,,154.0,0,Transient,35.0,0,0,2017-01-05 -City Hotel,1,88,2017,May,20,14,1,0,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,126.0,0,0,2017-04-10 -City Hotel,0,102,2016,October,41,2,2,1,2,0.0,0,SC,ITA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,108.0,0,1,2016-10-05 -City Hotel,1,47,2017,February,8,25,2,1,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,104.4,0,0,2017-01-09 -City Hotel,0,42,2016,January,3,16,0,1,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,80.3,0,1,2016-01-17 -Resort Hotel,0,159,2016,January,5,25,1,3,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,241.0,,0,Transient,38.86,0,1,2016-01-29 -City Hotel,0,160,2017,March,12,25,0,1,3,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,G,0,No Deposit,9.0,,0,Transient,135.0,0,1,2017-03-26 -City Hotel,0,89,2016,September,39,20,2,5,2,0.0,0,BB,ROU,Offline TA/TO,TA/TO,0,0,0,A,A,2,No Deposit,138.0,,0,Transient,95.0,0,2,2016-09-27 -City Hotel,1,32,2016,October,42,10,1,3,1,0.0,0,BB,CHN,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,148.28,0,0,2016-10-09 -Resort Hotel,0,21,2016,July,30,17,2,4,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,204.0,0,0,2016-07-23 -City Hotel,1,240,2017,July,30,23,2,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,110.0,0,0,2016-11-25 -Resort Hotel,1,203,2017,August,33,18,4,8,2,1.0,0,BB,AZE,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,15.0,,0,Transient,126.8,0,3,2017-02-25 -Resort Hotel,0,233,2017,June,24,15,0,2,2,0.0,0,BB,CN,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,99.0,0,2,2017-06-17 -City Hotel,0,11,2016,March,11,11,0,2,2,0.0,0,BB,BEL,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,138.0,,0,Transient,74.8,0,0,2016-03-13 -City Hotel,1,323,2016,May,22,26,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0,2015-10-21 -City Hotel,0,25,2015,October,41,6,0,4,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Contract,112.25,0,1,2015-10-10 -Resort Hotel,1,223,2017,May,18,4,2,5,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,1,No Deposit,177.0,,0,Transient,68.4,0,1,2016-11-02 -City Hotel,0,11,2016,August,36,28,2,2,3,1.0,0,BB,ESP,Direct,Direct,0,0,0,G,G,0,No Deposit,14.0,,0,Transient,246.0,0,0,2016-09-01 -City Hotel,1,128,2016,November,48,26,1,1,2,0.0,0,SC,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,79.2,0,0,2016-07-21 -Resort Hotel,0,23,2016,January,3,14,0,1,2,0.0,0,BB,USA,Direct,Direct,0,0,0,E,E,0,No Deposit,,,0,Transient,48.0,0,0,2016-01-15 -Resort Hotel,0,37,2016,July,30,23,2,5,3,1.0,0,BB,PRT,Online TA,TA/TO,0,0,0,H,H,0,No Deposit,,,0,Transient,210.0,1,0,2016-07-30 -City Hotel,0,24,2017,July,27,6,0,1,2,2.0,0,BB,DEU,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,7.0,,0,Transient,250.96,0,1,2017-07-07 -City Hotel,1,195,2015,September,38,17,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,No Deposit,20.0,,0,Transient,48.0,0,0,2015-07-23 -City Hotel,0,173,2017,April,16,20,0,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,9.0,,0,Transient,99.0,0,1,2017-04-21 -City Hotel,1,7,2017,May,19,8,1,0,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,160.0,0,3,2017-05-03 -City Hotel,1,159,2017,June,24,17,2,5,3,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,157.25,0,0,2017-05-07 -Resort Hotel,0,85,2016,July,28,3,2,3,3,0.0,0,FB,PRT,Offline TA/TO,TA/TO,0,0,0,A,C,1,No Deposit,5.0,,0,Transient-Party,168.48,1,0,2016-07-08 -City Hotel,1,312,2015,August,35,25,0,4,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.0,0,0,2015-07-06 -City Hotel,1,605,2016,October,43,17,1,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,60.0,0,0,2016-09-20 -City Hotel,0,12,2017,May,20,16,2,5,1,0.0,0,BB,CHN,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,8.0,,0,Transient-Party,156.75,0,1,2017-05-23 -City Hotel,0,108,2017,March,11,17,1,2,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,97.2,0,2,2017-03-20 -City Hotel,1,321,2015,September,36,3,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,1,1,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.8,0,0,2014-10-17 -Resort Hotel,1,172,2015,September,37,12,2,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,15.0,,0,Transient-Party,36.0,0,0,2015-08-06 -City Hotel,1,275,2017,July,28,11,0,1,2,0.0,0,SC,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,89.1,0,1,2016-10-18 -City Hotel,1,94,2016,March,10,4,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,Non Refund,154.0,,28,Transient,80.0,0,0,2015-12-29 -City Hotel,1,288,2017,July,27,3,1,4,2,0.0,0,SC,POL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,89.1,0,1,2017-06-08 -City Hotel,1,325,2015,September,37,7,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Contract,62.0,0,0,2015-01-01 -Resort Hotel,0,326,2017,June,25,23,3,7,2,0.0,0,HB,GBR,Offline TA/TO,TA/TO,0,0,0,A,A,1,No Deposit,40.0,,0,Contract,102.2,0,2,2017-07-03 -Resort Hotel,1,277,2016,June,25,12,2,2,2,0.0,0,Undefined,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,110.0,0,0,2016-03-29 -City Hotel,0,18,2016,February,7,11,0,3,2,1.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,9.0,,0,Transient,111.0,0,1,2016-02-14 -City Hotel,1,50,2017,July,28,10,1,2,2,1.0,0,BB,DNK,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,160.0,0,1,2017-06-27 -Resort Hotel,0,2,2016,October,45,30,2,0,2,0.0,0,BB,PRT,Complementary,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,6.0,1,0,2016-11-01 -City Hotel,0,56,2016,May,23,30,1,1,1,0.0,0,BB,BEL,Groups,TA/TO,0,0,0,A,A,1,No Deposit,,,0,Transient-Party,120.0,0,0,2016-06-01 -City Hotel,1,35,2017,May,19,10,0,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,39.0,,0,Transient-Party,130.0,0,0,2017-05-03 -City Hotel,0,74,2017,July,27,8,2,1,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,120.0,0,1,2017-07-11 -City Hotel,0,88,2017,June,23,5,1,3,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,83.0,,0,Transient,122.4,0,0,2017-06-09 -Resort Hotel,1,46,2016,March,11,11,0,1,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,375.0,,0,Transient-Party,25.0,0,0,2016-03-02 -Resort Hotel,1,172,2016,December,53,26,2,5,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,240.0,,0,Transient,52.46,0,1,2016-10-09 -City Hotel,0,33,2016,July,27,1,0,1,2,1.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,C,0,No Deposit,9.0,,0,Transient,152.1,0,2,2016-07-02 -City Hotel,1,277,2016,November,46,7,1,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,100.0,0,0,2016-04-04 -City Hotel,1,294,2015,August,32,7,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient-Party,62.0,0,0,2015-07-02 -City Hotel,0,86,2016,April,15,6,0,3,2,0.0,0,BB,NLD,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,99.3,0,1,2016-04-09 -City Hotel,0,10,2016,August,34,15,1,0,2,2.0,1,BB,PRT,Direct,Direct,0,0,0,G,G,0,No Deposit,,,0,Transient,276.0,0,2,2016-08-16 -Resort Hotel,0,1,2017,January,2,13,0,1,2,2.0,0,SC,PRT,Online TA,TA/TO,0,0,0,C,I,1,No Deposit,196.0,,0,Transient,0.0,0,0,2017-01-14 -City Hotel,1,68,2016,February,8,17,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,37.0,,0,Transient,75.0,0,0,2016-01-06 -City Hotel,0,135,2017,February,8,25,2,1,2,0.0,0,SC,ITA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,79.2,0,1,2017-02-28 -Resort Hotel,0,120,2015,October,43,24,4,6,2,0.0,0,BB,IRL,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,175.0,,0,Transient,34.56,0,0,2015-11-03 -City Hotel,1,105,2017,May,20,19,0,2,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,160.0,0,0,2017-02-03 -City Hotel,0,55,2016,April,15,6,0,1,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,56.0,,0,Transient-Party,104.0,0,0,2016-04-07 -City Hotel,1,30,2015,September,40,30,0,2,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,3.0,,0,Transient,121.0,0,0,2015-09-15 -City Hotel,0,10,2017,August,32,11,0,1,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,189.0,0,0,2017-08-12 -City Hotel,0,3,2016,October,45,31,1,1,1,0.0,0,BB,SWE,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,140.0,0,1,2016-11-02 -Resort Hotel,1,92,2016,February,9,26,1,2,2,0.0,0,Undefined,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,134.0,,0,Transient,79.0,0,0,2015-12-10 -City Hotel,1,19,2017,July,28,15,2,1,2,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,126.67,0,0,2017-06-27 -City Hotel,0,62,2016,November,46,6,2,2,1,0.0,0,SC,RUS,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,140.63,0,1,2016-11-10 -Resort Hotel,0,0,2016,April,15,4,1,0,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,1,No Deposit,,,0,Transient,56.0,0,1,2016-04-05 -City Hotel,1,100,2015,October,42,16,0,1,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,6.0,,0,Transient,108.0,0,0,2015-09-28 -Resort Hotel,0,234,2016,September,37,9,2,3,2,0.0,0,HB,DEU,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,241.0,,0,Transient,119.34,0,1,2016-09-14 -City Hotel,1,247,2016,October,44,28,1,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,37.0,,0,Transient,110.0,0,0,2016-03-15 -Resort Hotel,0,69,2017,August,33,16,2,4,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,230.0,0,2,2017-08-22 -Resort Hotel,1,192,2017,June,24,11,2,5,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,314.0,,0,Transient,88.0,0,0,2016-12-02 -City Hotel,0,35,2017,February,6,9,2,5,2,0.0,0,BB,FRA,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,71.0,0,0,2017-02-16 -City Hotel,1,272,2015,July,29,16,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,1,1,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.8,0,0,2014-10-17 -City Hotel,0,25,2016,April,17,17,2,2,2,1.0,0,BB,IRL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,125.5,0,0,2016-04-21 -City Hotel,0,69,2015,October,44,25,2,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,37.0,,58,Transient-Party,85.67,0,0,2015-10-28 -City Hotel,0,59,2016,November,47,18,2,2,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,28.0,,0,Transient,75.0,0,0,2016-11-22 -City Hotel,1,184,2016,October,41,4,0,3,2,0.0,0,BB,NOR,Online TA,TA/TO,0,0,0,B,B,1,No Deposit,9.0,,0,Transient,101.05,0,0,2016-04-09 -Resort Hotel,0,2,2016,January,4,23,0,1,2,0.0,0,BB,ESP,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,314.0,,0,Transient-Party,29.0,0,1,2016-01-24 -Resort Hotel,0,13,2017,June,23,7,0,2,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,2,A,A,0,No Deposit,,154.0,0,Transient,75.0,0,0,2017-06-09 -City Hotel,0,26,2016,January,5,30,1,1,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Transient,91.0,0,1,2016-02-01 -City Hotel,0,4,2017,May,22,29,1,1,1,0.0,0,BB,ESP,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,28.0,,0,Transient,94.0,0,0,2017-05-31 -City Hotel,0,64,2016,March,12,19,1,1,2,2.0,0,BB,PRT,Direct,Direct,0,0,0,E,F,0,No Deposit,,,0,Transient,159.6,0,2,2016-03-21 -Resort Hotel,1,221,2017,April,18,30,2,5,2,0.0,0,HB,PRT,Groups,TA/TO,0,0,0,D,D,0,No Deposit,,,0,Transient-Party,82.0,0,0,2016-10-07 -City Hotel,0,7,2017,February,9,26,2,3,2,0.0,0,SC,NLD,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,74.8,0,0,2017-03-03 -City Hotel,1,328,2015,September,37,10,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Contract,62.0,0,0,2015-01-01 -Resort Hotel,0,364,2016,October,42,12,0,4,1,0.0,0,BB,GBR,Groups,Corporate,0,0,0,A,A,1,No Deposit,,223.0,0,Transient-Party,60.0,0,0,2016-10-16 -City Hotel,0,96,2017,June,25,19,1,2,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,126.0,0,2,2017-06-22 -City Hotel,1,0,2017,May,20,18,0,1,2,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,144.0,0,1,2017-05-18 -City Hotel,0,56,2015,September,38,17,0,2,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,20.0,,0,Transient-Party,82.0,0,0,2015-09-19 -City Hotel,0,162,2017,June,22,3,2,1,2,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,27.0,,0,Transient,89.1,0,0,2017-06-06 -Resort Hotel,1,184,2017,August,34,26,2,5,2,0.0,0,FB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Transient,173.0,0,0,2017-03-07 -City Hotel,1,321,2015,September,36,3,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Contract,62.0,0,0,2015-01-01 -City Hotel,0,448,2017,June,24,17,2,1,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,229.0,,0,Transient-Party,0.0,0,0,2017-06-20 -Resort Hotel,0,0,2016,October,41,5,0,1,3,0.0,0,BB,ESP,Direct,Direct,1,0,0,D,D,0,No Deposit,,,0,Transient,141.0,1,0,2016-10-06 -City Hotel,0,5,2015,August,33,15,1,1,2,2.0,0,BB,PRT,Direct,Direct,0,0,0,F,F,0,No Deposit,,,0,Transient,122.0,0,1,2015-08-17 -City Hotel,0,15,2015,October,43,20,0,3,1,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,E,0,No Deposit,14.0,,0,Transient,109.0,0,0,2015-10-23 -Resort Hotel,0,27,2017,February,9,28,0,1,2,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient-Party,56.0,0,2,2017-03-01 -City Hotel,1,155,2017,August,35,31,0,3,2,0.0,0,SC,SWE,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,135.0,0,1,2017-04-04 -Resort Hotel,0,171,2016,September,36,1,0,3,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,132.6,1,0,2016-09-04 -Resort Hotel,0,16,2016,July,30,23,2,6,3,0.0,0,HB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,215.0,0,2,2016-07-31 -Resort Hotel,1,79,2016,October,44,26,2,6,2,0.0,0,BB,IRL,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,58.05,0,3,2016-08-30 -City Hotel,1,68,2017,April,16,18,0,4,2,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,126.0,0,0,2017-04-01 -Resort Hotel,1,112,2017,August,35,28,1,5,3,0.0,0,HB,PRT,Direct,Direct,0,0,0,D,D,0,No Deposit,250.0,,0,Transient,250.0,0,2,2017-05-09 -City Hotel,0,3,2016,June,27,27,1,0,1,0.0,0,SC,ESP,Offline TA/TO,GDS,0,0,0,A,A,0,No Deposit,195.0,,0,Transient,124.0,0,0,2016-06-28 -City Hotel,1,74,2017,July,30,23,4,5,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,145.56,0,1,2017-05-28 -City Hotel,1,109,2017,April,17,26,2,5,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,120.86,0,0,2017-04-24 -Resort Hotel,0,4,2017,January,5,30,1,0,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,D,0,No Deposit,,34.0,0,Transient,38.5,1,0,2017-01-31 -City Hotel,0,6,2016,August,33,7,2,2,2,1.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,180.0,0,1,2016-08-11 -City Hotel,0,1,2016,October,41,7,0,2,1,0.0,0,SC,SWE,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,133.98,0,1,2016-10-09 -City Hotel,0,98,2017,April,15,14,1,2,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,138.0,,0,Transient,90.1,0,0,2017-04-17 -City Hotel,0,137,2016,June,27,26,2,3,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,28.0,,0,Transient,112.0,0,0,2016-07-01 -City Hotel,0,30,2016,November,49,30,0,2,3,0.0,0,BB,ITA,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,22.0,,0,Transient,98.1,0,0,2016-12-02 -City Hotel,1,30,2016,June,23,2,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,235.0,,0,Transient,140.0,0,0,2016-05-03 -City Hotel,1,180,2016,October,40,1,2,1,2,0.0,0,BB,IRL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,108.9,0,0,2016-08-31 -Resort Hotel,0,3,2016,February,6,1,1,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,D,0,No Deposit,,43.0,0,Transient,30.0,0,0,2016-02-03 -City Hotel,1,19,2015,July,30,25,2,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.0,0,0,2015-07-22 -City Hotel,1,71,2016,June,25,14,0,3,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,236.0,,0,Transient,120.0,0,0,2016-04-27 -Resort Hotel,0,1,2016,August,34,16,0,1,2,0.0,0,BB,ISR,Online TA,TA/TO,0,0,0,A,C,0,No Deposit,240.0,,0,Transient,209.0,0,1,2016-08-17 -City Hotel,1,365,2015,October,42,17,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient,62.8,0,0,2015-01-30 -Resort Hotel,1,90,2016,December,52,23,2,5,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,37.8,0,0,2016-10-18 -City Hotel,1,67,2015,October,43,21,0,1,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,89.25,0,0,2015-09-24 -Resort Hotel,0,61,2016,October,41,3,3,8,2,0.0,0,BB,IRL,Direct,Direct,1,0,1,D,D,0,No Deposit,,,0,Transient,10.0,0,0,2016-10-14 -Resort Hotel,0,160,2017,March,11,18,2,5,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,240.0,,0,Transient,55.8,0,3,2017-03-25 -Resort Hotel,1,36,2015,August,32,4,0,3,1,0.0,0,BB,PRT,Direct,Direct,0,0,0,F,F,0,No Deposit,250.0,,0,Transient,221.0,0,0,2015-07-29 -Resort Hotel,0,49,2015,December,52,23,0,0,2,0.0,0,HB,PRT,Online TA,TA/TO,0,0,0,F,F,1,No Deposit,242.0,,0,Transient,0.0,0,0,2015-12-23 -Resort Hotel,0,1,2016,April,15,5,0,2,1,0.0,0,BB,UKR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,75.0,0,1,2016-04-07 -City Hotel,0,5,2017,May,22,29,1,0,1,0.0,0,BB,PRT,Complementary,Direct,1,4,12,D,A,0,No Deposit,,45.0,0,Transient,0.0,0,3,2017-05-30 -City Hotel,0,16,2017,March,12,23,0,2,1,0.0,0,BB,FIN,Corporate,Corporate,0,0,0,D,D,0,No Deposit,,,0,Transient-Party,108.0,0,0,2017-03-25 -City Hotel,0,209,2017,August,34,22,0,3,2,0.0,0,SC,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,108.0,1,1,2017-08-25 -City Hotel,0,1,2016,July,31,30,0,1,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,E,E,1,No Deposit,,,0,Transient,177.0,1,0,2016-07-31 -City Hotel,1,36,2016,March,11,8,2,5,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,71.1,0,0,2016-02-02 -City Hotel,1,29,2016,October,42,15,1,1,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,154.5,0,1,2016-09-16 -City Hotel,1,56,2017,March,12,21,0,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,86.0,,0,Transient,85.0,0,0,2017-01-24 -Resort Hotel,0,0,2017,January,3,18,0,1,1,0.0,0,BB,PRT,Direct,Direct,1,0,0,A,A,1,No Deposit,,,0,Transient,57.0,1,0,2017-01-19 -Resort Hotel,1,252,2017,July,29,17,1,5,2,0.0,0,BB,NOR,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,240.0,,0,Transient,192.0,0,0,2016-11-08 -Resort Hotel,0,27,2015,December,52,24,0,2,2,0.0,0,BB,BRA,Offline TA/TO,TA/TO,0,0,0,A,E,0,No Deposit,6.0,,0,Transient,50.0,0,0,2015-12-26 -Resort Hotel,1,55,2017,July,29,17,3,8,2,0.0,0,BB,RUS,Direct,Direct,0,0,0,F,F,0,No Deposit,250.0,,0,Transient,260.0,0,0,2017-05-29 -City Hotel,1,138,2016,August,34,16,0,3,1,0.0,0,BB,PRT,Direct,Direct,0,0,0,D,D,0,No Deposit,,,0,Transient,121.2,0,1,2016-08-04 -City Hotel,0,30,2016,September,39,24,1,1,2,0.0,0,BB,ZAF,Online TA,TA/TO,0,0,0,D,A,0,No Deposit,9.0,,0,Transient,143.55,0,0,2016-09-26 -City Hotel,1,309,2016,May,20,13,1,2,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,3.0,,215,Transient-Party,101.0,0,0,2016-02-09 -City Hotel,0,58,2016,November,47,16,0,3,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,28.0,,0,Transient,75.0,0,0,2016-11-19 -Resort Hotel,1,140,2016,February,7,12,0,2,2,0.0,0,HB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,50.0,0,0,2015-12-11 +hotel,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests +Resort Hotel,58,2016,May,20,8,2,0,3,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,I,3,No Deposit,393.0,,0,Transient-Party,75.6,0,1 +Resort Hotel,122,2016,August,35,23,1,5,3,1.0,0,BB,ESP,Online TA,TA/TO,0,0,0,H,H,2,No Deposit,240.0,,0,Transient,265.83,0,0 +Resort Hotel,101,2016,August,36,30,0,3,2,0.0,0,BB,CN,Direct,Direct,0,0,0,F,F,0,No Deposit,250.0,,0,Transient,197.67,0,0 +City Hotel,356,2015,October,41,8,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Contract,62.0,0,0 +City Hotel,48,2016,September,38,14,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,281.0,,0,Transient-Party,130.0,0,0 +City Hotel,167,2017,April,16,17,1,3,2,0.0,0,BB,CHE,Offline TA/TO,TA/TO,0,0,0,A,A,1,No Deposit,16.0,,0,Transient-Party,89.1,0,0 +City Hotel,3,2015,September,37,9,0,2,1,0.0,0,BB,FRA,Corporate,Corporate,0,0,0,A,B,0,No Deposit,,68.0,0,Transient-Party,90.0,0,0 +City Hotel,123,2017,May,19,8,1,4,2,0.0,0,BB,NOR,Online TA,TA/TO,0,0,0,A,A,3,No Deposit,9.0,,0,Transient,111.6,0,1 +City Hotel,0,2015,August,33,14,0,2,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,6.0,,0,Transient-Party,109.0,0,0 +City Hotel,158,2017,March,13,27,1,1,1,0.0,0,BB,ISR,Groups,TA/TO,0,0,0,A,A,1,No Deposit,37.0,,0,Transient-Party,95.0,0,0 +City Hotel,130,2016,April,16,13,0,4,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,29.0,,10,Transient,100.0,0,0 +City Hotel,138,2016,July,28,4,1,2,2,0.0,0,BB,IND,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,7.0,,0,Transient,91.48,0,1 +Resort Hotel,69,2017,February,8,25,2,5,2,0.0,0,BB,BEL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,53.14,0,2 +Resort Hotel,2,2016,October,43,19,0,1,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,81.0,1,2 +City Hotel,45,2016,March,13,21,1,2,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,28.0,,0,Transient,85.0,0,0 +City Hotel,26,2016,November,47,17,0,3,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,83.0,,0,Transient,91.2,0,0 +Resort Hotel,364,2016,October,42,12,0,4,2,0.0,0,BB,GBR,Groups,Corporate,0,0,0,A,D,0,No Deposit,,223.0,0,Transient-Party,69.0,1,0 +Resort Hotel,254,2015,September,37,12,2,2,2,0.0,0,BB,CN,Groups,TA/TO,0,0,0,A,A,0,No Deposit,15.0,,0,Transient-Party,0.0,0,0 +City Hotel,11,2016,December,50,6,0,1,2,0.0,0,SC,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,88.0,0,0 +Resort Hotel,0,2017,January,1,2,1,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,D,0,No Deposit,165.0,,0,Transient,41.0,1,0 +Resort Hotel,3,2016,April,16,14,0,1,1,0.0,0,HB,PRT,Direct,Direct,0,0,0,E,E,1,No Deposit,250.0,,0,Transient-Party,116.0,1,0 +Resort Hotel,1,2016,March,12,15,0,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,A,0,No Deposit,,165.0,0,Transient,30.0,0,1 +Resort Hotel,10,2016,December,53,31,0,1,2,2.0,0,BB,USA,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,240.0,,0,Transient,180.0,0,0 +City Hotel,0,2015,December,52,22,0,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,96.0,1,1 +City Hotel,179,2016,August,35,26,2,2,2,0.0,0,BB,AUT,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,7.0,,0,Transient,82.18,0,0 +City Hotel,65,2016,July,31,27,0,2,2,0.0,0,BB,AUS,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,9.0,,0,Transient,109.9,0,1 +City Hotel,73,2016,October,44,28,0,1,1,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,1,A,A,1,No Deposit,21.0,,0,Group,62.5,0,0 +City Hotel,152,2015,September,38,16,2,4,3,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,D,D,0,No Deposit,13.0,,0,Transient,136.68,0,0 +City Hotel,39,2015,August,33,14,0,2,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,6.0,,0,Transient-Party,109.0,0,0 +City Hotel,11,2016,March,11,7,1,3,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,28.0,,0,Transient,70.0,0,0 +City Hotel,267,2016,March,14,31,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0 +City Hotel,3,2016,January,3,15,2,3,2,0.0,0,BB,BEL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,11.0,,0,Transient,69.84,0,0 +City Hotel,81,2017,May,18,1,1,2,3,0.0,0,HB,ITA,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,9.0,,0,Transient,196.2,0,0 +City Hotel,99,2016,February,8,19,0,2,1,0.0,0,BB,PRT,Corporate,TA/TO,0,0,0,A,D,1,No Deposit,,67.0,0,Transient-Party,80.5,0,0 +City Hotel,10,2016,November,45,1,0,4,2,0.0,0,SC,COL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,102.0,0,0 +Resort Hotel,1,2015,December,52,22,2,5,1,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,1,No Deposit,8.0,,0,Transient,51.0,0,0 +Resort Hotel,274,2015,October,40,3,2,5,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,96.0,,0,Transient,36.05,0,0 +City Hotel,54,2017,January,2,14,0,1,3,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,138.6,0,0 +City Hotel,133,2017,July,28,9,2,1,2,0.0,0,BB,BEL,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient-Party,89.1,0,2 +City Hotel,396,2017,June,26,25,2,0,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,B,1,No Deposit,6.0,,0,Transient-Party,80.0,0,0 +City Hotel,69,2016,April,17,18,1,3,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient-Party,110.08,0,1 +City Hotel,44,2016,June,27,28,0,2,2,1.0,0,BB,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,134.1,0,0 +City Hotel,218,2017,July,27,5,0,4,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,107.1,0,0 +City Hotel,71,2016,August,35,26,2,2,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,131.4,0,1 +City Hotel,340,2017,April,16,21,2,2,1,1.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,98.1,0,2 +City Hotel,114,2016,July,29,13,0,4,2,0.0,0,SC,IRL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Transient,89.25,0,0 +Resort Hotel,232,2016,November,48,26,2,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,11.0,,0,Transient,30.0,0,0 +City Hotel,311,2015,August,35,24,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient-Party,62.0,0,0 +Resort Hotel,138,2016,July,30,23,2,4,3,0.0,0,Undefined,ESP,Direct,Direct,0,0,0,F,F,0,No Deposit,,,0,Transient,318.0,0,1 +City Hotel,301,2016,November,47,18,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,20.0,,0,Transient,52.0,0,0 +Resort Hotel,110,2016,August,33,10,1,4,1,0.0,0,BB,IRL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,189.0,0,2 +Resort Hotel,0,2017,February,6,8,0,1,2,0.0,0,BB,,Direct,Direct,0,0,0,F,F,0,No Deposit,,,0,Transient,73.0,0,0 +City Hotel,11,2016,November,45,5,0,1,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,130.0,0,1 +Resort Hotel,0,2017,May,20,15,1,0,1,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,79.0,1,0 +City Hotel,2,2016,November,48,23,0,1,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,103.0,0,1 +City Hotel,23,2017,January,2,8,2,3,2,1.0,0,HB,ITA,Direct,Direct,0,0,0,E,E,0,No Deposit,14.0,,0,Transient,168.0,1,1 +City Hotel,78,2016,December,50,8,1,3,2,0.0,0,BB,DZA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,88.4,0,1 +City Hotel,44,2016,October,41,2,2,1,2,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Transient,108.0,0,1 +Resort Hotel,0,2017,January,1,3,0,2,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,D,0,No Deposit,,154.0,0,Transient,35.0,0,0 +City Hotel,88,2017,May,20,14,1,0,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,126.0,0,0 +City Hotel,102,2016,October,41,2,2,1,2,0.0,0,SC,ITA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,108.0,0,1 +City Hotel,47,2017,February,8,25,2,1,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,104.4,0,0 +City Hotel,42,2016,January,3,16,0,1,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,80.3,0,1 +Resort Hotel,159,2016,January,5,25,1,3,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,241.0,,0,Transient,38.86,0,1 +City Hotel,160,2017,March,12,25,0,1,3,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,D,G,0,No Deposit,9.0,,0,Transient,135.0,0,1 +City Hotel,89,2016,September,39,20,2,5,2,0.0,0,BB,ROU,Offline TA/TO,TA/TO,0,0,0,A,A,2,No Deposit,138.0,,0,Transient,95.0,0,2 +City Hotel,32,2016,October,42,10,1,3,1,0.0,0,BB,CHN,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,148.28,0,0 +Resort Hotel,21,2016,July,30,17,2,4,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,204.0,0,0 +City Hotel,240,2017,July,30,23,2,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,110.0,0,0 +Resort Hotel,203,2017,August,33,18,4,8,2,1.0,0,BB,AZE,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,15.0,,0,Transient,126.8,0,3 +Resort Hotel,233,2017,June,24,15,0,2,2,0.0,0,BB,CN,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,99.0,0,2 +City Hotel,11,2016,March,11,11,0,2,2,0.0,0,BB,BEL,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,138.0,,0,Transient,74.8,0,0 +City Hotel,323,2016,May,22,26,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,62.0,0,0 +City Hotel,25,2015,October,41,6,0,4,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Contract,112.25,0,1 +Resort Hotel,223,2017,May,18,4,2,5,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,1,No Deposit,177.0,,0,Transient,68.4,0,1 +City Hotel,11,2016,August,36,28,2,2,3,1.0,0,BB,ESP,Direct,Direct,0,0,0,G,G,0,No Deposit,14.0,,0,Transient,246.0,0,0 +City Hotel,128,2016,November,48,26,1,1,2,0.0,0,SC,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,79.2,0,0 +Resort Hotel,23,2016,January,3,14,0,1,2,0.0,0,BB,USA,Direct,Direct,0,0,0,E,E,0,No Deposit,,,0,Transient,48.0,0,0 +Resort Hotel,37,2016,July,30,23,2,5,3,1.0,0,BB,PRT,Online TA,TA/TO,0,0,0,H,H,0,No Deposit,,,0,Transient,210.0,1,0 +City Hotel,24,2017,July,27,6,0,1,2,2.0,0,BB,DEU,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,7.0,,0,Transient,250.96,0,1 +City Hotel,195,2015,September,38,17,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,No Deposit,20.0,,0,Transient,48.0,0,0 +City Hotel,173,2017,April,16,20,0,1,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,1,No Deposit,9.0,,0,Transient,99.0,0,1 +City Hotel,7,2017,May,19,8,1,0,1,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,160.0,0,3 +City Hotel,159,2017,June,24,17,2,5,3,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,157.25,0,0 +Resort Hotel,85,2016,July,28,3,2,3,3,0.0,0,FB,PRT,Offline TA/TO,TA/TO,0,0,0,A,C,1,No Deposit,5.0,,0,Transient-Party,168.48,1,0 +City Hotel,312,2015,August,35,25,0,4,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.0,0,0 +City Hotel,605,2016,October,43,17,1,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,1.0,,0,Transient,60.0,0,0 +City Hotel,12,2017,May,20,16,2,5,1,0.0,0,BB,CHN,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,8.0,,0,Transient-Party,156.75,0,1 +City Hotel,108,2017,March,11,17,1,2,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,97.2,0,2 +City Hotel,321,2015,September,36,3,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,1,1,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.8,0,0 +Resort Hotel,172,2015,September,37,12,2,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,15.0,,0,Transient-Party,36.0,0,0 +City Hotel,275,2017,July,28,11,0,1,2,0.0,0,SC,DEU,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,89.1,0,1 +City Hotel,94,2016,March,10,4,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,1,0,A,A,0,Non Refund,154.0,,28,Transient,80.0,0,0 +City Hotel,288,2017,July,27,3,1,4,2,0.0,0,SC,POL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,89.1,0,1 +City Hotel,325,2015,September,37,7,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Contract,62.0,0,0 +Resort Hotel,326,2017,June,25,23,3,7,2,0.0,0,HB,GBR,Offline TA/TO,TA/TO,0,0,0,A,A,1,No Deposit,40.0,,0,Contract,102.2,0,2 +Resort Hotel,277,2016,June,25,12,2,2,2,0.0,0,Undefined,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,110.0,0,0 +City Hotel,18,2016,February,7,11,0,3,2,1.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,9.0,,0,Transient,111.0,0,1 +City Hotel,50,2017,July,28,10,1,2,2,1.0,0,BB,DNK,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,160.0,0,1 +Resort Hotel,2,2016,October,45,30,2,0,2,0.0,0,BB,PRT,Complementary,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,6.0,1,0 +City Hotel,56,2016,May,23,30,1,1,1,0.0,0,BB,BEL,Groups,TA/TO,0,0,0,A,A,1,No Deposit,,,0,Transient-Party,120.0,0,0 +City Hotel,35,2017,May,19,10,0,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,39.0,,0,Transient-Party,130.0,0,0 +City Hotel,74,2017,July,27,8,2,1,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,120.0,0,1 +City Hotel,88,2017,June,23,5,1,3,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,83.0,,0,Transient,122.4,0,0 +Resort Hotel,46,2016,March,11,11,0,1,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,375.0,,0,Transient-Party,25.0,0,0 +Resort Hotel,172,2016,December,53,26,2,5,2,0.0,0,BB,DEU,Online TA,TA/TO,0,0,0,A,A,2,No Deposit,240.0,,0,Transient,52.46,0,1 +City Hotel,33,2016,July,27,1,0,1,2,1.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,C,0,No Deposit,9.0,,0,Transient,152.1,0,2 +City Hotel,277,2016,November,46,7,1,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,100.0,0,0 +City Hotel,294,2015,August,32,7,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient-Party,62.0,0,0 +City Hotel,86,2016,April,15,6,0,3,2,0.0,0,BB,NLD,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,99.3,0,1 +City Hotel,10,2016,August,34,15,1,0,2,2.0,1,BB,PRT,Direct,Direct,0,0,0,G,G,0,No Deposit,,,0,Transient,276.0,0,2 +Resort Hotel,1,2017,January,2,13,0,1,2,2.0,0,SC,PRT,Online TA,TA/TO,0,0,0,C,I,1,No Deposit,196.0,,0,Transient,0.0,0,0 +City Hotel,68,2016,February,8,17,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,37.0,,0,Transient,75.0,0,0 +City Hotel,135,2017,February,8,25,2,1,2,0.0,0,SC,ITA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,79.2,0,1 +Resort Hotel,120,2015,October,43,24,4,6,2,0.0,0,BB,IRL,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,175.0,,0,Transient,34.56,0,0 +City Hotel,105,2017,May,20,19,0,2,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,160.0,0,0 +City Hotel,55,2016,April,15,6,0,1,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,56.0,,0,Transient-Party,104.0,0,0 +City Hotel,30,2015,September,40,30,0,2,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,3.0,,0,Transient,121.0,0,0 +City Hotel,10,2017,August,32,11,0,1,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,189.0,0,0 +City Hotel,3,2016,October,45,31,1,1,1,0.0,0,BB,SWE,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,140.0,0,1 +Resort Hotel,92,2016,February,9,26,1,2,2,0.0,0,Undefined,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,134.0,,0,Transient,79.0,0,0 +City Hotel,19,2017,July,28,15,2,1,2,0.0,0,SC,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,126.67,0,0 +City Hotel,62,2016,November,46,6,2,2,1,0.0,0,SC,RUS,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,140.63,0,1 +Resort Hotel,0,2016,April,15,4,1,0,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,1,No Deposit,,,0,Transient,56.0,0,1 +City Hotel,100,2015,October,42,16,0,1,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,6.0,,0,Transient,108.0,0,0 +Resort Hotel,234,2016,September,37,9,2,3,2,0.0,0,HB,DEU,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,241.0,,0,Transient,119.34,0,1 +City Hotel,247,2016,October,44,28,1,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,37.0,,0,Transient,110.0,0,0 +Resort Hotel,69,2017,August,33,16,2,4,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,230.0,0,2 +Resort Hotel,192,2017,June,24,11,2,5,2,0.0,0,BB,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,314.0,,0,Transient,88.0,0,0 +City Hotel,35,2017,February,6,9,2,5,2,0.0,0,BB,FRA,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,71.0,0,0 +City Hotel,272,2015,July,29,16,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,1,1,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.8,0,0 +City Hotel,25,2016,April,17,17,2,2,2,1.0,0,BB,IRL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,125.5,0,0 +City Hotel,69,2015,October,44,25,2,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,37.0,,58,Transient-Party,85.67,0,0 +City Hotel,59,2016,November,47,18,2,2,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,28.0,,0,Transient,75.0,0,0 +City Hotel,184,2016,October,41,4,0,3,2,0.0,0,BB,NOR,Online TA,TA/TO,0,0,0,B,B,1,No Deposit,9.0,,0,Transient,101.05,0,0 +Resort Hotel,2,2016,January,4,23,0,1,2,0.0,0,BB,ESP,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,314.0,,0,Transient-Party,29.0,0,1 +Resort Hotel,13,2017,June,23,7,0,2,1,0.0,0,BB,PRT,Corporate,Corporate,1,0,2,A,A,0,No Deposit,,154.0,0,Transient,75.0,0,0 +City Hotel,26,2016,January,5,30,1,1,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Transient,91.0,0,1 +City Hotel,4,2017,May,22,29,1,1,1,0.0,0,BB,ESP,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,28.0,,0,Transient,94.0,0,0 +City Hotel,64,2016,March,12,19,1,1,2,2.0,0,BB,PRT,Direct,Direct,0,0,0,E,F,0,No Deposit,,,0,Transient,159.6,0,2 +Resort Hotel,221,2017,April,18,30,2,5,2,0.0,0,HB,PRT,Groups,TA/TO,0,0,0,D,D,0,No Deposit,,,0,Transient-Party,82.0,0,0 +City Hotel,7,2017,February,9,26,2,3,2,0.0,0,SC,NLD,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,74.8,0,0 +City Hotel,328,2015,September,37,10,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Contract,62.0,0,0 +Resort Hotel,364,2016,October,42,12,0,4,1,0.0,0,BB,GBR,Groups,Corporate,0,0,0,A,A,1,No Deposit,,223.0,0,Transient-Party,60.0,0,0 +City Hotel,96,2017,June,25,19,1,2,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,126.0,0,2 +City Hotel,0,2017,May,20,18,0,1,2,0.0,0,SC,PRT,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,144.0,0,1 +City Hotel,56,2015,September,38,17,0,2,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,20.0,,0,Transient-Party,82.0,0,0 +City Hotel,162,2017,June,22,3,2,1,2,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,27.0,,0,Transient,89.1,0,0 +Resort Hotel,184,2017,August,34,26,2,5,2,0.0,0,FB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,8.0,,0,Transient,173.0,0,0 +City Hotel,321,2015,September,36,3,0,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Contract,62.0,0,0 +City Hotel,448,2017,June,24,17,2,1,1,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,229.0,,0,Transient-Party,0.0,0,0 +Resort Hotel,0,2016,October,41,5,0,1,3,0.0,0,BB,ESP,Direct,Direct,1,0,0,D,D,0,No Deposit,,,0,Transient,141.0,1,0 +City Hotel,5,2015,August,33,15,1,1,2,2.0,0,BB,PRT,Direct,Direct,0,0,0,F,F,0,No Deposit,,,0,Transient,122.0,0,1 +City Hotel,15,2015,October,43,20,0,3,1,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,E,0,No Deposit,14.0,,0,Transient,109.0,0,0 +Resort Hotel,27,2017,February,9,28,0,1,2,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient-Party,56.0,0,2 +City Hotel,155,2017,August,35,31,0,3,2,0.0,0,SC,SWE,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,135.0,0,1 +Resort Hotel,171,2016,September,36,1,0,3,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,132.6,1,0 +Resort Hotel,16,2016,July,30,23,2,6,3,0.0,0,HB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,215.0,0,2 +Resort Hotel,79,2016,October,44,26,2,6,2,0.0,0,BB,IRL,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,58.05,0,3 +City Hotel,68,2017,April,16,18,0,4,2,0.0,0,BB,ITA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,126.0,0,0 +Resort Hotel,112,2017,August,35,28,1,5,3,0.0,0,HB,PRT,Direct,Direct,0,0,0,D,D,0,No Deposit,250.0,,0,Transient,250.0,0,2 +City Hotel,3,2016,June,27,27,1,0,1,0.0,0,SC,ESP,Offline TA/TO,GDS,0,0,0,A,A,0,No Deposit,195.0,,0,Transient,124.0,0,0 +City Hotel,74,2017,July,30,23,4,5,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,145.56,0,1 +City Hotel,109,2017,April,17,26,2,5,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,120.86,0,0 +Resort Hotel,4,2017,January,5,30,1,0,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,D,0,No Deposit,,34.0,0,Transient,38.5,1,0 +City Hotel,6,2016,August,33,7,2,2,2,1.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,180.0,0,1 +City Hotel,1,2016,October,41,7,0,2,1,0.0,0,SC,SWE,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,7.0,,0,Transient,133.98,0,1 +City Hotel,98,2017,April,15,14,1,2,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,138.0,,0,Transient,90.1,0,0 +City Hotel,137,2016,June,27,26,2,3,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,28.0,,0,Transient,112.0,0,0 +City Hotel,30,2016,November,49,30,0,2,3,0.0,0,BB,ITA,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,22.0,,0,Transient,98.1,0,0 +City Hotel,30,2016,June,23,2,0,2,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,235.0,,0,Transient,140.0,0,0 +City Hotel,180,2016,October,40,1,2,1,2,0.0,0,BB,IRL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,108.9,0,0 +Resort Hotel,3,2016,February,6,1,1,1,1,0.0,0,BB,PRT,Corporate,Corporate,0,0,0,A,D,0,No Deposit,,43.0,0,Transient,30.0,0,0 +City Hotel,19,2015,July,30,25,2,2,2,0.0,0,BB,PRT,Groups,TA/TO,0,0,0,A,A,0,No Deposit,1.0,,0,Transient-Party,62.0,0,0 +City Hotel,71,2016,June,25,14,0,3,1,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,236.0,,0,Transient,120.0,0,0 +Resort Hotel,1,2016,August,34,16,0,1,2,0.0,0,BB,ISR,Online TA,TA/TO,0,0,0,A,C,0,No Deposit,240.0,,0,Transient,209.0,0,1 +City Hotel,365,2015,October,42,17,1,1,2,0.0,0,BB,PRT,Groups,TA/TO,0,1,0,A,A,0,Non Refund,1.0,,0,Transient,62.8,0,0 +Resort Hotel,90,2016,December,52,23,2,5,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,240.0,,0,Transient,37.8,0,0 +City Hotel,67,2015,October,43,21,0,1,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,14.0,,0,Transient,89.25,0,0 +Resort Hotel,61,2016,October,41,3,3,8,2,0.0,0,BB,IRL,Direct,Direct,1,0,1,D,D,0,No Deposit,,,0,Transient,10.0,0,0 +Resort Hotel,160,2017,March,11,18,2,5,2,0.0,0,BB,GBR,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,240.0,,0,Transient,55.8,0,3 +Resort Hotel,36,2015,August,32,4,0,3,1,0.0,0,BB,PRT,Direct,Direct,0,0,0,F,F,0,No Deposit,250.0,,0,Transient,221.0,0,0 +Resort Hotel,49,2015,December,52,23,0,0,2,0.0,0,HB,PRT,Online TA,TA/TO,0,0,0,F,F,1,No Deposit,242.0,,0,Transient,0.0,0,0 +Resort Hotel,1,2016,April,15,5,0,2,1,0.0,0,BB,UKR,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,240.0,,0,Transient,75.0,0,1 +City Hotel,5,2017,May,22,29,1,0,1,0.0,0,BB,PRT,Complementary,Direct,1,4,12,D,A,0,No Deposit,,45.0,0,Transient,0.0,0,3 +City Hotel,16,2017,March,12,23,0,2,1,0.0,0,BB,FIN,Corporate,Corporate,0,0,0,D,D,0,No Deposit,,,0,Transient-Party,108.0,0,0 +City Hotel,209,2017,August,34,22,0,3,2,0.0,0,SC,ESP,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,108.0,1,1 +City Hotel,1,2016,July,31,30,0,1,2,0.0,0,BB,PRT,Direct,Direct,0,0,0,E,E,1,No Deposit,,,0,Transient,177.0,1,0 +City Hotel,36,2016,March,11,8,2,5,2,0.0,0,SC,GBR,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,71.1,0,0 +City Hotel,29,2016,October,42,15,1,1,2,0.0,0,BB,FRA,Online TA,TA/TO,0,0,0,D,D,0,No Deposit,9.0,,0,Transient,154.5,0,1 +City Hotel,56,2017,March,12,21,0,3,2,0.0,0,BB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,Non Refund,86.0,,0,Transient,85.0,0,0 +Resort Hotel,0,2017,January,3,18,0,1,1,0.0,0,BB,PRT,Direct,Direct,1,0,0,A,A,1,No Deposit,,,0,Transient,57.0,1,0 +Resort Hotel,252,2017,July,29,17,1,5,2,0.0,0,BB,NOR,Online TA,TA/TO,0,0,0,E,E,0,No Deposit,240.0,,0,Transient,192.0,0,0 +Resort Hotel,27,2015,December,52,24,0,2,2,0.0,0,BB,BRA,Offline TA/TO,TA/TO,0,0,0,A,E,0,No Deposit,6.0,,0,Transient,50.0,0,0 +Resort Hotel,55,2017,July,29,17,3,8,2,0.0,0,BB,RUS,Direct,Direct,0,0,0,F,F,0,No Deposit,250.0,,0,Transient,260.0,0,0 +City Hotel,138,2016,August,34,16,0,3,1,0.0,0,BB,PRT,Direct,Direct,0,0,0,D,D,0,No Deposit,,,0,Transient,121.2,0,1 +City Hotel,30,2016,September,39,24,1,1,2,0.0,0,BB,ZAF,Online TA,TA/TO,0,0,0,D,A,0,No Deposit,9.0,,0,Transient,143.55,0,0 +City Hotel,309,2016,May,20,13,1,2,2,0.0,0,HB,PRT,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,3.0,,215,Transient-Party,101.0,0,0 +City Hotel,58,2016,November,47,16,0,3,2,0.0,0,BB,FRA,Offline TA/TO,TA/TO,0,0,0,A,D,0,No Deposit,28.0,,0,Transient,75.0,0,0 +Resort Hotel,140,2016,February,7,12,0,2,2,0.0,0,HB,PRT,Groups,TA/TO,0,0,0,A,A,0,Non Refund,,,0,Transient,50.0,0,0 diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_008/history.csv b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_008/history.csv index 17a64c1052e08eb39ded0ec4000f8d444bddf0a3..5445fd4b3a7160be3a926a389a3de7c17a695da6 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_008/history.csv +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_008/history.csv @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:3c88b23635383e01d6dbf6abe4ca1fe34dc8ead87d91c5f641793d36f47019b0 -size 16717311 +oid sha256:38fa82316e21619416bb6e5b7e3a6816838f5744880e9705721218cc56058387 +size 15181111 diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_008/info.json b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_008/info.json index 4816c05993309461836de8622438e342f2e503ff..20956c93d07801fe3772275e08832605a78593ed 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_008/info.json +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_008/info.json @@ -1,398 +1,391 @@ -{ - "name": "Hotel Booking Demand", - "source": "https://www.kaggle.com/datasets/jessemostipak/hotel-booking-demand/data", - "data_intro": "This dataset contains hotel booking data for a city hotel and a resort hotel, providing details on booking dates, stay duration, guest count, cancellation trends, and pricing. It is useful for demand forecasting, revenue management, and customer behavior analysis.", - "is_splited": false, - "overall_size": 119390, - "train_size": 0, - "test_size": 0, - "c_classes": 12, - "n_classes": 20, - "cat_feature_intro": { - "hotel": "Type of hotel (City Hotel or Resort Hotel).", - "arrival_date_month": "Month of arrival for the booking.", - "meal": "Type of meal plan booked (e.g., Breakfast included, Half board, Full board).", - "country": "Country of origin of the guest.", - "market_segment": "Booking distribution channel (e.g., direct, corporate, travel agent).", - "distribution_channel": "Channel through which the booking was made (e.g., online travel agents, direct).", - "reserved_room_type": "Type of room reserved by the guest before any changes.", - "assigned_room_type": "Final room type assigned to the guest.", - "deposit_type": "Indicates if a deposit was made and its type (e.g., No Deposit, Refundable, Non-Refundable).", - "customer_type": "Type of customer (e.g., transient, group, contract).", - "reservation_status": "Final status of the booking (e.g., Canceled, Checked-out, No-show).", - "reservation_status_date": "Date when the reservation status was last updated." - }, - "num_feature_intro": { - "is_canceled": "Indicates whether the booking was canceled (1) or not (0).", - "lead_time": "Number of days between booking and arrival.", - "arrival_date_year": "Year of arrival for the booking.", - "arrival_date_week_number": "Week number of the arrival date.", - "arrival_date_day_of_month": "Day of the month of arrival.", - "stays_in_weekend_nights": "Number of weekend nights (Saturday or Sunday) the guest stayed.", - "stays_in_week_nights": "Number of weekday nights (Monday to Friday) the guest stayed.", - "adults": "Number of adults included in the booking.", - "children": "Number of children included in the booking.", - "babies": "Number of babies included in the booking.", - "is_repeated_guest": "Indicates whether the guest is a returning customer (1) or not (0).", - "previous_cancellations": "Number of times the guest has canceled previous bookings.", - "previous_bookings_not_canceled": "Number of previous bookings not canceled by the guest.", - "booking_changes": "Number of changes made to the booking.", - "agent": "ID of the booking agent (if applicable).", - "company": "ID of the company making the booking (if applicable).", - "days_in_waiting_list": "Number of days the booking was on the waiting list before confirmation.", - "adr": "Average daily rate (price per night per room).", - "required_car_parking_spaces": "Number of car parking spaces required by the guest.", - "total_of_special_requests": "Number of special requests made by the guest (e.g., high floor, twin beds)." - }, - "evaluation_metric": null, - "task_type": "classification", - "target": "reservation_status", - "num_feature_value": { - "adr": [ - -6.38, - 5400.0 - ], - "adults": [ - 0.0, - 55.0 - ], - "agent": [ - 1.0, - 535.0 - ], - "arrival_date_day_of_month": [ - 1.0, - 31.0 - ], - "arrival_date_week_number": [ - 1.0, - 53.0 - ], - "arrival_date_year": [ - 2015.0, - 2017.0 - ], - "babies": [ - 0.0, - 10.0 - ], - "booking_changes": [ - 0.0, - 21.0 - ], - "children": [ - 0.0, - 10.0 - ], - "company": [ - 6.0, - 543.0 - ], - "days_in_waiting_list": [ - 0.0, - 391.0 - ], - "is_canceled": [ - 0.0, - 1.0 - ], - "is_repeated_guest": [ - 0.0, - 1.0 - ], - "lead_time": [ - 0.0, - 737.0 - ], - "previous_bookings_not_canceled": [ - 0.0, - 72.0 - ], - "previous_cancellations": [ - 0.0, - 26.0 - ], - "required_car_parking_spaces": [ - 0.0, - 8.0 - ], - "stays_in_week_nights": [ - 0.0, - 50.0 - ], - "stays_in_weekend_nights": [ - 0.0, - 19.0 - ], - "total_of_special_requests": [ - 0.0, - 5.0 - ] - }, - "cat_feature_value": { - "arrival_date_month": [ - "April", - "August", - "December", - "February", - "January", - "July", - "June", - "March", - "May", - "November", - "October", - "September" - ], - "assigned_room_type": [ - "A", - "B", - "C", - "D", - "E", - "F", - "G", - "H", - "I", - "K", - "L", - "P" - ], - "country": [ - "ABW", - "AGO", - "AIA", - "ALB", - "AND", - "ARE", - "ARG", - "ARM", - "ASM", - "ATA", - "ATF", - "AUS", - "AUT", - "AZE", - "BDI", - "BEL", - "BEN", - "BFA", - "BGD", - "BGR", - "BHR", - "BHS", - "BIH", - "BLR", - "BOL", - "BRA", - "BRB", - "BWA", - "CAF", - "CHE", - "CHL", - "CHN", - "CIV", - "CMR", - "CN", - "COL", - "COM", - "CPV", - "CRI", - "CUB", - "CYM", - "CYP", - "CZE", - "DEU", - "DJI", - "DMA", - "DNK", - "DOM", - "DZA", - "ECU", - "EGY", - "ESP", - "EST", - "ETH", - "FIN", - "FJI", - "FRA", - "FRO", - "GAB", - "GBR", - "GEO", - "GGY", - "GHA", - "GIB", - "GLP", - "GNB", - "GRC", - "GTM", - "GUY", - "HKG", - "HND", - "HRV", - "HUN", - "IDN", - "IMN", - "IND", - "IRL", - "IRN", - "IRQ", - "ISL", - "ISR", - "ITA", - "JAM", - "JEY", - "JOR", - "JPN", - "KAZ", - "KEN", - "KHM", - "KIR", - "KNA", - "KOR", - "KWT", - "LAO", - "LBN", - "LBY", - "LCA", - "LIE", - "LKA", - "LTU", - "LUX", - "LVA", - "MAC", - "MAR", - "MCO", - "MDG", - "MDV", - "MEX", - "MKD", - "MLI", - "MLT", - "MMR", - "MNE", - "MOZ", - "MRT", - "MUS", - "MWI", - "MYS", - "MYT", - "NAM", - "NCL", - "NGA", - "NIC", - "NLD", - "NOR", - "NPL", - "NZL", - "OMN", - "PAK", - "PAN", - "PER", - "PHL", - "PLW", - "POL", - "PRI", - "PRT", - "PRY", - "PYF", - "QAT", - "ROU", - "RUS", - "RWA", - "SAU", - "SDN", - "SEN", - "SGP", - "SLE", - "SLV", - "SMR", - "SRB", - "STP", - "SUR", - "SVK", - "SVN", - "SWE", - "SYC", - "SYR", - "TGO", - "THA", - "TJK", - "TMP", - "TUN", - "TUR", - "TWN", - "TZA", - "UGA", - "UKR", - "UMI", - "URY", - "USA", - "UZB", - "VEN", - "VGB", - "VNM", - "ZAF", - "ZMB", - "ZWE" - ], - "customer_type": [ - "Contract", - "Group", - "Transient", - "Transient-Party" - ], - "deposit_type": [ - "No Deposit", - "Non Refund", - "Refundable" - ], - "distribution_channel": [ - "Corporate", - "Direct", - "GDS", - "TA/TO", - "Undefined" - ], - "hotel": [ - "City Hotel", - "Resort Hotel" - ], - "market_segment": [ - "Aviation", - "Complementary", - "Corporate", - "Direct", - "Groups", - "Offline TA/TO", - "Online TA", - "Undefined" - ], - "meal": [ - "BB", - "FB", - "HB", - "SC", - "Undefined" - ], - "reservation_status": [ - "Canceled", - "Check-Out", - "No-Show" - ], - "reservation_status_date": "string", - "reserved_room_type": [ - "A", - "B", - "C", - "D", - "E", - "F", - "G", - "H", - "L", - "P" - ] - } -} \ No newline at end of file +{ + "name": "Hotel Booking Demand", + "source": "https://www.kaggle.com/datasets/jessemostipak/hotel-booking-demand/data", + "data_intro": "This dataset contains hotel booking data for a city hotel and a resort hotel, providing details on booking dates, stay duration, guest count, cancellation trends, and pricing. It is useful for demand forecasting, revenue management, and customer behavior analysis.", + "is_splited": false, + "overall_size": 119390, + "train_size": 0, + "test_size": 0, + "c_classes": 11, + "n_classes": 19, + "cat_feature_intro": { + "hotel": "Type of hotel (City Hotel or Resort Hotel).", + "arrival_date_month": "Month of arrival for the booking.", + "meal": "Type of meal plan booked (e.g., Breakfast included, Half board, Full board).", + "country": "Country of origin of the guest.", + "market_segment": "Booking distribution channel (e.g., direct, corporate, travel agent).", + "distribution_channel": "Channel through which the booking was made (e.g., online travel agents, direct).", + "reserved_room_type": "Type of room reserved by the guest before any changes.", + "assigned_room_type": "Final room type assigned to the guest.", + "deposit_type": "Indicates if a deposit was made and its type (e.g., No Deposit, Refundable, Non-Refundable).", + "customer_type": "Type of customer (e.g., transient, group, contract).", + "reservation_status": "Final status of the booking (e.g., Canceled, Checked-out, No-show)." + }, + "num_feature_intro": { + "lead_time": "Number of days between booking and arrival.", + "arrival_date_year": "Year of arrival for the booking.", + "arrival_date_week_number": "Week number of the arrival date.", + "arrival_date_day_of_month": "Day of the month of arrival.", + "stays_in_weekend_nights": "Number of weekend nights (Saturday or Sunday) the guest stayed.", + "stays_in_week_nights": "Number of weekday nights (Monday to Friday) the guest stayed.", + "adults": "Number of adults included in the booking.", + "children": "Number of children included in the booking.", + "babies": "Number of babies included in the booking.", + "is_repeated_guest": "Indicates whether the guest is a returning customer (1) or not (0).", + "previous_cancellations": "Number of times the guest has canceled previous bookings.", + "previous_bookings_not_canceled": "Number of previous bookings not canceled by the guest.", + "booking_changes": "Number of changes made to the booking.", + "agent": "ID of the booking agent (if applicable).", + "company": "ID of the company making the booking (if applicable).", + "days_in_waiting_list": "Number of days the booking was on the waiting list before confirmation.", + "adr": "Average daily rate (price per night per room).", + "required_car_parking_spaces": "Number of car parking spaces required by the guest.", + "total_of_special_requests": "Number of special requests made by the guest (e.g., high floor, twin beds)." + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "reservation_status", + "num_feature_value": { + "adr": [ + -6.38, + 5400.0 + ], + "adults": [ + 0.0, + 55.0 + ], + "agent": [ + 1.0, + 535.0 + ], + "arrival_date_day_of_month": [ + 1.0, + 31.0 + ], + "arrival_date_week_number": [ + 1.0, + 53.0 + ], + "arrival_date_year": [ + 2015.0, + 2017.0 + ], + "babies": [ + 0.0, + 10.0 + ], + "booking_changes": [ + 0.0, + 21.0 + ], + "children": [ + 0.0, + 10.0 + ], + "company": [ + 6.0, + 543.0 + ], + "days_in_waiting_list": [ + 0.0, + 391.0 + ], + "is_repeated_guest": [ + 0.0, + 1.0 + ], + "lead_time": [ + 0.0, + 737.0 + ], + "previous_bookings_not_canceled": [ + 0.0, + 72.0 + ], + "previous_cancellations": [ + 0.0, + 26.0 + ], + "required_car_parking_spaces": [ + 0.0, + 8.0 + ], + "stays_in_week_nights": [ + 0.0, + 50.0 + ], + "stays_in_weekend_nights": [ + 0.0, + 19.0 + ], + "total_of_special_requests": [ + 0.0, + 5.0 + ] + }, + "cat_feature_value": { + "arrival_date_month": [ + "April", + "August", + "December", + "February", + "January", + "July", + "June", + "March", + "May", + "November", + "October", + "September" + ], + "assigned_room_type": [ + "A", + "B", + "C", + "D", + "E", + "F", + "G", + "H", + "I", + "K", + "L", + "P" + ], + "country": [ + "ABW", + "AGO", + "AIA", + "ALB", + "AND", + "ARE", + "ARG", + "ARM", + "ASM", + "ATA", + "ATF", + "AUS", + "AUT", + "AZE", + "BDI", + "BEL", + "BEN", + "BFA", + "BGD", + "BGR", + "BHR", + "BHS", + "BIH", + "BLR", + "BOL", + "BRA", + "BRB", + "BWA", + "CAF", + "CHE", + "CHL", + "CHN", + "CIV", + "CMR", + "CN", + "COL", + "COM", + "CPV", + "CRI", + "CUB", + "CYM", + "CYP", + "CZE", + "DEU", + "DJI", + "DMA", + "DNK", + "DOM", + "DZA", + "ECU", + "EGY", + "ESP", + "EST", + "ETH", + "FIN", + "FJI", + "FRA", + "FRO", + "GAB", + "GBR", + "GEO", + "GGY", + "GHA", + "GIB", + "GLP", + "GNB", + "GRC", + "GTM", + "GUY", + "HKG", + "HND", + "HRV", + "HUN", + "IDN", + "IMN", + "IND", + "IRL", + "IRN", + "IRQ", + "ISL", + "ISR", + "ITA", + "JAM", + "JEY", + "JOR", + "JPN", + "KAZ", + "KEN", + "KHM", + "KIR", + "KNA", + "KOR", + "KWT", + "LAO", + "LBN", + "LBY", + "LCA", + "LIE", + "LKA", + "LTU", + "LUX", + "LVA", + "MAC", + "MAR", + "MCO", + "MDG", + "MDV", + "MEX", + "MKD", + "MLI", + "MLT", + "MMR", + "MNE", + "MOZ", + "MRT", + "MUS", + "MWI", + "MYS", + "MYT", + "NAM", + "NCL", + "NGA", + "NIC", + "NLD", + "NOR", + "NPL", + "NZL", + "OMN", + "PAK", + "PAN", + "PER", + "PHL", + "PLW", + "POL", + "PRI", + "PRT", + "PRY", + "PYF", + "QAT", + "ROU", + "RUS", + "RWA", + "SAU", + "SDN", + "SEN", + "SGP", + "SLE", + "SLV", + "SMR", + "SRB", + "STP", + "SUR", + "SVK", + "SVN", + "SWE", + "SYC", + "SYR", + "TGO", + "THA", + "TJK", + "TMP", + "TUN", + "TUR", + "TWN", + "TZA", + "UGA", + "UKR", + "UMI", + "URY", + "USA", + "UZB", + "VEN", + "VGB", + "VNM", + "ZAF", + "ZMB", + "ZWE" + ], + "customer_type": [ + "Contract", + "Group", + "Transient", + "Transient-Party" + ], + "deposit_type": [ + "No Deposit", + "Non Refund", + "Refundable" + ], + "distribution_channel": [ + "Corporate", + "Direct", + "GDS", + "TA/TO", + "Undefined" + ], + "hotel": [ + "City Hotel", + "Resort Hotel" + ], + "market_segment": [ + "Aviation", + "Complementary", + "Corporate", + "Direct", + "Groups", + "Offline TA/TO", + "Online TA", + "Undefined" + ], + "meal": [ + "BB", + "FB", + "HB", + "SC", + "Undefined" + ], + "reservation_status": [ + "Canceled", + "Check-Out", + "No-Show" + ], + "reserved_room_type": [ + "A", + "B", + "C", + "D", + "E", + "F", + "G", + "H", + "L", + "P" + ] + } +} diff --git a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_008/test_current.csv b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_008/test_current.csv index d62964edfe0599a30703507372e4b39d3142ff8b..b0fa134ebe516c56d0ce6f2cf898063760d379c1 100644 --- a/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_008/test_current.csv +++ b/ranking_and_filtering/daily/classification/Hotel_booking_demand_B4/Hotel_booking_demand_B4_008/test_current.csv @@ -1,16 +1,16 @@ -hotel,is_canceled,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests,reservation_status,reservation_status_date -Resort Hotel,0,122,2016,August,35,23,1,5,3,1.0,0,BB,ESP,Online TA,TA/TO,0,0,0,H,H,2,No Deposit,240.0,,0,Transient,265.83,0,0,Check-Out,2016-08-29 -Resort Hotel,0,10,2016,December,53,31,0,1,2,2.0,0,BB,USA,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,240.0,,0,Transient,180.0,0,0,Check-Out,2017-01-01 -City Hotel,0,340,2017,April,16,21,2,2,1,1.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,98.1,0,2,Check-Out,2017-04-25 -City Hotel,0,23,2017,January,2,8,2,3,2,1.0,0,HB,ITA,Direct,Direct,0,0,0,E,E,0,No Deposit,14.0,,0,Transient,168.0,1,1,Check-Out,2017-01-13 -City Hotel,0,11,2016,August,36,28,2,2,3,1.0,0,BB,ESP,Direct,Direct,0,0,0,G,G,0,No Deposit,14.0,,0,Transient,246.0,0,0,Check-Out,2016-09-01 -Resort Hotel,0,37,2016,July,30,23,2,5,3,1.0,0,BB,PRT,Online TA,TA/TO,0,0,0,H,H,0,No Deposit,,,0,Transient,210.0,1,0,Check-Out,2016-07-30 -City Hotel,0,24,2017,July,27,6,0,1,2,2.0,0,BB,DEU,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,7.0,,0,Transient,250.96,0,1,Check-Out,2017-07-07 -City Hotel,0,18,2016,February,7,11,0,3,2,1.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,9.0,,0,Transient,111.0,0,1,Check-Out,2016-02-14 -City Hotel,0,33,2016,July,27,1,0,1,2,1.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,C,0,No Deposit,9.0,,0,Transient,152.1,0,2,Check-Out,2016-07-02 -City Hotel,0,10,2016,August,34,15,1,0,2,2.0,1,BB,PRT,Direct,Direct,0,0,0,G,G,0,No Deposit,,,0,Transient,276.0,0,2,Check-Out,2016-08-16 -Resort Hotel,0,1,2017,January,2,13,0,1,2,2.0,0,SC,PRT,Online TA,TA/TO,0,0,0,C,I,1,No Deposit,196.0,,0,Transient,0.0,0,0,Check-Out,2017-01-14 -City Hotel,0,25,2016,April,17,17,2,2,2,1.0,0,BB,IRL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,125.5,0,0,Check-Out,2016-04-21 -City Hotel,0,64,2016,March,12,19,1,1,2,2.0,0,BB,PRT,Direct,Direct,0,0,0,E,F,0,No Deposit,,,0,Transient,159.6,0,2,Check-Out,2016-03-21 -City Hotel,0,5,2015,August,33,15,1,1,2,2.0,0,BB,PRT,Direct,Direct,0,0,0,F,F,0,No Deposit,,,0,Transient,122.0,0,1,Check-Out,2015-08-17 -City Hotel,0,6,2016,August,33,7,2,2,2,1.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,180.0,0,1,Check-Out,2016-08-11 +hotel,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests,reservation_status +Resort Hotel,122,2016,August,35,23,1,5,3,1.0,0,BB,ESP,Online TA,TA/TO,0,0,0,H,H,2,No Deposit,240.0,,0,Transient,265.83,0,0,Check-Out +Resort Hotel,10,2016,December,53,31,0,1,2,2.0,0,BB,USA,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,240.0,,0,Transient,180.0,0,0,Check-Out +City Hotel,340,2017,April,16,21,2,2,1,1.0,0,BB,PRT,Direct,Direct,0,0,0,A,A,0,No Deposit,,,0,Transient,98.1,0,2,Check-Out +City Hotel,23,2017,January,2,8,2,3,2,1.0,0,HB,ITA,Direct,Direct,0,0,0,E,E,0,No Deposit,14.0,,0,Transient,168.0,1,1,Check-Out +City Hotel,11,2016,August,36,28,2,2,3,1.0,0,BB,ESP,Direct,Direct,0,0,0,G,G,0,No Deposit,14.0,,0,Transient,246.0,0,0,Check-Out +Resort Hotel,37,2016,July,30,23,2,5,3,1.0,0,BB,PRT,Online TA,TA/TO,0,0,0,H,H,0,No Deposit,,,0,Transient,210.0,1,0,Check-Out +City Hotel,24,2017,July,27,6,0,1,2,2.0,0,BB,DEU,Online TA,TA/TO,0,0,0,G,G,0,No Deposit,7.0,,0,Transient,250.96,0,1,Check-Out +City Hotel,18,2016,February,7,11,0,3,2,1.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,D,0,No Deposit,9.0,,0,Transient,111.0,0,1,Check-Out +City Hotel,33,2016,July,27,1,0,1,2,1.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,C,0,No Deposit,9.0,,0,Transient,152.1,0,2,Check-Out +City Hotel,10,2016,August,34,15,1,0,2,2.0,1,BB,PRT,Direct,Direct,0,0,0,G,G,0,No Deposit,,,0,Transient,276.0,0,2,Check-Out +Resort Hotel,1,2017,January,2,13,0,1,2,2.0,0,SC,PRT,Online TA,TA/TO,0,0,0,C,I,1,No Deposit,196.0,,0,Transient,0.0,0,0,Check-Out +City Hotel,25,2016,April,17,17,2,2,2,1.0,0,BB,IRL,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,125.5,0,0,Check-Out +City Hotel,64,2016,March,12,19,1,1,2,2.0,0,BB,PRT,Direct,Direct,0,0,0,E,F,0,No Deposit,,,0,Transient,159.6,0,2,Check-Out +City Hotel,5,2015,August,33,15,1,1,2,2.0,0,BB,PRT,Direct,Direct,0,0,0,F,F,0,No Deposit,,,0,Transient,122.0,0,1,Check-Out +City Hotel,6,2016,August,33,7,2,2,2,1.0,0,BB,FRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,180.0,0,1,Check-Out diff --git a/ranking_and_filtering/daily/classification/Wine_Quality_Dataset_B4/Wine_Quality_Dataset_B4_002/Wine_Quality_Dataset_B4_002_current.json b/ranking_and_filtering/daily/classification/Wine_Quality_Dataset_B4/Wine_Quality_Dataset_B4_002/Wine_Quality_Dataset_B4_002_current.json index 8b066242ce4965b94558d74b502ce2f39aea747b..b8cbb8aa627fd1a98e5ff26f06412e40b9fcb638 100644 --- a/ranking_and_filtering/daily/classification/Wine_Quality_Dataset_B4/Wine_Quality_Dataset_B4_002/Wine_Quality_Dataset_B4_002_current.json +++ b/ranking_and_filtering/daily/classification/Wine_Quality_Dataset_B4/Wine_Quality_Dataset_B4_002/Wine_Quality_Dataset_B4_002_current.json @@ -1,3643 +1,3643 @@ { - "id": "002", - "task_type": "B4", - "subtask_type": "list", - "perspective": "data_holder", - "dataset_name": "Wine Quality Dataset", - "table_path": "output/daily/classification/Wine Quality Dataset_B4/Wine Quality Dataset_B4_002/test.csv", - "query": "Our logistics team needs to know which new wines from the incoming inventory are probable high-scoring candidates for our exclusive client club. Based on the chemical profiles and outcomes in the file I sent you, please identify all entries that are strong contenders for our highest quality tier.", - "meta_info": { - "domain": "daily" + "id": "002", + "task_type": "B4", + "subtask_type": "list", + "perspective": "data_holder", + "dataset_name": "Wine Quality Dataset", + "table_path": "output/daily/classification/Wine Quality Dataset_B4/Wine Quality Dataset_B4_002/test.csv", + "query": "Our logistics team needs to know which new wines from the incoming inventory are probable high-scoring candidates for our exclusive client club. Based on the chemical profiles and outcomes in the file I sent you, please identify all entries that are strong contenders for our highest quality tier (quality score 8).", + "meta_info": { + "domain": "daily" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "fixed acidity": 8.6, + "volatile acidity": 0.315, + "citric acid": 0.4, + "residual sugar": 2.2, + "chlorides": 0.079, + "free sulfur dioxide": 3.0, + "total sulfur dioxide": 6.0, + "density": 0.99512, + "pH": 3.27, + "sulphates": 0.67, + "alcohol": 11.9, + "quality": 6, + "Id": 915 + } + }, + { + "scenario_id": "002", + "features": { + "fixed acidity": 7.9, + "volatile acidity": 0.65, + "citric acid": 0.01, + "residual sugar": 2.5, + "chlorides": 0.078, + "free sulfur dioxide": 17.0, + "total sulfur dioxide": 38.0, + "density": 0.9963, + "pH": 3.34, + "sulphates": 0.74, + "alcohol": 11.7, + "quality": 7, + "Id": 334 + } + }, + { + "scenario_id": "003", + "features": { + "fixed acidity": 7.5, + "volatile acidity": 0.49, + "citric acid": 0.19, + "residual sugar": 1.9, + "chlorides": 0.076, + "free sulfur dioxide": 10.0, + "total sulfur dioxide": 44.0, + "density": 0.9957, + "pH": 3.39, + "sulphates": 0.54, + "alcohol": 9.7, + "quality": 5, + "Id": 218 + } + }, + { + "scenario_id": "004", + "features": { + "fixed acidity": 5.2, + "volatile acidity": 0.645, + "citric acid": 0.0, + "residual sugar": 2.15, + "chlorides": 0.08, + "free sulfur dioxide": 15.0, + "total sulfur dioxide": 28.0, + "density": 0.99444, + "pH": 3.78, + "sulphates": 0.61, + "alcohol": 12.5, + "quality": 6, + "Id": 1300 + } + }, + { + "scenario_id": "005", + "features": { + "fixed acidity": 7.0, + "volatile acidity": 0.805, + "citric acid": 0.0, + "residual sugar": 2.5, + "chlorides": 0.068, + "free sulfur dioxide": 7.0, + "total sulfur dioxide": 20.0, + "density": 0.9969, + "pH": 3.48, + "sulphates": 0.56, + "alcohol": 9.6, + "quality": 5, + "Id": 178 + } + }, + { + "scenario_id": "006", + "features": { + "fixed acidity": 7.2, + "volatile acidity": 0.33, + "citric acid": 0.33, + "residual sugar": 1.7, + "chlorides": 0.061, + "free sulfur dioxide": 3.0, + "total sulfur dioxide": 13.0, + "density": 0.996, + "pH": 3.23, + "sulphates": 1.1, + "alcohol": 10.0, + "quality": 8, + "Id": 1403 + } + }, + { + "scenario_id": "007", + "features": { + "fixed acidity": 8.0, + "volatile acidity": 0.42, + "citric acid": 0.17, + "residual sugar": 2.0, + "chlorides": 0.073, + "free sulfur dioxide": 6.0, + "total sulfur dioxide": 18.0, + "density": 0.9972, + "pH": 3.29, + "sulphates": 0.61, + "alcohol": 9.2, + "quality": 6, + "Id": 172 + } + }, + { + "scenario_id": "009", + "features": { + "fixed acidity": 8.4, + "volatile acidity": 0.29, + "citric acid": 0.4, + "residual sugar": 1.7, + "chlorides": 0.067, + "free sulfur dioxide": 8.0, + "total sulfur dioxide": 20.0, + "density": 0.99603, + "pH": 3.39, + "sulphates": 0.6, + "alcohol": 10.5, + 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acidity": 0.41, - "citric acid": 0.68, - "residual sugar": 1.7, - "chlorides": 0.467, - "free sulfur dioxide": 18.0, - "total sulfur dioxide": 69.0, - "density": 0.9973, - "pH": 3.08, - "sulphates": 1.31, - "alcohol": 9.3, - "quality": 5, - "Id": 106 - } - }, - { - "scenario_id": "228", - "features": { - "fixed acidity": 9.9, - "volatile acidity": 0.32, - "citric acid": 0.56, - "residual sugar": 2.0, - "chlorides": 0.073, - "free sulfur dioxide": 3.0, - "total sulfur dioxide": 8.0, - "density": 0.99534, - "pH": 3.15, - "sulphates": 0.73, - "alcohol": 11.4, - "quality": 6, - "Id": 1076 - } - } - ], - "target_column": "quality", - "task_sub_type": "classification", - "ranking_ground_truth": { - "top_k_ids": [ - "006", - "043", - "070", - "074", - "075", - "096", - "106", - "113", - "118", - "126", - "144", - "160", - "167", - "197", - "209" - ] - }, - "active_filters": [], - "target_class_value": "8" - }, - "response": "", - "evaluation_score": {} -} \ No newline at end of file + "active_filters": [], + "target_class_value": "8" + }, + "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/daily/regression/100000_UK_Used_Car_Data_set_B4/100000_UK_Used_Car_Data_set_B4_008/current.csv b/ranking_and_filtering/daily/regression/100000_UK_Used_Car_Data_set_B4/100000_UK_Used_Car_Data_set_B4_008/current.csv index 13579311f0c601c9c5c0bab79a0fb122ac0f0578..968366b1afb128644b55410f9bfb66829aa48122 100644 --- a/ranking_and_filtering/daily/regression/100000_UK_Used_Car_Data_set_B4/100000_UK_Used_Car_Data_set_B4_008/current.csv +++ b/ranking_and_filtering/daily/regression/100000_UK_Used_Car_Data_set_B4/100000_UK_Used_Car_Data_set_B4_008/current.csv @@ -50,7 +50,6 @@ Ford, Ka+,2017,8300,Manual,Petrol,145,57.7,1.2 Vauxhall, Corsa,2019,9870,Manual,Petrol,145,43.5,1.4 BMW, 1 Series,2019,18480,Manual,Diesel,145,70.6,1.5 Volkswagen, Tiguan,2011,7490,Manual,Petrol,205,42.2,1.4 -Ford, Fiesta,2018,10000,Manual,Petrol,150,65.7,1.0 Vauxhall, Insignia,2014,5488,Manual,Petrol,200,40.4,1.8 Audi, RS3,2019,43785,Semi-Auto,Petrol,145,30.1,2.5 Audi, A1,2016,11533,Manual,Petrol,30,55.4,1.4 diff --git a/ranking_and_filtering/daily/regression/Chocolate_Bar_Ratings_B4/Chocolate_Bar_Ratings_B4_001/history.csv b/ranking_and_filtering/daily/regression/Chocolate_Bar_Ratings_B4/Chocolate_Bar_Ratings_B4_001/history.csv index ce539ffdf8d32a6d87c8bf773517d86bcc033e2d..54f84c7cfa23bcca85f6d08af3049d3eec07df50 100644 --- a/ranking_and_filtering/daily/regression/Chocolate_Bar_Ratings_B4/Chocolate_Bar_Ratings_B4_001/history.csv +++ b/ranking_and_filtering/daily/regression/Chocolate_Bar_Ratings_B4/Chocolate_Bar_Ratings_B4_001/history.csv @@ -1190,7 +1190,7 @@ hexx,Venezuela,1546,2015,70%,U.S.A.,3.0, ,Venezuela Cravve,"Namau Village, N. Taileva P., batch a2812",975,2012,67%,Australia,3.25,Trinitario,Fiji Chaleur B,Uganda,1426,2014,70%,Canada,2.75,Forastero,Uganda Arete,Guatemala,1602,2015,70%,U.S.A.,3.25, ,Guatemala -Na�ve,Barinas,1399,2014,70%,Lithuania,3.0, ,Venezuela +Naïve,Barinas,1399,2014,70%,Lithuania,3.0, ,Venezuela Cacao Hunters,Tumaco,1430,2014,70%,Colombia,3.5, ,Colombia Zokoko,Alto Beni,697,2011,68%,Australia,3.5, ,Bolivia Rain Republic,Suchitepequez E.,470,2010,70%,Guatemala,2.75, ,Guatemala @@ -1371,7 +1371,7 @@ Pralus,Claudio Corallo w/ nibs,162,2007,75%,France,4.0,Forastero,Sao Tome Hummingbird,"Ocumare, Cumboto",1097,2013,70%,Canada,3.25,"Trinitario, Criollo",Venezuela Somerville,Nicaragua,1367,2014,70%,U.S.A.,3.25, ,Nicaragua Tan Ban Skrati,"Paramaribo, batch 20160043-01",1868,2016,70%,Suriname,3.25,Trinitario,Suriname -Na�ve,Chuao,1399,2014,75%,Lithuania,2.75,Trinitario,Venezuela +Naïve,Chuao,1399,2014,75%,Lithuania,2.75,Trinitario,Venezuela Habitual,Sharkey,1201,2014,66%,Canada,3.25, ,Hawaii Indaphoria,"Conacado, Manifesto",907,2012,70%,U.S.A.,2.75, ,Dominican Republic Grand Place,"Ben Tre, Dong Nai",741,2011,72%,Vietnam,3.0,Trinitario,Vietnam @@ -1486,7 +1486,7 @@ A. Morin,Chuao,1015,2013,70%,France,4.0,Trinitario,Venezuela Xocolla,"Sambirano, batch 170102",1948,2017,70%,U.S.A.,2.75, ,Madagascar Fresco,"Maranon, #230, DR, LC",1387,2014,70%,U.S.A.,3.5,Forastero (Nacional),Peru Cacao Atlanta,Tumbes,1215,2014,75%,U.S.A.,2.5,Criollo,Peru -Na�ve,Nicaragua,1379,2014,70%,Lithuania,3.25,Criollo,Nicaragua +Naïve,Nicaragua,1379,2014,70%,Lithuania,3.25,Criollo,Nicaragua Hoja Verde (Tulicorp),Arriba,414,2009,58%,Ecuador,3.25,Forastero (Arriba) ASS,Ecuador "Chocolate Tree, The",Maranon,1582,2015,69%,Scotland,3.75,Forastero (Nacional),Peru Coppeneur,Chuao 100hr,451,2009,70%,Germany,3.5,"Criollo, Trinitario",Venezuela diff --git a/ranking_and_filtering/daily/regression/Chocolate_Bar_Ratings_B4/Chocolate_Bar_Ratings_B4_002/history.csv b/ranking_and_filtering/daily/regression/Chocolate_Bar_Ratings_B4/Chocolate_Bar_Ratings_B4_002/history.csv index 0b8387321b592a570e755f735a09f224067de9f8..8570df939cc09509f90f61b9a31ba945ab396e59 100644 --- a/ranking_and_filtering/daily/regression/Chocolate_Bar_Ratings_B4/Chocolate_Bar_Ratings_B4_002/history.csv +++ b/ranking_and_filtering/daily/regression/Chocolate_Bar_Ratings_B4/Chocolate_Bar_Ratings_B4_002/history.csv @@ -762,7 +762,7 @@ Harper Macaw,"Tome Acu E., Amazon Rainforest",1700,2015,77%,U.S.A.,3.5, ,Brazil Pump Street Bakery,"Crayfish Bay Estate, 2014",1502,2015,70%,U.K.,3.5,Trinitario,Grenada Hotel Chocolat,"Marcial, single Cote, 2012",1030,2013,70%,U.K.,4.0,Trinitario,St. Lucia Cacao Sampaka,"Xoconusco, Chiapas",346,2009,70%,Spain,4.0,Criollo,Mexico -Na�ve,Chuao,1399,2014,75%,Lithuania,2.75,Trinitario,Venezuela +Naïve,Chuao,1399,2014,75%,Lithuania,2.75,Trinitario,Venezuela Castronovo,Elvesia P.,1407,2014,70%,U.S.A.,2.75,Trinitario,Dominican Republic Valrhona,"Sambirano, Ampamakia 2005, Millot P.",75,2006,64%,France,3.5,Trinitario,Madagascar Bonnat,"Xoconusco, cacao Real",395,2009,75%,France,3.0,Criollo,Mexico @@ -865,7 +865,7 @@ Cacao Store,Madagascar,1684,2015,70%,Japan,3.0,Trinitario,Madagascar Bahen & Co.,Bahia,999,2012,70%,Australia,2.5, ,Brazil Amedei,Toscano Black,40,2006,70%,Italy,5.0,Blend,  Cacaoyere (Ecuatoriana),Esmeraldas,237,2008,71%,Ecuador,3.5,Forastero (Arriba),Ecuador -Na�ve,Barinas,1399,2014,70%,Lithuania,3.0, ,Venezuela +Naïve,Barinas,1399,2014,70%,Lithuania,3.0, ,Venezuela Zokoko,"Tranquilidad, Baures",701,2011,72%,Australia,3.75, ,Bolivia Bahen & Co.,Sambirano,995,2012,70%,Australia,3.0,Trinitario,Madagascar Burnt Fork Bend,Blend,1303,2014,72%,U.S.A.,3.25,Blend,  @@ -1005,7 +1005,7 @@ Guido Castagna,Ghana,355,2009,64%,Italy,3.0,Forastero,Ghana Cacao Store,Vietnam,1684,2015,70%,Japan,3.5,Trinitario,Vietnam Domori,Hacienda Victoria,1672,2015,70%,Italy,3.75, ,Ecuador Benoit Nihant,"Somia Plantation, Sambirano, 70hr C",773,2011,72%,Belgium,3.75,Trinitario,Madagascar -Na�ve,Nicaragua,1379,2014,70%,Lithuania,3.25,Criollo,Nicaragua +Naïve,Nicaragua,1379,2014,70%,Lithuania,3.25,Criollo,Nicaragua Shark Mountain,"Belize, 2013",1335,2014,72%,U.S.A.,3.25,"Criollo, Trinitario",Belize Lonohana,"Haleiwa, O'ahu; Lonohana E., Kanahiku",1383,2014,70%,U.S.A.,3.75, ,Hawaii Montecristi,Manabi,1654,2015,63%,Ecuador,3.5, ,Ecuador diff --git a/ranking_and_filtering/daily/regression/Chocolate_Bar_Ratings_B4/Chocolate_Bar_Ratings_B4_003/history.csv b/ranking_and_filtering/daily/regression/Chocolate_Bar_Ratings_B4/Chocolate_Bar_Ratings_B4_003/history.csv index 0f102b47ac12efa32d6cfedf9b152cfc382dca8d..8623bf185f5ffcbfeb1c29f69e024e1b705882db 100644 --- a/ranking_and_filtering/daily/regression/Chocolate_Bar_Ratings_B4/Chocolate_Bar_Ratings_B4_003/history.csv +++ b/ranking_and_filtering/daily/regression/Chocolate_Bar_Ratings_B4/Chocolate_Bar_Ratings_B4_003/history.csv @@ -109,7 +109,7 @@ Bonnat,"Porcelana, Tabasco, Marfil de Blanco",336,2009,75%,France,3.0,Criollo,Me Majani,Ecuador,531,2010,70%,Italy,2.0, ,Ecuador Hotel Chocolat,"Marcial, single Cote, 2012",1030,2013,70%,U.K.,4.0,Trinitario,St. Lucia Zart Pralinen,"Kakao Kamili, Kilombero Valley",1824,2016,85%,Austria,3.0,"Criollo, Trinitario",Tanzania -Na�ve,Nicaragua,1379,2014,70%,Lithuania,3.25,Criollo,Nicaragua +Naïve,Nicaragua,1379,2014,70%,Lithuania,3.25,Criollo,Nicaragua Theo,"Ghana, Panama, Ecuador",188,2007,75%,U.S.A.,3.0,Blend,"Ghana, Panama, Ecuador" Bahen & Co.,Houseblend,999,2012,70%,Australia,2.5,Blend,  Stone Grindz,"Ecuador, Twilght Dark",1291,2014,70%,U.S.A.,3.5, ,Ecuador @@ -1234,7 +1234,7 @@ Timo A. Meyer,"Belize, med roast",1836,2016,72%,Germany,3.75,"Trinitario, TCGA", Carlotta Chocolat,Cordoba,1888,2016,65%,Colombia,3.25,Trinitario,Colombia Terroir,Belize,1478,2015,70%,U.S.A.,3.75,Trinitario,Belize Dandelion,Dominican Republic,654,2011,70%,U.S.A.,3.25, ,Dominican Republic -Na�ve,Barinas,1399,2014,70%,Lithuania,3.0, ,Venezuela +Naïve,Barinas,1399,2014,70%,Lithuania,3.0, ,Venezuela Chloe Chocolat,Blend No. 1,672,2011,70%,France,3.5, ,  Castronovo,Guaniamo,1153,2013,72%,U.S.A.,3.0, ,Venezuela Sibu Sura,Peru,911,2012,70%,U.S.A.,2.5, ,Peru @@ -1461,7 +1461,7 @@ Heirloom Cacao Preservation (Brasstown),"Maya Mtn, Moho R., Toledo D., 2015",174 Dean and Deluca (Belcolade),Madagascar,170,2007,66%,U.S.A.,2.5,Trinitario,Madagascar Dandelion,Tanzania,654,2011,70%,U.S.A.,3.25, ,Tanzania Soma,Orinoco,1173,2013,70%,Canada,3.25,Forastero,Venezuela -Na�ve,Chuao,1399,2014,75%,Lithuania,2.75,Trinitario,Venezuela +Naïve,Chuao,1399,2014,75%,Lithuania,2.75,Trinitario,Venezuela Salgado,Rio Arriba,292,2008,70%,Argentina,3.5,Forastero (Arriba) ASSS,Ecuador Hacienda El Castillo,Don Homero- Cerecita Valley,1327,2014,70%,Ecuador,3.0,Trinitario,Ecuador Tabal,Costa Rica,1101,2013,70%,U.S.A.,2.75, ,Costa Rica diff --git a/ranking_and_filtering/daily/regression/Chocolate_Bar_Ratings_B4/Chocolate_Bar_Ratings_B4_004/history.csv b/ranking_and_filtering/daily/regression/Chocolate_Bar_Ratings_B4/Chocolate_Bar_Ratings_B4_004/history.csv index 716da66e5a393e6e756d1eb6e1d1834edee91714..0a3ad26947f62496b9c6fda63b25ab59f6555979 100644 --- a/ranking_and_filtering/daily/regression/Chocolate_Bar_Ratings_B4/Chocolate_Bar_Ratings_B4_004/history.csv +++ b/ranking_and_filtering/daily/regression/Chocolate_Bar_Ratings_B4/Chocolate_Bar_Ratings_B4_004/history.csv @@ -675,7 +675,7 @@ Tejas,San Jose,971,2012,67%,U.S.A.,3.25, ,Bolivia Choklat,Brazilian,377,2009,80%,Canada,3.0, ,Brazil Arete,Phantom,1924,2016,70%,U.S.A.,4.0,Forastero (Nacional),Peru Willie's Cacao,Los Llanos,1227,2014,88%,U.K.,3.0,Trinitario,Colombia -Na�ve,Chuao,1399,2014,75%,Lithuania,2.75,Trinitario,Venezuela +Naïve,Chuao,1399,2014,75%,Lithuania,2.75,Trinitario,Venezuela Sirene,"Somia Plantation, 2012",1339,2014,73%,Canada,3.75,Trinitario,Madagascar Lonohana,"Opaeula Estate, O'ahu, Ele'ele",1097,2013,72%,U.S.A.,3.0, ,Hawaii Theo,Ivory Coast,184,2007,75%,U.S.A.,2.5, ,Ivory Coast @@ -728,7 +728,7 @@ Valrhona,Chuao 2002 P.,129,2007,65%,France,3.75,Trinitario,Venezuela Beschle (Felchlin),"Indigena Amazonia, Grand Cru, Quizas",636,2011,72%,Switzerland,3.5, ,Venezuela Palette de Bine,"San Juan Estate, Gran Couva",1570,2015,70%,Canada,3.25,Trinitario,Trinidad Potomac,Upala w/ nibs,647,2011,70%,U.S.A.,3.5,Matina,Costa Rica -Na�ve,Nicaragua,1379,2014,70%,Lithuania,3.25,Criollo,Nicaragua +Naïve,Nicaragua,1379,2014,70%,Lithuania,3.25,Criollo,Nicaragua Soma,"Bachelor's Hall E., St. Thomas Parish",1177,2013,70%,Canada,4.0,Trinitario,Jamaica Amatller (Simon Coll),Ecuador,327,2009,70%,Spain,2.75, ,Ecuador Bonnat,"Maracaibo, El Rosario",48,2006,75%,France,4.0,Criollo,Venezuela @@ -1458,7 +1458,7 @@ Choco Del Sol,Maya Mountain,1407,2014,82%,Germany,3.25,Trinitario,Belize Castronovo,"Duarte, Batch 360",1570,2015,70%,U.S.A.,3.75,"Criollo, Trinitario",Domincan Republic Sirene,Lachua w/ cane sugar,1614,2015,73%,Canada,3.0, ,Guatemala Soma,Patanemo,1077,2013,70%,Canada,3.75,Criollo,Venezuela -Na�ve,Barinas,1399,2014,70%,Lithuania,3.0, ,Venezuela +Naïve,Barinas,1399,2014,70%,Lithuania,3.0, ,Venezuela Marsatta,Dominican Republic,1189,2013,89%,U.S.A.,2.5, ,Dominican Republic Cacao Atlanta,Tumbes,1215,2014,75%,U.S.A.,2.5,Criollo,Peru Batch,"Dominican Republic, Batch 3",1840,2016,65%,U.S.A.,3.5, ,Domincan Republic diff --git a/ranking_and_filtering/daily/regression/Chocolate_Bar_Ratings_B4/Chocolate_Bar_Ratings_B4_005/history.csv b/ranking_and_filtering/daily/regression/Chocolate_Bar_Ratings_B4/Chocolate_Bar_Ratings_B4_005/history.csv index 765b20453f35e1ddf2ab362870f49cf008c854dc..66668cd4265bb62b32c5e14ab3d39b244ad9e4f3 100644 --- a/ranking_and_filtering/daily/regression/Chocolate_Bar_Ratings_B4/Chocolate_Bar_Ratings_B4_005/history.csv +++ b/ranking_and_filtering/daily/regression/Chocolate_Bar_Ratings_B4/Chocolate_Bar_Ratings_B4_005/history.csv @@ -299,7 +299,7 @@ Millcreek Cacao Roasters,Ecuador,1073,2013,70%,U.S.A.,2.5,Forastero (Nacional),E Lindt & Sprungli,Excellence (US Version),157,2007,85%,Switzerland,3.0, ,  Bonnat,"Ocumare, Puerto Cabello",32,2006,75%,France,4.0, ,Venezuela Soma,Orinoco,1173,2013,70%,Canada,3.25,Forastero,Venezuela -Na�ve,Chuao,1399,2014,75%,Lithuania,2.75,Trinitario,Venezuela +Naïve,Chuao,1399,2014,75%,Lithuania,2.75,Trinitario,Venezuela "Smooth Chocolator, The","Guasare, La Sierra de Perija, batch gua001",1740,2016,70%,Australia,3.75,Criollo,Venezuela Kto,Ocumare,1422,2014,72%,U.S.A.,2.75, ,Venezuela Beschle (Felchlin),Ecuador,636,2011,72%,Switzerland,4.0, ,Ecuador @@ -805,7 +805,7 @@ Pralus,"Madagascar, Nosy Be Isle.",5,2006,75%,France,3.75,Criollo,Madagascar Brazen,Ambolikapkly P.,1518,2015,60%,U.S.A.,3.25, ,Madagascar Somerville,Nicaragua,1367,2014,70%,U.S.A.,3.25, ,Nicaragua Caribeans,"Talamanca, Raul-Kekoldo community",1586,2015,72%,Costa Rica,3.25, ,Costa Rica -Na�ve,Barinas,1399,2014,70%,Lithuania,3.0, ,Venezuela +Naïve,Barinas,1399,2014,70%,Lithuania,3.0, ,Venezuela Valrhona,Le Noir Amer,157,2007,71%,France,2.75,Forastero,West Africa Heirloom Cacao Preservation (Guittard),"Los Rios, Hacienda Limon, Orecao, 2014",1243,2014,67%,U.S.A.,3.75,EET,Ecuador Mindo,Ecuador,607,2010,77%,U.S.A.,2.75, ,Ecuador @@ -922,7 +922,7 @@ Mast Brothers,Tanzania,1450,2015,74%,U.S.A.,3.25, ,Tanzania Willie's Cacao,San Martin,457,2009,70%,U.K.,3.0, ,Peru Akesson's (Pralus),"Bali (west), Sukrama Family, Melaya area",636,2011,75%,Switzerland,3.75,Trinitario,Indonesia Amedei,Jamaica,123,2007,70%,Italy,3.0,Trinitario,Jamaica -Na�ve,Nicaragua,1379,2014,70%,Lithuania,3.25,Criollo,Nicaragua +Naïve,Nicaragua,1379,2014,70%,Lithuania,3.25,Criollo,Nicaragua Nuance,"Ecuador, 2013",1458,2015,70%,U.S.A.,2.75,Trinitario,Ecuador Map Chocolate,Colombia,1610,2015,75%,U.S.A.,2.75, ,Colombia El Rey,"Rio Caribe, Cariaco",439,2009,60.5%,Venezuela,2.75,Trinitario,Venezuela diff --git a/ranking_and_filtering/daily/regression/Chocolate_Bar_Ratings_B4/Chocolate_Bar_Ratings_B4_006/history.csv b/ranking_and_filtering/daily/regression/Chocolate_Bar_Ratings_B4/Chocolate_Bar_Ratings_B4_006/history.csv index 8a2e348b09d1b17524504c46797d50a93564bfdf..e46b363188a1eed41ce0809131de5be5d4fb974c 100644 --- a/ranking_and_filtering/daily/regression/Chocolate_Bar_Ratings_B4/Chocolate_Bar_Ratings_B4_006/history.csv +++ b/ranking_and_filtering/daily/regression/Chocolate_Bar_Ratings_B4/Chocolate_Bar_Ratings_B4_006/history.csv @@ -175,7 +175,7 @@ Pralus,"Djakarta, Java and Ghana",99,2006,75%,France,3.5,"Criollo, Forastero","I Cacao Atlanta,Tumbes,1215,2014,75%,U.S.A.,2.5,Criollo,Peru Rogue,"Caranero, Choc. Garage Exclusive",1446,2015,70%,U.S.A.,3.75, ,Venezuela La Maison du Chocolat (Valrhona),"Acarigua, w/ nibs",1189,2013,56%,France,3.25, ,Ecuador -Na�ve,Chuao,1399,2014,75%,Lithuania,2.75,Trinitario,Venezuela +Naïve,Chuao,1399,2014,75%,Lithuania,2.75,Trinitario,Venezuela Ambrosia,Dominican Republic,1498,2015,75%,Canada,3.25, ,Dominican Republic Manoa,"Hamakua, Hawaiian Crown, #176",1007,2012,72%,U.S.A.,2.75, ,Hawaii Ocho,"PNG, Devotion",1411,2014,66%,New Zealand,2.75, ,Papua New Guinea @@ -263,7 +263,7 @@ El Rey,"Rio Caribe, Macuro",439,2009,70%,Venezuela,3.0,Trinitario,Venezuela Sibu,Oro,1586,2015,82%,Costa Rica,3.25, ,Costa Rica Cacao de Origin,Hacienda la Trinidad,1668,2015,74%,Venezuela,3.25, ,Venezuela Hogarth,Akesson's Estate,1712,2016,70%,New Zealand,2.75,Trinitario,Madagascar -Na�ve,Nicaragua,1379,2014,70%,Lithuania,3.25,Criollo,Nicaragua +Naïve,Nicaragua,1379,2014,70%,Lithuania,3.25,Criollo,Nicaragua Chocolarder,Dominican Republic,1514,2015,80%,U.K.,2.5, ,Dominican Republic Soma,Noula Coop,817,2012,70%,Canada,3.25, ,Haiti Willie's Cacao,Los Llanos,1227,2014,88%,U.K.,3.0,Trinitario,Colombia @@ -1315,7 +1315,7 @@ Cote d' Or (Kraft),Sensations Intense,48,2006,70%,Belgium,1.0, ,  K'ul,"Maranon, Fortunato No. 4",1852,2016,70%,U.S.A.,2.75,Forastero (Nacional),Peru Taza,Chiapan,451,2009,75%,U.S.A.,2.75, ,Mexico Coppeneur,Ocumare 61,558,2010,72%,Germany,3.25,Criollo (Ocumare 61),Venezuela -Na�ve,Barinas,1399,2014,70%,Lithuania,3.0, ,Venezuela +Naïve,Barinas,1399,2014,70%,Lithuania,3.0, ,Venezuela Santander (Compania Nacional),Colombian,32,2006,65%,Colombia,3.0,Forastero (Nacional),Colombia Beschle (Felchlin),Madagascar,636,2011,64%,Switzerland,3.0,Trinitario,Madagascar DeVries,Bolivian,241,2008,80%,U.S.A.,2.75, ,Bolivia diff --git a/ranking_and_filtering/daily/regression/Chocolate_Bar_Ratings_B4/Chocolate_Bar_Ratings_B4_007/history.csv b/ranking_and_filtering/daily/regression/Chocolate_Bar_Ratings_B4/Chocolate_Bar_Ratings_B4_007/history.csv index 98a8c8a7c9e7599b1020fe313326b582021302d8..04c054230c8d1dd1f76bed4a4218f1efebd2f264 100644 --- a/ranking_and_filtering/daily/regression/Chocolate_Bar_Ratings_B4/Chocolate_Bar_Ratings_B4_007/history.csv +++ b/ranking_and_filtering/daily/regression/Chocolate_Bar_Ratings_B4/Chocolate_Bar_Ratings_B4_007/history.csv @@ -283,7 +283,7 @@ Palette de Bine,Kokoa Kamili,1574,2015,72%,Canada,3.5, ,Tanzania Original Beans (Felchlin),Papua Kerafat,1438,2014,68%,Switzerland,2.75, ,Papua New Guinea Pralus,Jamaique,32,2006,75%,France,4.0,Trinitario,Jamaica Noble Bean aka Jerjobo,Moho Valley,1299,2014,75%,U.S.A.,3.0,Trinitario,Belize -Na�ve,Chuao,1399,2014,75%,Lithuania,2.75,Trinitario,Venezuela +Naïve,Chuao,1399,2014,75%,Lithuania,2.75,Trinitario,Venezuela Hummingbird,Lam Dong,1542,2015,70%,Canada,3.5,Trinitario,Vietnam Artisan du Chocolat,Jamaica,531,2010,72%,U.K.,3.75,Trinitario,Jamaica Altus aka Cao Artisan,CIAAB Coop,1732,2016,60%,U.S.A.,2.5, ,Bolivia @@ -522,7 +522,7 @@ Scharffen Berger,Cuyagua,135,2007,75%,U.S.A.,3.75, ,Venezuela Arete,Nacional,1534,2015,68%,U.S.A.,3.5,Forastero (Nacional),Peru Goodnow Farms,"Asochivite, batch 1005",1924,2016,77%,U.S.A.,3.25, ,Guatemala Map Chocolate,"San Juan Estate, Cherry Blossoms at Night",1606,2015,70%,U.S.A.,2.75,Trinitario,Trinidad -Na�ve,Nicaragua,1379,2014,70%,Lithuania,3.25,Criollo,Nicaragua +Naïve,Nicaragua,1379,2014,70%,Lithuania,3.25,Criollo,Nicaragua Pierre Marcolini,"Porcelana, Tabasco, Limited Ed.",81,2006,72%,Belgium,4.0,Criollo,Mexico Caribeans,Anselmo Paraiso Estate,1586,2015,72%,Costa Rica,3.5, ,Costa Rica Caribeans,Costa Rica,841,2012,72%,Costa Rica,3.0,Forastero,Costa Rica @@ -1468,7 +1468,7 @@ Dick Taylor,Mantuano,682,2011,75%,U.S.A.,2.75,Criollo,Venezuela Bittersweet Origins,Bocas del Toro,233,2008,75%,U.S.A.,3.5, ,Panama Felchlin,"Alto Beni, Cru Savage",56,2006,68%,Switzerland,4.0,Criollo (Wild),Bolivia Nova Monda,"Punta Galera, cacao Nacional, gold label",935,2012,80%,Nicaragua,3.25,Forastero (Nacional),Ecuador -Na�ve,Barinas,1399,2014,70%,Lithuania,3.0, ,Venezuela +Naïve,Barinas,1399,2014,70%,Lithuania,3.0, ,Venezuela Maglio,Cuba,308,2008,70%,Italy,3.25,Criollo,Cuba Dean and Deluca (Belcolade),Costa Rica,175,2007,64%,U.S.A.,3.0, ,Costa Rica Beschle (Felchlin),Madagascar,636,2011,64%,Switzerland,3.0,Trinitario,Madagascar diff --git a/ranking_and_filtering/daily/regression/Chocolate_Bar_Ratings_B4/Chocolate_Bar_Ratings_B4_008/history.csv b/ranking_and_filtering/daily/regression/Chocolate_Bar_Ratings_B4/Chocolate_Bar_Ratings_B4_008/history.csv index 5aa3d3e967c6328f6fa1e545c018145439af684c..b2e24837879ad71d7a2837421b813cd4dc75eb2d 100644 --- a/ranking_and_filtering/daily/regression/Chocolate_Bar_Ratings_B4/Chocolate_Bar_Ratings_B4_008/history.csv +++ b/ranking_and_filtering/daily/regression/Chocolate_Bar_Ratings_B4/Chocolate_Bar_Ratings_B4_008/history.csv @@ -152,7 +152,7 @@ Szanto Tibor,Cacao Blanco,1193,2013,70%,Hungary,3.25, ,Nicaragua Madecasse (Cinagra),Madagascar,284,2008,67%,Madagascar,3.5,Trinitario,Madagascar La Maison du Chocolat (Valrhona),Grenada,741,2011,68%,France,3.5,Trinitario,Grenada Pierre Marcolini,"Porcelana, Tabasco, Limited Ed.",81,2006,72%,Belgium,4.0,Criollo,Mexico -Na�ve,Nicaragua,1379,2014,70%,Lithuania,3.25,Criollo,Nicaragua +Naïve,Nicaragua,1379,2014,70%,Lithuania,3.25,Criollo,Nicaragua Indaphoria,"Conacado, Manifesto",907,2012,70%,U.S.A.,2.75, ,Dominican Republic Kallari (Ecuatoriana),Cacao Nacional W.F.,263,2008,75%,Ecuador,2.5, ,Ecuador Zak's,"Dominican Republic, Batch D2",1578,2015,70%,U.S.A.,3.25, ,Domincan Republic @@ -1471,7 +1471,7 @@ Carlotta Chocolat,Arauca,1884,2016,65%,Colombia,3.0,Trinitario,Colombia Soma,Carenero Superior,951,2012,70%,Canada,3.75,Trinitario,Venezuela Arete,San Juan,1724,2016,70%,U.S.A.,3.5,Trinitario,Trinidad Starchild,Cacao Verapaz,1692,2015,70%,U.S.A.,3.0, ,Guatemala -Na�ve,Chuao,1399,2014,75%,Lithuania,2.75,Trinitario,Venezuela +Naïve,Chuao,1399,2014,75%,Lithuania,2.75,Trinitario,Venezuela Arete,Chuno,1598,2015,70%,U.S.A.,4.0,"Criollo, Trinitario",Nicaragua Pascha,Peru,1137,2013,70%,Peru,2.75, ,Peru A. Morin,Atsane,1676,2015,70%,France,3.0, ,Togo @@ -1566,7 +1566,7 @@ Pacari,Los Rios,224,2008,72%,Ecuador,3.0,Forastero (Arriba),Ecuador Pralus,Dominican Republic-Organic,280,2008,75%,France,2.0, ,Dominican Republic Felchlin,Maracaibo Clasificado,48,2006,65%,Switzerland,3.0,Criollo,Venezuela French Broad,"Palo Blanco, Chulucanas",883,2012,61%,U.S.A.,3.5, ,Peru -Na�ve,Barinas,1399,2014,70%,Lithuania,3.0, ,Venezuela +Naïve,Barinas,1399,2014,70%,Lithuania,3.0, ,Venezuela Pump Street Bakery,"Akesson's, batch 4411",1530,2015,74%,U.K.,3.5,Criollo,Madagascar A. Morin,Chanchamayo Province,1019,2013,70%,France,3.5, ,Peru Soma,"Alto Beni, Wild Bolivian",676,2011,70%,Canada,3.5,Nacional,Bolivia diff --git a/ranking_and_filtering/daily/regression/Global_YouTube_Statistics_2023_B4/Global_YouTube_Statistics_2023_B4_001/info.json b/ranking_and_filtering/daily/regression/Global_YouTube_Statistics_2023_B4/Global_YouTube_Statistics_2023_B4_001/info.json index 32b2e724985eb4b8c7e1935cfdd4e4510dbfb775..2a4d0bb2870f7f26e8f34e8121139aaccc4b4a47 100644 --- a/ranking_and_filtering/daily/regression/Global_YouTube_Statistics_2023_B4/Global_YouTube_Statistics_2023_B4_001/info.json +++ b/ranking_and_filtering/daily/regression/Global_YouTube_Statistics_2023_B4/Global_YouTube_Statistics_2023_B4_001/info.json @@ -1,288 +1,288 @@ -{ - "name": "Global YouTube Statistics 2023", - "source": "https://www.kaggle.com/datasets/nelgiriyewithana/global-youtube-statistics-2023/data", - "data_intro": "A collection of YouTube giants, this dataset offers a perfect avenue to analyze and gain valuable insights from the luminaries of the platform. With comprehensive details on top creators' subscriber counts, video views, upload frequency, country of origin, earnings, and more, this treasure trove of information is a must-explore for aspiring content creators, data enthusiasts, and anyone intrigued by the ever-evolving online content landscape. Immerse yourself in the world of YouTube success and unlock a wealth of knowledge with this extraordinary dataset.", - "is_splited": false, - "overall_size": 995, - "train_size": 0, - "test_size": 0, - "c_classes": 7, - "n_classes": 21, - "cat_feature_intro": { - "Youtuber": "Name of the YouTube channel", - "category": "Category or niche of the channel", - "Title": "Title of the YouTube channel", - "Country": "Longitude coordinate of the country's location", - "Abbreviation": "Abbreviation of the country", - "channel_type": "Ranking of the channel based on its type (individual or brand)", - "created_month": "Month when the YouTube channel was created" - }, - "num_feature_intro": { - "rank": "Ranking of the channel based on its type (individual or brand)", - "subscribers": "Number of new subscribers gained in the last 30 days", - "video views": "Total video views in the last 30 days", - "uploads": "Total number of videos uploaded on the channel", - "video_views_rank": "Ranking of the channel based on total video views", - "country_rank": "Ranking of the channel based on the number of subscribers within its country", - "channel_type_rank": "Ranking of the channel based on its type (individual or brand)", - "video_views_for_the_last_30_days": "Total video views in the last 30 days", - "lowest_monthly_earnings": "Lowest estimated monthly earnings from the channel", - "highest_monthly_earnings": "Highest estimated monthly earnings from the channel", - "lowest_yearly_earnings": "Lowest estimated yearly earnings from the channel", - "highest_yearly_earnings": "Highest estimated yearly earnings from the channel", - "subscribers_for_last_30_days": "Number of new subscribers gained in the last 30 days", - "created_year": "Year when the YouTube channel was created", - "created_date": "Exact date of the YouTube channel's creation", - "Gross tertiary education enrollment (%)": "Percentage of the population enrolled in tertiary education in the country", - "Population": "Percentage of the population living in urban areas", - "Unemployment rate": "Unemployment rate in the country", - "Urban_population": "Percentage of the population living in urban areas", - "Latitude": "Latitude coordinate of the country's location", - "Longitude": "Longitude coordinate of the country's location" - }, - "evaluation_metric": null, - "task_type": "regression", - "target": "highest_monthly_earnings", - "num_feature_value": { - "Gross tertiary education enrollment (%)": [ - 7.6, - 113.1 - ], - "Latitude": [ - -38.416097, - 61.92411 - ], - "Longitude": [ - -172.104629, - 138.252924 - ], - "Population": [ - 202506.0, - 1397715000.0 - ], - "Unemployment rate": [ - 0.75, - 14.72 - ], - "Urban_population": [ - 35588.0, - 842933962.0 - ], - "channel_type_rank": [ - 1.0, - 7741.0 - ], - "country_rank": [ - 1.0, - 7741.0 - ], - "created_date": [ - 1.0, - 31.0 - ], - "created_year": [ - 1970.0, - 2022.0 - ], - "highest_monthly_earnings": [ - 0.0, - 13600000.0 - ], - "highest_yearly_earnings": [ - 0.0, - 163400000.0 - ], - "lowest_monthly_earnings": [ - 0.0, - 850900.0 - ], - "lowest_yearly_earnings": [ - 0.0, - 10200000.0 - ], - "rank": [ - 1.0, - 995.0 - ], - "subscribers": [ - 12300000.0, - 245000000.0 - ], - "subscribers_for_last_30_days": [ - 1.0, - 8000000.0 - ], - "uploads": [ - 0.0, - 301308.0 - ], - "video views": [ - 0.0, - 228000000000.0 - ], - "video_views_for_the_last_30_days": [ - 1.0, - 6589000000.0 - ], - "video_views_rank": [ - 1.0, - 4057944.0 - ] - }, - "cat_feature_value": { - "Abbreviation": [ - "AD", - "AE", - "AF", - "AR", - "AU", - "BB", - "BD", - "BR", - "CA", - "CH", - "CL", - "CN", - "CO", - "CU", - "DE", - "EC", - "EG", - "ES", - "FI", - "FR", - "GB", - "ID", - "IN", - "IQ", - "IT", - "JO", - "JP", - "KR", - "KW", - "LV", - "MA", - "MX", - "MY", - "NL", - "PE", - "PH", - "PK", - "RU", - "SA", - "SE", - "SG", - "SV", - "TH", - "TR", - "UA", - "US", - "VE", - "VN", - "WS" - ], - "Country": [ - "Afghanistan", - "Andorra", - "Argentina", - "Australia", - "Bangladesh", - "Barbados", - "Brazil", - "Canada", - "Chile", - "China", - "Colombia", - "Cuba", - "Ecuador", - "Egypt", - "El Salvador", - "Finland", - "France", - "Germany", - "India", - "Indonesia", - "Iraq", - "Italy", - "Japan", - "Jordan", - "Kuwait", - "Latvia", - "Malaysia", - "Mexico", - "Morocco", - "Netherlands", - "Pakistan", - "Peru", - "Philippines", - "Russia", - "Samoa", - "Saudi Arabia", - "Singapore", - "South Korea", - "Spain", - "Sweden", - "Switzerland", - "Thailand", - "Turkey", - "Ukraine", - "United Arab Emirates", - "United Kingdom", - "United States", - "Venezuela", - "Vietnam" - ], - "Title": "string", - "Youtuber": "string", - "category": [ - "Autos & Vehicles", - "Comedy", - "Education", - "Entertainment", - "Film & Animation", - "Gaming", - "Howto & Style", - "Movies", - "Music", - "News & Politics", - "Nonprofits & Activism", - "People & Blogs", - "Pets & Animals", - "Science & Technology", - "Shows", - "Sports", - "Trailers", - "Travel & Events" - ], - "channel_type": [ - "Animals", - "Autos", - "Comedy", - "Education", - "Entertainment", - "Film", - "Games", - "Howto", - "Music", - "News", - "Nonprofit", - "People", - "Sports", - "Tech" - ], - "created_month": [ - "Apr", - "Aug", - "Dec", - "Feb", - "Jan", - "Jul", - "Jun", - "Mar", - "May", - "Nov", - "Oct", - "Sep" - ] - } -} \ No newline at end of file +{ + "name": "Global YouTube Statistics 2023", + "source": "https://www.kaggle.com/datasets/nelgiriyewithana/global-youtube-statistics-2023/data", + "data_intro": "A collection of YouTube giants, this dataset offers a perfect avenue to analyze and gain valuable insights from the luminaries of the platform. With comprehensive details on top creators' subscriber counts, video views, upload frequency, country of origin, earnings, and more, this treasure trove of information is a must-explore for aspiring content creators, data enthusiasts, and anyone intrigued by the ever-evolving online content landscape. Immerse yourself in the world of YouTube success and unlock a wealth of knowledge with this extraordinary dataset.", + "is_splited": false, + "overall_size": 995, + "train_size": 0, + "test_size": 0, + "c_classes": 7, + "n_classes": 21, + "cat_feature_intro": { + "Youtuber": "Name of the YouTube channel", + "category": "Category or niche of the channel", + "Title": "Title of the YouTube channel", + "Country": "Country associated with the channel.", + "Abbreviation": "Abbreviation of the country", + "channel_type": "Type or category of the channel.", + "created_month": "Month when the YouTube channel was created" + }, + "num_feature_intro": { + "rank": "Ranking of the channel based on its type (individual or brand)", + "subscribers": "Number of new subscribers gained in the last 30 days", + "video views": "Total video views in the last 30 days", + "uploads": "Total number of videos uploaded on the channel", + "video_views_rank": "Ranking of the channel based on total video views", + "country_rank": "Ranking of the channel based on the number of subscribers within its country", + "channel_type_rank": "Ranking of the channel based on its type (individual or brand)", + "video_views_for_the_last_30_days": "Total video views in the last 30 days", + "lowest_monthly_earnings": "Lowest estimated monthly earnings from the channel", + "highest_monthly_earnings": "Highest estimated monthly earnings from the channel", + "lowest_yearly_earnings": "Lowest estimated yearly earnings from the channel", + "highest_yearly_earnings": "Highest estimated yearly earnings from the channel", + "subscribers_for_last_30_days": "Number of new subscribers gained in the last 30 days", + "created_year": "Year when the YouTube channel was created", + "created_date": "Exact date of the YouTube channel's creation", + "Gross tertiary education enrollment (%)": "Percentage of the population enrolled in tertiary education in the country", + "Population": "Percentage of the population living in urban areas", + "Unemployment rate": "Unemployment rate in the country", + "Urban_population": "Percentage of the population living in urban areas", + "Latitude": "Latitude coordinate of the country's location", + "Longitude": "Longitude coordinate of the country's location" + }, + "evaluation_metric": null, + "task_type": "regression", + "target": "highest_monthly_earnings", + "num_feature_value": { + "Gross tertiary education enrollment (%)": [ + 7.6, + 113.1 + ], + "Latitude": [ + -38.416097, + 61.92411 + ], + "Longitude": [ + -172.104629, + 138.252924 + ], + "Population": [ + 202506.0, + 1397715000.0 + ], + "Unemployment rate": [ + 0.75, + 14.72 + ], + "Urban_population": [ + 35588.0, + 842933962.0 + ], + "channel_type_rank": [ + 1.0, + 7741.0 + ], + "country_rank": [ + 1.0, + 7741.0 + ], + "created_date": [ + 1.0, + 31.0 + ], + "created_year": [ + 1970.0, + 2022.0 + ], + "highest_monthly_earnings": [ + 0.0, + 13600000.0 + ], + "highest_yearly_earnings": [ + 0.0, + 163400000.0 + ], + "lowest_monthly_earnings": [ + 0.0, + 850900.0 + ], + "lowest_yearly_earnings": [ + 0.0, + 10200000.0 + ], + "rank": [ + 1.0, + 995.0 + ], + "subscribers": [ + 12300000.0, + 245000000.0 + ], + "subscribers_for_last_30_days": [ + 1.0, + 8000000.0 + ], + "uploads": [ + 0.0, + 301308.0 + ], + "video views": [ + 0.0, + 228000000000.0 + ], + "video_views_for_the_last_30_days": [ + 1.0, + 6589000000.0 + ], + "video_views_rank": [ + 1.0, + 4057944.0 + ] + }, + "cat_feature_value": { + "Abbreviation": [ + "AD", + "AE", + "AF", + "AR", + "AU", + "BB", + "BD", + "BR", + "CA", + "CH", + "CL", + "CN", + "CO", + "CU", + "DE", + "EC", + "EG", + "ES", + "FI", + "FR", + "GB", + "ID", + "IN", + "IQ", + "IT", + "JO", + "JP", + "KR", + "KW", + "LV", + "MA", + "MX", + "MY", + "NL", + "PE", + "PH", + "PK", + "RU", + "SA", + "SE", + "SG", + "SV", + "TH", + "TR", + "UA", + "US", + "VE", + "VN", + "WS" + ], + "Country": [ + "Afghanistan", + "Andorra", + "Argentina", + "Australia", + "Bangladesh", + "Barbados", + "Brazil", + "Canada", + "Chile", + "China", + "Colombia", + "Cuba", + "Ecuador", + "Egypt", + "El Salvador", + "Finland", + "France", + "Germany", + "India", + "Indonesia", + "Iraq", + "Italy", + "Japan", + "Jordan", + "Kuwait", + "Latvia", + "Malaysia", + "Mexico", + "Morocco", + "Netherlands", + "Pakistan", + "Peru", + "Philippines", + "Russia", + "Samoa", + "Saudi Arabia", + "Singapore", + "South Korea", + "Spain", + "Sweden", + "Switzerland", + "Thailand", + "Turkey", + "Ukraine", + "United Arab Emirates", + "United Kingdom", + "United States", + "Venezuela", + "Vietnam" + ], + "Title": "string", + "Youtuber": "string", + "category": [ + "Autos & Vehicles", + "Comedy", + "Education", + "Entertainment", + "Film & Animation", + "Gaming", + "Howto & Style", + "Movies", + "Music", + "News & Politics", + "Nonprofits & Activism", + "People & Blogs", + "Pets & Animals", + "Science & Technology", + "Shows", + "Sports", + "Trailers", + "Travel & Events" + ], + "channel_type": [ + "Animals", + "Autos", + "Comedy", + "Education", + "Entertainment", + "Film", + "Games", + "Howto", + "Music", + "News", + "Nonprofit", + "People", + "Sports", + "Tech" + ], + "created_month": [ + "Apr", + "Aug", + "Dec", + "Feb", + "Jan", + "Jul", + "Jun", + "Mar", + "May", + "Nov", + "Oct", + "Sep" + ] + } +} diff --git a/ranking_and_filtering/daily/regression/Global_YouTube_Statistics_2023_B4/Global_YouTube_Statistics_2023_B4_002/Global_YouTube_Statistics_2023_B4_002_current.json b/ranking_and_filtering/daily/regression/Global_YouTube_Statistics_2023_B4/Global_YouTube_Statistics_2023_B4_002/Global_YouTube_Statistics_2023_B4_002_current.json index f4791eda5dfe10d4035ebc71e067b4b992dc845c..10a61f73a54a0989a22c71afa18c940c27efa2c6 100644 --- a/ranking_and_filtering/daily/regression/Global_YouTube_Statistics_2023_B4/Global_YouTube_Statistics_2023_B4_002/Global_YouTube_Statistics_2023_B4_002_current.json +++ b/ranking_and_filtering/daily/regression/Global_YouTube_Statistics_2023_B4/Global_YouTube_Statistics_2023_B4_002/Global_YouTube_Statistics_2023_B4_002_current.json @@ -1,6335 +1,6335 @@ { - "id": "002", - "task_type": "B4", - "subtask_type": "topk", - "perspective": "data_holder", - "dataset_name": "Global YouTube Statistics 2023", - "table_path": "output/Global YouTube Statistics 2023_B4/Global YouTube Statistics 2023_B4_002/test.csv", - "query": "We're conducting a risk assessment on creator burnout and sustainability. Based on the trends from our archive, identify the 5 creators with the lowest 'highest_monthly_earnings' values in the new data batch, indicating those whose top-end earnings are most at risk for the coming month.", - "meta_info": { - "domain": "daily" + "id": "002", + "task_type": "B4", + "subtask_type": "topk", + "perspective": "data_holder", + "dataset_name": "Global YouTube Statistics 2023", + "table_path": "output/Global YouTube Statistics 2023_B4/Global YouTube Statistics 2023_B4_002/test.csv", + "query": "We're conducting a risk assessment on creator burnout and sustainability. Based on the trends from our archive, identify the 5 creators with the lowest 'highest_monthly_earnings' values in the new data batch, indicating those whose top-end earnings are most at risk for the coming month.", + "meta_info": { + "domain": "daily" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "rank": 454, + "Youtuber": "GRAMMY GOLD OFFICIAL", + "subscribers": 18600000, + "video views": 20196704276.0, + "category": "Music", + "Title": "GRAMMY GOLD OFFICIAL", + "uploads": 6287, + "Country": "Thailand", + "Abbreviation": "TH", + "channel_type": "Music", + "video_views_rank": 121.0, + "country_rank": 5.0, + "channel_type_rank": 101.0, + "video_views_for_the_last_30_days": 168597000.0, + "lowest_monthly_earnings": 42100.0, + "highest_monthly_earnings": 674400.0, + "lowest_yearly_earnings": 505800.0, + "highest_yearly_earnings": 8100000.0, + "subscribers_for_last_30_days": 100000.0, + "created_year": 2014.0, + "created_month": "Jan", + "created_date": 15.0, + "Gross tertiary education enrollment (%)": 49.3, + "Population": 69625582.0, + "Unemployment rate": 0.75, + "Urban_population": 35294600.0, + "Latitude": 15.870032, + "Longitude": 100.992541 + } + }, + { + "scenario_id": "002", + "features": { + "rank": 601, + "Youtuber": "La Rosa de Guadalupe", + "subscribers": 16100000, + "video views": 9642146451.0, + "category": "Entertainment", + "Title": "larosadeguadalupe", + "uploads": 0, + "Country": "Mexico", + "Abbreviation": "MX", + "channel_type": "Games", + "video_views_rank": 4057944.0, + "country_rank": 4258.0, + "channel_type_rank": 7472.0, + "video_views_for_the_last_30_days": null, + "lowest_monthly_earnings": 0.0, + "highest_monthly_earnings": 0.0, + "lowest_yearly_earnings": 0.0, + "highest_yearly_earnings": 0.0, + "subscribers_for_last_30_days": null, + "created_year": 2008.0, + "created_month": "Sep", + "created_date": 3.0, + "Gross tertiary education enrollment (%)": 40.2, + "Population": 126014024.0, + "Unemployment rate": 3.42, + "Urban_population": 102626859.0, + "Latitude": 23.634501, + "Longitude": -102.552784 + } + }, + { + "scenario_id": "003", + "features": { + "rank": 980, + "Youtuber": "DaniRep | +6 V����������ï", + "subscribers": 12400000, + "video views": 6933660906.0, + "category": "Gaming", + "Title": "DaniRep | +6 V����������ï", + "uploads": 12419, + "Country": "Spain", + "Abbreviation": "ES", + "channel_type": "Games", + "video_views_rank": 847.0, + "country_rank": 19.0, + "channel_type_rank": 68.0, + "video_views_for_the_last_30_days": 82648000.0, + "lowest_monthly_earnings": 20700.0, + "highest_monthly_earnings": 330600.0, + "lowest_yearly_earnings": 247900.0, + "highest_yearly_earnings": 4000000.0, + "subscribers_for_last_30_days": 100000.0, + "created_year": 2012.0, + "created_month": "Oct", + "created_date": 29.0, + "Gross tertiary education enrollment (%)": 88.9, + "Population": 47076781.0, + "Unemployment rate": 13.96, + "Urban_population": 37927409.0, + "Latitude": 40.463667, + "Longitude": -3.74922 + } + }, + { + "scenario_id": "004", + "features": { + "rank": 618, + "Youtuber": "Volga Video", + "subscribers": 15700000, + "video views": 9787697838.0, + "category": "Film & Animation", + "Title": "Volga Video", + "uploads": 16874, + "Country": "India", + "Abbreviation": "IN", + "channel_type": "Entertainment", + "video_views_rank": 463.0, + "country_rank": 97.0, + "channel_type_rank": 139.0, + "video_views_for_the_last_30_days": 51315000.0, + "lowest_monthly_earnings": 12800.0, + "highest_monthly_earnings": 205300.0, + "lowest_yearly_earnings": 153900.0, + "highest_yearly_earnings": 2500000.0, + "subscribers_for_last_30_days": null, + "created_year": 2012.0, + "created_month": "Mar", + "created_date": 23.0, + "Gross tertiary education enrollment (%)": 28.1, + "Population": 1366417754.0, + "Unemployment rate": 5.36, + "Urban_population": 471031528.0, + "Latitude": 20.593684, + "Longitude": 78.96288 + } + }, + { + "scenario_id": "005", + "features": { + "rank": 638, + "Youtuber": "ýýýýýýýý ýýýýýýýýýýýýýý ýýýý 5 ýýýýýýýýýý", + "subscribers": 15500000, + "video views": 4499826716.0, + "category": "Howto & Style", + "Title": "ýýýýýýýý ýýýýýýýýýýýýýý ýýýý 5 ýýýýýýýýýý", + "uploads": 5183, + "Country": "Egypt", + "Abbreviation": "EG", + "channel_type": "Howto", + "video_views_rank": 1597.0, + "country_rank": 1.0, + "channel_type_rank": 22.0, + "video_views_for_the_last_30_days": 32916000.0, + "lowest_monthly_earnings": 8200.0, + "highest_monthly_earnings": 131700.0, + "lowest_yearly_earnings": 98700.0, + "highest_yearly_earnings": 1600000.0, + "subscribers_for_last_30_days": 100000.0, + "created_year": 2017.0, + "created_month": "Dec", + "created_date": 8.0, + "Gross tertiary education enrollment (%)": 35.2, + "Population": 100388073.0, + "Unemployment rate": 10.76, + "Urban_population": 42895824.0, + "Latitude": 26.820553, + "Longitude": 30.802498 + } + }, + { + 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"Unemployment rate": "Unemployment rate in the country", - "Urban_population": "Percentage of the population living in urban areas", - "Latitude": "Latitude coordinate of the country's location", - "Longitude": "Longitude coordinate of the country's location" - }, - "evaluation_metric": null, - "task_type": "regression", - "target": "highest_monthly_earnings", - "num_feature_value": { - "Gross tertiary education enrollment (%)": [ - 7.6, - 113.1 - ], - "Latitude": [ - -38.416097, - 61.92411 - ], - "Longitude": [ - -172.104629, - 138.252924 - ], - "Population": [ - 202506.0, - 1397715000.0 - ], - "Unemployment rate": [ - 0.75, - 14.72 - ], - "Urban_population": [ - 35588.0, - 842933962.0 - ], - "channel_type_rank": [ - 1.0, - 7741.0 - ], - "country_rank": [ - 1.0, - 7741.0 - ], - "created_date": [ - 1.0, - 31.0 - ], - "created_year": [ - 1970.0, - 2022.0 - ], - "highest_monthly_earnings": [ - 0.0, - 13600000.0 - ], - "highest_yearly_earnings": [ - 0.0, - 163400000.0 - ], - "lowest_monthly_earnings": [ - 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"China", - "Colombia", - "Cuba", - "Ecuador", - "Egypt", - "El Salvador", - "Finland", - "France", - "Germany", - "India", - "Indonesia", - "Iraq", - "Italy", - "Japan", - "Jordan", - "Kuwait", - "Latvia", - "Malaysia", - "Mexico", - "Morocco", - "Netherlands", - "Pakistan", - "Peru", - "Philippines", - "Russia", - "Samoa", - "Saudi Arabia", - "Singapore", - "South Korea", - "Spain", - "Sweden", - "Switzerland", - "Thailand", - "Turkey", - "Ukraine", - "United Arab Emirates", - "United Kingdom", - "United States", - "Venezuela", - "Vietnam" - ], - "Title": "string", - "Youtuber": "string", - "category": [ - "Autos & Vehicles", - "Comedy", - "Education", - "Entertainment", - "Film & Animation", - "Gaming", - "Howto & Style", - "Movies", - "Music", - "News & Politics", - "Nonprofits & Activism", - "People & Blogs", - "Pets & Animals", - "Science & Technology", - "Shows", - "Sports", - "Trailers", - "Travel & Events" - ], - "channel_type": [ - "Animals", - "Autos", - "Comedy", - "Education", - "Entertainment", - "Film", - "Games", - "Howto", - "Music", - "News", - "Nonprofit", - "People", - "Sports", - "Tech" - ], - "created_month": [ - "Apr", - "Aug", - "Dec", - "Feb", - "Jan", - "Jul", - "Jun", - "Mar", - "May", - "Nov", - "Oct", - "Sep" - ] - } -} \ No newline at end of file +{ + "name": "Global YouTube Statistics 2023", + "source": "https://www.kaggle.com/datasets/nelgiriyewithana/global-youtube-statistics-2023/data", + "data_intro": "A collection of YouTube giants, this dataset offers a perfect avenue to analyze and gain valuable insights from the luminaries of the platform. With comprehensive details on top creators' subscriber counts, video views, upload frequency, country of origin, earnings, and more, this treasure trove of information is a must-explore for aspiring content creators, data enthusiasts, and anyone intrigued by the ever-evolving online content landscape. Immerse yourself in the world of YouTube success and unlock a wealth of knowledge with this extraordinary dataset.", + "is_splited": false, + "overall_size": 995, + "train_size": 0, + "test_size": 0, + "c_classes": 7, + "n_classes": 21, + "cat_feature_intro": { + "Youtuber": "Name of the YouTube channel", + "category": "Category or niche of the channel", + "Title": "Title of the YouTube channel", + "Country": "Country associated with the channel.", + "Abbreviation": "Abbreviation of the country", + "channel_type": "Type or category of the channel.", + "created_month": "Month when the YouTube channel was created" + }, + "num_feature_intro": { + "rank": "Ranking of the channel based on its type (individual or brand)", + "subscribers": "Number of new subscribers gained in the last 30 days", + "video views": "Total video views in the last 30 days", + "uploads": "Total number of videos uploaded on the channel", + "video_views_rank": "Ranking of the channel based on total video views", + "country_rank": "Ranking of the channel based on the number of subscribers within its country", + "channel_type_rank": "Ranking of the channel based on its type (individual or brand)", + "video_views_for_the_last_30_days": "Total video views in the last 30 days", + "lowest_monthly_earnings": "Lowest estimated monthly earnings from the channel", + "highest_monthly_earnings": "Highest estimated monthly earnings from the channel", + "lowest_yearly_earnings": "Lowest estimated yearly earnings from the channel", + "highest_yearly_earnings": "Highest estimated yearly earnings from the channel", + "subscribers_for_last_30_days": "Number of new subscribers gained in the last 30 days", + "created_year": "Year when the YouTube channel was created", + "created_date": "Exact date of the YouTube channel's creation", + "Gross tertiary education enrollment (%)": "Percentage of the population enrolled in tertiary education in the country", + "Population": "Percentage of the population living in urban areas", + "Unemployment rate": "Unemployment rate in the country", + "Urban_population": "Percentage of the population living in urban areas", + "Latitude": "Latitude coordinate of the country's location", + "Longitude": "Longitude coordinate of the country's location" + }, + "evaluation_metric": null, + "task_type": "regression", + "target": "highest_monthly_earnings", + "num_feature_value": { + "Gross tertiary education enrollment (%)": [ + 7.6, + 113.1 + ], + "Latitude": [ + -38.416097, + 61.92411 + ], + "Longitude": [ + -172.104629, + 138.252924 + ], + "Population": [ + 202506.0, + 1397715000.0 + ], + "Unemployment rate": [ + 0.75, + 14.72 + ], + "Urban_population": [ + 35588.0, + 842933962.0 + ], + "channel_type_rank": [ + 1.0, + 7741.0 + ], + "country_rank": [ + 1.0, + 7741.0 + ], + "created_date": [ + 1.0, + 31.0 + ], + "created_year": [ + 1970.0, + 2022.0 + ], + "highest_monthly_earnings": [ + 0.0, + 13600000.0 + ], + "highest_yearly_earnings": [ + 0.0, + 163400000.0 + ], + "lowest_monthly_earnings": [ + 0.0, + 850900.0 + ], + "lowest_yearly_earnings": [ + 0.0, + 10200000.0 + ], + "rank": [ + 1.0, + 995.0 + ], + "subscribers": [ + 12300000.0, + 245000000.0 + ], + "subscribers_for_last_30_days": [ + 1.0, + 8000000.0 + ], + "uploads": [ + 0.0, + 301308.0 + ], + "video views": [ + 0.0, + 228000000000.0 + ], + "video_views_for_the_last_30_days": [ + 1.0, + 6589000000.0 + ], + "video_views_rank": [ + 1.0, + 4057944.0 + ] + }, + "cat_feature_value": { + "Abbreviation": [ + "AD", + "AE", + "AF", + "AR", + "AU", + "BB", + "BD", + "BR", + "CA", + "CH", + "CL", + "CN", + "CO", + "CU", + "DE", + "EC", + "EG", + "ES", + "FI", + "FR", + "GB", + "ID", + "IN", + "IQ", + "IT", + "JO", + "JP", + "KR", + "KW", + "LV", + "MA", + "MX", + "MY", + "NL", + "PE", + "PH", + "PK", + "RU", + "SA", + "SE", + "SG", + "SV", + "TH", + "TR", + "UA", + "US", + "VE", + "VN", + "WS" + ], + "Country": [ + "Afghanistan", + "Andorra", + "Argentina", + "Australia", + "Bangladesh", + "Barbados", + "Brazil", + "Canada", + "Chile", + "China", + "Colombia", + "Cuba", + "Ecuador", + "Egypt", + "El Salvador", + "Finland", + "France", + "Germany", + "India", + "Indonesia", + "Iraq", + "Italy", + "Japan", + "Jordan", + "Kuwait", + "Latvia", + "Malaysia", + "Mexico", + "Morocco", + "Netherlands", + "Pakistan", + "Peru", + "Philippines", + "Russia", + "Samoa", + "Saudi Arabia", + "Singapore", + "South Korea", + "Spain", + "Sweden", + "Switzerland", + "Thailand", + "Turkey", + "Ukraine", + "United Arab Emirates", + "United Kingdom", + "United States", + "Venezuela", + "Vietnam" + ], + "Title": "string", + "Youtuber": "string", + "category": [ + "Autos & Vehicles", + "Comedy", + "Education", + "Entertainment", + "Film & Animation", + "Gaming", + "Howto & Style", + "Movies", + "Music", + "News & Politics", + "Nonprofits & Activism", + "People & Blogs", + "Pets & Animals", + "Science & Technology", + "Shows", + "Sports", + "Trailers", + "Travel & Events" + ], + "channel_type": [ + "Animals", + "Autos", + "Comedy", + "Education", + "Entertainment", + "Film", + "Games", + "Howto", + "Music", + "News", + "Nonprofit", + "People", + "Sports", + "Tech" + ], + "created_month": [ + "Apr", + "Aug", + "Dec", + "Feb", + "Jan", + "Jul", + "Jun", + "Mar", + "May", + "Nov", + "Oct", + "Sep" + ] + } +} diff --git a/ranking_and_filtering/daily/regression/Global_YouTube_Statistics_2023_B4/Global_YouTube_Statistics_2023_B4_003/info.json b/ranking_and_filtering/daily/regression/Global_YouTube_Statistics_2023_B4/Global_YouTube_Statistics_2023_B4_003/info.json index 32b2e724985eb4b8c7e1935cfdd4e4510dbfb775..2a4d0bb2870f7f26e8f34e8121139aaccc4b4a47 100644 --- a/ranking_and_filtering/daily/regression/Global_YouTube_Statistics_2023_B4/Global_YouTube_Statistics_2023_B4_003/info.json +++ b/ranking_and_filtering/daily/regression/Global_YouTube_Statistics_2023_B4/Global_YouTube_Statistics_2023_B4_003/info.json @@ -1,288 +1,288 @@ -{ - "name": "Global YouTube Statistics 2023", - "source": "https://www.kaggle.com/datasets/nelgiriyewithana/global-youtube-statistics-2023/data", - "data_intro": "A collection of YouTube giants, this dataset offers a perfect avenue to analyze and gain valuable insights from the luminaries of the platform. With comprehensive details on top creators' subscriber counts, video views, upload frequency, country of origin, earnings, and more, this treasure trove of information is a must-explore for aspiring content creators, data enthusiasts, and anyone intrigued by the ever-evolving online content landscape. Immerse yourself in the world of YouTube success and unlock a wealth of knowledge with this extraordinary dataset.", - "is_splited": false, - "overall_size": 995, - "train_size": 0, - "test_size": 0, - "c_classes": 7, - "n_classes": 21, - "cat_feature_intro": { - "Youtuber": "Name of the YouTube channel", - "category": "Category or niche of the channel", - "Title": "Title of the YouTube channel", - "Country": "Longitude coordinate of the country's location", - "Abbreviation": "Abbreviation of the country", - "channel_type": "Ranking of the channel based on its type (individual or brand)", - "created_month": "Month when the YouTube channel was created" - }, - "num_feature_intro": { - "rank": "Ranking of the channel based on its type (individual or brand)", - "subscribers": "Number of new subscribers gained in the last 30 days", - "video views": "Total video views in the last 30 days", - "uploads": "Total number of videos uploaded on the channel", - "video_views_rank": "Ranking of the channel based on total video views", - "country_rank": "Ranking of the channel based on the number of subscribers within its country", - "channel_type_rank": "Ranking of the channel based on its type (individual or brand)", - "video_views_for_the_last_30_days": "Total video views in the last 30 days", - "lowest_monthly_earnings": "Lowest estimated monthly earnings from the channel", - "highest_monthly_earnings": "Highest estimated monthly earnings from the channel", - "lowest_yearly_earnings": "Lowest estimated yearly earnings from the channel", - "highest_yearly_earnings": "Highest estimated yearly earnings from the channel", - "subscribers_for_last_30_days": "Number of new subscribers gained in the last 30 days", - "created_year": "Year when the YouTube channel was created", - "created_date": "Exact date of the YouTube channel's creation", - "Gross tertiary education enrollment (%)": "Percentage of the population enrolled in tertiary education in the country", - "Population": "Percentage of the population living in urban areas", - "Unemployment rate": "Unemployment rate in the country", - "Urban_population": "Percentage of the population living in urban areas", - "Latitude": "Latitude coordinate of the country's location", - "Longitude": "Longitude coordinate of the country's location" - }, - "evaluation_metric": null, - "task_type": "regression", - "target": "highest_monthly_earnings", - "num_feature_value": { - "Gross tertiary education enrollment (%)": [ - 7.6, - 113.1 - ], - "Latitude": [ - -38.416097, - 61.92411 - ], - "Longitude": [ - -172.104629, - 138.252924 - ], - "Population": [ - 202506.0, - 1397715000.0 - ], - "Unemployment rate": [ - 0.75, - 14.72 - ], - "Urban_population": [ - 35588.0, - 842933962.0 - ], - "channel_type_rank": [ - 1.0, - 7741.0 - ], - "country_rank": [ - 1.0, - 7741.0 - ], - "created_date": [ - 1.0, - 31.0 - ], - "created_year": [ - 1970.0, - 2022.0 - ], - "highest_monthly_earnings": [ - 0.0, - 13600000.0 - ], - "highest_yearly_earnings": [ - 0.0, - 163400000.0 - ], - "lowest_monthly_earnings": [ - 0.0, - 850900.0 - ], - "lowest_yearly_earnings": [ - 0.0, - 10200000.0 - ], - "rank": [ - 1.0, - 995.0 - ], - "subscribers": [ - 12300000.0, - 245000000.0 - ], - "subscribers_for_last_30_days": [ - 1.0, - 8000000.0 - ], - "uploads": [ - 0.0, - 301308.0 - ], - "video views": [ - 0.0, - 228000000000.0 - ], - "video_views_for_the_last_30_days": [ - 1.0, - 6589000000.0 - ], - "video_views_rank": [ - 1.0, - 4057944.0 - ] - }, - "cat_feature_value": { - "Abbreviation": [ - "AD", - "AE", - "AF", - "AR", - "AU", - "BB", - "BD", - "BR", - "CA", - "CH", - "CL", - "CN", - "CO", - "CU", - "DE", - "EC", - "EG", - "ES", - "FI", - "FR", - "GB", - "ID", - "IN", - "IQ", - "IT", - "JO", - "JP", - "KR", - "KW", - "LV", - "MA", - "MX", - "MY", - "NL", - "PE", - "PH", - "PK", - "RU", - "SA", - "SE", - "SG", - "SV", - "TH", - "TR", - "UA", - "US", - "VE", - "VN", - "WS" - ], - "Country": [ - "Afghanistan", - "Andorra", - "Argentina", - "Australia", - "Bangladesh", - "Barbados", - "Brazil", - "Canada", - "Chile", - "China", - "Colombia", - "Cuba", - "Ecuador", - "Egypt", - "El Salvador", - "Finland", - "France", - "Germany", - "India", - "Indonesia", - "Iraq", - "Italy", - "Japan", - "Jordan", - "Kuwait", - "Latvia", - "Malaysia", - "Mexico", - "Morocco", - "Netherlands", - "Pakistan", - "Peru", - "Philippines", - "Russia", - "Samoa", - "Saudi Arabia", - "Singapore", - "South Korea", - "Spain", - "Sweden", - "Switzerland", - "Thailand", - "Turkey", - "Ukraine", - "United Arab Emirates", - "United Kingdom", - "United States", - "Venezuela", - "Vietnam" - ], - "Title": "string", - "Youtuber": "string", - "category": [ - "Autos & Vehicles", - "Comedy", - "Education", - "Entertainment", - "Film & Animation", - "Gaming", - "Howto & Style", - "Movies", - "Music", - "News & Politics", - "Nonprofits & Activism", - "People & Blogs", - "Pets & Animals", - "Science & Technology", - "Shows", - "Sports", - "Trailers", - "Travel & Events" - ], - "channel_type": [ - "Animals", - "Autos", - "Comedy", - "Education", - "Entertainment", - "Film", - "Games", - "Howto", - "Music", - "News", - "Nonprofit", - "People", - "Sports", - "Tech" - ], - "created_month": [ - "Apr", - "Aug", - "Dec", - "Feb", - "Jan", - "Jul", - "Jun", - "Mar", - "May", - "Nov", - "Oct", - "Sep" - ] - } -} \ No newline at end of file +{ + "name": "Global YouTube Statistics 2023", + "source": "https://www.kaggle.com/datasets/nelgiriyewithana/global-youtube-statistics-2023/data", + "data_intro": "A collection of YouTube giants, this dataset offers a perfect avenue to analyze and gain valuable insights from the luminaries of the platform. With comprehensive details on top creators' subscriber counts, video views, upload frequency, country of origin, earnings, and more, this treasure trove of information is a must-explore for aspiring content creators, data enthusiasts, and anyone intrigued by the ever-evolving online content landscape. Immerse yourself in the world of YouTube success and unlock a wealth of knowledge with this extraordinary dataset.", + "is_splited": false, + "overall_size": 995, + "train_size": 0, + "test_size": 0, + "c_classes": 7, + "n_classes": 21, + "cat_feature_intro": { + "Youtuber": "Name of the YouTube channel", + "category": "Category or niche of the channel", + "Title": "Title of the YouTube channel", + "Country": "Country associated with the channel.", + "Abbreviation": "Abbreviation of the country", + "channel_type": "Type or category of the channel.", + "created_month": "Month when the YouTube channel was created" + }, + "num_feature_intro": { + "rank": "Ranking of the channel based on its type (individual or brand)", + "subscribers": "Number of new subscribers gained in the last 30 days", + "video views": "Total video views in the last 30 days", + "uploads": "Total number of videos uploaded on the channel", + "video_views_rank": "Ranking of the channel based on total video views", + "country_rank": "Ranking of the channel based on the number of subscribers within its country", + "channel_type_rank": "Ranking of the channel based on its type (individual or brand)", + "video_views_for_the_last_30_days": "Total video views in the last 30 days", + "lowest_monthly_earnings": "Lowest estimated monthly earnings from the channel", + "highest_monthly_earnings": "Highest estimated monthly earnings from the channel", + "lowest_yearly_earnings": "Lowest estimated yearly earnings from the channel", + "highest_yearly_earnings": "Highest estimated yearly earnings from the channel", + "subscribers_for_last_30_days": "Number of new subscribers gained in the last 30 days", + "created_year": "Year when the YouTube channel was created", + "created_date": "Exact date of the YouTube channel's creation", + "Gross tertiary education enrollment (%)": "Percentage of the population enrolled in tertiary education in the country", + "Population": "Percentage of the population living in urban areas", + "Unemployment rate": "Unemployment rate in the country", + "Urban_population": "Percentage of the population living in urban areas", + "Latitude": "Latitude coordinate of the country's location", + "Longitude": "Longitude coordinate of the country's location" + }, + "evaluation_metric": null, + "task_type": "regression", + "target": "highest_monthly_earnings", + "num_feature_value": { + "Gross tertiary education enrollment (%)": [ + 7.6, + 113.1 + ], + "Latitude": [ + -38.416097, + 61.92411 + ], + "Longitude": [ + -172.104629, + 138.252924 + ], + "Population": [ + 202506.0, + 1397715000.0 + ], + "Unemployment rate": [ + 0.75, + 14.72 + ], + "Urban_population": [ + 35588.0, + 842933962.0 + ], + "channel_type_rank": [ + 1.0, + 7741.0 + ], + "country_rank": [ + 1.0, + 7741.0 + ], + "created_date": [ + 1.0, + 31.0 + ], + "created_year": [ + 1970.0, + 2022.0 + ], + "highest_monthly_earnings": [ + 0.0, + 13600000.0 + ], + "highest_yearly_earnings": [ + 0.0, + 163400000.0 + ], + "lowest_monthly_earnings": [ + 0.0, + 850900.0 + ], + "lowest_yearly_earnings": [ + 0.0, + 10200000.0 + ], + "rank": [ + 1.0, + 995.0 + ], + "subscribers": [ + 12300000.0, + 245000000.0 + ], + "subscribers_for_last_30_days": [ + 1.0, + 8000000.0 + ], + "uploads": [ + 0.0, + 301308.0 + ], + "video views": [ + 0.0, + 228000000000.0 + ], + "video_views_for_the_last_30_days": [ + 1.0, + 6589000000.0 + ], + "video_views_rank": [ + 1.0, + 4057944.0 + ] + }, + "cat_feature_value": { + "Abbreviation": [ + "AD", + "AE", + "AF", + "AR", + "AU", + "BB", + "BD", + "BR", + "CA", + "CH", + "CL", + "CN", + "CO", + "CU", + "DE", + "EC", + "EG", + "ES", + "FI", + "FR", + "GB", + "ID", + "IN", + "IQ", + "IT", + "JO", + "JP", + "KR", + "KW", + "LV", + "MA", + "MX", + "MY", + "NL", + "PE", + "PH", + "PK", + "RU", + "SA", + "SE", + "SG", + "SV", + "TH", + "TR", + "UA", + "US", + "VE", + "VN", + "WS" + ], + "Country": [ + "Afghanistan", + "Andorra", + "Argentina", + "Australia", + "Bangladesh", + "Barbados", + "Brazil", + "Canada", + "Chile", + "China", + "Colombia", + "Cuba", + "Ecuador", + "Egypt", + "El Salvador", + "Finland", + "France", + "Germany", + "India", + "Indonesia", + "Iraq", + "Italy", + "Japan", + "Jordan", + "Kuwait", + "Latvia", + "Malaysia", + "Mexico", + "Morocco", + "Netherlands", + "Pakistan", + "Peru", + "Philippines", + "Russia", + "Samoa", + "Saudi Arabia", + "Singapore", + "South Korea", + "Spain", + "Sweden", + "Switzerland", + "Thailand", + "Turkey", + "Ukraine", + "United Arab Emirates", + "United Kingdom", + "United States", + "Venezuela", + "Vietnam" + ], + "Title": "string", + "Youtuber": "string", + "category": [ + "Autos & Vehicles", + "Comedy", + "Education", + "Entertainment", + "Film & Animation", + "Gaming", + "Howto & Style", + "Movies", + "Music", + "News & Politics", + "Nonprofits & Activism", + "People & Blogs", + "Pets & Animals", + "Science & Technology", + "Shows", + "Sports", + "Trailers", + "Travel & Events" + ], + "channel_type": [ + "Animals", + "Autos", + "Comedy", + "Education", + "Entertainment", + "Film", + "Games", + "Howto", + "Music", + "News", + "Nonprofit", + "People", + "Sports", + "Tech" + ], + "created_month": [ + "Apr", + "Aug", + "Dec", + "Feb", + "Jan", + "Jul", + "Jun", + "Mar", + "May", + "Nov", + "Oct", + "Sep" + ] + } +} diff --git a/ranking_and_filtering/daily/regression/Global_YouTube_Statistics_2023_B4/Global_YouTube_Statistics_2023_B4_004/Global_YouTube_Statistics_2023_B4_004_current.json b/ranking_and_filtering/daily/regression/Global_YouTube_Statistics_2023_B4/Global_YouTube_Statistics_2023_B4_004/Global_YouTube_Statistics_2023_B4_004_current.json index a6083eeb58120454c9153136282dacf6138da757..7997bc9d4dbfe94bafa98c8353d42968162a8746 100644 --- a/ranking_and_filtering/daily/regression/Global_YouTube_Statistics_2023_B4/Global_YouTube_Statistics_2023_B4_004/Global_YouTube_Statistics_2023_B4_004_current.json +++ b/ranking_and_filtering/daily/regression/Global_YouTube_Statistics_2023_B4/Global_YouTube_Statistics_2023_B4_004/Global_YouTube_Statistics_2023_B4_004_current.json @@ -1,6205 +1,6205 @@ { - "id": "004", - "task_type": "B4", - "subtask_type": "topk", - "perspective": "data_holder", - "dataset_name": "Global YouTube Statistics 2023", - "table_path": "output/Global YouTube Statistics 2023_B4/Global YouTube Statistics 2023_B4_004/test.csv", - "query": "For our new incubator program supporting emerging talent, we have initial slots for 7 creators. From the file I shared, shortlist the 7 with the smallest values in the highest_monthly_earnings so we can offer them targeted growth resources.", - "meta_info": { - "domain": "daily" + "id": "004", + "task_type": "B4", + "subtask_type": "topk", + "perspective": "data_holder", + "dataset_name": "Global YouTube Statistics 2023", + "table_path": "output/Global YouTube Statistics 2023_B4/Global YouTube Statistics 2023_B4_004/test.csv", + "query": "For our new incubator program supporting emerging talent, we have initial slots for 7 creators. 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b/ranking_and_filtering/daily/regression/Global_YouTube_Statistics_2023_B4/Global_YouTube_Statistics_2023_B4_004/info.json @@ -1,288 +1,288 @@ -{ - "name": "Global YouTube Statistics 2023", - "source": "https://www.kaggle.com/datasets/nelgiriyewithana/global-youtube-statistics-2023/data", - "data_intro": "A collection of YouTube giants, this dataset offers a perfect avenue to analyze and gain valuable insights from the luminaries of the platform. 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Immerse yourself in the world of YouTube success and unlock a wealth of knowledge with this extraordinary dataset.", - "is_splited": false, - "overall_size": 995, - "train_size": 0, - "test_size": 0, - "c_classes": 7, - "n_classes": 21, - "cat_feature_intro": { - "Youtuber": "Name of the YouTube channel", - "category": "Category or niche of the channel", - "Title": "Title of the YouTube channel", - "Country": "Longitude coordinate of the country's location", - "Abbreviation": "Abbreviation of the country", - "channel_type": "Ranking of the channel based on its type (individual or brand)", - "created_month": "Month when the YouTube channel was created" - }, - "num_feature_intro": { - "rank": "Ranking of the channel based on its type (individual or brand)", - "subscribers": "Number of new subscribers gained in the last 30 days", - "video views": "Total video views in the last 30 days", - "uploads": "Total number of videos uploaded on the channel", - "video_views_rank": "Ranking of the channel based on total video views", - "country_rank": "Ranking of the channel based on the number of subscribers within its country", - "channel_type_rank": "Ranking of the channel based on its type (individual or brand)", - "video_views_for_the_last_30_days": "Total video views in the last 30 days", - "lowest_monthly_earnings": "Lowest estimated monthly earnings from the channel", - "highest_monthly_earnings": "Highest estimated monthly earnings from the channel", - "lowest_yearly_earnings": "Lowest estimated yearly earnings from the channel", - "highest_yearly_earnings": "Highest estimated yearly earnings from the channel", - "subscribers_for_last_30_days": "Number of new subscribers gained in the last 30 days", - "created_year": "Year when the YouTube channel was created", - "created_date": "Exact date of the YouTube channel's creation", - "Gross tertiary education enrollment (%)": "Percentage of the population enrolled in tertiary education in the country", - "Population": "Percentage of the population living in urban areas", - "Unemployment rate": "Unemployment rate in the country", - "Urban_population": "Percentage of the population living in urban areas", - "Latitude": "Latitude coordinate of the country's location", - "Longitude": "Longitude coordinate of the country's location" - }, - "evaluation_metric": null, - "task_type": "regression", - "target": "highest_monthly_earnings", - "num_feature_value": { - "Gross tertiary education enrollment (%)": [ - 7.6, - 113.1 - ], - "Latitude": [ - -38.416097, - 61.92411 - ], - "Longitude": [ - -172.104629, - 138.252924 - ], - "Population": [ - 202506.0, - 1397715000.0 - ], - "Unemployment rate": [ - 0.75, - 14.72 - ], - "Urban_population": [ - 35588.0, - 842933962.0 - ], - "channel_type_rank": [ - 1.0, - 7741.0 - ], - "country_rank": [ - 1.0, - 7741.0 - ], - "created_date": [ - 1.0, - 31.0 - ], - "created_year": [ - 1970.0, - 2022.0 - ], - "highest_monthly_earnings": [ - 0.0, - 13600000.0 - ], - "highest_yearly_earnings": [ - 0.0, - 163400000.0 - ], - "lowest_monthly_earnings": [ - 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"China", - "Colombia", - "Cuba", - "Ecuador", - "Egypt", - "El Salvador", - "Finland", - "France", - "Germany", - "India", - "Indonesia", - "Iraq", - "Italy", - "Japan", - "Jordan", - "Kuwait", - "Latvia", - "Malaysia", - "Mexico", - "Morocco", - "Netherlands", - "Pakistan", - "Peru", - "Philippines", - "Russia", - "Samoa", - "Saudi Arabia", - "Singapore", - "South Korea", - "Spain", - "Sweden", - "Switzerland", - "Thailand", - "Turkey", - "Ukraine", - "United Arab Emirates", - "United Kingdom", - "United States", - "Venezuela", - "Vietnam" - ], - "Title": "string", - "Youtuber": "string", - "category": [ - "Autos & Vehicles", - "Comedy", - "Education", - "Entertainment", - "Film & Animation", - "Gaming", - "Howto & Style", - "Movies", - "Music", - "News & Politics", - "Nonprofits & Activism", - "People & Blogs", - "Pets & Animals", - "Science & Technology", - "Shows", - "Sports", - "Trailers", - "Travel & Events" - ], - "channel_type": [ - "Animals", - "Autos", - "Comedy", - "Education", - "Entertainment", - "Film", - "Games", - "Howto", - "Music", - "News", - "Nonprofit", - "People", - "Sports", - "Tech" - ], - "created_month": [ - "Apr", - "Aug", - "Dec", - "Feb", - "Jan", - "Jul", - "Jun", - "Mar", - "May", - "Nov", - "Oct", - "Sep" - ] - } -} \ No newline at end of file +{ + "name": "Global YouTube Statistics 2023", + "source": "https://www.kaggle.com/datasets/nelgiriyewithana/global-youtube-statistics-2023/data", + "data_intro": "A collection of YouTube giants, this dataset offers a perfect avenue to analyze and gain valuable insights from the luminaries of the platform. With comprehensive details on top creators' subscriber counts, video views, upload frequency, country of origin, earnings, and more, this treasure trove of information is a must-explore for aspiring content creators, data enthusiasts, and anyone intrigued by the ever-evolving online content landscape. Immerse yourself in the world of YouTube success and unlock a wealth of knowledge with this extraordinary dataset.", + "is_splited": false, + "overall_size": 995, + "train_size": 0, + "test_size": 0, + "c_classes": 7, + "n_classes": 21, + "cat_feature_intro": { + "Youtuber": "Name of the YouTube channel", + "category": "Category or niche of the channel", + "Title": "Title of the YouTube channel", + "Country": "Country associated with the channel.", + "Abbreviation": "Abbreviation of the country", + "channel_type": "Type or category of the channel.", + "created_month": "Month when the YouTube channel was created" + }, + "num_feature_intro": { + "rank": "Ranking of the channel based on its type (individual or brand)", + "subscribers": "Number of new subscribers gained in the last 30 days", + "video views": "Total video views in the last 30 days", + "uploads": "Total number of videos uploaded on the channel", + "video_views_rank": "Ranking of the channel based on total video views", + "country_rank": "Ranking of the channel based on the number of subscribers within its country", + "channel_type_rank": "Ranking of the channel based on its type (individual or brand)", + "video_views_for_the_last_30_days": "Total video views in the last 30 days", + "lowest_monthly_earnings": "Lowest estimated monthly earnings from the channel", + "highest_monthly_earnings": "Highest estimated monthly earnings from the channel", + "lowest_yearly_earnings": "Lowest estimated yearly earnings from the channel", + "highest_yearly_earnings": "Highest estimated yearly earnings from the channel", + "subscribers_for_last_30_days": "Number of new subscribers gained in the last 30 days", + "created_year": "Year when the YouTube channel was created", + "created_date": "Exact date of the YouTube channel's creation", + "Gross tertiary education enrollment (%)": "Percentage of the population enrolled in tertiary education in the country", + "Population": "Percentage of the population living in urban 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"lowest_monthly_earnings": [ + 0.0, + 850900.0 + ], + "lowest_yearly_earnings": [ + 0.0, + 10200000.0 + ], + "rank": [ + 1.0, + 995.0 + ], + "subscribers": [ + 12300000.0, + 245000000.0 + ], + "subscribers_for_last_30_days": [ + 1.0, + 8000000.0 + ], + "uploads": [ + 0.0, + 301308.0 + ], + "video views": [ + 0.0, + 228000000000.0 + ], + "video_views_for_the_last_30_days": [ + 1.0, + 6589000000.0 + ], + "video_views_rank": [ + 1.0, + 4057944.0 + ] + }, + "cat_feature_value": { + "Abbreviation": [ + "AD", + "AE", + "AF", + "AR", + "AU", + "BB", + "BD", + "BR", + "CA", + "CH", + "CL", + "CN", + "CO", + "CU", + "DE", + "EC", + "EG", + "ES", + "FI", + "FR", + "GB", + "ID", + "IN", + "IQ", + "IT", + "JO", + "JP", + "KR", + "KW", + "LV", + "MA", + "MX", + "MY", + "NL", + "PE", + "PH", + "PK", + "RU", + "SA", + "SE", + "SG", + "SV", + "TH", + "TR", + "UA", + "US", + "VE", + "VN", + "WS" + ], + "Country": [ + "Afghanistan", + "Andorra", + "Argentina", + "Australia", + "Bangladesh", + "Barbados", + "Brazil", + "Canada", + "Chile", + "China", + "Colombia", + "Cuba", + "Ecuador", + "Egypt", + "El Salvador", + "Finland", + "France", + "Germany", + "India", + "Indonesia", + "Iraq", + "Italy", + "Japan", + "Jordan", + "Kuwait", + "Latvia", + "Malaysia", + "Mexico", + "Morocco", + "Netherlands", + "Pakistan", + "Peru", + "Philippines", + "Russia", + "Samoa", + "Saudi Arabia", + "Singapore", + "South Korea", + "Spain", + "Sweden", + "Switzerland", + "Thailand", + "Turkey", + "Ukraine", + "United Arab Emirates", + "United Kingdom", + "United States", + "Venezuela", + "Vietnam" + ], + "Title": "string", + "Youtuber": "string", + "category": [ + "Autos & Vehicles", + "Comedy", + "Education", + "Entertainment", + "Film & Animation", + "Gaming", + "Howto & Style", + "Movies", + "Music", + "News & Politics", + "Nonprofits & Activism", + "People & Blogs", + "Pets & Animals", + "Science & Technology", + "Shows", + "Sports", + "Trailers", + "Travel & Events" + ], + "channel_type": [ + "Animals", + "Autos", + "Comedy", + "Education", + "Entertainment", + "Film", + "Games", + "Howto", + "Music", + "News", + "Nonprofit", + "People", + "Sports", + "Tech" + ], + "created_month": [ + "Apr", + "Aug", + "Dec", + "Feb", + "Jan", + "Jul", + "Jun", + "Mar", + "May", + "Nov", + "Oct", + "Sep" + ] + } +} diff --git a/ranking_and_filtering/daily/regression/Global_YouTube_Statistics_2023_B4/Global_YouTube_Statistics_2023_B4_005/info.json b/ranking_and_filtering/daily/regression/Global_YouTube_Statistics_2023_B4/Global_YouTube_Statistics_2023_B4_005/info.json index 32b2e724985eb4b8c7e1935cfdd4e4510dbfb775..2a4d0bb2870f7f26e8f34e8121139aaccc4b4a47 100644 --- a/ranking_and_filtering/daily/regression/Global_YouTube_Statistics_2023_B4/Global_YouTube_Statistics_2023_B4_005/info.json +++ b/ranking_and_filtering/daily/regression/Global_YouTube_Statistics_2023_B4/Global_YouTube_Statistics_2023_B4_005/info.json @@ -1,288 +1,288 @@ -{ - "name": "Global YouTube Statistics 2023", - "source": "https://www.kaggle.com/datasets/nelgiriyewithana/global-youtube-statistics-2023/data", - "data_intro": "A collection of YouTube giants, this dataset offers a perfect avenue to analyze and gain valuable insights from the luminaries of the platform. With comprehensive details on top creators' subscriber counts, video views, upload frequency, country of origin, earnings, and more, this treasure trove of information is a must-explore for aspiring content creators, data enthusiasts, and anyone intrigued by the ever-evolving online content landscape. Immerse yourself in the world of YouTube success and unlock a wealth of knowledge with this extraordinary dataset.", - "is_splited": false, - "overall_size": 995, - "train_size": 0, - "test_size": 0, - "c_classes": 7, - "n_classes": 21, - "cat_feature_intro": { - "Youtuber": "Name of the YouTube channel", - "category": "Category or niche of the channel", - "Title": "Title of the YouTube channel", - "Country": "Longitude coordinate of the country's location", - "Abbreviation": "Abbreviation of the country", - "channel_type": "Ranking of the channel based on its type (individual or brand)", - "created_month": "Month when the YouTube channel was created" - }, - "num_feature_intro": { - "rank": "Ranking of the channel based on its type (individual or brand)", - "subscribers": "Number of new subscribers gained in the last 30 days", - "video views": "Total video views in the last 30 days", - "uploads": "Total number of videos uploaded on the channel", - "video_views_rank": "Ranking of the channel based on total video views", - "country_rank": "Ranking of the channel based on the number of subscribers within its country", - "channel_type_rank": "Ranking of the channel based on its type (individual or brand)", - "video_views_for_the_last_30_days": "Total video views in the last 30 days", - "lowest_monthly_earnings": "Lowest estimated monthly earnings from the channel", - "highest_monthly_earnings": "Highest estimated monthly earnings from the channel", - "lowest_yearly_earnings": "Lowest estimated yearly earnings from the channel", - "highest_yearly_earnings": "Highest estimated yearly earnings from the channel", - "subscribers_for_last_30_days": "Number of new subscribers gained in the last 30 days", - "created_year": "Year when the YouTube channel was created", - "created_date": "Exact date of the YouTube channel's creation", - "Gross tertiary education enrollment (%)": "Percentage of the population enrolled in tertiary education in the country", - "Population": "Percentage of the population living in urban areas", - "Unemployment rate": "Unemployment rate in the country", - "Urban_population": "Percentage of the population living in urban areas", - "Latitude": "Latitude coordinate of the country's location", - "Longitude": "Longitude coordinate of the country's location" - }, - "evaluation_metric": null, - "task_type": "regression", - "target": "highest_monthly_earnings", - "num_feature_value": { - "Gross tertiary education enrollment (%)": [ - 7.6, - 113.1 - ], - "Latitude": [ - -38.416097, - 61.92411 - ], - "Longitude": [ - -172.104629, - 138.252924 - ], - "Population": [ - 202506.0, - 1397715000.0 - ], - "Unemployment rate": [ - 0.75, - 14.72 - ], - "Urban_population": [ - 35588.0, - 842933962.0 - ], - "channel_type_rank": [ - 1.0, - 7741.0 - ], - "country_rank": [ - 1.0, - 7741.0 - ], - "created_date": [ - 1.0, - 31.0 - ], - "created_year": [ - 1970.0, - 2022.0 - ], - "highest_monthly_earnings": [ - 0.0, - 13600000.0 - ], - "highest_yearly_earnings": [ - 0.0, - 163400000.0 - ], - "lowest_monthly_earnings": [ - 0.0, - 850900.0 - ], - "lowest_yearly_earnings": [ - 0.0, - 10200000.0 - ], - "rank": [ - 1.0, - 995.0 - ], - "subscribers": [ - 12300000.0, - 245000000.0 - ], - "subscribers_for_last_30_days": [ - 1.0, - 8000000.0 - ], - "uploads": [ - 0.0, - 301308.0 - ], - "video views": [ - 0.0, - 228000000000.0 - ], - "video_views_for_the_last_30_days": [ - 1.0, - 6589000000.0 - ], - "video_views_rank": [ - 1.0, - 4057944.0 - ] - }, - "cat_feature_value": { - "Abbreviation": [ - "AD", - "AE", - "AF", - "AR", - "AU", - "BB", - "BD", - "BR", - "CA", - "CH", - "CL", - "CN", - "CO", - "CU", - "DE", - "EC", - "EG", - "ES", - "FI", - "FR", - "GB", - "ID", - "IN", - "IQ", - "IT", - "JO", - "JP", - "KR", - "KW", - "LV", - "MA", - "MX", - "MY", - "NL", - "PE", - "PH", - "PK", - "RU", - "SA", - "SE", - "SG", - "SV", - "TH", - "TR", - "UA", - "US", - "VE", - "VN", - "WS" - ], - "Country": [ - "Afghanistan", - "Andorra", - "Argentina", - "Australia", - "Bangladesh", - "Barbados", - "Brazil", - "Canada", - "Chile", - "China", - "Colombia", - "Cuba", - "Ecuador", - "Egypt", - "El Salvador", - "Finland", - "France", - "Germany", - "India", - "Indonesia", - "Iraq", - "Italy", - "Japan", - "Jordan", - "Kuwait", - "Latvia", - "Malaysia", - "Mexico", - "Morocco", - "Netherlands", - "Pakistan", - "Peru", - "Philippines", - "Russia", - "Samoa", - "Saudi Arabia", - "Singapore", - "South Korea", - "Spain", - "Sweden", - "Switzerland", - "Thailand", - "Turkey", - "Ukraine", - "United Arab Emirates", - "United Kingdom", - "United States", - "Venezuela", - "Vietnam" - ], - "Title": "string", - "Youtuber": "string", - "category": [ - "Autos & Vehicles", - "Comedy", - "Education", - "Entertainment", - "Film & Animation", - "Gaming", - "Howto & Style", - "Movies", - "Music", - "News & Politics", - "Nonprofits & Activism", - "People & Blogs", - "Pets & Animals", - "Science & Technology", - "Shows", - "Sports", - "Trailers", - "Travel & Events" - ], - "channel_type": [ - "Animals", - "Autos", - "Comedy", - "Education", - 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With comprehensive details on top creators' subscriber counts, video views, upload frequency, country of origin, earnings, and more, this treasure trove of information is a must-explore for aspiring content creators, data enthusiasts, and anyone intrigued by the ever-evolving online content landscape. Immerse yourself in the world of YouTube success and unlock a wealth of knowledge with this extraordinary dataset.", + "is_splited": false, + "overall_size": 995, + "train_size": 0, + "test_size": 0, + "c_classes": 7, + "n_classes": 21, + "cat_feature_intro": { + "Youtuber": "Name of the YouTube channel", + "category": "Category or niche of the channel", + "Title": "Title of the YouTube channel", + "Country": "Country associated with the channel.", + "Abbreviation": "Abbreviation of the country", + "channel_type": "Type or category of the channel.", + "created_month": "Month when the YouTube channel was created" + }, + "num_feature_intro": { + "rank": "Ranking of the channel based on its type (individual or brand)", + "subscribers": "Number of new subscribers gained in the last 30 days", + "video views": "Total video views in the last 30 days", + "uploads": "Total number of videos uploaded on the channel", + "video_views_rank": "Ranking of the channel based on total video views", + "country_rank": "Ranking of the channel based on the number of subscribers within its country", + "channel_type_rank": "Ranking of the channel based on its type (individual or brand)", + "video_views_for_the_last_30_days": "Total video views in the last 30 days", + "lowest_monthly_earnings": "Lowest estimated monthly earnings from the channel", + "highest_monthly_earnings": "Highest estimated monthly earnings from the channel", + "lowest_yearly_earnings": "Lowest estimated yearly earnings from the channel", + "highest_yearly_earnings": "Highest estimated yearly earnings from the channel", + "subscribers_for_last_30_days": "Number of new subscribers gained in the last 30 days", + "created_year": "Year when the YouTube channel was created", + "created_date": "Exact date of the YouTube channel's creation", + "Gross tertiary education enrollment (%)": "Percentage of the population enrolled in tertiary education in the country", + "Population": "Percentage of the population living in urban areas", + "Unemployment rate": "Unemployment rate in the country", + "Urban_population": "Percentage of the population living in urban areas", + "Latitude": "Latitude coordinate of the country's location", + "Longitude": "Longitude coordinate of the country's location" + }, + "evaluation_metric": null, + "task_type": "regression", + "target": "highest_monthly_earnings", + "num_feature_value": { + "Gross tertiary education enrollment (%)": [ + 7.6, + 113.1 + ], + "Latitude": [ + -38.416097, + 61.92411 + ], + "Longitude": [ + -172.104629, + 138.252924 + ], + "Population": [ + 202506.0, + 1397715000.0 + ], + "Unemployment rate": [ + 0.75, + 14.72 + ], + "Urban_population": [ + 35588.0, + 842933962.0 + ], + "channel_type_rank": [ + 1.0, + 7741.0 + ], + "country_rank": [ + 1.0, + 7741.0 + ], + "created_date": [ + 1.0, + 31.0 + ], + "created_year": [ + 1970.0, + 2022.0 + ], + "highest_monthly_earnings": [ + 0.0, + 13600000.0 + ], + "highest_yearly_earnings": [ + 0.0, + 163400000.0 + ], + "lowest_monthly_earnings": [ + 0.0, + 850900.0 + ], + "lowest_yearly_earnings": [ + 0.0, + 10200000.0 + ], + "rank": [ + 1.0, + 995.0 + ], + "subscribers": [ + 12300000.0, + 245000000.0 + ], + "subscribers_for_last_30_days": [ + 1.0, + 8000000.0 + ], + "uploads": [ + 0.0, + 301308.0 + ], + "video views": [ + 0.0, + 228000000000.0 + ], + "video_views_for_the_last_30_days": [ + 1.0, + 6589000000.0 + ], + "video_views_rank": [ + 1.0, + 4057944.0 + ] + }, + "cat_feature_value": { + "Abbreviation": [ + "AD", + "AE", + "AF", + "AR", + "AU", + "BB", + "BD", + "BR", + "CA", + "CH", + "CL", + "CN", + "CO", + "CU", + "DE", + "EC", + "EG", + "ES", + "FI", + "FR", + "GB", + "ID", + "IN", + "IQ", + "IT", + "JO", + "JP", + "KR", + "KW", + "LV", + "MA", + "MX", + "MY", + "NL", + "PE", + "PH", + "PK", + "RU", + "SA", + "SE", + "SG", + "SV", + "TH", + "TR", + "UA", + "US", + "VE", + "VN", + "WS" + ], + "Country": [ + "Afghanistan", + "Andorra", + "Argentina", + "Australia", + "Bangladesh", + "Barbados", + "Brazil", + "Canada", + "Chile", + "China", + "Colombia", + "Cuba", + "Ecuador", + "Egypt", + "El Salvador", + "Finland", + "France", + "Germany", + "India", + "Indonesia", + "Iraq", + "Italy", + "Japan", + "Jordan", + "Kuwait", + "Latvia", + "Malaysia", + "Mexico", + "Morocco", + "Netherlands", + "Pakistan", + "Peru", + "Philippines", + "Russia", + "Samoa", + "Saudi Arabia", + "Singapore", + "South Korea", + "Spain", + "Sweden", + "Switzerland", + "Thailand", + "Turkey", + "Ukraine", + "United Arab Emirates", + "United Kingdom", + "United States", + "Venezuela", + "Vietnam" + ], + "Title": "string", + "Youtuber": "string", + "category": [ + "Autos & Vehicles", + "Comedy", + "Education", + "Entertainment", + "Film & Animation", + "Gaming", + "Howto & Style", + "Movies", + "Music", + "News & Politics", + "Nonprofits & Activism", + "People & Blogs", + "Pets & Animals", + "Science & Technology", + "Shows", + "Sports", + "Trailers", + "Travel & Events" + ], + "channel_type": [ + "Animals", + "Autos", + "Comedy", + "Education", + "Entertainment", + "Film", + "Games", + "Howto", + "Music", + "News", + "Nonprofit", + "People", + "Sports", + "Tech" + ], + "created_month": [ + "Apr", + "Aug", + "Dec", + "Feb", + "Jan", + "Jul", + "Jun", + "Mar", + "May", + "Nov", + "Oct", + "Sep" + ] + } +} diff --git a/ranking_and_filtering/daily/regression/Global_YouTube_Statistics_2023_B4/Global_YouTube_Statistics_2023_B4_006/info.json b/ranking_and_filtering/daily/regression/Global_YouTube_Statistics_2023_B4/Global_YouTube_Statistics_2023_B4_006/info.json index 32b2e724985eb4b8c7e1935cfdd4e4510dbfb775..2a4d0bb2870f7f26e8f34e8121139aaccc4b4a47 100644 --- a/ranking_and_filtering/daily/regression/Global_YouTube_Statistics_2023_B4/Global_YouTube_Statistics_2023_B4_006/info.json +++ b/ranking_and_filtering/daily/regression/Global_YouTube_Statistics_2023_B4/Global_YouTube_Statistics_2023_B4_006/info.json @@ -1,288 +1,288 @@ -{ - "name": "Global YouTube Statistics 2023", - "source": "https://www.kaggle.com/datasets/nelgiriyewithana/global-youtube-statistics-2023/data", - "data_intro": "A collection of YouTube giants, this dataset offers a perfect avenue to analyze and gain valuable insights from the luminaries of the platform. With comprehensive details on top creators' subscriber counts, video views, upload frequency, country of origin, earnings, and more, this treasure trove of information is a must-explore for aspiring content creators, data enthusiasts, and anyone intrigued by the ever-evolving online content landscape. Immerse yourself in the world of YouTube success and unlock a wealth of knowledge with this extraordinary dataset.", - "is_splited": false, - "overall_size": 995, - "train_size": 0, - "test_size": 0, - "c_classes": 7, - "n_classes": 21, - "cat_feature_intro": { - "Youtuber": "Name of the YouTube channel", - "category": "Category or niche of the channel", - "Title": "Title of the YouTube channel", - "Country": "Longitude coordinate of the country's location", - "Abbreviation": "Abbreviation of the country", - "channel_type": "Ranking of the channel based on its type (individual or brand)", - "created_month": "Month when the YouTube channel was created" - }, - "num_feature_intro": { - "rank": "Ranking of the channel based on its type (individual or brand)", - "subscribers": "Number of new subscribers gained in the last 30 days", - "video views": "Total video views in the last 30 days", - "uploads": "Total number of videos uploaded on the channel", - "video_views_rank": "Ranking of the channel based on total video views", - "country_rank": "Ranking of the channel based on the number of subscribers within its country", - "channel_type_rank": "Ranking of the channel based on its type (individual or brand)", - "video_views_for_the_last_30_days": "Total video views in the last 30 days", - "lowest_monthly_earnings": "Lowest estimated monthly earnings from the channel", - "highest_monthly_earnings": "Highest estimated monthly earnings from the channel", - "lowest_yearly_earnings": "Lowest estimated yearly earnings from the channel", - "highest_yearly_earnings": "Highest estimated yearly earnings from the channel", - "subscribers_for_last_30_days": "Number of new subscribers gained in the last 30 days", - "created_year": "Year when the YouTube channel was created", - "created_date": "Exact date of the YouTube channel's creation", - "Gross tertiary education enrollment (%)": "Percentage of the population enrolled in tertiary education in the country", - "Population": "Percentage of the population living in urban areas", - "Unemployment rate": "Unemployment rate in the country", - "Urban_population": "Percentage of the population living in urban areas", - "Latitude": "Latitude coordinate of the country's location", - "Longitude": "Longitude coordinate of the country's location" - }, - "evaluation_metric": null, - "task_type": "regression", - "target": "highest_monthly_earnings", - "num_feature_value": { - "Gross tertiary education enrollment (%)": [ - 7.6, - 113.1 - ], - "Latitude": [ - -38.416097, - 61.92411 - ], - "Longitude": [ - -172.104629, - 138.252924 - ], - "Population": [ - 202506.0, - 1397715000.0 - ], - "Unemployment rate": [ - 0.75, - 14.72 - ], - "Urban_population": [ - 35588.0, - 842933962.0 - ], - "channel_type_rank": [ - 1.0, - 7741.0 - ], - "country_rank": [ - 1.0, - 7741.0 - ], - "created_date": [ - 1.0, - 31.0 - ], - "created_year": [ - 1970.0, - 2022.0 - ], - "highest_monthly_earnings": [ - 0.0, - 13600000.0 - ], - "highest_yearly_earnings": [ - 0.0, - 163400000.0 - ], - "lowest_monthly_earnings": [ - 0.0, - 850900.0 - ], - "lowest_yearly_earnings": [ - 0.0, - 10200000.0 - ], - "rank": [ - 1.0, - 995.0 - ], - "subscribers": [ - 12300000.0, - 245000000.0 - ], - "subscribers_for_last_30_days": [ - 1.0, - 8000000.0 - ], - "uploads": [ - 0.0, - 301308.0 - ], - "video views": [ - 0.0, - 228000000000.0 - ], - "video_views_for_the_last_30_days": [ - 1.0, - 6589000000.0 - ], - "video_views_rank": [ - 1.0, - 4057944.0 - ] - }, - "cat_feature_value": { - "Abbreviation": [ - "AD", - "AE", - "AF", - "AR", - "AU", - "BB", - "BD", - "BR", - "CA", - "CH", - "CL", - "CN", - "CO", - "CU", - "DE", - "EC", - "EG", - "ES", - "FI", - "FR", - "GB", - "ID", - "IN", - "IQ", - "IT", - "JO", - "JP", - "KR", - "KW", - "LV", - "MA", - "MX", - "MY", - "NL", - "PE", - "PH", - "PK", - "RU", - "SA", - "SE", - "SG", - "SV", - "TH", - "TR", - "UA", - "US", - "VE", - "VN", - "WS" - ], - "Country": [ - "Afghanistan", - "Andorra", - "Argentina", - "Australia", - "Bangladesh", - "Barbados", - "Brazil", - "Canada", - "Chile", - "China", - "Colombia", - "Cuba", - "Ecuador", - "Egypt", - "El Salvador", - "Finland", - "France", - "Germany", - "India", - "Indonesia", - "Iraq", - "Italy", - "Japan", - "Jordan", - "Kuwait", - "Latvia", - "Malaysia", - "Mexico", - "Morocco", - "Netherlands", - "Pakistan", - "Peru", - "Philippines", - "Russia", - "Samoa", - "Saudi Arabia", - "Singapore", - "South Korea", - "Spain", - "Sweden", - "Switzerland", - "Thailand", - "Turkey", - "Ukraine", - "United Arab Emirates", - "United Kingdom", - "United States", - "Venezuela", - "Vietnam" - ], - "Title": "string", - "Youtuber": "string", - "category": [ - "Autos & Vehicles", - "Comedy", - "Education", - "Entertainment", - "Film & Animation", - "Gaming", - "Howto & Style", - "Movies", - "Music", - "News & Politics", - "Nonprofits & Activism", - "People & Blogs", - "Pets & Animals", - "Science & Technology", - "Shows", - "Sports", - "Trailers", - "Travel & Events" - ], - "channel_type": [ - "Animals", - "Autos", - "Comedy", - "Education", - "Entertainment", - "Film", - "Games", - "Howto", - "Music", - "News", - "Nonprofit", - "People", - "Sports", - "Tech" - ], - "created_month": [ - "Apr", - "Aug", - "Dec", - "Feb", - "Jan", - "Jul", - "Jun", - "Mar", - "May", - "Nov", - "Oct", - "Sep" - ] - } -} \ No newline at end of file +{ + "name": "Global YouTube Statistics 2023", + "source": "https://www.kaggle.com/datasets/nelgiriyewithana/global-youtube-statistics-2023/data", + "data_intro": "A collection of YouTube giants, this dataset offers a perfect avenue to analyze and gain valuable insights from the luminaries of the platform. With comprehensive details on top creators' subscriber counts, video views, upload frequency, country of origin, earnings, and more, this treasure trove of information is a must-explore for aspiring content creators, data enthusiasts, and anyone intrigued by the ever-evolving online content landscape. Immerse yourself in the world of YouTube success and unlock a wealth of knowledge with this extraordinary dataset.", + "is_splited": false, + "overall_size": 995, + "train_size": 0, + "test_size": 0, + "c_classes": 7, + "n_classes": 21, + "cat_feature_intro": { + "Youtuber": "Name of the YouTube channel", + "category": "Category or niche of the channel", + "Title": "Title of the YouTube channel", + "Country": "Country associated with the channel.", + "Abbreviation": "Abbreviation of the country", + "channel_type": "Type or category of the channel.", + "created_month": "Month when the YouTube channel was created" + }, + "num_feature_intro": { + "rank": "Ranking of the channel based on its type (individual or brand)", + "subscribers": "Number of new subscribers gained in the last 30 days", + "video views": "Total video views in the last 30 days", + "uploads": "Total number of videos uploaded on the channel", + "video_views_rank": "Ranking of the channel based on total video views", + "country_rank": "Ranking of the channel based on the number of subscribers within its country", + "channel_type_rank": "Ranking of the channel based on its type (individual or brand)", + "video_views_for_the_last_30_days": "Total video views in the last 30 days", + "lowest_monthly_earnings": "Lowest estimated monthly earnings from the channel", + "highest_monthly_earnings": "Highest estimated monthly earnings from the channel", + "lowest_yearly_earnings": "Lowest estimated yearly earnings from the channel", + "highest_yearly_earnings": "Highest estimated yearly earnings from the channel", + "subscribers_for_last_30_days": "Number of new subscribers gained in the last 30 days", + "created_year": "Year when the YouTube channel was created", + "created_date": "Exact date of the YouTube channel's creation", + "Gross tertiary education enrollment (%)": "Percentage of the population enrolled in tertiary education in the country", + "Population": "Percentage of the population living in urban areas", + "Unemployment rate": "Unemployment rate in the country", + "Urban_population": "Percentage of the population living in urban areas", + "Latitude": "Latitude coordinate of the country's location", + "Longitude": "Longitude coordinate of the country's location" + }, + "evaluation_metric": null, + "task_type": "regression", + "target": "highest_monthly_earnings", + "num_feature_value": { + "Gross tertiary education enrollment (%)": [ + 7.6, + 113.1 + ], + "Latitude": [ + -38.416097, + 61.92411 + ], + "Longitude": [ + -172.104629, + 138.252924 + ], + "Population": [ + 202506.0, + 1397715000.0 + ], + "Unemployment rate": [ + 0.75, + 14.72 + ], + "Urban_population": [ + 35588.0, + 842933962.0 + ], + "channel_type_rank": [ + 1.0, + 7741.0 + ], + "country_rank": [ + 1.0, + 7741.0 + ], + "created_date": [ + 1.0, + 31.0 + ], + "created_year": [ + 1970.0, + 2022.0 + ], + "highest_monthly_earnings": [ + 0.0, + 13600000.0 + ], + "highest_yearly_earnings": [ + 0.0, + 163400000.0 + ], + "lowest_monthly_earnings": [ + 0.0, + 850900.0 + ], + "lowest_yearly_earnings": [ + 0.0, + 10200000.0 + ], + "rank": [ + 1.0, + 995.0 + ], + "subscribers": [ + 12300000.0, + 245000000.0 + ], + "subscribers_for_last_30_days": [ + 1.0, + 8000000.0 + ], + "uploads": [ + 0.0, + 301308.0 + ], + "video views": [ + 0.0, + 228000000000.0 + ], + "video_views_for_the_last_30_days": [ + 1.0, + 6589000000.0 + ], + "video_views_rank": [ + 1.0, + 4057944.0 + ] + }, + "cat_feature_value": { + "Abbreviation": [ + "AD", + "AE", + "AF", + "AR", + "AU", + "BB", + "BD", + "BR", + "CA", + "CH", + "CL", + "CN", + "CO", + "CU", + "DE", + "EC", + "EG", + "ES", + "FI", + "FR", + "GB", + "ID", + "IN", + "IQ", + "IT", + "JO", + "JP", + "KR", + "KW", + "LV", + "MA", + "MX", + "MY", + "NL", + "PE", + "PH", + "PK", + "RU", + "SA", + "SE", + "SG", + "SV", + "TH", + "TR", + "UA", + "US", + "VE", + "VN", + "WS" + ], + "Country": [ + "Afghanistan", + "Andorra", + "Argentina", + "Australia", + "Bangladesh", + "Barbados", + "Brazil", + "Canada", + "Chile", + "China", + "Colombia", + "Cuba", + "Ecuador", + "Egypt", + "El Salvador", + "Finland", + "France", + "Germany", + "India", + "Indonesia", + "Iraq", + "Italy", + "Japan", + "Jordan", + "Kuwait", + "Latvia", + "Malaysia", + "Mexico", + "Morocco", + "Netherlands", + "Pakistan", + "Peru", + "Philippines", + "Russia", + "Samoa", + "Saudi Arabia", + "Singapore", + "South Korea", + "Spain", + "Sweden", + "Switzerland", + "Thailand", + "Turkey", + "Ukraine", + "United Arab Emirates", + "United Kingdom", + "United States", + "Venezuela", + "Vietnam" + ], + "Title": "string", + "Youtuber": "string", + "category": [ + "Autos & Vehicles", + "Comedy", + "Education", + "Entertainment", + "Film & Animation", + "Gaming", + "Howto & Style", + "Movies", + "Music", + "News & Politics", + "Nonprofits & Activism", + "People & Blogs", + "Pets & Animals", + "Science & Technology", + "Shows", + "Sports", + "Trailers", + "Travel & Events" + ], + "channel_type": [ + "Animals", + "Autos", + "Comedy", + "Education", + "Entertainment", + "Film", + "Games", + "Howto", + "Music", + "News", + "Nonprofit", + "People", + "Sports", + "Tech" + ], + "created_month": [ + "Apr", + "Aug", + "Dec", + "Feb", + "Jan", + "Jul", + "Jun", + "Mar", + "May", + "Nov", + "Oct", + "Sep" + ] + } +} diff --git a/ranking_and_filtering/daily/regression/Global_YouTube_Statistics_2023_B4/Global_YouTube_Statistics_2023_B4_007/info.json b/ranking_and_filtering/daily/regression/Global_YouTube_Statistics_2023_B4/Global_YouTube_Statistics_2023_B4_007/info.json index 32b2e724985eb4b8c7e1935cfdd4e4510dbfb775..2a4d0bb2870f7f26e8f34e8121139aaccc4b4a47 100644 --- a/ranking_and_filtering/daily/regression/Global_YouTube_Statistics_2023_B4/Global_YouTube_Statistics_2023_B4_007/info.json +++ b/ranking_and_filtering/daily/regression/Global_YouTube_Statistics_2023_B4/Global_YouTube_Statistics_2023_B4_007/info.json @@ -1,288 +1,288 @@ -{ - "name": "Global YouTube Statistics 2023", - "source": "https://www.kaggle.com/datasets/nelgiriyewithana/global-youtube-statistics-2023/data", - "data_intro": "A collection of YouTube giants, this dataset offers a perfect avenue to analyze and gain valuable insights from the luminaries of the platform. With comprehensive details on top creators' subscriber counts, video views, upload frequency, country of origin, earnings, and more, this treasure trove of information is a must-explore for aspiring content creators, data enthusiasts, and anyone intrigued by the ever-evolving online content landscape. Immerse yourself in the world of YouTube success and unlock a wealth of knowledge with this extraordinary dataset.", - "is_splited": false, - "overall_size": 995, - "train_size": 0, - "test_size": 0, - "c_classes": 7, - "n_classes": 21, - "cat_feature_intro": { - "Youtuber": "Name of the YouTube channel", - "category": "Category or niche of the channel", - "Title": "Title of the YouTube channel", - "Country": "Longitude coordinate of the country's location", - "Abbreviation": "Abbreviation of the country", - "channel_type": "Ranking of the channel based on its type (individual or brand)", - "created_month": "Month when the YouTube channel was created" - }, - "num_feature_intro": { - "rank": "Ranking of the channel based on its type (individual or brand)", - "subscribers": "Number of new subscribers gained in the last 30 days", - "video views": "Total video views in the last 30 days", - "uploads": "Total number of videos uploaded on the channel", - "video_views_rank": "Ranking of the channel based on total video views", - "country_rank": "Ranking of the channel based on the number of subscribers within its country", - "channel_type_rank": "Ranking of the channel based on its type (individual or brand)", - "video_views_for_the_last_30_days": "Total video views in the last 30 days", - "lowest_monthly_earnings": "Lowest estimated monthly earnings from the channel", - "highest_monthly_earnings": "Highest estimated monthly earnings from the channel", - "lowest_yearly_earnings": "Lowest estimated yearly earnings from the channel", - "highest_yearly_earnings": "Highest estimated yearly earnings from the channel", - "subscribers_for_last_30_days": "Number of new subscribers gained in the last 30 days", - "created_year": "Year when the YouTube channel was created", - "created_date": "Exact date of the YouTube channel's creation", - "Gross tertiary education enrollment (%)": "Percentage of the population enrolled in tertiary education in the country", - "Population": "Percentage of the population living in urban areas", - "Unemployment rate": "Unemployment rate in the country", - "Urban_population": "Percentage of the population living in urban areas", - "Latitude": "Latitude coordinate of the country's location", - "Longitude": "Longitude coordinate of the country's location" - }, - "evaluation_metric": null, - "task_type": "regression", - "target": "highest_monthly_earnings", - "num_feature_value": { - "Gross tertiary education enrollment (%)": [ - 7.6, - 113.1 - ], - "Latitude": [ - -38.416097, - 61.92411 - ], - "Longitude": [ - -172.104629, - 138.252924 - ], - "Population": [ - 202506.0, - 1397715000.0 - ], - "Unemployment rate": [ - 0.75, - 14.72 - ], - "Urban_population": [ - 35588.0, - 842933962.0 - ], - "channel_type_rank": [ - 1.0, - 7741.0 - ], - "country_rank": [ - 1.0, - 7741.0 - ], - "created_date": [ - 1.0, - 31.0 - ], - "created_year": [ - 1970.0, - 2022.0 - ], - "highest_monthly_earnings": [ - 0.0, - 13600000.0 - ], - "highest_yearly_earnings": [ - 0.0, - 163400000.0 - ], - "lowest_monthly_earnings": [ - 0.0, - 850900.0 - ], - "lowest_yearly_earnings": [ - 0.0, - 10200000.0 - ], - "rank": [ - 1.0, - 995.0 - ], - "subscribers": [ - 12300000.0, - 245000000.0 - ], - "subscribers_for_last_30_days": [ - 1.0, - 8000000.0 - ], - "uploads": [ - 0.0, - 301308.0 - ], - "video views": [ - 0.0, - 228000000000.0 - ], - "video_views_for_the_last_30_days": [ - 1.0, - 6589000000.0 - ], - "video_views_rank": [ - 1.0, - 4057944.0 - ] - }, - "cat_feature_value": { - "Abbreviation": [ - "AD", - "AE", - "AF", - "AR", - "AU", - "BB", - "BD", - "BR", - "CA", - "CH", - "CL", - "CN", - "CO", - "CU", - "DE", - "EC", - "EG", - "ES", - "FI", - "FR", - "GB", - "ID", - "IN", - "IQ", - "IT", - "JO", - "JP", - "KR", - "KW", - "LV", - "MA", - "MX", - "MY", - "NL", - "PE", - "PH", - "PK", - "RU", - "SA", - "SE", - "SG", - "SV", - "TH", - "TR", - "UA", - "US", - "VE", - "VN", - "WS" - ], - "Country": [ - "Afghanistan", - "Andorra", - "Argentina", - "Australia", - "Bangladesh", - "Barbados", - "Brazil", - "Canada", - "Chile", - "China", - "Colombia", - "Cuba", - "Ecuador", - "Egypt", - "El Salvador", - "Finland", - "France", - "Germany", - "India", - "Indonesia", - "Iraq", - "Italy", - "Japan", - "Jordan", - "Kuwait", - "Latvia", - "Malaysia", - "Mexico", - "Morocco", - "Netherlands", - "Pakistan", - "Peru", - "Philippines", - "Russia", - "Samoa", - "Saudi Arabia", - "Singapore", - "South Korea", - "Spain", - "Sweden", - "Switzerland", - "Thailand", - "Turkey", - "Ukraine", - "United Arab Emirates", - "United Kingdom", - "United States", - "Venezuela", - "Vietnam" - ], - "Title": "string", - "Youtuber": "string", - "category": [ - "Autos & Vehicles", - "Comedy", - "Education", - "Entertainment", - "Film & Animation", - "Gaming", - "Howto & Style", - "Movies", - "Music", - "News & Politics", - "Nonprofits & Activism", - "People & Blogs", - "Pets & Animals", - "Science & Technology", - "Shows", - "Sports", - "Trailers", - "Travel & Events" - ], - "channel_type": [ - "Animals", - "Autos", - "Comedy", - "Education", - "Entertainment", - "Film", - "Games", - "Howto", - "Music", - "News", - "Nonprofit", - "People", - "Sports", - "Tech" - ], - "created_month": [ - "Apr", - "Aug", - "Dec", - "Feb", - "Jan", - "Jul", - "Jun", - "Mar", - "May", - "Nov", - "Oct", - "Sep" - ] - } -} \ No newline at end of file +{ + "name": "Global YouTube Statistics 2023", + "source": "https://www.kaggle.com/datasets/nelgiriyewithana/global-youtube-statistics-2023/data", + "data_intro": "A collection of YouTube giants, this dataset offers a perfect avenue to analyze and gain valuable insights from the luminaries of the platform. With comprehensive details on top creators' subscriber counts, video views, upload frequency, country of origin, earnings, and more, this treasure trove of information is a must-explore for aspiring content creators, data enthusiasts, and anyone intrigued by the ever-evolving online content landscape. Immerse yourself in the world of YouTube success and unlock a wealth of knowledge with this extraordinary dataset.", + "is_splited": false, + "overall_size": 995, + "train_size": 0, + "test_size": 0, + "c_classes": 7, + "n_classes": 21, + "cat_feature_intro": { + "Youtuber": "Name of the YouTube channel", + "category": "Category or niche of the channel", + "Title": "Title of the YouTube channel", + "Country": "Country associated with the channel.", + "Abbreviation": "Abbreviation of the country", + "channel_type": "Type or category of the channel.", + "created_month": "Month when the YouTube channel was created" + }, + "num_feature_intro": { + "rank": "Ranking of the channel based on its type (individual or brand)", + "subscribers": "Number of new subscribers gained in the last 30 days", + "video views": "Total video views in the last 30 days", + "uploads": "Total number of videos uploaded on the channel", + "video_views_rank": "Ranking of the channel based on total video views", + "country_rank": "Ranking of the channel based on the number of subscribers within its country", + "channel_type_rank": "Ranking of the channel based on its type (individual or brand)", + "video_views_for_the_last_30_days": "Total video views in the last 30 days", + "lowest_monthly_earnings": "Lowest estimated monthly earnings from the channel", + "highest_monthly_earnings": "Highest estimated monthly earnings from the channel", + "lowest_yearly_earnings": "Lowest estimated yearly earnings from the channel", + "highest_yearly_earnings": "Highest estimated yearly earnings from the channel", + "subscribers_for_last_30_days": "Number of new subscribers gained in the last 30 days", + "created_year": "Year when the YouTube channel was created", + "created_date": "Exact date of the YouTube channel's creation", + "Gross tertiary education enrollment (%)": "Percentage of the population enrolled in tertiary education in the country", + "Population": "Percentage of the population living in urban areas", + "Unemployment rate": "Unemployment rate in the country", + "Urban_population": "Percentage of the population living in urban areas", + "Latitude": "Latitude coordinate of the country's location", + "Longitude": "Longitude coordinate of the country's location" + }, + "evaluation_metric": null, + "task_type": "regression", + "target": "highest_monthly_earnings", + "num_feature_value": { + "Gross tertiary education enrollment (%)": [ + 7.6, + 113.1 + ], + "Latitude": [ + -38.416097, + 61.92411 + ], + "Longitude": [ + -172.104629, + 138.252924 + ], + "Population": [ + 202506.0, + 1397715000.0 + ], + "Unemployment rate": [ + 0.75, + 14.72 + ], + "Urban_population": [ + 35588.0, + 842933962.0 + ], + "channel_type_rank": [ + 1.0, + 7741.0 + ], + "country_rank": [ + 1.0, + 7741.0 + ], + "created_date": [ + 1.0, + 31.0 + ], + "created_year": [ + 1970.0, + 2022.0 + ], + "highest_monthly_earnings": [ + 0.0, + 13600000.0 + ], + "highest_yearly_earnings": [ + 0.0, + 163400000.0 + ], + "lowest_monthly_earnings": [ + 0.0, + 850900.0 + ], + "lowest_yearly_earnings": [ + 0.0, + 10200000.0 + ], + "rank": [ + 1.0, + 995.0 + ], + "subscribers": [ + 12300000.0, + 245000000.0 + ], + "subscribers_for_last_30_days": [ + 1.0, + 8000000.0 + ], + "uploads": [ + 0.0, + 301308.0 + ], + "video views": [ + 0.0, + 228000000000.0 + ], + "video_views_for_the_last_30_days": [ + 1.0, + 6589000000.0 + ], + "video_views_rank": [ + 1.0, + 4057944.0 + ] + }, + "cat_feature_value": { + "Abbreviation": [ + "AD", + "AE", + "AF", + "AR", + "AU", + "BB", + "BD", + "BR", + "CA", + "CH", + "CL", + "CN", + "CO", + "CU", + "DE", + "EC", + "EG", + "ES", + "FI", + "FR", + "GB", + "ID", + "IN", + "IQ", + "IT", + "JO", + "JP", + "KR", + "KW", + "LV", + "MA", + "MX", + "MY", + "NL", + "PE", + "PH", + "PK", + "RU", + "SA", + "SE", + "SG", + "SV", + "TH", + "TR", + "UA", + "US", + "VE", + "VN", + "WS" + ], + "Country": [ + "Afghanistan", + "Andorra", + "Argentina", + "Australia", + "Bangladesh", + "Barbados", + "Brazil", + "Canada", + "Chile", + "China", + "Colombia", + "Cuba", + "Ecuador", + "Egypt", + "El Salvador", + "Finland", + "France", + "Germany", + "India", + "Indonesia", + "Iraq", + "Italy", + "Japan", + "Jordan", + "Kuwait", + "Latvia", + "Malaysia", + "Mexico", + "Morocco", + "Netherlands", + "Pakistan", + "Peru", + "Philippines", + "Russia", + "Samoa", + "Saudi Arabia", + "Singapore", + "South Korea", + "Spain", + "Sweden", + "Switzerland", + "Thailand", + "Turkey", + "Ukraine", + "United Arab Emirates", + "United Kingdom", + "United States", + "Venezuela", + "Vietnam" + ], + "Title": "string", + "Youtuber": "string", + "category": [ + "Autos & Vehicles", + "Comedy", + "Education", + "Entertainment", + "Film & Animation", + "Gaming", + "Howto & Style", + "Movies", + "Music", + "News & Politics", + "Nonprofits & Activism", + "People & Blogs", + "Pets & Animals", + "Science & Technology", + "Shows", + "Sports", + "Trailers", + "Travel & Events" + ], + "channel_type": [ + "Animals", + "Autos", + "Comedy", + "Education", + "Entertainment", + "Film", + "Games", + "Howto", + "Music", + "News", + "Nonprofit", + "People", + "Sports", + "Tech" + ], + "created_month": [ + "Apr", + "Aug", + "Dec", + "Feb", + "Jan", + "Jul", + "Jun", + "Mar", + "May", + "Nov", + "Oct", + "Sep" + ] + } +} diff --git a/ranking_and_filtering/daily/regression/Global_YouTube_Statistics_2023_B4/Global_YouTube_Statistics_2023_B4_008/info.json b/ranking_and_filtering/daily/regression/Global_YouTube_Statistics_2023_B4/Global_YouTube_Statistics_2023_B4_008/info.json index 32b2e724985eb4b8c7e1935cfdd4e4510dbfb775..2a4d0bb2870f7f26e8f34e8121139aaccc4b4a47 100644 --- a/ranking_and_filtering/daily/regression/Global_YouTube_Statistics_2023_B4/Global_YouTube_Statistics_2023_B4_008/info.json +++ b/ranking_and_filtering/daily/regression/Global_YouTube_Statistics_2023_B4/Global_YouTube_Statistics_2023_B4_008/info.json @@ -1,288 +1,288 @@ -{ - "name": "Global YouTube Statistics 2023", - "source": "https://www.kaggle.com/datasets/nelgiriyewithana/global-youtube-statistics-2023/data", - "data_intro": "A collection of YouTube giants, this dataset offers a perfect avenue to analyze and gain valuable insights from the luminaries of the platform. With comprehensive details on top creators' subscriber counts, video views, upload frequency, country of origin, earnings, and more, this treasure trove of information is a must-explore for aspiring content creators, data enthusiasts, and anyone intrigued by the ever-evolving online content landscape. Immerse yourself in the world of YouTube success and unlock a wealth of knowledge with this extraordinary dataset.", - "is_splited": false, - "overall_size": 995, - "train_size": 0, - "test_size": 0, - "c_classes": 7, - "n_classes": 21, - "cat_feature_intro": { - "Youtuber": "Name of the YouTube channel", - "category": "Category or niche of the channel", - "Title": "Title of the YouTube channel", - "Country": "Longitude coordinate of the country's location", - "Abbreviation": "Abbreviation of the country", - "channel_type": "Ranking of the channel based on its type (individual or brand)", - "created_month": "Month when the YouTube channel was created" - }, - "num_feature_intro": { - "rank": "Ranking of the channel based on its type (individual or brand)", - "subscribers": "Number of new subscribers gained in the last 30 days", - "video views": "Total video views in the last 30 days", - "uploads": "Total number of videos uploaded on the channel", - "video_views_rank": "Ranking of the channel based on total video views", - "country_rank": "Ranking of the channel based on the number of subscribers within its country", - "channel_type_rank": "Ranking of the channel based on its type (individual or brand)", - "video_views_for_the_last_30_days": "Total video views in the last 30 days", - "lowest_monthly_earnings": "Lowest estimated monthly earnings from the channel", - "highest_monthly_earnings": "Highest estimated monthly earnings from the channel", - "lowest_yearly_earnings": "Lowest estimated yearly earnings from the channel", - "highest_yearly_earnings": "Highest estimated yearly earnings from the channel", - "subscribers_for_last_30_days": "Number of new subscribers gained in the last 30 days", - "created_year": "Year when the YouTube channel was created", - "created_date": "Exact date of the YouTube channel's creation", - "Gross tertiary education enrollment (%)": "Percentage of the population enrolled in tertiary education in the country", - "Population": "Percentage of the population living in urban areas", - "Unemployment rate": "Unemployment rate in the country", - "Urban_population": "Percentage of the population living in urban areas", - "Latitude": "Latitude coordinate of the country's location", - "Longitude": "Longitude coordinate of the country's location" - }, - "evaluation_metric": null, - "task_type": "regression", - "target": "highest_monthly_earnings", - "num_feature_value": { - "Gross tertiary education enrollment (%)": [ - 7.6, - 113.1 - ], - "Latitude": [ - -38.416097, - 61.92411 - ], - "Longitude": [ - -172.104629, - 138.252924 - ], - "Population": [ - 202506.0, - 1397715000.0 - ], - "Unemployment rate": [ - 0.75, - 14.72 - ], - "Urban_population": [ - 35588.0, - 842933962.0 - ], - "channel_type_rank": [ - 1.0, - 7741.0 - ], - "country_rank": [ - 1.0, - 7741.0 - ], - "created_date": [ - 1.0, - 31.0 - ], - "created_year": [ - 1970.0, - 2022.0 - ], - "highest_monthly_earnings": [ - 0.0, - 13600000.0 - ], - "highest_yearly_earnings": [ - 0.0, - 163400000.0 - ], - "lowest_monthly_earnings": [ - 0.0, - 850900.0 - ], - "lowest_yearly_earnings": [ - 0.0, - 10200000.0 - ], - "rank": [ - 1.0, - 995.0 - ], - "subscribers": [ - 12300000.0, - 245000000.0 - ], - "subscribers_for_last_30_days": [ - 1.0, - 8000000.0 - ], - "uploads": [ - 0.0, - 301308.0 - ], - "video views": [ - 0.0, - 228000000000.0 - ], - "video_views_for_the_last_30_days": [ - 1.0, - 6589000000.0 - ], - "video_views_rank": [ - 1.0, - 4057944.0 - ] - }, - "cat_feature_value": { - "Abbreviation": [ - "AD", - "AE", - "AF", - "AR", - "AU", - "BB", - "BD", - "BR", - "CA", - "CH", - "CL", - "CN", - "CO", - "CU", - "DE", - "EC", - "EG", - "ES", - "FI", - "FR", - "GB", - "ID", - "IN", - "IQ", - "IT", - "JO", - "JP", - "KR", - "KW", - "LV", - "MA", - "MX", - "MY", - "NL", - "PE", - "PH", - "PK", - "RU", - "SA", - "SE", - "SG", - "SV", - "TH", - "TR", - "UA", - "US", - "VE", - "VN", - "WS" - ], - "Country": [ - "Afghanistan", - "Andorra", - "Argentina", - "Australia", - "Bangladesh", - "Barbados", - "Brazil", - "Canada", - "Chile", - "China", - "Colombia", - "Cuba", - "Ecuador", - "Egypt", - "El Salvador", - "Finland", - "France", - "Germany", - "India", - "Indonesia", - "Iraq", - "Italy", - "Japan", - "Jordan", - "Kuwait", - "Latvia", - "Malaysia", - "Mexico", - "Morocco", - "Netherlands", - "Pakistan", - "Peru", - "Philippines", - "Russia", - "Samoa", - "Saudi Arabia", - "Singapore", - "South Korea", - "Spain", - "Sweden", - "Switzerland", - "Thailand", - "Turkey", - "Ukraine", - "United Arab Emirates", - "United Kingdom", - "United States", - "Venezuela", - "Vietnam" - ], - "Title": "string", - "Youtuber": "string", - "category": [ - "Autos & Vehicles", - "Comedy", - "Education", - "Entertainment", - "Film & Animation", - "Gaming", - "Howto & Style", - "Movies", - "Music", - "News & Politics", - "Nonprofits & Activism", - "People & Blogs", - "Pets & Animals", - "Science & Technology", - "Shows", - "Sports", - "Trailers", - "Travel & Events" - ], - "channel_type": [ - "Animals", - "Autos", - "Comedy", - "Education", - "Entertainment", - "Film", - "Games", - "Howto", - "Music", - "News", - "Nonprofit", - "People", - "Sports", - "Tech" - ], - "created_month": [ - "Apr", - "Aug", - "Dec", - "Feb", - "Jan", - "Jul", - "Jun", - "Mar", - "May", - "Nov", - "Oct", - "Sep" - ] - } -} \ No newline at end of file +{ + "name": "Global YouTube Statistics 2023", + "source": "https://www.kaggle.com/datasets/nelgiriyewithana/global-youtube-statistics-2023/data", + "data_intro": "A collection of YouTube giants, this dataset offers a perfect avenue to analyze and gain valuable insights from the luminaries of the platform. With comprehensive details on top creators' subscriber counts, video views, upload frequency, country of origin, earnings, and more, this treasure trove of information is a must-explore for aspiring content creators, data enthusiasts, and anyone intrigued by the ever-evolving online content landscape. Immerse yourself in the world of YouTube success and unlock a wealth of knowledge with this extraordinary dataset.", + "is_splited": false, + "overall_size": 995, + "train_size": 0, + "test_size": 0, + "c_classes": 7, + "n_classes": 21, + "cat_feature_intro": { + "Youtuber": "Name of the YouTube channel", + "category": "Category or niche of the channel", + "Title": "Title of the YouTube channel", + "Country": "Country associated with the channel.", + "Abbreviation": "Abbreviation of the country", + "channel_type": "Type or category of the channel.", + "created_month": "Month when the YouTube channel was created" + }, + "num_feature_intro": { + "rank": "Ranking of the channel based on its type (individual or brand)", + "subscribers": "Number of new subscribers gained in the last 30 days", + "video views": "Total video views in the last 30 days", + "uploads": "Total number of videos uploaded on the channel", + "video_views_rank": "Ranking of the channel based on total video views", + "country_rank": "Ranking of the channel based on the number of subscribers within its country", + "channel_type_rank": "Ranking of the channel based on its type (individual or brand)", + "video_views_for_the_last_30_days": "Total video views in the last 30 days", + "lowest_monthly_earnings": "Lowest estimated monthly earnings from the channel", + "highest_monthly_earnings": "Highest estimated monthly earnings from the channel", + "lowest_yearly_earnings": "Lowest estimated yearly earnings from the channel", + "highest_yearly_earnings": "Highest estimated yearly earnings from the channel", + "subscribers_for_last_30_days": "Number of new subscribers gained in the last 30 days", + "created_year": "Year when the YouTube channel was created", + "created_date": "Exact date of the YouTube channel's creation", + "Gross tertiary education enrollment (%)": "Percentage of the population enrolled in tertiary education in the country", + "Population": "Percentage of the population living in urban areas", + "Unemployment rate": "Unemployment rate in the country", + "Urban_population": "Percentage of the population living in urban areas", + "Latitude": "Latitude coordinate of the country's location", + "Longitude": "Longitude coordinate of the country's location" + }, + "evaluation_metric": null, + "task_type": "regression", + "target": "highest_monthly_earnings", + "num_feature_value": { + "Gross tertiary education enrollment (%)": [ + 7.6, + 113.1 + ], + "Latitude": [ + -38.416097, + 61.92411 + ], + "Longitude": [ + -172.104629, + 138.252924 + ], + "Population": [ + 202506.0, + 1397715000.0 + ], + "Unemployment rate": [ + 0.75, + 14.72 + ], + "Urban_population": [ + 35588.0, + 842933962.0 + ], + "channel_type_rank": [ + 1.0, + 7741.0 + ], + "country_rank": [ + 1.0, + 7741.0 + ], + "created_date": [ + 1.0, + 31.0 + ], + "created_year": [ + 1970.0, + 2022.0 + ], + "highest_monthly_earnings": [ + 0.0, + 13600000.0 + ], + "highest_yearly_earnings": [ + 0.0, + 163400000.0 + ], + "lowest_monthly_earnings": [ + 0.0, + 850900.0 + ], + "lowest_yearly_earnings": [ + 0.0, + 10200000.0 + ], + "rank": [ + 1.0, + 995.0 + ], + "subscribers": [ + 12300000.0, + 245000000.0 + ], + "subscribers_for_last_30_days": [ + 1.0, + 8000000.0 + ], + "uploads": [ + 0.0, + 301308.0 + ], + "video views": [ + 0.0, + 228000000000.0 + ], + "video_views_for_the_last_30_days": [ + 1.0, + 6589000000.0 + ], + "video_views_rank": [ + 1.0, + 4057944.0 + ] + }, + "cat_feature_value": { + "Abbreviation": [ + "AD", + "AE", + "AF", + "AR", + "AU", + "BB", + "BD", + "BR", + "CA", + "CH", + "CL", + "CN", + "CO", + "CU", + "DE", + "EC", + "EG", + "ES", + "FI", + "FR", + "GB", + "ID", + "IN", + "IQ", + "IT", + "JO", + "JP", + "KR", + "KW", + "LV", + "MA", + "MX", + "MY", + "NL", + "PE", + "PH", + "PK", + "RU", + "SA", + "SE", + "SG", + "SV", + "TH", + "TR", + "UA", + "US", + "VE", + "VN", + "WS" + ], + "Country": [ + "Afghanistan", + "Andorra", + "Argentina", + "Australia", + "Bangladesh", + "Barbados", + "Brazil", + "Canada", + "Chile", + "China", + "Colombia", + "Cuba", + "Ecuador", + "Egypt", + "El Salvador", + "Finland", + "France", + "Germany", + "India", + "Indonesia", + "Iraq", + "Italy", + "Japan", + "Jordan", + "Kuwait", + "Latvia", + "Malaysia", + "Mexico", + "Morocco", + "Netherlands", + "Pakistan", + "Peru", + "Philippines", + "Russia", + "Samoa", + "Saudi Arabia", + "Singapore", + "South Korea", + "Spain", + "Sweden", + "Switzerland", + "Thailand", + "Turkey", + "Ukraine", + "United Arab Emirates", + "United Kingdom", + "United States", + "Venezuela", + "Vietnam" + ], + "Title": "string", + "Youtuber": "string", + "category": [ + "Autos & Vehicles", + "Comedy", + "Education", + "Entertainment", + "Film & Animation", + "Gaming", + "Howto & Style", + "Movies", + "Music", + "News & Politics", + "Nonprofits & Activism", + "People & Blogs", + "Pets & Animals", + "Science & Technology", + "Shows", + "Sports", + "Trailers", + "Travel & Events" + ], + "channel_type": [ + "Animals", + "Autos", + "Comedy", + "Education", + "Entertainment", + "Film", + "Games", + "Howto", + "Music", + "News", + "Nonprofit", + "People", + "Sports", + "Tech" + ], + "created_month": [ + "Apr", + "Aug", + "Dec", + "Feb", + "Jan", + "Jul", + "Jun", + "Mar", + "May", + "Nov", + "Oct", + "Sep" + ] + } +} diff --git a/ranking_and_filtering/daily/regression/Gun_Violence_Data_B4/Gun_Violence_Data_B4_001/Gun_Violence_Data_B4_001_current.json b/ranking_and_filtering/daily/regression/Gun_Violence_Data_B4/Gun_Violence_Data_B4_001/Gun_Violence_Data_B4_001_current.json index e4a8192b8aae6209cb6d5c6a6b193fd51e524582..8932e80ce7c5d8d56b887c4248fd496a32b15d92 100644 --- a/ranking_and_filtering/daily/regression/Gun_Violence_Data_B4/Gun_Violence_Data_B4_001/Gun_Violence_Data_B4_001_current.json +++ b/ranking_and_filtering/daily/regression/Gun_Violence_Data_B4/Gun_Violence_Data_B4_001/Gun_Violence_Data_B4_001_current.json @@ -1,1732 +1,1732 @@ { - "id": "001", - "task_type": "B4", - "subtask_type": "topk", - "perspective": "data_holder", - "dataset_name": "Gun Violence Data", - "table_path": "output/Gun Violence Data_B4/Gun Violence Data_B4_001/test.csv", - "query": "For our annual safety report to the state legislature, I want to highlight areas where interventions have been most effective. Using the archive of past events, shortlist the 5 cities from the new data that are forecast to have the fewest fatalities, indicating potential success stories.", - "meta_info": { - "domain": "daily" + "id": "001", + "task_type": "B4", + "subtask_type": "topk", + "perspective": "data_holder", + "dataset_name": "Gun Violence Data", + "table_path": "output/Gun Violence Data_B4/Gun Violence Data_B4_001/test.csv", + "query": "For our annual safety report to the state legislature, I want to highlight areas where interventions have been most effective. Using the archive of past events, shortlist the 5 cities from the new data that are forecast to have the fewest fatalities, indicating potential success stories.", + "meta_info": { + "domain": "daily" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "incident_id": 832945, + "date": "2017-04-30", + "state": "West Virginia", + "city_or_county": "Charleston", + "address": "1006 1st Avenue", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/832945", + "source_url": "http://www.wvgazettemail.com/article/20170516/GZ0118/170519652", + "incident_url_fields_missing": "False", + "congressional_district": 2.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Drug involvement", + "latitude": 38.3629, + "location_description": null, + "longitude": -81.6577, + "n_guns_involved": 1.0, + "notes": "Address is 1006 1/2, West Side, one shot and killed", + "participant_age": "0::52||1::27", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": "0::Bryan Keith Johnson||1::John Stanley Ford III", + "participant_relationship": null, + "participant_status": "0::Killed||1::Unharmed, Arrested", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://www.wsaz.com/content/news/Crews-respond-to-call-for-shooting-in-Charleston--420862374.html||http://www.wvgazettemail.com/article/20170501/GZ0118/170509987||http://www.tristateupdate.com/story/35289511/charleston-police-identify-man-fatally-shot-overnight||http://www.wvgazettemail.com/article/20170516/GZ0118/170519652", + "state_house_district": 37.0, + "state_senate_district": 8.0 + } + }, + { + "scenario_id": "002", + "features": { + "incident_id": 1027000, + "date": "2018-01-13", + "state": "Tennessee", + "city_or_county": "Powell", + "address": "601 Edgemoor Rd", + "n_killed": 0, + "n_injured": 2, + "incident_url": "http://www.gunviolencearchive.org/incident/1027000", + "source_url": "http://www.wate.com/news/top-stories/anderson-county-sheriff-deputies-investigate-shooting-on-clinton-hwy/914342994", + "incident_url_fields_missing": "False", + "congressional_district": 3.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Wounded/Injured", + "latitude": 36.0357, + "location_description": "Bread Box", + "longitude": -84.131, + "n_guns_involved": 1.0, + "notes": "Anderson Co, also found on Clinton Hey, Claxton; 3rd vic shot at, no inj", + "participant_age": "0::36||1::34||2::23||3::24", + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+||3::Adult 18+", + "participant_gender": "0::Male||1::Male||2::Male||3::Female", + "participant_name": "0::Nathan Lynn Phillips||1::William Ernest Foster||2::Jacob Lynn Rutherford||3::Marrie Taylor Atteberry", + "participant_relationship": null, + "participant_status": "0::Injured||1::Injured||2::Unharmed, Arrested||3::Unharmed, Arrested", + "participant_type": "0::Victim||1::Victim||2::Subject-Suspect||3::Subject-Suspect", + "sources": "http://www.wate.com/news/top-stories/anderson-county-sheriff-deputies-investigate-shooting-on-clinton-hwy/914342994", + "state_house_district": 33.0, + "state_senate_district": 5.0 + } + }, + { + "scenario_id": "003", + "features": { + "incident_id": 465538, + "date": "2015-12-13", + "state": "Oregon", + "city_or_county": "Medford", + "address": "Bartlett Street", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/465538", + "source_url": "http://www.mailtribune.com/article/20151215/NEWS/151219747/101034/NEWS?rssfeed=true", + "incident_url_fields_missing": "False", + "congressional_district": 2.0, + "gun_stolen": "0::Unknown||1::Unknown", + "gun_type": "0::Handgun||1::Handgun", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 42.3302, + "location_description": null, + "longitude": -122.874, + "n_guns_involved": 2.0, + "notes": null, + "participant_age": "0::27||1::25||2::30", + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+", + "participant_gender": "0::Male||1::Male||2::Male", + "participant_name": "0::Isaac Deleon||1::Shane Anthony \"Scarface\" Zornes||2::Ruben Escareno Marmolejo", + "participant_relationship": null, + "participant_status": "0::Killed||1::Unharmed, Arrested||2::Unharmed, Arrested", + "participant_type": "0::Victim||1::Subject-Suspect||2::Subject-Suspect", + "sources": "http://www.mailtribune.com/article/20151213/NEWS/151219833/101033/NEWS?rssfeed=true||http://www.mailtribune.com/article/20151216/NEWS/151219710||http://www.mailtribune.com/article/20151215/NEWS/151219747/101034/NEWS?rssfeed=true", + "state_house_district": 6.0, + "state_senate_district": 3.0 + } + }, + { + "scenario_id": "004", + "features": { + "incident_id": 774253, + "date": "2017-02-13", + "state": "Florida", + "city_or_county": "Palm Beach Gardens (Riviera Beach)", + "address": "W 5th St and Ave H", + "n_killed": 0, + "n_injured": 2, + "incident_url": "http://www.gunviolencearchive.org/incident/774253", + "source_url": "http://www.wptv.com/news/region-c-palm-beach-county/riviera-beach/officials-on-scene-of-shooting-in-riviera-beach", + "incident_url_fields_missing": "False", + "congressional_district": 20.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Wounded/Injured||Drive-by (car to street, car to car)", + "latitude": 26.7671, + "location_description": null, + "longitude": -80.0638, + "n_guns_involved": 1.0, + "notes": "26.767057, -80.063831", + "participant_age": null, + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Female||1::Female", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured||1::Injured", + "participant_type": "0::Victim||1::Victim", + "sources": "http://www.wptv.com/news/region-c-palm-beach-county/riviera-beach/officials-on-scene-of-shooting-in-riviera-beach", + "state_house_district": 88.0, + "state_senate_district": 27.0 + } + }, + { + "scenario_id": "005", + "features": { + "incident_id": 688645, + "date": "2016-10-30", + "state": "North Carolina", + "city_or_county": "Charlotte", + "address": "3215 Freedom Drive", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/688645", + "source_url": "http://wbtw.com/2016/10/31/nc-pizza-hut-worker-uses-gun-to-kill-suspect-during-robbery-attempt/", + "incident_url_fields_missing": "False", + "congressional_district": null, + "gun_stolen": "0::Not-stolen||1::Unknown", + "gun_type": "0::Handgun||1::Handgun", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Institution/Group/Business||Defensive Use||Defensive Use - Victim stops crime||Defensive Use - Crime occurs, victim shoots subject/suspect/perpetrator||Defensive Use - Stand Your Ground/Castle Doctrine established||Armed robbery with injury/death and/or evidence of DGU found||Brandishing/flourishing/open carry/lost/found", + "latitude": null, + "location_description": "Pizza Hut", + "longitude": null, + "n_guns_involved": 2.0, + "notes": "restaurant closed, attempt ar, employee shot 1 suspect with personal handgun, unclear if other 2 were armed", + "participant_age": "1::28", + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+||3::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": "1::Michael Renard Grace", + "participant_relationship": "1::Armed Robbery||2::Armed Robbery||3::Armed Robbery", + "participant_status": "0::Unharmed||1::Killed||2::Unharmed||3::Unharmed", + "participant_type": "0::Victim||1::Subject-Suspect||2::Subject-Suspect||3::Subject-Suspect", + "sources": "http://www.wfmynews2.com/news/crime/pizza-hut-employee-shoots-kills-robbery-suspect-police/344296829||http://wbtw.com/2016/10/31/nc-pizza-hut-worker-uses-gun-to-kill-suspect-during-robbery-attempt/", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "006", + "features": { + "incident_id": 365714, + "date": "2015-06-28", + "state": "Florida", + "city_or_county": "Jacksonville", + "address": "Arlington Expressway", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/365714", + "source_url": "http://www.news4jax.com/news/man-shot-in-stomach-recovering/33823874", + "incident_url_fields_missing": "False", + "congressional_district": 5.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured", + "latitude": 30.3275, + "location_description": "Summer Oaks Apartments", + "longitude": -81.5774, + "n_guns_involved": null, + "notes": null, + "participant_age": "0::35", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured", + "participant_type": "0::Victim", + "sources": "http://www.news4jax.com/news/man-shot-in-stomach-recovering/33823874", + "state_house_district": 13.0, + "state_senate_district": 9.0 + } + }, + { + "scenario_id": "007", + "features": { + "incident_id": 323729, + "date": "2015-04-15", + "state": "Indiana", + "city_or_county": "Indianapolis", + "address": "300 block of Hillside Ave", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/323729", + "source_url": "http://fox59.com/2015/04/15/person-shot-in-leg-on-indys-near-northeast-side/", + "incident_url_fields_missing": "False", + "congressional_district": 7.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured", + "latitude": 39.8286, + "location_description": null, + "longitude": -86.1232, + "n_guns_involved": null, + "notes": "Victim injured, leg; unclear.", + "participant_age": null, + "participant_age_group": null, + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured", + "participant_type": "0::Victim", + "sources": "http://fox59.com/2015/04/15/person-shot-in-leg-on-indys-near-northeast-side/", + "state_house_district": 96.0, + "state_senate_district": 34.0 + } + }, + { + "scenario_id": "008", + "features": { + "incident_id": 801080, + "date": "2017-03-23", + "state": "Georgia", + "city_or_county": "Acworth", + "address": "4335 Clairesbrook Lane", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/801080", + "source_url": "http://www.11alive.com/news/police-investigate-shooting-in-kennesaw-subdivision/424972790", + "incident_url_fields_missing": "False", + "congressional_district": 11.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Wounded/Injured||LOCKDOWN/ALERT ONLY: No GV Incident Occurred Onsite", + "latitude": 34.057, + "location_description": "Baker Heights subdivision", + "longitude": -84.626, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": "0::16", + "participant_age_group": "0::Teen 12-17", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured", + "participant_type": "0::Victim", + "sources": "http://www.11alive.com/news/police-investigate-shooting-in-kennesaw-subdivision/424972790", + "state_house_district": 35.0, + "state_senate_district": 37.0 + } + }, + { + "scenario_id": "009", + "features": { + "incident_id": 997676, + "date": "2017-11-27", + "state": "Virginia", + "city_or_county": "Newport News", + "address": "1st block of Museum Dr", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/997676", + "source_url": "http://www.dailypress.com/news/newport-news/dp-newport-news-police-investigating-shooting-on-museum-drive-20171127-story.html", + "incident_url_fields_missing": "False", + "congressional_district": null, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Suicide^", + "latitude": 37.0489, + "location_description": null, + "longitude": -76.4883, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": null, + "participant_age_group": null, + "participant_gender": null, + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Victim", + "sources": "http://www.dailypress.com/news/newport-news/dp-newport-news-police-investigating-shooting-on-museum-drive-20171127-story.html", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "018", + "features": { + "incident_id": 179415, + "date": "2014-08-22", + "state": "California", + "city_or_county": "Mountain View", + "address": "1 Amphitheatre Parkway", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/179415", + "source_url": "http://abc7news.com/news/young-jeezy-arrested-in-firearms-probe-after-deadly-shooting/280597/", + "incident_url_fields_missing": "False", + "congressional_district": 18.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Possession (gun(s) found during commission of other crimes)", + "latitude": 37.4233, + "location_description": null, + "longitude": -122.078, + "n_guns_involved": null, + "notes": "Wiz Khalifa concert at the Shoreline Ampitheater", + "participant_age": "0::38||2::39||3::38||4::31||5::48||6::53", + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+||3::Adult 18+||4::Adult 18+||5::Adult 18+||6::Adult 18+", + "participant_gender": "0::Male||1::Male||2::Male||3::Male||4::Male||5::Male||6::Female", + "participant_name": "0::Eric Johnson||1::Jay \"Young Jeezy\" Jenkins||2::Kena Marshall||3::Peter Maynard||4::David Kuniansky-Altman||5::William Gilmore||6::Alexa Beason", + "participant_relationship": null, + "participant_status": "0::Killed||1::Unharmed||2::Unharmed||3::Unharmed||4::Unharmed||5::Unharmed||6::Unharmed", + "participant_type": "0::Victim||1::Subject-Suspect||2::Subject-Suspect||3::Subject-Suspect||4::Subject-Suspect||5::Subject-Suspect||6::Subject-Suspect", + "sources": "http://www.mercurynews.com/crime-courts/ci_26392919/mountain-view-one-person-shot-at-shoreline-ampitheater||http://abc7news.com/news/young-jeezy-arrested-in-firearms-probe-after-deadly-shooting/280597/", + "state_house_district": 24.0, + "state_senate_district": 13.0 + } + }, + { + "scenario_id": "024", + "features": { + "incident_id": 869272, + "date": "2017-06-16", + "state": "Indiana", + "city_or_county": "Richmond", + "address": "100 block of Richmond Ave", + "n_killed": 2, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/869272", + "source_url": "http://www.theindychannel.com/news/crime/two-killed-one-wounded-in-shooting-at-richmond-home-following-argument", + "incident_url_fields_missing": "False", + "congressional_district": 6.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Wounded/Injured||Shot - Dead (murder, accidental, suicide)", + "latitude": 39.8347, + "location_description": null, + "longitude": -84.9014, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": null, + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+", + "participant_gender": "0::Male||1::Male||2::Male", + "participant_name": "0::Bishop Oliver||1::Shawn Sams||2::Chris Crawley", + "participant_relationship": null, + "participant_status": "0::Killed||1::Killed||2::Injured", + "participant_type": "0::Victim||1::Victim||2::Victim", + "sources": "http://www.theindychannel.com/news/crime/two-killed-one-wounded-in-shooting-at-richmond-home-following-argument", + "state_house_district": 56.0, + "state_senate_district": 27.0 + } + }, + { + "scenario_id": "040", + "features": { + "incident_id": 364918, + "date": "2015-06-24", + "state": "Oklahoma", + "city_or_county": "Oklahoma City", + "address": "N.W. 150th Street and Western Avenue", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/364918", + "source_url": "http://kfor.com/2018/03/26/three-arrested-in-connection-to-2015-home-invasion-homicide-in-oklahoma-city/", + "incident_url_fields_missing": "False", + "congressional_district": null, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Drug involvement", + "latitude": null, + "location_description": null, + "longitude": null, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": "0::37", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male||1::Male||2::Male||3::Male||4::Male", + "participant_name": "0::Tuan Lam||1::Mikecala Goosby||2::Stevequam Orange||3::David Love IV||4::Devon Shaw", + "participant_relationship": null, + "participant_status": "0::Killed||1::Unharmed, Arrested||2::Unharmed, Arrested||3::Unharmed, Arrested||4::Unharmed", + "participant_type": "0::Victim||1::Subject-Suspect||2::Subject-Suspect||3::Subject-Suspect||4::Subject-Suspect", + "sources": "http://kfor.com/2015/06/24/police-one-dead-after-possible-home-invasion/||http://kfor.com/2015/08/25/four-charged-one-arrested-in-metro-murder/||http://kfor.com/2018/03/26/three-arrested-in-connection-to-2015-home-invasion-homicide-in-oklahoma-city/", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "042", + "features": { + "incident_id": 982529, + "date": "2017-11-05", + "state": "Oklahoma", + "city_or_county": "Oklahoma City", + "address": "2000 W Memorial Rd", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/982529", + "source_url": "http://www.news9.com/story/36789734/17-year-old-arrested-in-connection-with-deadly-shooting-at-nw-okc-wal-mart", + "incident_url_fields_missing": "False", + "congressional_district": 5.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 35.6084, + "location_description": "Walmart", + "longitude": -97.548, + "n_guns_involved": 1.0, + "notes": "Shooting victim crashed car into Walmart parking lot; unclear whether shooting took place at that location.", + "participant_age": "0::26||1::17", + "participant_age_group": "0::Adult 18+||1::Teen 12-17", + "participant_gender": "0::Male||1::Male", + "participant_name": "0::Kendall Dejuan Neal||1::Nicholas Daniel", + "participant_relationship": null, + "participant_status": "0::Killed||1::Unharmed, Arrested", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://www.news9.com/story/36772480/one-found-fatally-shot-at-nw-okc-walmart||http://www.news9.com/story/36768335/police-investigate-fatal-shooting-in-nw-okc-walmart-parking-lot||http://www.news9.com/story/36789734/17-year-old-arrested-in-connection-with-deadly-shooting-at-nw-okc-wal-mart", + "state_house_district": 83.0, + "state_senate_district": 48.0 + } + }, + { + "scenario_id": "043", + "features": { + "incident_id": 759616, + "date": "2017-01-23", + "state": "Maryland", + "city_or_county": "Parkton", + "address": "1000 block of Dairy Rd", + "n_killed": 2, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/759616", + "source_url": "http://www.baltimoresun.com/news/maryland/baltimore-county/bs-md-co-murder-suicide-20170124-story.html", + "incident_url_fields_missing": "False", + "congressional_district": 1.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Suicide^||Murder/Suicide||Domestic Violence", + "latitude": 39.6495, + "location_description": null, + "longitude": -76.6798, + "n_guns_involved": 1.0, + "notes": "murder suicide, unclear who shot who", + "participant_age": "0::49||1::56", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Female||1::Male", + "participant_name": "0::Dawn Longo Rowe||1::Lyttelton Waddell Rowe", + "participant_relationship": "1::Significant others - current or former", + "participant_status": "0::Killed||1::Killed", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://www.baltimoresun.com/news/maryland/baltimore-county/bs-md-co-murder-suicide-20170124-story.html", + "state_house_district": null, + "state_senate_district": 42.0 + } + }, + { + "scenario_id": "051", + "features": { + "incident_id": 443428, + "date": "2015-10-23", + "state": "District of Columbia", + "city_or_county": "Washington", + "address": "1400 block of First Street SW", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/443428", + "source_url": "http://www.wusa9.com/story/news/local/dc/2015/11/05/19-year-old-dies-after-october-shooting-near-nats-park/75265300/", + "incident_url_fields_missing": "False", + "congressional_district": 1.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 38.8728, + "location_description": "Nationals Park", + "longitude": -77.0119, + "n_guns_involved": null, + "notes": null, + "participant_age": "0::19", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Daniel Brown", + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Victim", + "sources": "http://www.wusa9.com/story/news/local/dc/2015/11/05/19-year-old-dies-after-october-shooting-near-nats-park/75265300/", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "053", + "features": { + "incident_id": 200837, + "date": "2014-10-04", + "state": "California", + "city_or_county": "San Bernardino", + "address": "7100 block of Argyle Ave", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/200837", + "source_url": "http://www.pe.com/articles/car-751330-highland-smith.html", + "incident_url_fields_missing": "False", + "congressional_district": 8.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 34.125, + "location_description": null, + "longitude": -117.243, + "n_guns_involved": 1.0, + "notes": "34.124968, -117.242982", + "participant_age": "0::31", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Edgar Smith", + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Victim", + "sources": "http://www.pe.com/articles/car-751330-highland-smith.html", + "state_house_district": 40.0, + "state_senate_district": 23.0 + } + }, + { + "scenario_id": "057", + "features": { + "incident_id": 289665, + "date": "2015-02-07", + "state": "Virginia", + "city_or_county": "Richmond", + "address": "3000 block of Jefferson Davis Highway", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/289665", + "source_url": "http://www.wave3.com/story/28049911/richmond-police-woman-found-dead-in-hotel-room", + "incident_url_fields_missing": "False", + "congressional_district": 3.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 37.4829, + "location_description": null, + "longitude": -77.4448, + "n_guns_involved": null, + "notes": "Southside, woman found shot dead in motel room", + "participant_age": "0::35||1::33||2::23", + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+", + "participant_gender": "0::Female||1::Male||2::Male", + "participant_name": "0::Lynette Gregory||1::Wayne Martin||2::Davon Creer", + "participant_relationship": null, + "participant_status": "0::Killed||1::Unharmed, Arrested||2::Unharmed, Arrested", + "participant_type": "0::Victim||1::Subject-Suspect||2::Subject-Suspect", + "sources": "http://www.nbc12.com/story/28049911/richmond-police-woman-found-dead-in-hotel-room||http://www.wave3.com/story/28049911/richmond-police-woman-found-dead-in-hotel-room", + "state_house_district": 70.0, + "state_senate_district": 16.0 + } + }, + { + "scenario_id": "067", + "features": { + "incident_id": 1039798, + "date": "2018-01-30", + "state": "Texas", + "city_or_county": "Fort Worth", + "address": "2700 block of S Meadow Dr", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/1039798", + "source_url": "http://www.star-telegram.com/news/local/community/fort-worth/article199847539.html", + "incident_url_fields_missing": "False", + "congressional_district": 12.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 32.6411, + "location_description": null, + "longitude": -97.3604, + "n_guns_involved": 1.0, + "notes": "Vic was a rapper and club promoter who went by the name Monte Wayne.", + "participant_age": "0::36||1::41", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": "0::Demonta Tanksley||1::Ronald E. Berry", + "participant_relationship": null, + "participant_status": "0::Killed||1::Arrested", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://www.star-telegram.com/news/local/community/fort-worth/article197378844.html||https://www.nbcdfw.com/news/local/Shooting-in-Fort-Worth-Leaves-1-Dead-471780493.html||http://www.star-telegram.com/news/local/community/fort-worth/article199847539.html", + "state_house_district": 95.0, + "state_senate_district": 10.0 + } + }, + { + "scenario_id": "069", + "features": { + "incident_id": 904080, + "date": "2017-08-02", + "state": "Louisiana", + "city_or_county": "Coushatta", + "address": "Banks Street", + "n_killed": 1, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/904080", + "source_url": "http://www.arklatexhomepage.com/news/lsp-investigating-officer-involved-shooting-in-red-river-parish/781173487", + "incident_url_fields_missing": "False", + "congressional_district": 4.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Wounded/Injured||Shot - Dead (murder, accidental, suicide)||Shots Fired - No Injuries||Officer Involved Incident||Officer Involved Shooting - Officer shot||Officer Involved Shooting - subject/suspect/perpetrator killed||Shootout (where VENN diagram of shooters and victims overlap)", + "latitude": 32.0022, + "location_description": "Red River Parish", + "longitude": -93.3289, + "n_guns_involved": 1.0, + "notes": "ALT: LA 480, shots exchanged between Vet and Deputies", + "participant_age": "1::29", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "1::Male", + "participant_name": "0::Deputy||1::Preston Thornton", + "participant_relationship": null, + "participant_status": "0::Injured||1::Killed", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://www.ksla.com/story/36031367/lsp-investigating-officer-involved-shooting-in-red-river-parish||http://www.ksla.com/story/36031367/lsp-investigating-officer-involved-shooting-in-red-river-parish||http://www.arklatexhomepage.com/news/lsp-investigating-officer-involved-shooting-in-red-river-parish/781173487", + "state_house_district": 23.0, + "state_senate_district": 31.0 + } + }, + { + "scenario_id": "070", + "features": { + "incident_id": 874980, + "date": "2017-06-24", + "state": "Kansas", + "city_or_county": "Hutchinson", + "address": "100 block of S Elm", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/874980", + "source_url": "http://www.kansas.com/news/local/crime/article158214864.html", + "incident_url_fields_missing": "False", + "congressional_district": 1.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 38.0506, + "location_description": null, + "longitude": -97.9242, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": "0::24||1::19", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": "0::Kenneth Thompson||1::Leo Lamont Wells", + "participant_relationship": null, + "participant_status": "0::Killed||1::Unharmed, Arrested", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://www.kwch.com/content/news/1-hurt-in-Hutchinson-shooting-430522703.html||http://www.kansas.com/news/local/crime/article158214864.html", + "state_house_district": 102.0, + "state_senate_district": 34.0 + } + }, + { + "scenario_id": "075", + "features": { + "incident_id": 142460, + "date": "2014-05-26", + "state": "Alabama", + "city_or_county": "Covington County", + "address": null, + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/142460", + "source_url": "http://www.waka.com/2014/06/02/covington-co-man-killed-in-shooting/", + "incident_url_fields_missing": "False", + "congressional_district": 2.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 31.3066, + "location_description": null, + "longitude": -86.4997, + "n_guns_involved": 1.0, + "notes": "1 male victim shot - killed", + "participant_age": "0::30", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Shawn Bolden", + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Victim", + "sources": "http://www.waka.com/2014/06/02/covington-co-man-killed-in-shooting/", + "state_house_district": 92.0, + "state_senate_district": 31.0 + } + }, + { + "scenario_id": "076", + "features": { + "incident_id": 220978, + "date": "2014-11-13", + "state": "Colorado", + "city_or_county": "Evans", + "address": "2400 block of Marina Street", + "n_killed": 2, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/220978", + "source_url": "http://www.denverpost.com/news/ci_26962545/evans-police-man-shot-teen-daughter-wife-murder", + "incident_url_fields_missing": "False", + "congressional_district": 4.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Wounded/Injured||Shot - Dead (murder, accidental, suicide)||Suicide^||Murder/Suicide||Domestic Violence", + "latitude": 40.3753, + "location_description": null, + "longitude": -104.722, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": "0::47||1::61||2::17", + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Teen 12-17", + "participant_gender": "0::Female||1::Male||2::Female", + "participant_name": "0::Natalya Perrin||1::Richard Perrin||2::Danika Perrin", + "participant_relationship": null, + "participant_status": "0::Injured||1::Killed||2::Killed", + "participant_type": "0::Victim||1::Victim||2::Victim", + "sources": "http://www.9news.com/story/news/local/2014/11/13/2-bodies-found-in-evans-home/18999799/||http://www.denverpost.com/news/ci_26962545/evans-police-man-shot-teen-daughter-wife-murder", + "state_house_district": 50.0, + "state_senate_district": 13.0 + } + }, + { + "scenario_id": "085", + "features": { + "incident_id": 367627, + "date": "2015-06-30", + "state": "North Carolina", + "city_or_county": "Franklin", + "address": "Woodland Hills Drive and Highway 28", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/367627", + "source_url": "http://www.wlos.com//news/features/top-stories/stories/officer-involved-shooting-investigation-21743.shtml#.VZjd97Xy2GM", + "incident_url_fields_missing": "False", + "congressional_district": 11.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Officer Involved Incident||Officer Involved Shooting - subject/suspect/perpetrator killed", + "latitude": 35.2023, + "location_description": "The Bluff Townhouses", + "longitude": -83.3924, + "n_guns_involved": null, + "notes": "police trying to serve arrest warrant shot and killed man standing in driveway of home who fired at them", + "participant_age": "0::52", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Clay Alan Lickteig", + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Subject-Suspect", + "sources": "http://www.wsoctv.com/news/news/local/man-shot-death-police-western-north-carolina/nmqjr/||http://www.wlos.com//news/features/top-stories/stories/deadly-officerinvolved-shooting-franklin-21752.shtml#.VZjZILXy2GM||http://www.wlos.com//news/features/top-stories/stories/officer-involved-shooting-investigation-21743.shtml#.VZjd97Xy2GM", + "state_house_district": 120.0, + "state_senate_district": 50.0 + } + }, + { + "scenario_id": "088", + "features": { + "incident_id": 997079, + "date": "2017-11-28", + "state": "Washington", + "city_or_county": "Seattle", + "address": "3000 block of 14th Ave W", + "n_killed": 1, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/997079", + "source_url": "http://www.kiro7.com/news/local/woman-shot-man-dead-in-queen-anne-attempted-murder-suicide/655129991", + "incident_url_fields_missing": "False", + "congressional_district": 7.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Wounded/Injured||Shot - Dead (murder, accidental, suicide)||Suicide^||Attempted Murder/Suicide (one variable unsuccessful)||Domestic Violence", + "latitude": 47.6466, + "location_description": null, + "longitude": -122.375, + "n_guns_involved": 1.0, + "notes": "shot wife then self", + "participant_age": "0::70||1::70", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Female||1::Male", + "participant_name": null, + "participant_relationship": "1::Significant others - current or former", + "participant_status": "0::Injured||1::Killed", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://komonews.com/news/local/1-dead-woman-injured-after-shooting-in-seattles-interbay-area||http://www.kiro7.com/news/local/woman-shot-man-dead-in-queen-anne-attempted-murder-suicide/655129991", + "state_house_district": 36.0, + "state_senate_district": 36.0 + } + }, + { + "scenario_id": "090", + "features": { + "incident_id": 463642, + "date": "2015-12-11", + "state": "Maryland", + "city_or_county": "Baltimore", + "address": "5100 block of Park Heights Avenue", + "n_killed": 1, + "n_injured": 3, + "incident_url": "http://www.gunviolencearchive.org/incident/463642", + "source_url": "http://www.baltimoresun.com/news/maryland/crime/blog/bs-md-ci-homicide-20151211-story.html", + "incident_url_fields_missing": "False", + "congressional_district": 7.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured||Shot - Dead (murder, accidental, suicide)||Mass Shooting (4+ victims injured or killed excluding the subject/suspect/perpetrator, one location)", + "latitude": 39.3483, + "location_description": null, + "longitude": -76.6751, + "n_guns_involved": null, + "notes": null, + "participant_age": "0::27||1::17||2::21||3::24", + "participant_age_group": "0::Adult 18+||1::Teen 12-17||2::Adult 18+||3::Adult 18+", + "participant_gender": "0::Male||1::Male||2::Female||3::Male", + "participant_name": "0::Evan Davis", + "participant_relationship": null, + "participant_status": "0::Killed||1::Injured||2::Injured||3::Injured", + "participant_type": "0::Victim||1::Victim||2::Victim||3::Victim", + "sources": "http://www.baltimoresun.com/news/maryland/crime/blog/bs-md-ci-homicide-20151211-story.html", + "state_house_district": 41.0, + "state_senate_district": 41.0 + } + }, + { + "scenario_id": "091", + "features": { + "incident_id": 262012, + "date": "2014-12-11", + "state": "Missouri", + "city_or_county": "Springfield", + "address": "1040 S. Missouri Avenue", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/262012", + "source_url": "http://www.ky3.com/news/local/neighbor-heard-4-shots-before-man-was-found-dead/21048998_30181160", + "incident_url_fields_missing": "False", + "congressional_district": 7.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 37.1953, + "location_description": null, + "longitude": -93.3016, + "n_guns_involved": null, + "notes": null, + "participant_age": "0::26", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Joshua Kelley", + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Victim", + "sources": "http://www.ky3.com/news/local/neighbor-heard-4-shots-before-man-was-found-dead/21048998_30181160", + "state_house_district": 132.0, + "state_senate_district": 30.0 + } + }, + { + "scenario_id": "095", + "features": { + "incident_id": 140995, + "date": "2014-05-29", + "state": "Michigan", + "city_or_county": "Detroit", + "address": "12828 Fenkell", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/140995", + "source_url": "http://www.mlive.com/news/detroit/index.ssf/2014/06/cpl_holder_accidentally_kills.html", + "incident_url_fields_missing": "False", + "congressional_district": 14.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Accidental Shooting||Accidental Shooting - Death||Defensive Use", + "latitude": 42.4023, + "location_description": null, + "longitude": -83.1708, + "n_guns_involved": null, + "notes": "Djs R Us - Storeowner shot at thief who ducked. Bullet hit innocent bystander, killing him. Legal CPL weapon", + "participant_age": "0::19", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Senghore Ball ", + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Victim", + "sources": "http://www.freep.com/article/20140530/NEWS01/305300181/detroit-fatal-shooting||http://www.mlive.com/news/detroit/index.ssf/2014/06/cpl_holder_accidentally_kills.html", + "state_house_district": 8.0, + "state_senate_district": 3.0 + } + }, + { + "scenario_id": "105", + "features": { + "incident_id": 170557, + "date": "2014-01-19", + "state": "Indiana", + "city_or_county": "Indianapolis", + "address": "1800 block of South Belmont Avenue.", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/170557", + "source_url": "http://www.indystar.com/story/news/crime/2014/02/02/slain-pizza-driver-was-local-musician/5105763/", + "incident_url_fields_missing": "False", + "congressional_district": 7.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Armed robbery with injury/death and/or evidence of DGU found||Car-jacking||Possession (gun(s) found during commission of other crimes)", + "latitude": 39.7424, + "location_description": null, + "longitude": -86.1967, + "n_guns_involved": null, + "notes": "PIzza driver robbed, shot, and killed. Car abandoned by 2 suspects in Wyoming; apprehended right after.", + "participant_age": "0::49||1::26||2::22", + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+", + "participant_gender": "0::Male||1::Male||2::Male", + "participant_name": "0::John Sullivan||1::Jeremiah Roberts||2::Derek Romano", + "participant_relationship": null, + "participant_status": "0::Killed||1::Unharmed||2::Unharmed", + "participant_type": "0::Victim||1::Subject-Suspect||2::Subject-Suspect", + "sources": "http://www.indystar.com/story/news/crime/2014/02/02/slain-pizza-driver-was-local-musician/5105763/", + "state_house_district": 97.0, + "state_senate_district": 35.0 + } + }, + { + "scenario_id": "106", + "features": { + "incident_id": 276091, + "date": "2015-01-05", + "state": "Florida", + "city_or_county": "Tampa", + "address": "3100 block of North Jefferson Street", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/276091", + "source_url": "http://tbo.com/news/crime/tampa-police-investigating-two-unrelated-shooting-deaths-20150105/", + "incident_url_fields_missing": "False", + "congressional_district": 14.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 27.9632, + "location_description": null, + "longitude": -82.4566, + "n_guns_involved": null, + "notes": "Male shot killed", + "participant_age": "0::18", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Lyfe Coleman", + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Victim", + "sources": "http://tbo.com/news/crime/tampa-police-investigating-two-unrelated-shooting-deaths-20150105/", + "state_house_district": 61.0, + "state_senate_district": 19.0 + } + }, + { + "scenario_id": "110", + "features": { + "incident_id": 96062, + "date": "2014-01-12", + "state": "Pennsylvania", + "city_or_county": "Chester", + "address": "2900 block of West 3rd Street. ", + "n_killed": 1, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/96062", + "source_url": "http://articles.philly.com/2014-01-14/news/46153194_1_robbery-victim-chester-bar-dead-man", + "incident_url_fields_missing": "False", + "congressional_district": 1.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured||Shot - Dead (murder, accidental, suicide)||Armed robbery with injury/death and/or evidence of DGU found||Possession (gun(s) found during commission of other crimes)||Brandishing/flourishing/open carry/lost/found||Defensive Use", + "latitude": 39.8314, + "location_description": null, + "longitude": -75.3923, + "n_guns_involved": null, + "notes": null, + "participant_age": "1::31", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured||1::Killed", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://abclocal.go.com/wpvi/story||http://articles.philly.com/2014-01-14/news/46153194_1_robbery-victim-chester-bar-dead-man", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "111", + "features": { + "incident_id": 386694, + "date": "2015-07-12", + "state": "Maryland", + "city_or_county": "Baltimore", + "address": "1200 block of North Caroline Street", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/386694", + "source_url": "http://data.baltimoresun.com/bing-maps/homicides/index.php?show_results=UPDATE+MAP&range=30&district=all&zipcode=All&cause=shooting&age=all&gender=all&race=all&article=all", + "incident_url_fields_missing": "False", + "congressional_district": 7.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 39.3043, + "location_description": null, + "longitude": -76.5984, + "n_guns_involved": null, + "notes": null, + "participant_age": "0::39", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Tyrone Johnson", + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Victim", + "sources": "http://data.baltimoresun.com/bing-maps/homicides/index.php?show_results=UPDATE+MAP&range=30&district=all&zipcode=All&cause=shooting&age=all&gender=all&race=all&article=all", + "state_house_district": 45.0, + "state_senate_district": 45.0 + } + }, + { + "scenario_id": "118", + "features": { + "incident_id": 478385, + "date": "2016-01-03", + "state": "Florida", + "city_or_county": "Fort Pierce", + "address": "18th Street", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/478385", + "source_url": "http://www.wpbf.com/news/man-shot-to-death-while-walking-in-fort-pierce/37251662", + "incident_url_fields_missing": "False", + "congressional_district": 18.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 27.4566, + "location_description": null, + "longitude": -80.3427, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": "0::20", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": "0::Anthony Guzman", + "participant_relationship": null, + "participant_status": "0::Killed||1::Unharmed", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://www.wpbf.com/news/man-shot-to-death-while-walking-in-fort-pierce/37251662", + "state_house_district": 84.0, + "state_senate_district": 32.0 + } + }, + { + "scenario_id": "126", + "features": { + "incident_id": 866691, + "date": "2017-06-13", + "state": "Oklahoma", + "city_or_county": "Oklahoma City", + "address": "9401 S Shartel Ave", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/866691", + "source_url": "http://www.koco.com/article/man-dies-after-tuesday-night-shooting-at-southwest-okc-apartment-complex/10027216", + "incident_url_fields_missing": "False", + "congressional_district": 4.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 35.3744, + "location_description": "Brookwood Village Apartments", + "longitude": -97.524, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": "0::19", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Steven Morgan", + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Victim", + "sources": "http://kfor.com/2017/06/13/police-investigating-shooting-in-southwest-oklahoma-city/||http://www.koco.com/article/man-dies-after-tuesday-night-shooting-at-southwest-okc-apartment-complex/10027216", + "state_house_district": 91.0, + "state_senate_district": 45.0 + } + }, + { + "scenario_id": "136", + "features": { + "incident_id": 308050, + "date": "2015-03-13", + "state": "Arkansas", + "city_or_county": "Springdale", + "address": "32 Applegate Drive", + "n_killed": 1, + "n_injured": 2, + "incident_url": "http://www.gunviolencearchive.org/incident/308050", + "source_url": "http://www.arkansasonline.com/news/2015/mar/14/1-dead-2-hurt-teen-held-in-springdale-s/?f=crime", + "incident_url_fields_missing": "False", + "congressional_district": 3.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured||Shot - Dead (murder, accidental, suicide)", + "latitude": 36.1764, + "location_description": null, + "longitude": -94.1223, + "n_guns_involved": null, + "notes": "1 killed, 2 wounded", + "participant_age": "0::18||1::21||2::14||3::17", + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Teen 12-17||3::Teen 12-17", + "participant_gender": "0::Male||1::Female||2::Male||3::Male", + "participant_name": "0::Fabian Rodriguez||1::Savannah Smith", + "participant_relationship": null, + "participant_status": "0::Killed||1::Injured||2::Injured||3::Unharmed, Arrested", + "participant_type": "0::Victim||1::Victim||2::Victim||3::Subject-Suspect", + "sources": "http://www.arkansasonline.com/news/2015/mar/14/1-dead-2-hurt-teen-held-in-springdale-s/?f=crime", + "state_house_district": 89.0, + "state_senate_district": 7.0 + } + }, + { + "scenario_id": "137", + "features": { + "incident_id": 106345, + "date": "2014-02-13", + "state": "Arizona", + "city_or_county": "Phoenix", + "address": "I-17 and Osborne ", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/106345", + "source_url": "http://www.myfoxphoenix.com/story/24712468/2014/02/13/possibly-shooting-at-phoenix-apartments", + "incident_url_fields_missing": "False", + "congressional_district": 7.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 33.5362, + "location_description": null, + "longitude": -112.095, + "n_guns_involved": null, + "notes": null, + "participant_age": null, + "participant_age_group": "0::Adult 18+", + "participant_gender": null, + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Victim", + "sources": "http://www.myfoxphoenix.com/story/24712468/2014/02/13/possibly-shooting-at-phoenix-apartments", + "state_house_district": 24.0, + "state_senate_district": 24.0 + } + }, + { + "scenario_id": "141", + "features": { + "incident_id": 877721, + "date": "2017-06-27", + "state": "Texas", + "city_or_county": "Amarillo", + "address": "601 S Nelson St", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/877721", + "source_url": "http://abc7amarillo.com/news/local/one-dead-in-officer-involved-shooting-in-north-amarillo", + "incident_url_fields_missing": "False", + "congressional_district": 13.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Handgun", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Officer Involved Incident||Officer Involved Shooting - subject/suspect/perpetrator killed||Domestic Violence||Brandishing/flourishing/open carry/lost/found", + "latitude": 35.204, + "location_description": "Nelson Street Apartments", + "longitude": -101.806, + "n_guns_involved": 1.0, + "notes": "respond to dv, shot suspect with handgun after fail tase", + "participant_age": "0::35", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Jason Magana Herrera", + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Subject-Suspect", + "sources": "http://amarillo.com/news/local-news/2017-06-27/amarillo-police-officer-shoots-kills-man-who-pointed-gut||http://abc7amarillo.com/news/local/one-dead-in-officer-involved-shooting-in-north-amarillo", + "state_house_district": null, + "state_senate_district": 31.0 + } + }, + { + "scenario_id": "143", + "features": { + "incident_id": 360045, + "date": "2015-06-18", + "state": "Texas", + "city_or_county": "Fort Worth", + "address": "9000 block of Brian Way Circle", + "n_killed": 1, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/360045", + "source_url": "http://www.star-telegram.com/news/local/community/fort-worth/article100424737.html", + "incident_url_fields_missing": "False", + "congressional_district": 12.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::22 LR", + "incident_characteristics": "Shot - Wounded/Injured||Shot - Dead (murder, accidental, suicide)||Armed robbery with injury/death and/or evidence of DGU found", + "latitude": 32.7344, + "location_description": "Bent Tree Apartments", + "longitude": -97.4734, + "n_guns_involved": 1.0, + "notes": "1 killed, 1 inj. Armed robbery.", + "participant_age": "0::21||1::23||2::17||3::17||4::20||5::16", + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Teen 12-17||3::Teen 12-17||4::Adult 18+||5::Teen 12-17", + "participant_gender": "0::Male||1::Male||2::Male||3::Male||4::Male", + "participant_name": "1::Eleaquin Cardona||2::Duane C. Thomas||3::Jamal Lee Washington||4::Erik Morales", + "participant_relationship": "2::Armed Robbery||3::Armed Robbery||4::Armed Robbery", + "participant_status": "0::Injured||1::Killed||2::Unharmed, Arrested||3::Unharmed, Arrested||4::Unharmed, Arrested||5::Unharmed, Arrested", + "participant_type": "0::Victim||1::Victim||2::Subject-Suspect||3::Subject-Suspect||4::Subject-Suspect||5::Subject-Suspect", + "sources": "http://www.wfaa.com/story/news/crime/2015/06/18/fwpd-searching-for-suspects-in-fatal-apartment-shooting/28912863/||http://dfw.cbslocal.com/2015/06/18/suspects-sought-in-fatal-fort-worth-shooting/||http://www.star-telegram.com/news/local/community/fort-worth/article99973317.html||http://www.star-telegram.com/news/local/community/fort-worth/article100424737.html", + "state_house_district": 97.0, + "state_senate_district": 12.0 + } + }, + { + "scenario_id": "144", + "features": { + "incident_id": 866729, + "date": "2017-06-13", + "state": "Illinois", + "city_or_county": "Chicago", + "address": "5000 block of S Drexel", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/866729", + "source_url": "http://chicago.suntimes.com/news/police-person-found-dead-with-gunshot-wounds-in-south-side-building/", + "incident_url_fields_missing": "False", + "congressional_district": 1.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 41.8041, + "location_description": "Kenwood neighborhood", + "longitude": -87.6042, + "n_guns_involved": 1.0, + "notes": "Victim's body found in building, shot head, rt. side;", + "participant_age": "0::28", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Deandre Smith", + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Victim", + "sources": "http://chicago.suntimes.com/news/police-person-found-dead-with-gunshot-wounds-in-south-side-building/", + "state_house_district": 26.0, + "state_senate_district": 13.0 + } + }, + { + "scenario_id": "150", + "features": { + "incident_id": 646672, + "date": "2016-09-02", + "state": "Ohio", + "city_or_county": "Port Clinton", + "address": "State Rt. 2", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/646672", + "source_url": "http://www.toledoblade.com/Police-Fire/2016/09/02/Man-shoots-self-during-traffic-stop-near-Port-Clinton.html", + "incident_url_fields_missing": "False", + "congressional_district": 9.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Suicide^||Officer Involved Incident||Officer Involved Shooting - subject/suspect/perpetrator surrender at standoff", + "latitude": 41.5051, + "location_description": null, + "longitude": -82.9792, + "n_guns_involved": 1.0, + "notes": "Man wanted on felony arrest warrants commits suicide during traffic stop;", + "participant_age": "0::48", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Douglas Kapel", + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Victim", + "sources": "http://www.toledoblade.com/Police-Fire/2016/09/02/Man-shoots-self-during-traffic-stop-near-Port-Clinton.html", + "state_house_district": 89.0, + "state_senate_district": 2.0 + } + }, + { + "scenario_id": "152", + "features": { + "incident_id": 828186, + "date": "2017-04-25", + "state": "Illinois", + "city_or_county": "Chicago", + "address": "1400 block of West Jonquil", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/828186", + "source_url": "https://www.dnainfo.com/chicago/20170425/rogers-park/man-shot-dead-rogers-park-at-7-am-tuesday", + "incident_url_fields_missing": "False", + "congressional_district": 9.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 42.0211, + "location_description": "Rogers Park", + "longitude": -87.6666, + "n_guns_involved": 1.0, + "notes": "Shot, head, killed;", + "participant_age": "0::18", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Darlis Nkolomoni", + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Victim", + "sources": "http://chicago.suntimes.com/news/police-man-fatally-shot-in-rogers-park/||https://www.dnainfo.com/chicago/20170425/rogers-park/man-shot-dead-rogers-park-at-7-am-tuesday", + "state_house_district": 14.0, + "state_senate_district": 7.0 + } + }, + { + "scenario_id": "153", + "features": { + "incident_id": 888994, + "date": "2017-07-11", + "state": "Nebraska", + "city_or_county": "Hershey", + "address": "E 2nd St", + "n_killed": 1, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/888994", + "source_url": "http://krvn.com/regional-news/shooting-in-hershey/", + "incident_url_fields_missing": "False", + "congressional_district": 3.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::40 SW", + "incident_characteristics": "Shot - Wounded/Injured||Shot - Dead (murder, accidental, suicide)||Suicide^||Domestic Violence", + "latitude": 41.1575, + "location_description": null, + "longitude": -100.998, + "n_guns_involved": 1.0, + "notes": "struggle between suspect and girlfriend's mother, shot vic in hand, then shot self with handgun", + "participant_age": "1::22", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Female||1::Male", + "participant_name": "0::Alisha Imler||1::Taylon Thompson", + "participant_relationship": null, + "participant_status": "0::Injured||1::Killed", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://krvn.com/regional-news/hershey-shooting-and-apparent-suicide/||http://krvn.com/regional-news/shooting-in-hershey/", + "state_house_district": null, + "state_senate_district": 42.0 + } + }, + { + "scenario_id": "156", + "features": { + "incident_id": 453113, + "date": "2015-11-23", + "state": "New Jersey", + "city_or_county": "Jersey City", + "address": "Old Bergen Road and Neptune Avenue", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/453113", + "source_url": "http://www.nj.com/hudson/index.ssf/2015/11/prosecutors_officer_confirms_fatal_afternoon_shoot.html#incart_river_index", + "incident_url_fields_missing": "False", + "congressional_district": 10.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 40.6934, + "location_description": null, + "longitude": -74.0959, + "n_guns_involved": null, + "notes": null, + "participant_age": "0::33", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Markice Hatten", + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Victim", + "sources": "http://www.nj.com/hudson/index.ssf/2015/11/prosecutors_officer_confirms_fatal_afternoon_shoot.html#incart_river_index||http://www.nj.com/hudson/index.ssf/2015/11/prosecutors_officer_confirms_fatal_afternoon_shoot.html#incart_river_index", + "state_house_district": 31.0, + "state_senate_district": 31.0 + } + }, + { + "scenario_id": "158", + "features": { + "incident_id": 787669, + "date": "2017-03-04", + "state": "Texas", + "city_or_county": "Denton", + "address": "200 block of Royal Oaks Pl", + "n_killed": 2, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/787669", + "source_url": "http://www.star-telegram.com/news/local/community/northeast-tarrant/article136678643.html", + "incident_url_fields_missing": "False", + "congressional_district": 26.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Suicide^||Murder/Suicide||Domestic Violence", + "latitude": 33.1631, + "location_description": null, + "longitude": -97.1303, + "n_guns_involved": 1.0, + "notes": "2 killed, domestic violence murder-suicide. man shoots wife and then himself", + "participant_age": "0::70||1::70", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Female||1::Male", + "participant_name": "0::Ruth Lenora Bain||1::Joe William Bain", + "participant_relationship": "1::Family", + "participant_status": "0::Killed||1::Killed", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://dfw.cbslocal.com/2017/03/04/eldery-woman-shot-and-killed-in-denton-suspect-found-with-gunshot-wound/||http://www.dentonrc.com/local-news/local-news-headlines/20170305-couple-in-denton-shooting-identified.ece||http://www.wfaa.com/news/local/suspect-calls-911-after-shooting-elderly-woman-in-denton/419836402||http://www.star-telegram.com/news/local/community/northeast-tarrant/article136678643.html", + "state_house_district": null, + "state_senate_district": 12.0 + } + }, + { + "scenario_id": "159", + "features": { + "incident_id": 329835, + "date": "2015-04-26", + "state": "Oklahoma", + "city_or_county": "Muskogee", + "address": "4010 South 32nd Street", + "n_killed": 2, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/329835", + "source_url": "http://www.tulsaworld.com/communities/muskogee/man-charged-in-april-double-murder-in-muskogee/article_138ecbf1-a20f-5d9f-a492-585a78e1ee7a.html", + "incident_url_fields_missing": "False", + "congressional_district": 2.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 35.7097, + "location_description": null, + "longitude": -95.4026, + "n_guns_involved": null, + "notes": null, + "participant_age": "0::25||1::19||2::24", + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+", + "participant_gender": "0::Male||1::Female||2::Male", + "participant_name": "0::Nicholas Rutherford||1::Abby Seals||2::Eleno Maldonado Jr.", + "participant_relationship": null, + "participant_status": "0::Killed||1::Killed||2::Unharmed, Arrested", + "participant_type": "0::Victim||1::Victim||2::Subject-Suspect", + "sources": "http://www.news9.com/story/28903090/two-found-dead-near-muskogee-sheriffs-office-investigating||http://www.newson6.com/story/28908798/sheriff-ids-victims-in-muskogee-shooting-near-strip-club||http://www.tulsaworld.com/communities/muskogee/man-charged-in-april-double-murder-in-muskogee/article_138ecbf1-a20f-5d9f-a492-585a78e1ee7a.html", + "state_house_district": 13.0, + "state_senate_district": 9.0 + } + }, + { + "scenario_id": "163", + "features": { + "incident_id": 497617, + "date": "2016-02-01", + "state": "Indiana", + "city_or_county": "Hammond", + "address": "7800 block of Northcote Avenue", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/497617", + "source_url": "http://www.nwitimes.com/news/local/lake/update-hammond-standoff-ends-in-suicide/article_84bfacab-6018-5073-987e-25c0f832abad.html", + "incident_url_fields_missing": "False", + "congressional_district": 1.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::9mm", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Suicide^||Officer Involved Incident||Officer Involved Shooting - subject/suspect/perpetrator suicide at standoff", + "latitude": 41.572, + "location_description": null, + "longitude": -87.4858, + "n_guns_involved": 1.0, + "notes": "Man believed to be suicidal found shot, killed, suicide, in home, after SWAT standoff of 2 hours;", + "participant_age": "0::25", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Luis Torres Jr.", + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Subject-Suspect", + "sources": "http://www.nwitimes.com/news/local/lake/update-hammond-standoff-ends-in-suicide/article_84bfacab-6018-5073-987e-25c0f832abad.html", + "state_house_district": 12.0, + "state_senate_district": 1.0 + } + }, + { + "scenario_id": "168", + "features": { + "incident_id": 593725, + "date": "2016-06-28", + "state": "Missouri", + "city_or_county": "Windsor", + "address": "300 Winchell Street", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/593725", + "source_url": "http://www.dothaneagle.com/news/ap/state/year-old-missouri-man-charged-in-deadly-shooting/article_5d9bb9e3-cbb3-5ae6-bd4d-7085cd384b17.html", + "incident_url_fields_missing": "False", + "congressional_district": 4.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 38.5394, + "location_description": null, + "longitude": -93.5155, + "n_guns_involved": null, + "notes": null, + "participant_age": "0::45||1::44", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": "0::Terry Lynn Maples||1::Jeffrey Dean Watring", + "participant_relationship": null, + "participant_status": "0::Killed||1::Unharmed, Arrested", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://sedaliademocrat.com/news/13443/windsor-man-charged-in-tuesday-murder||http://www.dothaneagle.com/news/ap/state/year-old-missouri-man-charged-in-deadly-shooting/article_5d9bb9e3-cbb3-5ae6-bd4d-7085cd384b17.html", + "state_house_district": 57.0, + "state_senate_district": 31.0 + } + }, + { + "scenario_id": "170", + "features": { + "incident_id": 311878, + "date": "2015-03-22", + "state": "Montana", + "city_or_county": "Billings", + "address": "3400 block of Cherylon Street", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/311878", + "source_url": "http://billingsgazette.com/news/local/crime-and-courts/woman-shot-in-heights-on-sunday-dies-monday-officials-release/article_753e9e0d-0ac8-5a7e-ae8e-2b96212864f1.html", + "incident_url_fields_missing": "False", + "congressional_district": 1.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Handgun", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Drug involvement", + "latitude": 45.864, + "location_description": null, + "longitude": -108.45, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": "0::22||1::31", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Female||1::Male", + "participant_name": "0::Kayla Jeanette Kinder||1::John Edward Szydlowski", + "participant_relationship": null, + "participant_status": "0::Killed||1::Unharmed, Arrested", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://billingsgazette.com/news/local/man-arrested-in-sunday-shooting-walked-away-from-prerelease-on/article_8f9a9c63-3756-57cc-a644-9be936e5e4f0.html||http://billingsgazette.com/news/local/crime-and-courts/woman-shot-in-heights-on-sunday-dies-monday-officials-release/article_753e9e0d-0ac8-5a7e-ae8e-2b96212864f1.html", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "172", + "features": { + "incident_id": 668611, + "date": "2016-06-26", + "state": "Louisiana", + "city_or_county": "Harvey", + "address": "4100 block of Peters Road", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/668611", + "source_url": "http://www.nola.com/crime/index.ssf/2016/09/cordaryle_robert_terrance_call.html#incart_river_index", + "incident_url_fields_missing": "False", + "congressional_district": 2.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Armed robbery with injury/death and/or evidence of DGU found||Car-jacking", + "latitude": 29.8465, + "location_description": null, + "longitude": -90.0559, + "n_guns_involved": 1.0, + "notes": "1 killed. Armed robbery. Car-jacking. Female suspect lures victim to a meeting via Facebook and then male suspec shoots him.", + "participant_age": "0::25||1::29||2::23", + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+", + "participant_gender": "0::Male||1::Male||2::Female", + "participant_name": "0::Cordaryle Robert||1::Terrance Calloway||2::Dana Calloway", + "participant_relationship": null, + "participant_status": "0::Killed||1::Unharmed, Arrested||2::Unharmed, Arrested", + "participant_type": "0::Victim||1::Subject-Suspect||2::Subject-Suspect", + "sources": "http://www.nola.com/crime/index.ssf/2016/09/cordaryle_robert_terrance_call.html#incart_river_index", + "state_house_district": 87.0, + "state_senate_district": 8.0 + } + }, + { + "scenario_id": "176", + "features": { + "incident_id": 402318, + "date": "2015-08-25", + "state": "Virginia", + "city_or_county": "Hampton", + "address": "815 Lasalle Avenue", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/402318", + "source_url": "http://www.dailypress.com/news/crime/dp-man-shot-killed-in-hampton-shooting-near-lasalle-avenue-police-say-20150825-story.html", + "incident_url_fields_missing": "False", + "congressional_district": 3.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 37.0232, + "location_description": "LaSalle Garden Apartment", + "longitude": -76.3636, + "n_guns_involved": null, + "notes": "man shot to death after argument", + "participant_age": "0::25||1::24", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": "0::Shaolin Chanham||1::Keenan A. Palmer", + "participant_relationship": null, + "participant_status": "0::Killed||1::Unharmed, Arrested", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://wtkr.com/2015/08/25/person-fighting-for-life-after-shooting-in-hampton/||http://www.dailypress.com/news/crime/dp-man-shot-killed-in-hampton-shooting-near-lasalle-avenue-police-say-20150825-story.html", + "state_house_district": 92.0, + "state_senate_district": 2.0 + } + }, + { + "scenario_id": "188", + "features": { + "incident_id": 124637, + "date": "2014-04-11", + "state": "California", + "city_or_county": "Indio", + "address": "Avenue 44", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/124637", + "source_url": "http://www.kesq.com/news/police-investigating-suspicious-death-in-indio/25430260", + "incident_url_fields_missing": "False", + "congressional_district": 36.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 33.7295, + "location_description": null, + "longitude": -116.199, + "n_guns_involved": null, + "notes": "at Mountain View apartment complex ", + "participant_age": "0::36", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Victim", + "sources": "http://www.kesq.com/news/police-investigating-suspicious-death-in-indio/25430260", + "state_house_district": 56.0, + "state_senate_district": 28.0 + } + } + ], + "target_column": "n_killed", + "task_sub_type": "regression", + "ranking_ground_truth": { + "top_k_ids": [ + "002", + "004", + "006", + "007", + "008" + ] }, - "final_decision": {}, - "what_if": {}, - "ground_truth": { - "extracted_features": [ - { - "scenario_id": "001", - "features": { - "incident_id": 832945, - "date": "2017-04-30", - "state": "West Virginia", - "city_or_county": "Charleston", - "address": "1006 1st Avenue", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/832945", - "source_url": "http://www.wvgazettemail.com/article/20170516/GZ0118/170519652", - "incident_url_fields_missing": "False", - "congressional_district": 2.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Drug involvement", - "latitude": 38.3629, - "location_description": null, - "longitude": -81.6577, - "n_guns_involved": 1.0, - "notes": "Address is 1006 1/2, West Side, one shot and killed", - "participant_age": "0::52||1::27", - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Male||1::Male", - "participant_name": "0::Bryan Keith Johnson||1::John Stanley Ford III", - "participant_relationship": null, - "participant_status": "0::Killed||1::Unharmed, Arrested", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://www.wsaz.com/content/news/Crews-respond-to-call-for-shooting-in-Charleston--420862374.html||http://www.wvgazettemail.com/article/20170501/GZ0118/170509987||http://www.tristateupdate.com/story/35289511/charleston-police-identify-man-fatally-shot-overnight||http://www.wvgazettemail.com/article/20170516/GZ0118/170519652", - "state_house_district": 37.0, - "state_senate_district": 8.0 - } - }, - { - "scenario_id": "002", - "features": { - "incident_id": 1027000, - "date": "2018-01-13", - "state": "Tennessee", - "city_or_county": "Powell", - "address": "601 Edgemoor Rd", - "n_killed": 0, - "n_injured": 2, - "incident_url": "http://www.gunviolencearchive.org/incident/1027000", - "source_url": "http://www.wate.com/news/top-stories/anderson-county-sheriff-deputies-investigate-shooting-on-clinton-hwy/914342994", - "incident_url_fields_missing": "False", - "congressional_district": 3.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Wounded/Injured", - "latitude": 36.0357, - "location_description": "Bread Box", - "longitude": -84.131, - "n_guns_involved": 1.0, - "notes": "Anderson Co, also found on Clinton Hey, Claxton; 3rd vic shot at, no inj", - "participant_age": "0::36||1::34||2::23||3::24", - "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+||3::Adult 18+", - "participant_gender": "0::Male||1::Male||2::Male||3::Female", - "participant_name": "0::Nathan Lynn Phillips||1::William Ernest Foster||2::Jacob Lynn Rutherford||3::Marrie Taylor Atteberry", - "participant_relationship": null, - "participant_status": "0::Injured||1::Injured||2::Unharmed, Arrested||3::Unharmed, Arrested", - "participant_type": "0::Victim||1::Victim||2::Subject-Suspect||3::Subject-Suspect", - "sources": "http://www.wate.com/news/top-stories/anderson-county-sheriff-deputies-investigate-shooting-on-clinton-hwy/914342994", - "state_house_district": 33.0, - "state_senate_district": 5.0 - } - }, - { - "scenario_id": "003", - "features": { - "incident_id": 465538, - "date": "2015-12-13", - "state": "Oregon", - "city_or_county": "Medford", - "address": "Bartlett Street", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/465538", - "source_url": "http://www.mailtribune.com/article/20151215/NEWS/151219747/101034/NEWS?rssfeed=true", - "incident_url_fields_missing": "False", - "congressional_district": 2.0, - "gun_stolen": "0::Unknown||1::Unknown", - "gun_type": "0::Handgun||1::Handgun", - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", - "latitude": 42.3302, - "location_description": null, - "longitude": -122.874, - "n_guns_involved": 2.0, - "notes": null, - "participant_age": "0::27||1::25||2::30", - "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+", - "participant_gender": "0::Male||1::Male||2::Male", - "participant_name": "0::Isaac Deleon||1::Shane Anthony \"Scarface\" Zornes||2::Ruben Escareno Marmolejo", - "participant_relationship": null, - "participant_status": "0::Killed||1::Unharmed, Arrested||2::Unharmed, Arrested", - "participant_type": "0::Victim||1::Subject-Suspect||2::Subject-Suspect", - "sources": "http://www.mailtribune.com/article/20151213/NEWS/151219833/101033/NEWS?rssfeed=true||http://www.mailtribune.com/article/20151216/NEWS/151219710||http://www.mailtribune.com/article/20151215/NEWS/151219747/101034/NEWS?rssfeed=true", - "state_house_district": 6.0, - "state_senate_district": 3.0 - } - }, - { - "scenario_id": "004", - "features": { - "incident_id": 774253, - "date": "2017-02-13", - "state": "Florida", - "city_or_county": "Palm Beach Gardens (Riviera Beach)", - "address": "W 5th St and Ave H", - "n_killed": 0, - "n_injured": 2, - "incident_url": "http://www.gunviolencearchive.org/incident/774253", - "source_url": "http://www.wptv.com/news/region-c-palm-beach-county/riviera-beach/officials-on-scene-of-shooting-in-riviera-beach", - "incident_url_fields_missing": "False", - "congressional_district": 20.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Wounded/Injured||Drive-by (car to street, car to car)", - "latitude": 26.7671, - "location_description": null, - "longitude": -80.0638, - "n_guns_involved": 1.0, - "notes": "26.767057, -80.063831", - "participant_age": null, - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Female||1::Female", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Injured||1::Injured", - "participant_type": "0::Victim||1::Victim", - "sources": "http://www.wptv.com/news/region-c-palm-beach-county/riviera-beach/officials-on-scene-of-shooting-in-riviera-beach", - "state_house_district": 88.0, - "state_senate_district": 27.0 - } - }, - { - "scenario_id": "005", - "features": { - "incident_id": 688645, - "date": "2016-10-30", - "state": "North Carolina", - "city_or_county": "Charlotte", - "address": "3215 Freedom Drive", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/688645", - "source_url": "http://wbtw.com/2016/10/31/nc-pizza-hut-worker-uses-gun-to-kill-suspect-during-robbery-attempt/", - "incident_url_fields_missing": "False", - "congressional_district": null, - "gun_stolen": "0::Not-stolen||1::Unknown", - "gun_type": "0::Handgun||1::Handgun", - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Institution/Group/Business||Defensive Use||Defensive Use - Victim stops crime||Defensive Use - Crime occurs, victim shoots subject/suspect/perpetrator||Defensive Use - Stand Your Ground/Castle Doctrine established||Armed robbery with injury/death and/or evidence of DGU found||Brandishing/flourishing/open carry/lost/found", - "latitude": null, - "location_description": "Pizza Hut", - "longitude": null, - "n_guns_involved": 2.0, - "notes": "restaurant closed, attempt ar, employee shot 1 suspect with personal handgun, unclear if other 2 were armed", - "participant_age": "1::28", - 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"participant_status": "0::Killed", - "participant_type": "0::Victim", - "sources": "http://www.dailypress.com/news/newport-news/dp-newport-news-police-investigating-shooting-on-museum-drive-20171127-story.html", - "state_house_district": null, - "state_senate_district": null - } - }, - { - "scenario_id": "018", - "features": { - "incident_id": 179415, - "date": "2014-08-22", - "state": "California", - "city_or_county": "Mountain View", - "address": "1 Amphitheatre Parkway", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/179415", - "source_url": "http://abc7news.com/news/young-jeezy-arrested-in-firearms-probe-after-deadly-shooting/280597/", - "incident_url_fields_missing": "False", - "congressional_district": 18.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Possession (gun(s) found during commission of other crimes)", - "latitude": 37.4233, - "location_description": null, - "longitude": -122.078, - "n_guns_involved": null, - "notes": "Wiz Khalifa concert at the Shoreline Ampitheater", - "participant_age": "0::38||2::39||3::38||4::31||5::48||6::53", - "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+||3::Adult 18+||4::Adult 18+||5::Adult 18+||6::Adult 18+", - "participant_gender": "0::Male||1::Male||2::Male||3::Male||4::Male||5::Male||6::Female", - "participant_name": "0::Eric Johnson||1::Jay \"Young Jeezy\" Jenkins||2::Kena Marshall||3::Peter Maynard||4::David Kuniansky-Altman||5::William Gilmore||6::Alexa Beason", - "participant_relationship": null, - "participant_status": "0::Killed||1::Unharmed||2::Unharmed||3::Unharmed||4::Unharmed||5::Unharmed||6::Unharmed", - "participant_type": "0::Victim||1::Subject-Suspect||2::Subject-Suspect||3::Subject-Suspect||4::Subject-Suspect||5::Subject-Suspect||6::Subject-Suspect", - "sources": "http://www.mercurynews.com/crime-courts/ci_26392919/mountain-view-one-person-shot-at-shoreline-ampitheater||http://abc7news.com/news/young-jeezy-arrested-in-firearms-probe-after-deadly-shooting/280597/", - 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"participant_name": "0::Bishop Oliver||1::Shawn Sams||2::Chris Crawley", - "participant_relationship": null, - "participant_status": "0::Killed||1::Killed||2::Injured", - "participant_type": "0::Victim||1::Victim||2::Victim", - "sources": "http://www.theindychannel.com/news/crime/two-killed-one-wounded-in-shooting-at-richmond-home-following-argument", - "state_house_district": 56.0, - "state_senate_district": 27.0 - } - }, - { - "scenario_id": "040", - "features": { - "incident_id": 364918, - "date": "2015-06-24", - "state": "Oklahoma", - "city_or_county": "Oklahoma City", - "address": "N.W. 150th Street and Western Avenue", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/364918", - "source_url": "http://kfor.com/2018/03/26/three-arrested-in-connection-to-2015-home-invasion-homicide-in-oklahoma-city/", - "incident_url_fields_missing": "False", - "congressional_district": null, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - 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"state_house_district": 23.0, - "state_senate_district": 31.0 - } - }, - { - "scenario_id": "070", - "features": { - "incident_id": 874980, - "date": "2017-06-24", - "state": "Kansas", - "city_or_county": "Hutchinson", - "address": "100 block of S Elm", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/874980", - "source_url": "http://www.kansas.com/news/local/crime/article158214864.html", - "incident_url_fields_missing": "False", - "congressional_district": 1.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", - "latitude": 38.0506, - "location_description": null, - "longitude": -97.9242, - "n_guns_involved": 1.0, - "notes": null, - "participant_age": "0::24||1::19", - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Male||1::Male", - "participant_name": "0::Kenneth Thompson||1::Leo Lamont Wells", - "participant_relationship": null, - "participant_status": "0::Killed||1::Unharmed, Arrested", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://www.kwch.com/content/news/1-hurt-in-Hutchinson-shooting-430522703.html||http://www.kansas.com/news/local/crime/article158214864.html", - "state_house_district": 102.0, - "state_senate_district": 34.0 - } - }, - { - "scenario_id": "075", - "features": { - "incident_id": 142460, - "date": "2014-05-26", - "state": "Alabama", - "city_or_county": "Covington County", - "address": null, - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/142460", - "source_url": "http://www.waka.com/2014/06/02/covington-co-man-killed-in-shooting/", - "incident_url_fields_missing": "False", - "congressional_district": 2.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", - "latitude": 31.3066, - "location_description": null, - "longitude": -86.4997, - "n_guns_involved": 1.0, - "notes": "1 male victim shot - killed", - "participant_age": "0::30", - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": "0::Shawn Bolden", - "participant_relationship": null, - "participant_status": "0::Killed", - "participant_type": "0::Victim", - "sources": "http://www.waka.com/2014/06/02/covington-co-man-killed-in-shooting/", - "state_house_district": 92.0, - "state_senate_district": 31.0 - } - }, - { - "scenario_id": "076", - "features": { - "incident_id": 220978, - "date": "2014-11-13", - "state": "Colorado", - "city_or_county": "Evans", - "address": "2400 block of Marina Street", - "n_killed": 2, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/220978", - "source_url": "http://www.denverpost.com/news/ci_26962545/evans-police-man-shot-teen-daughter-wife-murder", - "incident_url_fields_missing": "False", - "congressional_district": 4.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Wounded/Injured||Shot - Dead (murder, accidental, suicide)||Suicide^||Murder/Suicide||Domestic Violence", - "latitude": 40.3753, - "location_description": null, - "longitude": -104.722, - "n_guns_involved": 1.0, - "notes": null, - "participant_age": "0::47||1::61||2::17", - "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Teen 12-17", - "participant_gender": "0::Female||1::Male||2::Female", - "participant_name": "0::Natalya Perrin||1::Richard Perrin||2::Danika Perrin", - "participant_relationship": null, - "participant_status": "0::Injured||1::Killed||2::Killed", - "participant_type": "0::Victim||1::Victim||2::Victim", - "sources": "http://www.9news.com/story/news/local/2014/11/13/2-bodies-found-in-evans-home/18999799/||http://www.denverpost.com/news/ci_26962545/evans-police-man-shot-teen-daughter-wife-murder", - "state_house_district": 50.0, - "state_senate_district": 13.0 - } - }, - { - "scenario_id": "085", - "features": { - "incident_id": 367627, - "date": "2015-06-30", - "state": "North Carolina", - "city_or_county": "Franklin", - "address": "Woodland Hills Drive and Highway 28", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/367627", - "source_url": "http://www.wlos.com//news/features/top-stories/stories/officer-involved-shooting-investigation-21743.shtml#.VZjd97Xy2GM", - "incident_url_fields_missing": "False", - "congressional_district": 11.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Officer Involved Incident||Officer Involved Shooting - subject/suspect/perpetrator killed", - "latitude": 35.2023, - "location_description": "The Bluff Townhouses", - "longitude": -83.3924, - "n_guns_involved": null, - "notes": "police trying to serve arrest warrant shot and killed man standing in driveway of home who fired at them", - "participant_age": "0::52", - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": "0::Clay Alan Lickteig", - "participant_relationship": null, - "participant_status": "0::Killed", - "participant_type": "0::Subject-Suspect", - "sources": "http://www.wsoctv.com/news/news/local/man-shot-death-police-western-north-carolina/nmqjr/||http://www.wlos.com//news/features/top-stories/stories/deadly-officerinvolved-shooting-franklin-21752.shtml#.VZjZILXy2GM||http://www.wlos.com//news/features/top-stories/stories/officer-involved-shooting-investigation-21743.shtml#.VZjd97Xy2GM", - "state_house_district": 120.0, - "state_senate_district": 50.0 - } - }, - { - "scenario_id": "088", - "features": { - "incident_id": 997079, - "date": "2017-11-28", - "state": "Washington", - "city_or_county": "Seattle", - "address": "3000 block of 14th Ave W", - "n_killed": 1, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/997079", - "source_url": "http://www.kiro7.com/news/local/woman-shot-man-dead-in-queen-anne-attempted-murder-suicide/655129991", - "incident_url_fields_missing": "False", - "congressional_district": 7.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Wounded/Injured||Shot - Dead (murder, accidental, suicide)||Suicide^||Attempted Murder/Suicide (one variable unsuccessful)||Domestic Violence", - "latitude": 47.6466, - "location_description": null, - "longitude": -122.375, - "n_guns_involved": 1.0, - "notes": "shot wife then self", - "participant_age": "0::70||1::70", - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Female||1::Male", - "participant_name": null, - "participant_relationship": "1::Significant others - current or former", - "participant_status": "0::Injured||1::Killed", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://komonews.com/news/local/1-dead-woman-injured-after-shooting-in-seattles-interbay-area||http://www.kiro7.com/news/local/woman-shot-man-dead-in-queen-anne-attempted-murder-suicide/655129991", - "state_house_district": 36.0, - "state_senate_district": 36.0 - } - }, - { - "scenario_id": "090", - "features": { - "incident_id": 463642, - "date": "2015-12-11", - "state": "Maryland", - "city_or_county": "Baltimore", - "address": "5100 block of Park Heights Avenue", - "n_killed": 1, - "n_injured": 3, - "incident_url": "http://www.gunviolencearchive.org/incident/463642", - "source_url": "http://www.baltimoresun.com/news/maryland/crime/blog/bs-md-ci-homicide-20151211-story.html", - "incident_url_fields_missing": "False", - "congressional_district": 7.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Wounded/Injured||Shot - Dead (murder, accidental, suicide)||Mass Shooting (4+ victims injured or killed excluding the subject/suspect/perpetrator, one location)", - "latitude": 39.3483, - "location_description": null, - "longitude": -76.6751, - "n_guns_involved": null, - "notes": null, - "participant_age": "0::27||1::17||2::21||3::24", - "participant_age_group": "0::Adult 18+||1::Teen 12-17||2::Adult 18+||3::Adult 18+", - "participant_gender": "0::Male||1::Male||2::Female||3::Male", - "participant_name": "0::Evan Davis", - "participant_relationship": null, - "participant_status": "0::Killed||1::Injured||2::Injured||3::Injured", - "participant_type": "0::Victim||1::Victim||2::Victim||3::Victim", - "sources": "http://www.baltimoresun.com/news/maryland/crime/blog/bs-md-ci-homicide-20151211-story.html", - "state_house_district": 41.0, - "state_senate_district": 41.0 - } - }, - { - "scenario_id": "091", - "features": { - "incident_id": 262012, - "date": "2014-12-11", - "state": "Missouri", - "city_or_county": "Springfield", - "address": "1040 S. Missouri Avenue", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/262012", - "source_url": "http://www.ky3.com/news/local/neighbor-heard-4-shots-before-man-was-found-dead/21048998_30181160", - "incident_url_fields_missing": "False", - "congressional_district": 7.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", - "latitude": 37.1953, - "location_description": null, - "longitude": -93.3016, - "n_guns_involved": null, - "notes": null, - "participant_age": "0::26", - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": "0::Joshua Kelley", - "participant_relationship": null, - "participant_status": "0::Killed", - "participant_type": "0::Victim", - "sources": "http://www.ky3.com/news/local/neighbor-heard-4-shots-before-man-was-found-dead/21048998_30181160", - "state_house_district": 132.0, - "state_senate_district": 30.0 - } - }, - { - "scenario_id": "095", - "features": { - "incident_id": 140995, - "date": "2014-05-29", - "state": "Michigan", - "city_or_county": "Detroit", - "address": "12828 Fenkell", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/140995", - "source_url": "http://www.mlive.com/news/detroit/index.ssf/2014/06/cpl_holder_accidentally_kills.html", - "incident_url_fields_missing": "False", - "congressional_district": 14.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Accidental Shooting||Accidental Shooting - Death||Defensive Use", - "latitude": 42.4023, - "location_description": null, - "longitude": -83.1708, - "n_guns_involved": null, - "notes": "Djs R Us - Storeowner shot at thief who ducked. Bullet hit innocent bystander, killing him. Legal CPL weapon", - "participant_age": "0::19", - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": "0::Senghore Ball ", - "participant_relationship": null, - "participant_status": "0::Killed", - "participant_type": "0::Victim", - "sources": "http://www.freep.com/article/20140530/NEWS01/305300181/detroit-fatal-shooting||http://www.mlive.com/news/detroit/index.ssf/2014/06/cpl_holder_accidentally_kills.html", - "state_house_district": 8.0, - "state_senate_district": 3.0 - } - }, - { - "scenario_id": "105", - "features": { - "incident_id": 170557, - "date": "2014-01-19", - "state": "Indiana", - "city_or_county": "Indianapolis", - "address": "1800 block of South Belmont Avenue.", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/170557", - "source_url": "http://www.indystar.com/story/news/crime/2014/02/02/slain-pizza-driver-was-local-musician/5105763/", - "incident_url_fields_missing": "False", - "congressional_district": 7.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Armed robbery with injury/death and/or evidence of DGU found||Car-jacking||Possession (gun(s) found during commission of other crimes)", - "latitude": 39.7424, - "location_description": null, - "longitude": -86.1967, - "n_guns_involved": null, - "notes": "PIzza driver robbed, shot, and killed. Car abandoned by 2 suspects in Wyoming; apprehended right after.", - "participant_age": "0::49||1::26||2::22", - "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+", - "participant_gender": "0::Male||1::Male||2::Male", - "participant_name": "0::John Sullivan||1::Jeremiah Roberts||2::Derek Romano", - "participant_relationship": null, - "participant_status": "0::Killed||1::Unharmed||2::Unharmed", - "participant_type": "0::Victim||1::Subject-Suspect||2::Subject-Suspect", - "sources": "http://www.indystar.com/story/news/crime/2014/02/02/slain-pizza-driver-was-local-musician/5105763/", - "state_house_district": 97.0, - "state_senate_district": 35.0 - } - }, - { - "scenario_id": "106", - "features": { - "incident_id": 276091, - "date": "2015-01-05", - "state": "Florida", - "city_or_county": "Tampa", - "address": "3100 block of North Jefferson Street", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/276091", - "source_url": "http://tbo.com/news/crime/tampa-police-investigating-two-unrelated-shooting-deaths-20150105/", - "incident_url_fields_missing": "False", - "congressional_district": 14.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", - "latitude": 27.9632, - "location_description": null, - "longitude": -82.4566, - "n_guns_involved": null, - "notes": "Male shot killed", - "participant_age": "0::18", - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": "0::Lyfe Coleman", - "participant_relationship": null, - "participant_status": "0::Killed", - "participant_type": "0::Victim", - "sources": "http://tbo.com/news/crime/tampa-police-investigating-two-unrelated-shooting-deaths-20150105/", - "state_house_district": 61.0, - "state_senate_district": 19.0 - } - }, - { - "scenario_id": "110", - "features": { - "incident_id": 96062, - "date": "2014-01-12", - "state": "Pennsylvania", - "city_or_county": "Chester", - "address": "2900 block of West 3rd Street. ", - "n_killed": 1, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/96062", - "source_url": "http://articles.philly.com/2014-01-14/news/46153194_1_robbery-victim-chester-bar-dead-man", - "incident_url_fields_missing": "False", - "congressional_district": 1.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Wounded/Injured||Shot - Dead (murder, accidental, suicide)||Armed robbery with injury/death and/or evidence of DGU found||Possession (gun(s) found during commission of other crimes)||Brandishing/flourishing/open carry/lost/found||Defensive Use", - "latitude": 39.8314, - "location_description": null, - "longitude": -75.3923, - "n_guns_involved": null, - "notes": null, - "participant_age": "1::31", - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Male||1::Male", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Injured||1::Killed", - "participant_type": "0::Victim||1::Subject-Suspect", - 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"participant_type": "0::Victim||1::Victim||2::Victim||3::Subject-Suspect", - "sources": "http://www.arkansasonline.com/news/2015/mar/14/1-dead-2-hurt-teen-held-in-springdale-s/?f=crime", - "state_house_district": 89.0, - "state_senate_district": 7.0 - } - }, - { - "scenario_id": "137", - "features": { - "incident_id": 106345, - "date": "2014-02-13", - "state": "Arizona", - "city_or_county": "Phoenix", - "address": "I-17 and Osborne ", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/106345", - "source_url": "http://www.myfoxphoenix.com/story/24712468/2014/02/13/possibly-shooting-at-phoenix-apartments", - "incident_url_fields_missing": "False", - "congressional_district": 7.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", - "latitude": 33.5362, - "location_description": null, - "longitude": -112.095, - "n_guns_involved": null, - "notes": null, - "participant_age": null, - 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} - }, - { - "scenario_id": "156", - "features": { - "incident_id": 453113, - "date": "2015-11-23", - "state": "New Jersey", - "city_or_county": "Jersey City", - "address": "Old Bergen Road and Neptune Avenue", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/453113", - "source_url": "http://www.nj.com/hudson/index.ssf/2015/11/prosecutors_officer_confirms_fatal_afternoon_shoot.html#incart_river_index", - "incident_url_fields_missing": "False", - "congressional_district": 10.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", - "latitude": 40.6934, - "location_description": null, - "longitude": -74.0959, - "n_guns_involved": null, - "notes": null, - "participant_age": "0::33", - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": "0::Markice Hatten", - "participant_relationship": null, - "participant_status": "0::Killed", - "participant_type": "0::Victim", - 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"incident_url_fields_missing": "False", - "congressional_district": 1.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::9mm", - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Suicide^||Officer Involved Incident||Officer Involved Shooting - subject/suspect/perpetrator suicide at standoff", - "latitude": 41.572, - "location_description": null, - "longitude": -87.4858, - "n_guns_involved": 1.0, - "notes": "Man believed to be suicidal found shot, killed, suicide, in home, after SWAT standoff of 2 hours;", - "participant_age": "0::25", - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": "0::Luis Torres Jr.", - "participant_relationship": null, - "participant_status": "0::Killed", - "participant_type": "0::Subject-Suspect", - "sources": "http://www.nwitimes.com/news/local/lake/update-hammond-standoff-ends-in-suicide/article_84bfacab-6018-5073-987e-25c0f832abad.html", - "state_house_district": 12.0, - "state_senate_district": 1.0 - } - }, - { - "scenario_id": "168", - "features": { - "incident_id": 593725, - "date": "2016-06-28", - "state": "Missouri", - "city_or_county": "Windsor", - "address": "300 Winchell Street", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/593725", - "source_url": "http://www.dothaneagle.com/news/ap/state/year-old-missouri-man-charged-in-deadly-shooting/article_5d9bb9e3-cbb3-5ae6-bd4d-7085cd384b17.html", - "incident_url_fields_missing": "False", - "congressional_district": 4.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", - "latitude": 38.5394, - "location_description": null, - "longitude": -93.5155, - "n_guns_involved": null, - "notes": null, - "participant_age": "0::45||1::44", - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Male||1::Male", - "participant_name": "0::Terry Lynn Maples||1::Jeffrey Dean Watring", - "participant_relationship": null, - "participant_status": "0::Killed||1::Unharmed, Arrested", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://sedaliademocrat.com/news/13443/windsor-man-charged-in-tuesday-murder||http://www.dothaneagle.com/news/ap/state/year-old-missouri-man-charged-in-deadly-shooting/article_5d9bb9e3-cbb3-5ae6-bd4d-7085cd384b17.html", - "state_house_district": 57.0, - "state_senate_district": 31.0 - } - }, - { - "scenario_id": "170", - "features": { - "incident_id": 311878, - "date": "2015-03-22", - "state": "Montana", - "city_or_county": "Billings", - "address": "3400 block of Cherylon Street", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/311878", - "source_url": "http://billingsgazette.com/news/local/crime-and-courts/woman-shot-in-heights-on-sunday-dies-monday-officials-release/article_753e9e0d-0ac8-5a7e-ae8e-2b96212864f1.html", - "incident_url_fields_missing": "False", - "congressional_district": 1.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Handgun", - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Drug involvement", - "latitude": 45.864, - "location_description": null, - "longitude": -108.45, - "n_guns_involved": 1.0, - "notes": null, - "participant_age": "0::22||1::31", - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Female||1::Male", - "participant_name": "0::Kayla Jeanette Kinder||1::John Edward Szydlowski", - "participant_relationship": null, - "participant_status": "0::Killed||1::Unharmed, Arrested", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://billingsgazette.com/news/local/man-arrested-in-sunday-shooting-walked-away-from-prerelease-on/article_8f9a9c63-3756-57cc-a644-9be936e5e4f0.html||http://billingsgazette.com/news/local/crime-and-courts/woman-shot-in-heights-on-sunday-dies-monday-officials-release/article_753e9e0d-0ac8-5a7e-ae8e-2b96212864f1.html", - "state_house_district": null, - "state_senate_district": null - } - }, - { - "scenario_id": "172", - "features": { - "incident_id": 668611, - "date": "2016-06-26", - "state": "Louisiana", - "city_or_county": "Harvey", - "address": "4100 block of Peters Road", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/668611", - "source_url": "http://www.nola.com/crime/index.ssf/2016/09/cordaryle_robert_terrance_call.html#incart_river_index", - "incident_url_fields_missing": "False", - "congressional_district": 2.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Armed robbery with injury/death and/or evidence of DGU found||Car-jacking", - "latitude": 29.8465, - "location_description": null, - "longitude": -90.0559, - "n_guns_involved": 1.0, - "notes": "1 killed. Armed robbery. Car-jacking. Female suspect lures victim to a meeting via Facebook and then male suspec shoots him.", - "participant_age": "0::25||1::29||2::23", - "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+", - "participant_gender": "0::Male||1::Male||2::Female", - "participant_name": "0::Cordaryle Robert||1::Terrance Calloway||2::Dana Calloway", - "participant_relationship": null, - "participant_status": "0::Killed||1::Unharmed, Arrested||2::Unharmed, Arrested", - "participant_type": "0::Victim||1::Subject-Suspect||2::Subject-Suspect", - "sources": "http://www.nola.com/crime/index.ssf/2016/09/cordaryle_robert_terrance_call.html#incart_river_index", - "state_house_district": 87.0, - "state_senate_district": 8.0 - } - }, - { - "scenario_id": "176", - "features": { - "incident_id": 402318, - "date": "2015-08-25", - "state": "Virginia", - "city_or_county": "Hampton", - "address": "815 Lasalle Avenue", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/402318", - "source_url": "http://www.dailypress.com/news/crime/dp-man-shot-killed-in-hampton-shooting-near-lasalle-avenue-police-say-20150825-story.html", - "incident_url_fields_missing": "False", - "congressional_district": 3.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", - "latitude": 37.0232, - "location_description": "LaSalle Garden Apartment", - "longitude": -76.3636, - "n_guns_involved": null, - "notes": "man shot to death after argument", - "participant_age": "0::25||1::24", - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Male||1::Male", - "participant_name": "0::Shaolin Chanham||1::Keenan A. Palmer", - "participant_relationship": null, - "participant_status": "0::Killed||1::Unharmed, Arrested", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://wtkr.com/2015/08/25/person-fighting-for-life-after-shooting-in-hampton/||http://www.dailypress.com/news/crime/dp-man-shot-killed-in-hampton-shooting-near-lasalle-avenue-police-say-20150825-story.html", - "state_house_district": 92.0, - "state_senate_district": 2.0 - } - }, - { - "scenario_id": "188", - "features": { - "incident_id": 124637, - "date": "2014-04-11", - "state": "California", - "city_or_county": "Indio", - "address": "Avenue 44", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/124637", - "source_url": "http://www.kesq.com/news/police-investigating-suspicious-death-in-indio/25430260", - "incident_url_fields_missing": "False", - "congressional_district": 36.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", - "latitude": 33.7295, - "location_description": null, - "longitude": -116.199, - "n_guns_involved": null, - "notes": "at Mountain View apartment complex ", - "participant_age": "0::36", - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Killed", - "participant_type": "0::Victim", - "sources": "http://www.kesq.com/news/police-investigating-suspicious-death-in-indio/25430260", - "state_house_district": 56.0, - "state_senate_district": 28.0 - } - } - ], - "target_column": "n_killed", - "task_sub_type": "regression", - "ranking_ground_truth": { - "top_k_ids": [ - "002", - "004", - "006", - "007", - "008" - ] - }, - "sort_direction": "high" - }, - "response": "", - "evaluation_score": {} -} \ No newline at end of file + "sort_direction": "low" + }, + "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/daily/regression/Gun_Violence_Data_B4/Gun_Violence_Data_B4_004/Gun_Violence_Data_B4_004_current.json b/ranking_and_filtering/daily/regression/Gun_Violence_Data_B4/Gun_Violence_Data_B4_004/Gun_Violence_Data_B4_004_current.json index 0716c5473e9195b6629ec552a6a3b5bbb77dee06..20f7519e2b1e83f8cc3bee474877476b81ef3c7c 100644 --- a/ranking_and_filtering/daily/regression/Gun_Violence_Data_B4/Gun_Violence_Data_B4_004/Gun_Violence_Data_B4_004_current.json +++ b/ranking_and_filtering/daily/regression/Gun_Violence_Data_B4/Gun_Violence_Data_B4_004/Gun_Violence_Data_B4_004_current.json @@ -1,1946 +1,1946 @@ { - "id": "004", - "task_type": "B4", - "subtask_type": "topk", - "perspective": "data_holder", - "dataset_name": "Gun Violence Data", - "table_path": "output/Gun Violence Data_B4/Gun Violence Data_B4_004/test.csv", - "query": "For a longitudinal study on community resilience, we need a control group of lower-impact events. From the candidate set, and using our historical archives for context, identify the 15 incidents with the lowest forecasted fatalities.", - "meta_info": { - "domain": "daily" + "id": "004", + "task_type": "B4", + "subtask_type": "topk", + "perspective": "data_holder", + "dataset_name": "Gun Violence Data", + "table_path": "output/Gun Violence Data_B4/Gun Violence Data_B4_004/test.csv", + "query": "For a longitudinal study on community resilience, we need a control group of lower-impact events. From the candidate set, and using our historical archives for context, identify the 15 incidents with the lowest forecasted fatalities.", + "meta_info": { + "domain": "daily" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "incident_id": 652279, + "date": "2016-09-09", + "state": "North Carolina", + "city_or_county": "Charlotte", + "address": "100 block of East Park Avenue", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/652279", + "source_url": "http://www.wsoctv.com/news/local/man-arrested-in-murder-case-connected-to-slaying-of-school-teacher/468034409", + "incident_url_fields_missing": "False", + "congressional_district": null, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Armed robbery with injury/death and/or evidence of DGU found", + "latitude": null, + "location_description": "SouthEnd", + "longitude": null, + "n_guns_involved": 1.0, + "notes": "Teacher Victim died 9/14/16 as a result of injuries from gunshot", + "participant_age": "0::33", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Female", + "participant_name": "0::Ruijuan Guo", + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Victim", + "sources": "http://wccbcharlotte.com/2016/09/10/police-investigating-shooting-central-division/||http://www.wcnc.com/news/local/shooting-investigation-underway-in-charlotte-central-division/316419372||http://www.wcnc.com/news/local/shooting-investigation-underway-in-charlotte-central-division/316419372||http://www.wsoctv.com/news/local/man-arrested-in-murder-case-connected-to-slaying-of-school-teacher/468034409", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "002", + "features": { + "incident_id": 141637, + "date": "2014-05-31", + "state": "Nebraska", + "city_or_county": "Omaha", + "address": "132nd Street and West Center Road", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/141637", + "source_url": "http://www.ketv.com/news/pharmacy-robbed-overnight/26275626#!TwVEg", + "incident_url_fields_missing": "False", + "congressional_district": 2.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Armed robbery with injury/death and/or evidence of DGU found", + "latitude": 41.234, + "location_description": null, + "longitude": -96.1194, + "n_guns_involved": null, + "notes": null, + "participant_age": null, + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Unharmed", + "participant_type": "0::Subject-Suspect", + "sources": "http://www.ketv.com/news/pharmacy-robbed-overnight/26275626#!TwVEg", + "state_house_district": null, + "state_senate_district": 20.0 + } + }, + { + "scenario_id": "003", + "features": { + "incident_id": 193765, + "date": "2014-09-20", + "state": "Oregon", + "city_or_county": "Clackamas County", + "address": "30800 block of Short Fellows Road ", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/193765", + "source_url": "http://www.oregonlive.com/clackamascounty/index.ssf/2014/09/man_shoots_burglary_suspect_in.html#incart_river", + "incident_url_fields_missing": "False", + "congressional_district": 5.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured||Home Invasion||Home Invasion - subject/suspect/perpetrator injured||Defensive Use", + "latitude": 45.1609, + "location_description": null, + "longitude": -122.485, + "n_guns_involved": null, + "notes": null, + "participant_age": "1::23", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": "1::Konstatntin Kutsov", + "participant_relationship": null, + "participant_status": "0::Unharmed||1::Injured", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://www.oregonlive.com/clackamascounty/index.ssf/2014/09/man_shoots_burglary_suspect_in.html#incart_river", + "state_house_district": 18.0, + "state_senate_district": 9.0 + } + }, + { + "scenario_id": "004", + "features": { + "incident_id": 389169, + "date": "2015-08-01", + "state": "Oklahoma", + "city_or_county": "Lawton", + "address": "6500 block of Elm", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/389169", + "source_url": "http://www.kswo.com/story/29688413/shots-fired-into-west-side-home", + "incident_url_fields_missing": "False", + "congressional_district": 4.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shots Fired - No Injuries", + "latitude": 34.6205, + "location_description": null, + "longitude": -98.473, + "n_guns_involved": null, + "notes": null, + "participant_age": null, + "participant_age_group": null, + "participant_gender": null, + "participant_name": null, + "participant_relationship": null, + "participant_status": null, + "participant_type": null, + "sources": "http://www.kswo.com/story/29688413/shots-fired-into-west-side-home", + "state_house_district": 62.0, + "state_senate_district": 32.0 + } + }, + { + "scenario_id": "005", + "features": { + "incident_id": 115681, + "date": "2014-03-15", + "state": "South Carolina", + "city_or_county": "Columbia", + "address": "1600 block of Fairlamb Avenue", + "n_killed": 1, + "n_injured": 2, + "incident_url": "http://www.gunviolencearchive.org/incident/115681", + "source_url": "http://www.thestate.com/2014/03/16/3330111/update-name-of-victim-in-saturdays.html", + "incident_url_fields_missing": "False", + "congressional_district": 2.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)|Pistol-whipping", + "latitude": 34.0474, + "location_description": null, + "longitude": -80.93, + "n_guns_involved": null, + "notes": null, + "participant_age": "0:39", + "participant_age_group": "0:Adult 18+|1:Adult 18+|2:Adult 18+|3:Adult 18+|4:Adult 18+|5:Adult 18+", + "participant_gender": "0:Male|3:Male|4:Male|5:Female", + "participant_name": "0:Luis Enrique Contreras-Teran", + "participant_relationship": null, + "participant_status": "0:Killed|1:Injured|2:Injured|3:Unharmed|4:Unharmed|5:Unharmed", + "participant_type": "0:Victim|1:Victim|2:Victim|3:Subject-Suspect|4:Subject-Suspect|5:Subject-Suspect", + "sources": "http://www.thestate.com/2014/03/16/3330111/update-name-of-victim-in-saturdays.html", + "state_house_district": 76.0, + "state_senate_district": 22.0 + } + }, + { + "scenario_id": "006", + "features": { + "incident_id": 1044649, + "date": "2018-02-07", + "state": "Ohio", + "city_or_county": "Columbus", + "address": "2800 block of E 13th Ave", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/1044649", + "source_url": "https://www.10tv.com/article/man-critical-condition-after-northeast-columbus-shooting", + "incident_url_fields_missing": "False", + "congressional_district": 3.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Wounded/Injured", + "latitude": 39.9973, + "location_description": null, + "longitude": -82.9955, + "n_guns_involved": 1.0, + "notes": "shot in torso, crit", + "participant_age": "0::45", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Delbert H. Hollingsworth", + "participant_relationship": null, + "participant_status": "0::Injured", + "participant_type": "0::Victim", + "sources": "https://www.10tv.com/article/man-critical-condition-after-northeast-columbus-shooting", + "state_house_district": 18.0, + "state_senate_district": 15.0 + } + }, + { + "scenario_id": "007", + "features": { + "incident_id": 336020, + "date": "2015-05-07", + "state": "Georgia", + "city_or_county": "Macon", + "address": "3044 Bloomfield Drive", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/336020", + "source_url": "http://www.macon.com/2015/05/07/3734779_bloomfield-apartments-cars-hit.html?rh=1", + "incident_url_fields_missing": "False", + "congressional_district": 2.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shots Fired - No Injuries", + "latitude": 32.8089, + "location_description": null, + "longitude": -83.7003, + "n_guns_involved": null, + "notes": "Sandy Springs apt, up to 10 shots heard, 3 apt, 2 cars hit, no inj", + "participant_age": null, + "participant_age_group": null, + "participant_gender": null, + "participant_name": null, + "participant_relationship": null, + "participant_status": null, + "participant_type": null, + "sources": "http://www.macon.com/2015/05/07/3734779_bloomfield-apartments-cars-hit.html?rh=1", + "state_house_district": 142.0, + "state_senate_district": 26.0 + } + }, + { + "scenario_id": "008", + "features": { + "incident_id": 943750, + "date": "2017-09-27", + "state": "Pennsylvania", + "city_or_county": "Philadelphia", + "address": "58th St and Kingsessing Ave", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/943750", + "source_url": "http://philadelphia.cbslocal.com/2017/09/28/3-wanted-kingsessing-crime-spree/", + "incident_url_fields_missing": "False", + "congressional_district": null, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Non-Shooting Incident||Armed robbery with injury/death and/or evidence of DGU found||Pistol-whipping", + "latitude": 39.9347, + "location_description": "(Kingsessing)", + "longitude": -75.2273, + "n_guns_involved": 1.0, + "notes": "approx. 11:45 pm, PW and robbed, LE believe same suspects as 9/27/17 shooting on N 63rd St", + "participant_age": null, + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+||3::Adult 18+", + "participant_gender": "0::Male||1::Male||2::Male||3::Male", + "participant_name": null, + "participant_relationship": "1::Armed Robbery||2::Armed Robbery||3::Armed Robbery", + "participant_status": "0::Injured", + "participant_type": "0::Victim||1::Subject-Suspect||2::Subject-Suspect||3::Subject-Suspect", + "sources": "http://philadelphia.cbslocal.com/2017/09/28/3-wanted-kingsessing-crime-spree/", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "009", + "features": { + "incident_id": 440535, + "date": "2015-11-01", + "state": "New Jersey", + "city_or_county": "Paterson", + "address": "East Main Street", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/440535", + "source_url": "http://www.northjersey.com/news/man-injured-in-paterson-shooting-1.1446306", + "incident_url_fields_missing": "False", + "congressional_district": 9.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured", + "latitude": 40.9292, + "location_description": null, + "longitude": -74.1683, + "n_guns_involved": null, + "notes": null, + "participant_age": "0::32", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured", + "participant_type": "0::Victim", + "sources": "http://www.northjersey.com/news/man-injured-in-paterson-shooting-1.1446306", + "state_house_district": 35.0, + "state_senate_district": 35.0 + } + }, + { + "scenario_id": "010", + "features": { + "incident_id": 295713, + "date": "2015-02-21", + "state": "California", + "city_or_county": "Los Angeles", + "address": "9900 block of Budlong Avenue", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/295713", + "source_url": "http://homicide.latimes.com/post/tramel-laqune-kerl/", + "incident_url_fields_missing": "False", + "congressional_district": 43.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 33.946, + "location_description": null, + "longitude": -118.296, + "n_guns_involved": null, + "notes": null, + "participant_age": "0::39", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Tramel Laqune Kerl", + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Victim", + "sources": "http://ktla.com/2015/02/21/man-shot-to-death-in-westmont-area-of-south-l-a-investigation-underway/||http://homicide.latimes.com/post/tramel-laqune-kerl/", + "state_house_district": 62.0, + "state_senate_district": 30.0 + } + }, + { + "scenario_id": "011", + "features": { + "incident_id": 135485, + "date": "2014-05-16", + "state": "New York", + "city_or_county": "Manhattan", + "address": "FDR Drive", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/135485", + "source_url": "http://newyork.cbslocal.com/2014/05/19/nypd-suspect-in-east-harlem-police-involved-shooting-arrested/", + "incident_url_fields_missing": "False", + "congressional_district": 12.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Officer Involved Incident||Officer Involved Shooting - subject/suspect/perpetrator killed", + "latitude": 40.7516, + "location_description": null, + "longitude": -73.9652, + "n_guns_involved": null, + "notes": "ois pharmacy", + "participant_age": "0::45", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": "0::Scott Kato", + "participant_relationship": null, + "participant_status": "0::Killed||1::Unharmed", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://www.nydailynews.com/new-york/nyc-crime/cops-shoot-kill-pharmacy-robber-manhattan-article-1.1795228||http://newyork.cbslocal.com/2014/05/19/nypd-suspect-in-east-harlem-police-involved-shooting-arrested/", + "state_house_district": 74.0, + "state_senate_district": 28.0 + } + }, + { + "scenario_id": "012", + "features": { + "incident_id": 371030, + "date": "2015-07-05", + "state": "South Carolina", + "city_or_county": "Eutawville", + "address": "157 State Road S-38-1641", + "n_killed": 2, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/371030", + "source_url": "http://thetandd.com/news/amid-cries-family-calls-fatal-shooting-senseless/article_b46321b1-094f-55d9-afc5-9874a3ad18c7.html", + "incident_url_fields_missing": "False", + "congressional_district": 6.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 33.4228, + "location_description": "Indian Bluff Recreation Park", + "longitude": -80.369, + "n_guns_involved": null, + "notes": "dispute over jet ski", + "participant_age": "1::30", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": "0::Adrian Jamil Eaglin||1::Luther Brandell Green", + "participant_relationship": null, + "participant_status": "0::Killed||1::Killed", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://thetandd.com/news/amid-cries-family-calls-fatal-shooting-senseless/article_b46321b1-094f-55d9-afc5-9874a3ad18c7.html", + "state_house_district": 66.0, + "state_senate_district": 39.0 + } + }, + { + "scenario_id": "013", + "features": { + "incident_id": 1014813, + "date": "2017-12-22", + "state": "Nebraska", + "city_or_county": "Lincoln", + "address": "6200 block of Woodstock Cir", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/1014813", + "source_url": "http://www.1011now.com/content/news/Three-juveniles-busted-for-larcenies-possible-connection-to-other-crimes--466599223.html", + "incident_url_fields_missing": "False", + "congressional_district": null, + "gun_stolen": "0::Stolen", + "gun_type": "0::32 Auto", + "incident_characteristics": "Non-Shooting Incident||Gun(s) stolen from owner", + "latitude": 40.7503, + "location_description": null, + "longitude": -96.694, + "n_guns_involved": 1.0, + "notes": "Gun stolen from vehicle between 12/22 and 12/23.", + "participant_age": null, + "participant_age_group": null, + "participant_gender": null, + "participant_name": null, + "participant_relationship": null, + "participant_status": null, + "participant_type": null, + "sources": "http://www.1011now.com/content/news/Three-juveniles-busted-for-larcenies-possible-connection-to-other-crimes--466599223.html", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "014", + "features": { + "incident_id": 896182, + "date": "2017-07-21", + "state": "South Carolina", + "city_or_county": "Greenville", + "address": "Laurens Rd", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/896182", + "source_url": "http://wspa.com/2017/07/22/guns-ammo-seized-during-traffic-stop-in-greenville-police-say/", + "incident_url_fields_missing": "False", + "congressional_district": 4.0, + "gun_stolen": "0::Stolen||1::Unknown||2::Unknown", + "gun_type": "0::Unknown||1::Unknown||2::Unknown", + "incident_characteristics": "Non-Shooting Incident||ATF/LE Confiscation/Raid/Arrest||Possession (gun(s) found during commission of other crimes)||Possession of gun by felon or prohibited person||Stolen/Illegally owned gun{s} recovered during arrest/warrant", + "latitude": 34.8476, + "location_description": null, + "longitude": -82.3992, + "n_guns_involved": 3.0, + "notes": "traffic stop, 3 guns incl at least 1 stolen", + "participant_age": null, + "participant_age_group": null, + "participant_gender": null, + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Unharmed, Arrested", + "participant_type": "0::Subject-Suspect", + "sources": "http://wspa.com/2017/07/22/guns-ammo-seized-during-traffic-stop-in-greenville-police-say/", + "state_house_district": 23.0, + "state_senate_district": 7.0 + } + }, + { + "scenario_id": "015", + "features": { + "incident_id": 132695, + "date": "2014-05-05", + "state": "Georgia", + "city_or_county": "Albany", + "address": "814 North Jefferson Street", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/132695", + "source_url": "http://www.wmcactionnews5.com/story/25436211/man-on-the-run-after-convenience-store-robbery", + "incident_url_fields_missing": "False", + "congressional_district": 2.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Armed robbery with injury/death and/or evidence of DGU found", + "latitude": 31.5878, + "location_description": null, + "longitude": -84.156, + "n_guns_involved": null, + "notes": "convenience store ", + "participant_age": "0::39", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Kevin Peleel", + "participant_relationship": null, + "participant_status": "0::Unharmed", + "participant_type": "0::Subject-Suspect", + "sources": "http://www.wmcactionnews5.com/story/25436211/man-on-the-run-after-convenience-store-robbery", + "state_house_district": 153.0, + "state_senate_district": 12.0 + } + }, + { + "scenario_id": "016", + "features": { + "incident_id": 872977, + "date": "2017-04-19", + "state": "California", + "city_or_county": "Long Beach", + "address": "500 block of Cherry Ave", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/872977", + "source_url": "http://www.longbeach.gov/police/press-releases/murder-500-block-cherry/", + "incident_url_fields_missing": "False", + "congressional_district": 47.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 33.7736, + "location_description": null, + "longitude": -118.167, + "n_guns_involved": 1.0, + "notes": "Arrest May 3, 2017 personal conflict led to the shooting where suspect was injured and later died", + "participant_age": "0::38||1::33", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": "0::William Luther Hayes||1::Jason Daniels", + "participant_relationship": null, + "participant_status": "0::Killed||1::Unharmed, Arrested", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://www.longbeach.gov/police/press-releases/murder-500-block-cherry/", + "state_house_district": 70.0, + "state_senate_district": 33.0 + } + }, + { + "scenario_id": "017", + "features": { + "incident_id": 1074215, + "date": "2018-03-07", + "state": "California", + "city_or_county": "Los Angeles", + "address": "615 Normandie Ave", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/1074215", + "source_url": "https://www.atf.gov/news/pr/felon-possession-receives-hefty-prison-sentence", + "incident_url_fields_missing": "False", + "congressional_district": 34.0, + "gun_stolen": "0::Unknown||1::Unknown||2::Unknown||3::Unknown||4::Unknown", + "gun_type": "0::9mm||1::9mm||2::22 LR||3::9mm||4::45 Auto", + "incident_characteristics": "Non-Shooting Incident||Drug involvement||ATF/LE Confiscation/Raid/Arrest||Possession of gun by felon or prohibited person", + "latitude": 34.0631, + "location_description": null, + "longitude": -118.3, + "n_guns_involved": 5.0, + "notes": null, + "participant_age": "0::22", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Michael Anthony Lopez", + "participant_relationship": null, + "participant_status": "0::Unharmed, Arrested", + "participant_type": "0::Subject-Suspect", + "sources": "https://www.atf.gov/news/pr/felon-possession-receives-hefty-prison-sentence", + "state_house_district": 53.0, + "state_senate_district": 24.0 + } + }, + { + "scenario_id": "018", + "features": { + "incident_id": 1036577, + "date": "2018-01-26", + "state": "Wisconsin", + "city_or_county": "Sun Prairie", + "address": "3700 Heatherstone Dr", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/1036577", + "source_url": "https://www.channel3000.com/news/officials-sun-prairie-man-who-accidentally-shot-himself-while-cleaning-gun-suffers-serious-injuries/691691239", + "incident_url_fields_missing": "False", + "congressional_district": 2.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::9mm", + "incident_characteristics": "Shot - Wounded/Injured||Accidental Shooting||Accidental Shooting - Injury||Accidental/Negligent Discharge||Self-Inflicted (not suicide or suicide attempt - NO PERP)||Cleaning gun", + "latitude": 43.1941, + "location_description": null, + "longitude": -89.2821, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": "0::49", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured", + "participant_type": "0::Victim", + "sources": "https://www.channel3000.com/news/officials-sun-prairie-man-who-accidentally-shot-himself-while-cleaning-gun-suffers-serious-injuries/691691239", + "state_house_district": 79.0, + "state_senate_district": 27.0 + } + }, + { + "scenario_id": "019", + "features": { + "incident_id": 295514, + "date": "2015-02-20", + "state": "Texas", + "city_or_county": "Harris County", + "address": "1200 block of E. Airtex", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/295514", + "source_url": "http://www.khou.com/story/news/local/2015/02/20/officer-involved-shooting-reported-in-n-harris-county/23751883/", + "incident_url_fields_missing": "False", + "congressional_district": 18.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Officer Involved Incident||Officer Involved Shooting - subject/suspect/perpetrator killed", + "latitude": 29.9816, + "location_description": null, + "longitude": -95.4281, + "n_guns_involved": null, + "notes": "Harris County Sheriff's Office investigating after perp aimed gun at a U.S. marshall and the perp was shot.", + "participant_age": null, + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Subject-Suspect", + "sources": "http://www.khou.com/story/news/local/2015/02/20/officer-involved-shooting-reported-in-n-harris-county/23751883/", + "state_house_district": 141.0, + "state_senate_district": 15.0 + } + }, + { + "scenario_id": "020", + "features": { + "incident_id": 870237, + "date": "2017-06-18", + "state": "Louisiana", + "city_or_county": "New Orleans", + "address": "E I 10 and Orleans Ave", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/870237", + "source_url": "http://Orleans Avenuewww.nola.com/crime/index.ssf/2017/06/shooting_interstate_10_new_orl.html", + "incident_url_fields_missing": "False", + "congressional_district": 2.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Wounded/Injured||Shots Fired - No Injuries||Drive-by (car to street, car to car)", + "latitude": 29.9652, + "location_description": null, + "longitude": -90.0739, + "n_guns_involved": 1.0, + "notes": "exit 235A; 2nd scene North Claiborne Avenue and Pauger Street", + "participant_age": null, + "participant_age_group": null, + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured", + "participant_type": "0::Victim", + "sources": "http://www.nola.com/crime/index.ssf/2017/06/shooting_interstate_10_new_orl.html", + "state_house_district": 93.0, + "state_senate_district": 5.0 + } + }, + { + "scenario_id": "021", + "features": { + "incident_id": 793399, + "date": "2017-03-11", + "state": "Virginia", + "city_or_county": "Chantilly", + "address": "13930 Metrotech Drive", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/793399", + "source_url": "http://www.wusa9.com/news/local/3-men-charged-in-recent-gun-store-burglaries/424004124", + "incident_url_fields_missing": "False", + "congressional_district": 10.0, + "gun_stolen": "0::Stolen||1::Stolen||2::Stolen||3::Stolen||4::Stolen||5::Stolen||6::Stolen||7::Stolen||8::Stolen||9::Stolen||10::Stolen||11::Stolen||12::Stolen||13::Stolen||14::Stolen||15::Stolen||16::Stolen||17::Stolen||18::Stolen||19::Stolen||20::Stolen||21::Stolen||22::Stolen||23::Stolen||24::Stolen||25::Stolen||26::Stolen||27::Stolen||28::Stolen||29::Stolen||30::Stolen||31::Stolen||32::Stolen||33::Stolen||34::Stolen", + "gun_type": "0::Unknown||1::Unknown||2::Unknown||3::Unknown||4::Unknown||5::Unknown||6::Unknown||7::Unknown||8::Unknown||9::Unknown||10::Unknown||11::Unknown||12::Unknown||13::Unknown||14::Unknown||15::Unknown||16::Unknown||17::Unknown||18::Unknown||19::Unknown||20::Unknown||21::Unknown||22::Unknown||23::Unknown||24::Unknown||25::Unknown||26::Unknown||27::Unknown||28::Unknown||29::Unknown||30::Unknown||31::Unknown||32::Unknown||33::Unknown||34::Unknown", + "incident_characteristics": "Institution/Group/Business||Non-Shooting Incident||Gun shop robbery or burglary", + "latitude": 38.8962, + "location_description": "50 West Armory", + "longitude": -77.4275, + "n_guns_involved": 35.0, + "notes": "35 firearms stolen from gun store, suspects also tried to rob two gun stores in Fredericksburg on March 14", + "participant_age": "0::19||1::23||2::23", + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+", + "participant_gender": "0::Male||1::Male||2::Male", + "participant_name": "0::Daniel Quarles||1::Preston Holmes||2::Matthew Jones", + "participant_relationship": null, + "participant_status": "0::Unharmed, Arrested||1::Unharmed, Arrested||2::Unharmed, Arrested", + "participant_type": "0::Subject-Suspect||1::Subject-Suspect||2::Subject-Suspect", + "sources": "http://www.alexandrianews.org/2017/03/dozens-of-firearms-stolen-from-chantilly-gun-store/||http://www.wusa9.com/news/local/3-arrested-in-va-gun-store-burglaries/424118061||http://www.wusa9.com/news/local/3-men-charged-in-recent-gun-store-burglaries/424004124", + "state_house_district": 86.0, + "state_senate_district": 32.0 + } + }, + { + "scenario_id": "022", + "features": { + "incident_id": 92228, + "date": "2014-01-01", + "state": "Arkansas", + "city_or_county": "Pine Bluff", + "address": "2300 block of Port Road", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/92228", + "source_url": "http://www.thv11.com/news/crime/293574/370/Man-wanted-in-New-Years-Day-shooting-in-Pine-Bluff", + "incident_url_fields_missing": "False", + "congressional_district": 4.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured", + "latitude": 34.2291, + "location_description": null, + "longitude": -91.9779, + "n_guns_involved": null, + "notes": null, + "participant_age": "1::26", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": "1::Ronald Maxwell Jr.", + "participant_relationship": null, + "participant_status": "0::Injured||1::Unharmed", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://www.katv.com/story/24341851/pine-bluff-man-shot-in-leg-suspect-in-custody||http://www.thv11.com/news/crime/293574/370/Man-wanted-in-New-Years-Day-shooting-in-Pine-Bluff", + "state_house_district": 16.0, + "state_senate_district": 25.0 + } + }, + { + "scenario_id": "031", + "features": { + "incident_id": 135042, + "date": "2014-05-14", + "state": "California", + "city_or_county": "Sacramento", + "address": "6800 block of Woodbine Ave", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/135042", + "source_url": "http://www.sacbee.com/news/local/crime/article202709659.html", + "incident_url_fields_missing": "False", + "congressional_district": 6.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Shotgun", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Domestic Violence||Possession of gun by felon or prohibited person", + "latitude": 38.5024, + "location_description": null, + "longitude": -121.48, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": "0::31||1::34", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Female||1::Male", + "participant_name": "0::Tamisha Evette Ridge||1::Dameshlo Green", + "participant_relationship": "1::Significant others - current or former", + "participant_status": "0::Killed||1::Unharmed, Arrested", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://www.sacbee.com/2014/05/14/6403951/woman-shot-to-death-in-south-sacramento.html||http://www.sacbee.com/news/local/crime/article202709659.html", + "state_house_district": 9.0, + "state_senate_district": 6.0 + } + }, + { + "scenario_id": "033", + "features": { + "incident_id": 320323, + "date": "2015-04-08", + "state": "Illinois", + "city_or_county": "Chicago", + "address": "84th Street and Essex", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/320323", + "source_url": "http://www.chicagotribune.com/news/local/breaking/chi-man-shot-in-head-on-south-side-20150408-story.html", + "incident_url_fields_missing": "False", + "congressional_district": 2.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 41.7427, + "location_description": null, + "longitude": -87.5637, + "n_guns_involved": null, + "notes": "Man shot, head, critical; unclear. Died in hospital.", + "participant_age": "0::21", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": "0::Devante Warnsby", + "participant_relationship": null, + "participant_status": "0::Killed||1::Unharmed", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://abc7chicago.com/news/man-shot-in-the-head-in-south-shore/641586/||http://www.chicagotribune.com/news/local/breaking/chi-man-shot-in-head-on-south-side-20150408-story.html", + "state_house_district": 33.0, + "state_senate_district": 17.0 + } + }, + { + "scenario_id": "036", + "features": { + "incident_id": 990412, + "date": "2017-11-16", + "state": "Illinois", + "city_or_county": "Des Plaines (Rosemont)", + "address": "9700 Higgins Rd", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/990412", + "source_url": "http://www.dailyherald.com/news/20171116/rosemont-police-investigating-death", + "incident_url_fields_missing": "False", + "congressional_district": 5.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 41.9942, + "location_description": null, + "longitude": -87.8689, + "n_guns_involved": 1.0, + "notes": "Man's body found w/ gun n hand in truck;", + "participant_age": null, + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Victim", + "sources": "http://www.dailyherald.com/news/20171116/rosemont-police-investigating-death", + "state_house_district": 20.0, + "state_senate_district": 10.0 + } + }, + { + "scenario_id": "047", + "features": { + "incident_id": 881671, + "date": "2017-07-02", + "state": "Mississippi", + "city_or_county": "Cruger", + "address": "2183 Holly Grove Meeks Rd", + "n_killed": 1, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/881671", + "source_url": "http://www.gwcommonwealth.com/news/article_94d705aa-7eb8-11e7-bf0c-8b51a12ae789.html", + "incident_url_fields_missing": "False", + "congressional_district": 2.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Wounded/Injured||Shot - Dead (murder, accidental, suicide)||Bar/club incident - in or around establishment", + "latitude": 33.3094, + "location_description": "Club CJ's", + "longitude": -90.2041, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": "2::37||3::32", + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+||3::Adult 18+||4::Adult 18+", + "participant_gender": "0::Male||1::Male||2::Male||3::Male||4::Male", + "participant_name": "0::Bernardo Washington||1::Joe Holmes||2::Demantreous Love||3::Jamar Newsome||4::Montrell Pretzea Love", + "participant_relationship": null, + "participant_status": "0::Killed||1::Injured||2::Unharmed||3::Unharmed||4::Unharmed, Arrested", + "participant_type": "0::Victim||1::Victim||2::Subject-Suspect||3::Subject-Suspect||4::Subject-Suspect", + "sources": "http://www.walb.com/story/35801551/double-shooting-at-holmes-co-club-leaves-one-dead||http://www.msnewsnow.com/story/36091244/two-men-wanted-for-capital-murder-in-holmes-county||http://wjtv.com/2017/07/03/1-dead-1-injured-in-holmes-county-club-shooting-deputies-search-for-suspect/||http://www.gwcommonwealth.com/news/article_94d705aa-7eb8-11e7-bf0c-8b51a12ae789.html", + "state_house_district": 34.0, + "state_senate_district": 24.0 + } + }, + { + "scenario_id": "062", + "features": { + "incident_id": 883282, + "date": "2017-07-05", + "state": "Illinois", + "city_or_county": "Chicago (Roseland)", + "address": "11000 block of S Eberhart", + "n_killed": 2, + "n_injured": 2, + "incident_url": "http://www.gunviolencearchive.org/incident/883282", + "source_url": "http://homicides.suntimes.com/2017/07/07/neighbor-fatal-shooting-of-derwin-moore-and-maurice-willis-in-roseland-are-so-normal-in-the-black-community/", + "incident_url_fields_missing": "False", + "congressional_district": 2.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Wounded/Injured||Shot - Dead (murder, accidental, suicide)||Drive-by (car to street, car to car)||Mass Shooting (4+ victims injured or killed excluding the subject/suspect/perpetrator, one location)", + "latitude": 41.6945, + "location_description": null, + "longitude": -87.611, + "n_guns_involved": 1.0, + "notes": "2 killed, 2 wounded, stable; drive-by by several shooters as they stood on street;", + "participant_age": "0::33||1::35||2::29||3::37", + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+||3::Adult 18+", + "participant_gender": "0::Male||1::Male||2::Male||3::Male", + "participant_name": "0::Maurice Anthony Willis||1::Derwin Moore", + "participant_relationship": null, + "participant_status": "0::Killed||1::Killed||2::Injured||3::Injured", + "participant_type": "0::Victim||1::Victim||2::Victim||3::Victim", + "sources": "http://chicago.suntimes.com/news/police-2-dead-2-wounded-in-roseland-shooting/||http://homicides.suntimes.com/2017/07/07/neighbor-fatal-shooting-of-derwin-moore-and-maurice-willis-in-roseland-are-so-normal-in-the-black-community/", + "state_house_district": 34.0, + "state_senate_district": 17.0 + } + }, + { + "scenario_id": "066", + "features": { + "incident_id": 218620, + "date": "2014-11-08", + "state": "California", + "city_or_county": "Lucerne", + "address": "16th street", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/218620", + "source_url": "http://blog.sfgate.com/crime/2014/11/10/lake-county-officers-shoot-and-kill-man/", + "incident_url_fields_missing": "False", + "congressional_district": 3.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Officer Involved Incident||Officer Involved Shooting - subject/suspect/perpetrator killed", + "latitude": 39.0858, + "location_description": null, + "longitude": -122.79, + "n_guns_involved": null, + "notes": null, + "participant_age": "0::56", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Christopher O’Neal", + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Subject-Suspect", + "sources": "http://blog.sfgate.com/crime/2014/11/10/lake-county-officers-shoot-and-kill-man/", + "state_house_district": 4.0, + "state_senate_district": 2.0 + } + }, + { + "scenario_id": "068", + "features": { + "incident_id": 337544, + "date": "2015-05-10", + "state": "Alabama", + "city_or_county": "Mobile (county)", + "address": "Spring Street", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/337544", + "source_url": "http://www.wsfa.com/story/29025992/apparent-homicide-on-spring-street", + "incident_url_fields_missing": "False", + "congressional_district": 1.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 30.7221, + "location_description": null, + "longitude": -88.0865, + "n_guns_involved": null, + "notes": null, + "participant_age": null, + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Victim", + "sources": "http://www.wsfa.com/story/29025992/apparent-homicide-on-spring-street", + "state_house_district": 99.0, + "state_senate_district": 33.0 + } + }, + { + "scenario_id": "069", + "features": { + "incident_id": 1023959, + "date": "2018-01-09", + "state": "Illinois", + "city_or_county": "Chicago", + "address": "1900 N Albany Ave", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/1023959", + "source_url": "https://chicago.suntimes.com/news/felon-charged-with-fatally-shooting-logan-square-man-during-argument/", + "incident_url_fields_missing": "False", + "congressional_district": 4.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Road rage", + "latitude": 41.916, + "location_description": null, + "longitude": -87.7053, + "n_guns_involved": 1.0, + "notes": "Victim shot outside store during fight after road rage incident; suspect convicted/murder, 1988;", + "participant_age": "0::34||1::51", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": "0::Peter Puleo||1::Marc Johnson", + "participant_relationship": null, + "participant_status": "0::Killed||1::Unharmed, Arrested", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "https://chicago.suntimes.com/news/man-killed-in-logan-square-drive-by-shooting-2/||https://chicago.suntimes.com/news/felon-charged-with-fatally-shooting-logan-square-man-during-argument/", + "state_house_district": 4.0, + "state_senate_district": 2.0 + } + }, + { + "scenario_id": "082", + "features": { + "incident_id": 153275, + "date": "2014-06-30", + "state": "Kentucky", + "city_or_county": "Lexington", + "address": "Scottsdale Circle", + "n_killed": 1, + "n_injured": 3, + "incident_url": "http://www.gunviolencearchive.org/incident/153275", + "source_url": "http://www.wkyt.com/home/headlines/Lexington-police-on-scene-of-reported-shooting-265316871.html", + "incident_url_fields_missing": "False", + "congressional_district": 6.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured||Shot - Dead (murder, accidental, suicide)||Mass Shooting (4+ victims injured or killed excluding the subject/suspect/perpetrator, one location)", + "latitude": 38.0763, + "location_description": null, + "longitude": -84.4772, + "n_guns_involved": null, + "notes": "Hollow Creek neighborhood off Russell Cave Road, one of four women shot inside home died", + "participant_age": "0::18", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Female||1::Female||2::Female||3::Female", + "participant_name": "0::Kieara Green", + "participant_relationship": null, + "participant_status": "0::Killed||1::Injured||2::Injured||3::Injured", + "participant_type": "0::Victim||1::Victim||2::Victim||3::Victim", + "sources": "http://www.wkyt.com/home/headlines/Lexington-police-on-scene-of-reported-shooting-265316871.html", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "084", + "features": { + "incident_id": 713419, + "date": "2016-11-28", + "state": "Minnesota", + "city_or_county": "Moorhead", + "address": "19th Avenue and 20th Street South", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/713419", + "source_url": "http://www.valleynewslive.com/content/news/Moorhead-Police-identify-man-involved-in-standoff-403434376.html", + "incident_url_fields_missing": "False", + "congressional_district": null, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Suicide^||Officer Involved Incident||Officer Involved Shooting - subject/suspect/perpetrator killed||Officer Involved Shooting - subject/suspect/perpetrator suicide at standoff||Domestic Violence", + "latitude": null, + "location_description": null, + "longitude": null, + "n_guns_involved": 1.0, + "notes": "Suspect fatally shot self during standoff.", + "participant_age": "0::28", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Joseph Scheibel", + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Subject-Suspect", + "sources": "http://www.myndnow.com/news/bismarck-news/man-involved-in-fargo-swat-incident-is-dead||http://www.valleynewslive.com/content/news/Moorhead-Police-identify-man-involved-in-standoff-403434376.html", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "085", + "features": { + "incident_id": 311067, + "date": "2015-03-21", + "state": "Texas", + "city_or_county": "Houston", + "address": "3300 block of Sadler", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/311067", + "source_url": "http://www.chron.com/news/houston-texas/article/Relatives-find-man-dead-in-bedroom-from-gunshot-6151117.php", + "incident_url_fields_missing": "False", + "congressional_district": 29.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 29.8345, + "location_description": null, + "longitude": -95.3363, + "n_guns_involved": null, + "notes": null, + "participant_age": "0::35", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Anthony Walton", + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Victim", + "sources": "http://www.chron.com/news/houston-texas/article/Relatives-find-man-dead-in-bedroom-from-gunshot-6151117.php", + "state_house_district": 142.0, + "state_senate_district": 6.0 + } + }, + { + "scenario_id": "087", + "features": { + "incident_id": 758480, + "date": "2017-01-23", + "state": "Delaware", + "city_or_county": "Wilmington", + "address": "1700 block of W 13th St", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/758480", + "source_url": "http://www.wdel.com/news/wilmington-man-id-d-as-victim-of-fatal-police-involved/article_2955a524-e26f-11e6-a289-9b53383bcc5f.html", + "incident_url_fields_missing": "False", + "congressional_district": 1.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Handgun", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Officer Involved Incident||Officer Involved Shooting - subject/suspect/perpetrator killed||Brandishing/flourishing/open carry/lost/found", + "latitude": 39.7556, + "location_description": "Trolley Square", + "longitude": -75.5647, + "n_guns_involved": 1.0, + "notes": "distraught man armed with handgun", + "participant_age": "0::60", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Bruce Altenburger", + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Subject-Suspect", + "sources": "http://www.delawareonline.com/story/news/crime/2017/01/23/wilmington-police-fatally-shoot-armed-man-near-trolley-square/96974292/||http://www.wdel.com/news/wilmington-man-id-d-as-victim-of-fatal-police-involved/article_2955a524-e26f-11e6-a289-9b53383bcc5f.html", + "state_house_district": 4.0, + "state_senate_district": 1.0 + } + }, + { + "scenario_id": "089", + "features": { + "incident_id": 757787, + "date": "2017-01-23", + "state": "Oklahoma", + "city_or_county": "Oklahoma City (Del City)", + "address": "4000 SE 15th St", + "n_killed": 2, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/757787", + "source_url": "http://newsok.com/del-city-police-make-arrest-in-laundromat-killings/article/5535964", + "incident_url_fields_missing": "False", + "congressional_district": 5.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Armed robbery with injury/death and/or evidence of DGU found", + "latitude": 35.4496, + "location_description": "Laundry Station", + "longitude": -97.4426, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": "0::60||1::42||2::33", + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+", + "participant_gender": "0::Male||1::Female||2::Male", + "participant_name": "0::Russ Roberts||1::Nekia Jackson||2::Roshaun Bernard Jones", + "participant_relationship": null, + "participant_status": "0::Killed||1::Killed||2::Unharmed, Arrested", + "participant_type": "0::Victim||1::Victim||2::Subject-Suspect", + "sources": "http://kfor.com/2017/01/23/police-investigating-double-homicide-after-two-people-found-dead-at-local-laundromat/||http://newsok.com/del-city-police-make-arrest-in-laundromat-killings/article/5535964", + "state_house_district": 94.0, + "state_senate_district": 42.0 + } + }, + { + "scenario_id": "100", + "features": { + "incident_id": 278698, + "date": "2015-01-15", + "state": "Texas", + "city_or_county": "Desoto", + "address": "1200 block of E. Pleasant Run Rd.", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/278698", + "source_url": "http://www.wfaa.com/story/news/crime/2015/01/16/woman-arrested-in-desoto-apartment-shooting/21882967/", + "incident_url_fields_missing": "False", + "congressional_district": 30.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 32.6041, + "location_description": null, + "longitude": -96.8283, + "n_guns_involved": null, + "notes": null, + "participant_age": "1::30||2::36", + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+", + "participant_gender": "0::Male||1::Female||2::Male", + "participant_name": "0::Vincent Dorsey||1::Latoya Vaughns||2::Terrance Brigham", + "participant_relationship": null, + "participant_status": "0::Killed||1::Unharmed, Arrested||2::Unharmed", + "participant_type": "0::Victim||1::Subject-Suspect||2::Subject-Suspect", + "sources": "http://www.wfaa.com/story/news/2015/01/15/man-found-dead-desoto-apartment-complex/21799555/||http://www.wfaa.com/story/news/crime/2015/01/16/woman-arrested-in-desoto-apartment-shooting/21882967/", + "state_house_district": 109.0, + "state_senate_district": 23.0 + } + }, + { + "scenario_id": "106", + "features": { + "incident_id": 276129, + "date": "2015-01-09", + "state": "Virginia", + "city_or_county": "Appomattox", + "address": "1523 Coleman Mountain Rd", + "n_killed": 2, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/276129", + "source_url": "http://www.timesvirginian.com/news/article_7a54c132-9c01-11e4-9730-3b1319e63883.html", + "incident_url_fields_missing": "False", + "congressional_district": 5.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Domestic Violence", + "latitude": 37.361, + "location_description": null, + "longitude": -78.6553, + "n_guns_involved": 1.0, + "notes": "man and woman shot each other to death, 4-year-old child found unharmed inside of home", + "participant_age": "0::45||1::42", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Female", + "participant_name": "0::Walker Clay Thornhill||1::Leilani Maria Valle", + "participant_relationship": null, + "participant_status": "0::Killed||1::Killed", + "participant_type": "0::Victim||1::Victim", + "sources": "http://www.newsadvance.com/news/local/two-dead-after-shooting-at-appomattox-house/article_724fb7a6-985c-11e4-b739-ff57e6045029.html||http://www.timesvirginian.com/news/article_7a54c132-9c01-11e4-9730-3b1319e63883.html", + "state_house_district": 59.0, + "state_senate_district": 22.0 + } + }, + { + "scenario_id": "114", + "features": { + "incident_id": 881976, + "date": "2017-07-03", + "state": "Texas", + "city_or_county": "Houston", + "address": "5400 block of Braesvalley Drive", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/881976", + "source_url": "http://www.chron.com/news/houston-texas/houston/article/HPD-responds-to-fatal-shooting-in-southwest-11264217.php", + "incident_url_fields_missing": "False", + "congressional_district": 7.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 29.6793, + "location_description": null, + "longitude": -95.4801, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": null, + "participant_age_group": null, + "participant_gender": null, + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Victim", + "sources": "http://www.chron.com/news/houston-texas/houston/article/HPD-responds-to-fatal-shooting-in-southwest-11264217.php", + "state_house_district": null, + "state_senate_district": 17.0 + } + }, + { + "scenario_id": "117", + "features": { + "incident_id": 322620, + "date": "2015-04-12", + "state": "Mississippi", + "city_or_county": "Greenville", + "address": "Edison and Union", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/322620", + "source_url": "http://www.ddtonline.com/news/article_01d004b2-e1f0-11e4-9427-2b68e0eab0e6.html", + "incident_url_fields_missing": "False", + "congressional_district": 2.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 33.4137, + "location_description": null, + "longitude": -91.047, + "n_guns_involved": null, + "notes": null, + "participant_age": "0::40", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Darryl Harris", + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Victim", + "sources": "http://www.ddtonline.com/news/article_01d004b2-e1f0-11e4-9427-2b68e0eab0e6.html", + "state_house_district": 49.0, + "state_senate_district": 12.0 + } + }, + { + "scenario_id": "119", + "features": { + "incident_id": 928218, + "date": "2017-08-26", + "state": "Pennsylvania", + "city_or_county": "Philadelphia", + "address": "1700 block of N Newkirk St", + "n_killed": 1, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/928218", + "source_url": "http://www.philly.com/philly/blogs/dncrime/Police-ID-man-killed-in-double-shooting.html", + "incident_url_fields_missing": "False", + "congressional_district": null, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Wounded/Injured||Shot - Dead (murder, accidental, suicide)", + "latitude": null, + "location_description": "(Brewerytown area)", + "longitude": null, + "n_guns_involved": 1.0, + "notes": "approx. 9:25 pm", + "participant_age": "0::25||1::45", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": "0::Nashawn Jones", + "participant_relationship": null, + "participant_status": "0::Killed||1::Injured", + "participant_type": "0::Victim||1::Victim", + "sources": "http://www.philly.com/philly/blogs/dncrime/Police-ID-man-killed-in-double-shooting.html", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "128", + "features": { + "incident_id": 144549, + "date": "2014-06-09", + "state": "Washington", + "city_or_county": "Vancouver", + "address": "Northeast 52nd Street at 131st Avenue", + "n_killed": 2, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/144549", + "source_url": "http://www.katu.com/news/local/Three-people-found-shot-in-Vancouver-home-262365831.html", + "incident_url_fields_missing": "False", + "congressional_district": 3.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured||Shot - Dead (murder, accidental, suicide)||Suicide^||Murder/Suicide", + "latitude": 45.6599, + "location_description": null, + "longitude": -122.538, + "n_guns_involved": null, + "notes": null, + "participant_age": "0::49||1::49||2::23", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Female||2::Male", + "participant_name": "0::Danny Lee Luster||1::Sandra Luster", + "participant_relationship": null, + "participant_status": "0::Killed||1::Killed||2::Injured", + "participant_type": "0::Victim||1::Victim||2::Victim", + "sources": "http://www.katu.com/news/local/Three-people-found-shot-in-Vancouver-home-262365831.html", + "state_house_district": 17.0, + "state_senate_district": 17.0 + } + }, + { + "scenario_id": "141", + "features": { + "incident_id": 299653, + "date": "2015-02-27", + "state": "California", + "city_or_county": "Sacramento", + "address": "2200 block of Edison Avenue", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/299653", + "source_url": "http://www.sacbee.com/news/local/crime/article11907812.html", + "incident_url_fields_missing": "False", + "congressional_district": 6.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 38.6316, + "location_description": null, + "longitude": -121.39, + "n_guns_involved": null, + "notes": null, + "participant_age": "0::26", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Robert Klose", + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Victim", + "sources": "http://www.sacbee.com/news/local/crime/article11907812.html", + "state_house_district": 8.0, + "state_senate_district": 6.0 + } + }, + { + "scenario_id": "147", + "features": { + "incident_id": 496075, + "date": "2016-01-29", + "state": "California", + "city_or_county": "Los Angeles", + "address": "Slauson Avenue and Crenshaw Boulevard", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/496075", + "source_url": "http://www.dailynews.com/general-news/20160130/1-killed-in-hyde-park-shooting-attack", + "incident_url_fields_missing": "False", + "congressional_district": 37.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 33.989, + "location_description": null, + "longitude": -118.331, + "n_guns_involved": null, + "notes": null, + "participant_age": null, + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Victim||1::Subject-Suspect||2::Subject-Suspect||3::Subject-Suspect||4::Subject-Suspect||5::Subject-Suspect||6::Subject-Suspect", + "sources": "http://www.dailynews.com/general-news/20160130/1-killed-in-hyde-park-shooting-attack", + "state_house_district": 54.0, + "state_senate_district": 30.0 + } + }, + { + "scenario_id": "152", + "features": { + "incident_id": 639901, + "date": "2016-08-26", + "state": "Kentucky", + "city_or_county": "Russell Springs", + "address": "Little Clifty Creek Road", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/639901", + "source_url": "http://www.lex18.com/story/32853837/man-killed-in-russell-co-shooting", + "incident_url_fields_missing": "False", + "congressional_district": 1.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 37.064, + "location_description": null, + "longitude": -85.1172, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": "0::66||1::39", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": "0::Montie Pendleton||1::Eric Pendleton", + "participant_relationship": "1::Family", + "participant_status": "0::Killed||1::Arrested", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://www.lex18.com/story/32853837/man-killed-in-russell-co-shooting", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "159", + "features": { + "incident_id": 457883, + "date": "2015-11-29", + "state": "North Dakota", + "city_or_county": "Minot", + "address": "Central Avenue and Broadway Street", + "n_killed": 1, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/457883", + "source_url": "http://www.kfyrtv.com/content/news/Man-wanted-for-Minot-murder-arrested-in-Las-Vegas-385271691.html", + "incident_url_fields_missing": "False", + "congressional_district": 1.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured||Shot - Dead (murder, accidental, suicide)||Gang involvement", + "latitude": 48.2365, + "location_description": null, + "longitude": -101.296, + "n_guns_involved": null, + "notes": null, + "participant_age": "0::30||1::43||2::25", + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+", + "participant_gender": "0::Male||1::Female||2::Male", + "participant_name": "0::Roger Falana||2::Johnny C. Norwood Jr.", + "participant_relationship": "2::Gang vs Gang", + "participant_status": "0::Killed||1::Injured||2::Unharmed, Arrested", + "participant_type": "0::Victim||1::Victim||2::Subject-Suspect", + "sources": "http://www.kxnet.com/story/30624719/one-killed-one-wounded-in-shooting||http://www.valleynewslive.com/home/headlines/Police-Issue-Warrant-for-Murder-Suspect-in-Minot-Fatal-Shooting-360487911.html||http://www.kxnet.com/story/30660346/arrests-made-investigation-continues-for-shooter||http://www.grandforksherald.com/news/crime-and-courts/3895916-minot-shooting-was-gang-related-police-say||http://www.kfyrtv.com/content/news/Man-wanted-for-Minot-murder-arrested-in-Las-Vegas-385271691.html", + "state_house_district": 5.0, + "state_senate_district": 5.0 + } + }, + { + "scenario_id": "161", + "features": { + "incident_id": 1052188, + "date": "2018-02-17", + "state": "Indiana", + "city_or_county": "South Bend", + "address": "4300 block of Silver Ln", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/1052188", + "source_url": "http://wsbt.com/news/local/police-respond-to-shooting-in-south-bend", + "incident_url_fields_missing": "False", + "congressional_district": 2.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 41.6627, + "location_description": null, + "longitude": -86.3075, + "n_guns_involved": 1.0, + "notes": "near Gladstone", + "participant_age": "0::17", + "participant_age_group": "0::Teen 12-17", + "participant_gender": "0::Male", + "participant_name": "0::Daekwon Tobar", + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Victim", + "sources": "http://wsbt.com/news/local/police-respond-to-shooting-in-south-bend", + "state_house_district": 7.0, + "state_senate_district": 10.0 + } + }, + { + "scenario_id": "165", + "features": { + "incident_id": 533170, + "date": "2016-03-31", + "state": "Missouri", + "city_or_county": "Saint Louis", + "address": "1700 block of Andros Ct", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/533170", + "source_url": "http://www.kmov.com/story/31622905/man-fatally-shot-in-ferguson-late-thursday-night-police-say", + "incident_url_fields_missing": "False", + "congressional_district": 1.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 38.7399, + "location_description": null, + "longitude": -90.2709, + "n_guns_involved": 1.0, + "notes": "38.739926, -90.270946", + "participant_age": "0::31", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Victim", + "sources": "http://www.kmov.com/story/31622905/man-fatally-shot-in-ferguson-late-thursday-night-police-say", + "state_house_district": 75.0, + "state_senate_district": 14.0 + } + }, + { + "scenario_id": "172", + "features": { + "incident_id": 459184, + "date": "2015-12-04", + "state": "Louisiana", + "city_or_county": "Baton Rouge", + "address": "8300 block of Amy Drive", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/459184", + "source_url": "http://www.wbrz.com/news/husband-charged-in-wife-s-murder", + "incident_url_fields_missing": "False", + "congressional_district": 6.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Domestic Violence", + "latitude": 30.3527, + "location_description": null, + "longitude": -91.1317, + "n_guns_involved": null, + "notes": "Woman shot and killed in incident of domestic violence. Husband charged", + "participant_age": "0::27||1::27", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Female||1::Male", + "participant_name": "0::Louisa Garcia||1::Juan Angel Delarca Martinez", + "participant_relationship": "1::Significant others - current or former", + "participant_status": "0::Killed||1::Arrested", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://www.ksla.com/story/30669337/deputies-investigate-shooting-on-amy-drive-in-baton-rouge||http://www.wbrz.com/news/husband-charged-in-wife-s-murder", + "state_house_district": 70.0, + "state_senate_district": 14.0 + } + }, + { + "scenario_id": "174", + "features": { + "incident_id": 217607, + "date": "2014-11-08", + "state": "Massachusetts", + "city_or_county": "Boston", + "address": "45 Bowdoin St", + "n_killed": 2, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/217607", + "source_url": "http://boston.homicidewatch.org/2014/11/21/audilia-daveiga-33-shot-and-killed-in-dorchester/", + "incident_url_fields_missing": "False", + "congressional_district": 8.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - 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Dead (murder, accidental, suicide)||Domestic Violence", + "latitude": 32.8128, + "location_description": "Moss Creek Apartments", + "longitude": -80.0265, + "n_guns_involved": null, + "notes": null, + "participant_age": "0::22||1::20", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Female", + "participant_name": "0::Parrish Quantize Greene||1::Teonna Cooper", + "participant_relationship": "1::Significant others - current or former", + "participant_status": "0::Killed||1::Unharmed", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://www.postandcourier.com/article/20151023/PC16/151029665", + "state_house_district": 114.0, + "state_senate_district": 41.0 + } + }, + { + "scenario_id": "189", + "features": { + "incident_id": 983062, + "date": "2017-11-07", + "state": "District of Columbia", + "city_or_county": "Washington", + "address": "1300 block of Rittenhouse St", + "n_killed": 1, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/983062", + "source_url": "https://www.washingtonpost.com/local/public-safety/police-arrest-second-suspect-in-killing-of-teenager-on-rittenhouse-street/2017/12/01/4ba9c9ee-d692-11e7-a986-d0a9770d9a3e_story.html?utm_term=.e620d8836434", + "incident_url_fields_missing": "False", + "congressional_district": null, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Wounded/Injured||Shot - Dead (murder, accidental, suicide)||Drive-by (car to street, car to car)", + "latitude": 38.9654, + "location_description": null, + "longitude": -77.0297, + "n_guns_involved": 1.0, + "notes": "Approx 8pm, lookout for red Mustang", + "participant_age": "0::16||2::20||3::17", + "participant_age_group": "0::Teen 12-17||1::Adult 18+||2::Adult 18+||3::Teen 12-17", + "participant_gender": "0::Male||1::Male||2::Male||3::Male", + "participant_name": "0::Yoselis Regino Barrios||2::Kevin Sorto||3::Victor Hernandez", + "participant_relationship": null, + "participant_status": "0::Killed||1::Injured||2::Unharmed, Arrested||3::Unharmed, Arrested", + "participant_type": "0::Victim||1::Victim||2::Subject-Suspect||3::Subject-Suspect", + "sources": "https://twitter.com/DCPoliceDept/status/928074129080836096||https://www.washingtonpost.com/local/public-safety/man-is-gravely-wounded-in-double-shooting-in-the-district/2017/11/07/28de3670-c42c-11e7-afe9-4f60b5a6c4a0_story.html?utm_term=.906b4dc763c2||https://www.washingtonpost.com/local/public-safety/man-killed-another-wounded-in-drive-by-shooting-in-northwest-washington/2017/11/08/af546f8e-c47d-11e7-aae0-cb18a8c29c65_story.html?utm_term=.81971a2fdb7f||https://www.washingtonpost.com/local/public-safety/police-arrest-20-year-old-man-for-killing-district-teen/2017/11/22/1e825e40-cfe4-11e7-a1a3-0d1e45a6de3d_story.html?utm_term=.c1d47de9ddc6||http://wjla.com/news/local/wanted-dc-police-search-for-second-suspect-in-nw-homicide||https://www.washingtonpost.com/local/public-safety/police-arrest-second-suspect-in-killing-of-teenager-on-rittenhouse-street/2017/12/01/4ba9c9ee-d692-11e7-a986-d0a9770d9a3e_story.html?utm_term=.e620d8836434", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "190", + "features": { + "incident_id": 218138, + "date": "2014-11-09", + "state": "Texas", + "city_or_county": "Irving", + "address": "Airport Freeway", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/218138", + "source_url": "http://www.nbcdfw.com/news/local/Irving-Police-Kill-Man-in-Hotel-Parking-Lot-282095011.html", + "incident_url_fields_missing": "False", + "congressional_district": 24.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Officer Involved Incident||Officer Involved Shooting - subject/suspect/perpetrator killed", + "latitude": 32.8371, + "location_description": null, + "longitude": -96.9683, + "n_guns_involved": null, + "notes": "perp rammed a van and police shot him; Irving police investigating", + "participant_age": "0::38", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Ashif Anwar", + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Subject-Suspect", + "sources": "http://www.ksat.com/content/pns/ksat/news/2014/11/10/tx-police-shooting-irving.html||http://dfw.cbslocal.com/2014/11/10/irving-police-shoot-kill-man-wanted-on-felony-warrants/||http://www.nbcdfw.com/news/local/Irving-Police-Kill-Man-in-Hotel-Parking-Lot-282095011.html", + "state_house_district": 105.0, + "state_senate_district": 16.0 + } + }, + { + "scenario_id": "200", + "features": { + "incident_id": 158453, + "date": "2014-06-30", + "state": "Illinois", + "city_or_county": "Danville", + "address": "1200 block of Marion Street", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/158453", + "source_url": "http://www.illinoishomepage.net/story/d/story/2-shootings-investigated/32506/AwJGx4dtj0SdIWuaf_ZRhQ", + "incident_url_fields_missing": "False", + "congressional_district": 15.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - 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"incident_id": 652279, - "date": "2016-09-09", - "state": "North Carolina", - "city_or_county": "Charlotte", - "address": "100 block of East Park Avenue", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/652279", - "source_url": "http://www.wsoctv.com/news/local/man-arrested-in-murder-case-connected-to-slaying-of-school-teacher/468034409", - "incident_url_fields_missing": "False", - "congressional_district": null, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Armed robbery with injury/death and/or evidence of DGU found", - "latitude": null, - "location_description": "SouthEnd", - "longitude": null, - "n_guns_involved": 1.0, - "notes": "Teacher Victim died 9/14/16 as a result of injuries from gunshot", - "participant_age": "0::33", - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Female", - "participant_name": "0::Ruijuan Guo", - "participant_relationship": null, - "participant_status": "0::Killed", - "participant_type": "0::Victim", - "sources": "http://wccbcharlotte.com/2016/09/10/police-investigating-shooting-central-division/||http://www.wcnc.com/news/local/shooting-investigation-underway-in-charlotte-central-division/316419372||http://www.wcnc.com/news/local/shooting-investigation-underway-in-charlotte-central-division/316419372||http://www.wsoctv.com/news/local/man-arrested-in-murder-case-connected-to-slaying-of-school-teacher/468034409", - "state_house_district": null, - "state_senate_district": null - } - }, - { - "scenario_id": "002", - "features": { - "incident_id": 141637, - "date": "2014-05-31", - "state": "Nebraska", - "city_or_county": "Omaha", - "address": "132nd Street and West Center Road", - "n_killed": 0, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/141637", - "source_url": "http://www.ketv.com/news/pharmacy-robbed-overnight/26275626#!TwVEg", - "incident_url_fields_missing": "False", - "congressional_district": 2.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Armed robbery with injury/death and/or evidence of DGU found", - "latitude": 41.234, - "location_description": null, - "longitude": -96.1194, - "n_guns_involved": null, - "notes": null, - "participant_age": null, - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Unharmed", - "participant_type": "0::Subject-Suspect", - "sources": "http://www.ketv.com/news/pharmacy-robbed-overnight/26275626#!TwVEg", - "state_house_district": null, - "state_senate_district": 20.0 - } - }, - { - "scenario_id": "003", - "features": { - "incident_id": 193765, - "date": "2014-09-20", - "state": "Oregon", - "city_or_county": "Clackamas County", - "address": "30800 block of Short Fellows Road ", - "n_killed": 0, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/193765", - "source_url": "http://www.oregonlive.com/clackamascounty/index.ssf/2014/09/man_shoots_burglary_suspect_in.html#incart_river", - 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"city_or_county": "Lawton", - "address": "6500 block of Elm", - "n_killed": 0, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/389169", - "source_url": "http://www.kswo.com/story/29688413/shots-fired-into-west-side-home", - "incident_url_fields_missing": "False", - "congressional_district": 4.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shots Fired - No Injuries", - "latitude": 34.6205, - "location_description": null, - "longitude": -98.473, - "n_guns_involved": null, - "notes": null, - "participant_age": null, - "participant_age_group": null, - "participant_gender": null, - "participant_name": null, - "participant_relationship": null, - "participant_status": null, - "participant_type": null, - "sources": "http://www.kswo.com/story/29688413/shots-fired-into-west-side-home", - "state_house_district": 62.0, - "state_senate_district": 32.0 - } - }, - { - "scenario_id": "005", - "features": { - "incident_id": 115681, - "date": "2014-03-15", - 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"participant_type": "0:Victim|1:Victim|2:Victim|3:Subject-Suspect|4:Subject-Suspect|5:Subject-Suspect", - "sources": "http://www.thestate.com/2014/03/16/3330111/update-name-of-victim-in-saturdays.html", - "state_house_district": 76.0, - "state_senate_district": 22.0 - } - }, - { - "scenario_id": "006", - "features": { - "incident_id": 1044649, - "date": "2018-02-07", - "state": "Ohio", - "city_or_county": "Columbus", - "address": "2800 block of E 13th Ave", - "n_killed": 0, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/1044649", - "source_url": "https://www.10tv.com/article/man-critical-condition-after-northeast-columbus-shooting", - "incident_url_fields_missing": "False", - "congressional_district": 3.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Wounded/Injured", - "latitude": 39.9973, - "location_description": null, - "longitude": -82.9955, - "n_guns_involved": 1.0, - "notes": "shot in torso, crit", - "participant_age": "0::45", - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": "0::Delbert H. Hollingsworth", - "participant_relationship": null, - "participant_status": "0::Injured", - "participant_type": "0::Victim", - "sources": "https://www.10tv.com/article/man-critical-condition-after-northeast-columbus-shooting", - "state_house_district": 18.0, - "state_senate_district": 15.0 - } - }, - { - "scenario_id": "007", - "features": { - "incident_id": 336020, - "date": "2015-05-07", - "state": "Georgia", - "city_or_county": "Macon", - "address": "3044 Bloomfield Drive", - "n_killed": 0, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/336020", - "source_url": "http://www.macon.com/2015/05/07/3734779_bloomfield-apartments-cars-hit.html?rh=1", - "incident_url_fields_missing": "False", - "congressional_district": 2.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shots Fired - No Injuries", - "latitude": 32.8089, - "location_description": null, - "longitude": -83.7003, - "n_guns_involved": null, - "notes": "Sandy Springs apt, up to 10 shots heard, 3 apt, 2 cars hit, no inj", - "participant_age": null, - "participant_age_group": null, - "participant_gender": null, - "participant_name": null, - "participant_relationship": null, - "participant_status": null, - "participant_type": null, - "sources": "http://www.macon.com/2015/05/07/3734779_bloomfield-apartments-cars-hit.html?rh=1", - "state_house_district": 142.0, - "state_senate_district": 26.0 - } - }, - { - "scenario_id": "008", - "features": { - "incident_id": 943750, - "date": "2017-09-27", - "state": "Pennsylvania", - "city_or_county": "Philadelphia", - "address": "58th St and Kingsessing Ave", - "n_killed": 0, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/943750", - "source_url": "http://philadelphia.cbslocal.com/2017/09/28/3-wanted-kingsessing-crime-spree/", - "incident_url_fields_missing": "False", - "congressional_district": null, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Non-Shooting Incident||Armed robbery with injury/death and/or evidence of DGU found||Pistol-whipping", - "latitude": 39.9347, - "location_description": "(Kingsessing)", - "longitude": -75.2273, - "n_guns_involved": 1.0, - "notes": "approx. 11:45 pm, PW and robbed, LE believe same suspects as 9/27/17 shooting on N 63rd St", - "participant_age": null, - "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+||3::Adult 18+", - "participant_gender": "0::Male||1::Male||2::Male||3::Male", - "participant_name": null, - "participant_relationship": "1::Armed Robbery||2::Armed Robbery||3::Armed Robbery", - "participant_status": "0::Injured", - "participant_type": "0::Victim||1::Subject-Suspect||2::Subject-Suspect||3::Subject-Suspect", - "sources": "http://philadelphia.cbslocal.com/2017/09/28/3-wanted-kingsessing-crime-spree/", - "state_house_district": null, - "state_senate_district": null - } - }, - { - "scenario_id": "009", - "features": { - "incident_id": 440535, - "date": "2015-11-01", - "state": "New Jersey", - "city_or_county": "Paterson", - "address": "East Main Street", - "n_killed": 0, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/440535", - "source_url": "http://www.northjersey.com/news/man-injured-in-paterson-shooting-1.1446306", - "incident_url_fields_missing": "False", - "congressional_district": 9.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Wounded/Injured", - "latitude": 40.9292, - "location_description": null, - "longitude": -74.1683, - "n_guns_involved": null, - "notes": null, - "participant_age": "0::32", - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Injured", - "participant_type": "0::Victim", - "sources": "http://www.northjersey.com/news/man-injured-in-paterson-shooting-1.1446306", - "state_house_district": 35.0, - "state_senate_district": 35.0 - } - }, - { - "scenario_id": "010", - "features": { - "incident_id": 295713, - "date": "2015-02-21", - "state": "California", - 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"state_house_district": 62.0, - "state_senate_district": 30.0 - } - }, - { - "scenario_id": "011", - "features": { - "incident_id": 135485, - "date": "2014-05-16", - "state": "New York", - "city_or_county": "Manhattan", - "address": "FDR Drive", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/135485", - "source_url": "http://newyork.cbslocal.com/2014/05/19/nypd-suspect-in-east-harlem-police-involved-shooting-arrested/", - "incident_url_fields_missing": "False", - "congressional_district": 12.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Officer Involved Incident||Officer Involved Shooting - subject/suspect/perpetrator killed", - "latitude": 40.7516, - "location_description": null, - "longitude": -73.9652, - "n_guns_involved": null, - "notes": "ois pharmacy", - "participant_age": "0::45", - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Male||1::Male", - "participant_name": "0::Scott Kato", - "participant_relationship": null, - "participant_status": "0::Killed||1::Unharmed", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://www.nydailynews.com/new-york/nyc-crime/cops-shoot-kill-pharmacy-robber-manhattan-article-1.1795228||http://newyork.cbslocal.com/2014/05/19/nypd-suspect-in-east-harlem-police-involved-shooting-arrested/", - "state_house_district": 74.0, - "state_senate_district": 28.0 - } - }, - { - "scenario_id": "012", - "features": { - "incident_id": 371030, - "date": "2015-07-05", - "state": "South Carolina", - "city_or_county": "Eutawville", - "address": "157 State Road S-38-1641", - "n_killed": 2, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/371030", - "source_url": "http://thetandd.com/news/amid-cries-family-calls-fatal-shooting-senseless/article_b46321b1-094f-55d9-afc5-9874a3ad18c7.html", - "incident_url_fields_missing": "False", - "congressional_district": 6.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", - "latitude": 33.4228, - "location_description": "Indian Bluff Recreation Park", - "longitude": -80.369, - "n_guns_involved": null, - "notes": "dispute over jet ski", - "participant_age": "1::30", - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Male||1::Male", - "participant_name": "0::Adrian Jamil Eaglin||1::Luther Brandell Green", - "participant_relationship": null, - "participant_status": "0::Killed||1::Killed", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://thetandd.com/news/amid-cries-family-calls-fatal-shooting-senseless/article_b46321b1-094f-55d9-afc5-9874a3ad18c7.html", - "state_house_district": 66.0, - "state_senate_district": 39.0 - } - }, - { - "scenario_id": "013", - "features": { - "incident_id": 1014813, - "date": "2017-12-22", - "state": "Nebraska", - "city_or_county": "Lincoln", - "address": "6200 block of Woodstock Cir", - 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"state_senate_district": null - } - }, - { - "scenario_id": "014", - "features": { - "incident_id": 896182, - "date": "2017-07-21", - "state": "South Carolina", - "city_or_county": "Greenville", - "address": "Laurens Rd", - "n_killed": 0, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/896182", - "source_url": "http://wspa.com/2017/07/22/guns-ammo-seized-during-traffic-stop-in-greenville-police-say/", - "incident_url_fields_missing": "False", - "congressional_district": 4.0, - "gun_stolen": "0::Stolen||1::Unknown||2::Unknown", - "gun_type": "0::Unknown||1::Unknown||2::Unknown", - "incident_characteristics": "Non-Shooting Incident||ATF/LE Confiscation/Raid/Arrest||Possession (gun(s) found during commission of other crimes)||Possession of gun by felon or prohibited person||Stolen/Illegally owned gun{s} recovered during arrest/warrant", - "latitude": 34.8476, - "location_description": null, - "longitude": -82.3992, - "n_guns_involved": 3.0, - "notes": "traffic stop, 3 guns incl at least 1 stolen", - 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"city_or_county": "Los Angeles", - "address": "615 Normandie Ave", - "n_killed": 0, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/1074215", - "source_url": "https://www.atf.gov/news/pr/felon-possession-receives-hefty-prison-sentence", - "incident_url_fields_missing": "False", - "congressional_district": 34.0, - "gun_stolen": "0::Unknown||1::Unknown||2::Unknown||3::Unknown||4::Unknown", - "gun_type": "0::9mm||1::9mm||2::22 LR||3::9mm||4::45 Auto", - "incident_characteristics": "Non-Shooting Incident||Drug involvement||ATF/LE Confiscation/Raid/Arrest||Possession of gun by felon or prohibited person", - "latitude": 34.0631, - "location_description": null, - "longitude": -118.3, - "n_guns_involved": 5.0, - "notes": null, - "participant_age": "0::22", - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": "0::Michael Anthony Lopez", - "participant_relationship": null, - "participant_status": "0::Unharmed, Arrested", - "participant_type": "0::Subject-Suspect", - "sources": "https://www.atf.gov/news/pr/felon-possession-receives-hefty-prison-sentence", - "state_house_district": 53.0, - "state_senate_district": 24.0 - } - }, - { - "scenario_id": "018", - "features": { - "incident_id": 1036577, - "date": "2018-01-26", - "state": "Wisconsin", - "city_or_county": "Sun Prairie", - "address": "3700 Heatherstone Dr", - "n_killed": 0, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/1036577", - "source_url": "https://www.channel3000.com/news/officials-sun-prairie-man-who-accidentally-shot-himself-while-cleaning-gun-suffers-serious-injuries/691691239", - "incident_url_fields_missing": "False", - "congressional_district": 2.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::9mm", - "incident_characteristics": "Shot - Wounded/Injured||Accidental Shooting||Accidental Shooting - Injury||Accidental/Negligent Discharge||Self-Inflicted (not suicide or suicide attempt - NO PERP)||Cleaning gun", - "latitude": 43.1941, - "location_description": null, - "longitude": -89.2821, - "n_guns_involved": 1.0, - "notes": null, - "participant_age": "0::49", - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Injured", - "participant_type": "0::Victim", - "sources": "https://www.channel3000.com/news/officials-sun-prairie-man-who-accidentally-shot-himself-while-cleaning-gun-suffers-serious-injuries/691691239", - "state_house_district": 79.0, - "state_senate_district": 27.0 - } - }, - { - "scenario_id": "019", - "features": { - "incident_id": 295514, - "date": "2015-02-20", - "state": "Texas", - "city_or_county": "Harris County", - "address": "1200 block of E. Airtex", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/295514", - "source_url": "http://www.khou.com/story/news/local/2015/02/20/officer-involved-shooting-reported-in-n-harris-county/23751883/", - "incident_url_fields_missing": "False", - "congressional_district": 18.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Officer Involved Incident||Officer Involved Shooting - subject/suspect/perpetrator killed", - "latitude": 29.9816, - "location_description": null, - "longitude": -95.4281, - "n_guns_involved": null, - "notes": "Harris County Sheriff's Office investigating after perp aimed gun at a U.S. marshall and the perp was shot.", - "participant_age": null, - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Killed", - "participant_type": "0::Subject-Suspect", - "sources": "http://www.khou.com/story/news/local/2015/02/20/officer-involved-shooting-reported-in-n-harris-county/23751883/", - "state_house_district": 141.0, - "state_senate_district": 15.0 - } - }, - { - "scenario_id": "020", - "features": { - "incident_id": 870237, - "date": "2017-06-18", - "state": "Louisiana", - "city_or_county": "New Orleans", - "address": "E I 10 and Orleans Ave", - "n_killed": 0, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/870237", - "source_url": "http://Orleans Avenuewww.nola.com/crime/index.ssf/2017/06/shooting_interstate_10_new_orl.html", - "incident_url_fields_missing": "False", - "congressional_district": 2.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Wounded/Injured||Shots Fired - No Injuries||Drive-by (car to street, car to car)", - "latitude": 29.9652, - "location_description": null, - "longitude": -90.0739, - "n_guns_involved": 1.0, - "notes": "exit 235A; 2nd scene North Claiborne Avenue and Pauger Street", - "participant_age": null, - "participant_age_group": null, - "participant_gender": "0::Male", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Injured", - "participant_type": "0::Victim", - "sources": "http://www.nola.com/crime/index.ssf/2017/06/shooting_interstate_10_new_orl.html", - "state_house_district": 93.0, - "state_senate_district": 5.0 - } - }, - { - "scenario_id": "021", - "features": { - "incident_id": 793399, - "date": "2017-03-11", - "state": "Virginia", - "city_or_county": "Chantilly", - "address": "13930 Metrotech Drive", - "n_killed": 0, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/793399", - "source_url": "http://www.wusa9.com/news/local/3-men-charged-in-recent-gun-store-burglaries/424004124", - "incident_url_fields_missing": "False", - "congressional_district": 10.0, - "gun_stolen": "0::Stolen||1::Stolen||2::Stolen||3::Stolen||4::Stolen||5::Stolen||6::Stolen||7::Stolen||8::Stolen||9::Stolen||10::Stolen||11::Stolen||12::Stolen||13::Stolen||14::Stolen||15::Stolen||16::Stolen||17::Stolen||18::Stolen||19::Stolen||20::Stolen||21::Stolen||22::Stolen||23::Stolen||24::Stolen||25::Stolen||26::Stolen||27::Stolen||28::Stolen||29::Stolen||30::Stolen||31::Stolen||32::Stolen||33::Stolen||34::Stolen", - "gun_type": "0::Unknown||1::Unknown||2::Unknown||3::Unknown||4::Unknown||5::Unknown||6::Unknown||7::Unknown||8::Unknown||9::Unknown||10::Unknown||11::Unknown||12::Unknown||13::Unknown||14::Unknown||15::Unknown||16::Unknown||17::Unknown||18::Unknown||19::Unknown||20::Unknown||21::Unknown||22::Unknown||23::Unknown||24::Unknown||25::Unknown||26::Unknown||27::Unknown||28::Unknown||29::Unknown||30::Unknown||31::Unknown||32::Unknown||33::Unknown||34::Unknown", - "incident_characteristics": "Institution/Group/Business||Non-Shooting Incident||Gun shop robbery or burglary", - "latitude": 38.8962, - "location_description": "50 West Armory", - "longitude": -77.4275, - "n_guns_involved": 35.0, - "notes": "35 firearms stolen from gun store, suspects also tried to rob two gun stores in Fredericksburg on March 14", - "participant_age": "0::19||1::23||2::23", - "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+", - "participant_gender": "0::Male||1::Male||2::Male", - "participant_name": "0::Daniel Quarles||1::Preston Holmes||2::Matthew Jones", - "participant_relationship": null, - "participant_status": "0::Unharmed, Arrested||1::Unharmed, Arrested||2::Unharmed, Arrested", - "participant_type": "0::Subject-Suspect||1::Subject-Suspect||2::Subject-Suspect", - "sources": "http://www.alexandrianews.org/2017/03/dozens-of-firearms-stolen-from-chantilly-gun-store/||http://www.wusa9.com/news/local/3-arrested-in-va-gun-store-burglaries/424118061||http://www.wusa9.com/news/local/3-men-charged-in-recent-gun-store-burglaries/424004124", - "state_house_district": 86.0, - "state_senate_district": 32.0 - } - }, - { - "scenario_id": "022", - "features": { - "incident_id": 92228, - "date": "2014-01-01", - "state": "Arkansas", - "city_or_county": "Pine Bluff", - "address": "2300 block of Port Road", - "n_killed": 0, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/92228", - "source_url": "http://www.thv11.com/news/crime/293574/370/Man-wanted-in-New-Years-Day-shooting-in-Pine-Bluff", - "incident_url_fields_missing": "False", - "congressional_district": 4.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Wounded/Injured", - "latitude": 34.2291, - "location_description": null, - "longitude": -91.9779, - "n_guns_involved": null, - "notes": null, - "participant_age": "1::26", - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Male||1::Male", - "participant_name": "1::Ronald Maxwell Jr.", - "participant_relationship": null, - "participant_status": "0::Injured||1::Unharmed", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://www.katv.com/story/24341851/pine-bluff-man-shot-in-leg-suspect-in-custody||http://www.thv11.com/news/crime/293574/370/Man-wanted-in-New-Years-Day-shooting-in-Pine-Bluff", - "state_house_district": 16.0, - "state_senate_district": 25.0 - } - }, - { - "scenario_id": "031", - "features": { - "incident_id": 135042, - "date": "2014-05-14", - "state": "California", - "city_or_county": "Sacramento", - "address": "6800 block of Woodbine Ave", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/135042", - "source_url": "http://www.sacbee.com/news/local/crime/article202709659.html", - "incident_url_fields_missing": "False", - "congressional_district": 6.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Shotgun", - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Domestic Violence||Possession of gun by felon or prohibited person", - "latitude": 38.5024, - "location_description": null, - "longitude": -121.48, - "n_guns_involved": 1.0, - "notes": null, - "participant_age": "0::31||1::34", - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Female||1::Male", - "participant_name": "0::Tamisha Evette Ridge||1::Dameshlo Green", - "participant_relationship": "1::Significant others - current or former", - "participant_status": "0::Killed||1::Unharmed, Arrested", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://www.sacbee.com/2014/05/14/6403951/woman-shot-to-death-in-south-sacramento.html||http://www.sacbee.com/news/local/crime/article202709659.html", - "state_house_district": 9.0, - "state_senate_district": 6.0 - } - }, - { - "scenario_id": "033", - "features": { - "incident_id": 320323, - "date": "2015-04-08", - "state": "Illinois", - "city_or_county": "Chicago", - "address": "84th Street and Essex", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/320323", - "source_url": "http://www.chicagotribune.com/news/local/breaking/chi-man-shot-in-head-on-south-side-20150408-story.html", - "incident_url_fields_missing": "False", - "congressional_district": 2.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", - "latitude": 41.7427, - "location_description": null, - "longitude": -87.5637, - "n_guns_involved": null, - "notes": "Man shot, head, critical; unclear. Died in hospital.", - "participant_age": "0::21", - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male||1::Male", - "participant_name": "0::Devante Warnsby", - "participant_relationship": null, - "participant_status": "0::Killed||1::Unharmed", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://abc7chicago.com/news/man-shot-in-the-head-in-south-shore/641586/||http://www.chicagotribune.com/news/local/breaking/chi-man-shot-in-head-on-south-side-20150408-story.html", - "state_house_district": 33.0, - "state_senate_district": 17.0 - } - }, - { - "scenario_id": "036", - "features": { - "incident_id": 990412, - "date": "2017-11-16", - "state": "Illinois", - "city_or_county": "Des Plaines (Rosemont)", - "address": "9700 Higgins Rd", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/990412", - "source_url": "http://www.dailyherald.com/news/20171116/rosemont-police-investigating-death", - "incident_url_fields_missing": "False", - 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"source_url": "http://www.gwcommonwealth.com/news/article_94d705aa-7eb8-11e7-bf0c-8b51a12ae789.html", - "incident_url_fields_missing": "False", - "congressional_district": 2.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Wounded/Injured||Shot - Dead (murder, accidental, suicide)||Bar/club incident - in or around establishment", - "latitude": 33.3094, - "location_description": "Club CJ's", - "longitude": -90.2041, - "n_guns_involved": 1.0, - "notes": null, - "participant_age": "2::37||3::32", - "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+||3::Adult 18+||4::Adult 18+", - "participant_gender": "0::Male||1::Male||2::Male||3::Male||4::Male", - "participant_name": "0::Bernardo Washington||1::Joe Holmes||2::Demantreous Love||3::Jamar Newsome||4::Montrell Pretzea Love", - "participant_relationship": null, - "participant_status": "0::Killed||1::Injured||2::Unharmed||3::Unharmed||4::Unharmed, Arrested", - "participant_type": "0::Victim||1::Victim||2::Subject-Suspect||3::Subject-Suspect||4::Subject-Suspect", - "sources": "http://www.walb.com/story/35801551/double-shooting-at-holmes-co-club-leaves-one-dead||http://www.msnewsnow.com/story/36091244/two-men-wanted-for-capital-murder-in-holmes-county||http://wjtv.com/2017/07/03/1-dead-1-injured-in-holmes-county-club-shooting-deputies-search-for-suspect/||http://www.gwcommonwealth.com/news/article_94d705aa-7eb8-11e7-bf0c-8b51a12ae789.html", - "state_house_district": 34.0, - "state_senate_district": 24.0 - } - }, - { - "scenario_id": "062", - "features": { - "incident_id": 883282, - "date": "2017-07-05", - "state": "Illinois", - "city_or_county": "Chicago (Roseland)", - "address": "11000 block of S Eberhart", - "n_killed": 2, - "n_injured": 2, - "incident_url": "http://www.gunviolencearchive.org/incident/883282", - "source_url": "http://homicides.suntimes.com/2017/07/07/neighbor-fatal-shooting-of-derwin-moore-and-maurice-willis-in-roseland-are-so-normal-in-the-black-community/", - "incident_url_fields_missing": "False", - "congressional_district": 2.0, - 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"state_house_district": 34.0, - "state_senate_district": 17.0 - } - }, - { - "scenario_id": "066", - "features": { - "incident_id": 218620, - "date": "2014-11-08", - "state": "California", - "city_or_county": "Lucerne", - "address": "16th street", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/218620", - "source_url": "http://blog.sfgate.com/crime/2014/11/10/lake-county-officers-shoot-and-kill-man/", - "incident_url_fields_missing": "False", - "congressional_district": 3.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Officer Involved Incident||Officer Involved Shooting - subject/suspect/perpetrator killed", - "latitude": 39.0858, - "location_description": null, - "longitude": -122.79, - "n_guns_involved": null, - "notes": null, - "participant_age": "0::56", - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": "0::Christopher O’Neal", - "participant_relationship": null, - "participant_status": "0::Killed", - "participant_type": "0::Subject-Suspect", - "sources": "http://blog.sfgate.com/crime/2014/11/10/lake-county-officers-shoot-and-kill-man/", - "state_house_district": 4.0, - "state_senate_district": 2.0 - } - }, - { - "scenario_id": "068", - "features": { - "incident_id": 337544, - "date": "2015-05-10", - "state": "Alabama", - "city_or_county": "Mobile (county)", - "address": "Spring Street", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/337544", - "source_url": "http://www.wsfa.com/story/29025992/apparent-homicide-on-spring-street", - "incident_url_fields_missing": "False", - "congressional_district": 1.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", - "latitude": 30.7221, - "location_description": null, - "longitude": -88.0865, - "n_guns_involved": null, - "notes": null, - "participant_age": null, - 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"latitude": 41.916, - "location_description": null, - "longitude": -87.7053, - "n_guns_involved": 1.0, - "notes": "Victim shot outside store during fight after road rage incident; suspect convicted/murder, 1988;", - "participant_age": "0::34||1::51", - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Male||1::Male", - "participant_name": "0::Peter Puleo||1::Marc Johnson", - "participant_relationship": null, - "participant_status": "0::Killed||1::Unharmed, Arrested", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "https://chicago.suntimes.com/news/man-killed-in-logan-square-drive-by-shooting-2/||https://chicago.suntimes.com/news/felon-charged-with-fatally-shooting-logan-square-man-during-argument/", - "state_house_district": 4.0, - "state_senate_district": 2.0 - } - }, - { - "scenario_id": "082", - "features": { - "incident_id": 153275, - "date": "2014-06-30", - "state": "Kentucky", - "city_or_county": "Lexington", - "address": "Scottsdale Circle", - 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"participant_type": "0::Victim||1::Victim||2::Victim||3::Victim", - "sources": "http://www.wkyt.com/home/headlines/Lexington-police-on-scene-of-reported-shooting-265316871.html", - "state_house_district": null, - "state_senate_district": null - } - }, - { - "scenario_id": "084", - "features": { - "incident_id": 713419, - "date": "2016-11-28", - "state": "Minnesota", - "city_or_county": "Moorhead", - "address": "19th Avenue and 20th Street South", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/713419", - "source_url": "http://www.valleynewslive.com/content/news/Moorhead-Police-identify-man-involved-in-standoff-403434376.html", - "incident_url_fields_missing": "False", - "congressional_district": null, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Suicide^||Officer Involved Incident||Officer Involved Shooting - subject/suspect/perpetrator killed||Officer Involved Shooting - 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"date": "2015-01-15", - "state": "Texas", - "city_or_county": "Desoto", - "address": "1200 block of E. 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"source_url": "http://www.lex18.com/story/32853837/man-killed-in-russell-co-shooting", - "incident_url_fields_missing": "False", - "congressional_district": 1.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", - "latitude": 37.064, - "location_description": null, - "longitude": -85.1172, - "n_guns_involved": 1.0, - "notes": null, - "participant_age": "0::66||1::39", - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Male||1::Male", - "participant_name": "0::Montie Pendleton||1::Eric Pendleton", - "participant_relationship": "1::Family", - "participant_status": "0::Killed||1::Arrested", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://www.lex18.com/story/32853837/man-killed-in-russell-co-shooting", - "state_house_district": null, - "state_senate_district": null - } - }, - { - "scenario_id": "159", - "features": { - "incident_id": 457883, - "date": "2015-11-29", - "state": "North Dakota", - "city_or_county": "Minot", - "address": "Central Avenue and Broadway Street", - "n_killed": 1, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/457883", - "source_url": "http://www.kfyrtv.com/content/news/Man-wanted-for-Minot-murder-arrested-in-Las-Vegas-385271691.html", - "incident_url_fields_missing": "False", - "congressional_district": 1.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Wounded/Injured||Shot - Dead (murder, accidental, suicide)||Gang involvement", - "latitude": 48.2365, - "location_description": null, - "longitude": -101.296, - "n_guns_involved": null, - "notes": null, - "participant_age": "0::30||1::43||2::25", - "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+", - "participant_gender": "0::Male||1::Female||2::Male", - "participant_name": "0::Roger Falana||2::Johnny C. Norwood Jr.", - "participant_relationship": "2::Gang vs Gang", - "participant_status": "0::Killed||1::Injured||2::Unharmed, Arrested", - "participant_type": "0::Victim||1::Victim||2::Subject-Suspect", - "sources": "http://www.kxnet.com/story/30624719/one-killed-one-wounded-in-shooting||http://www.valleynewslive.com/home/headlines/Police-Issue-Warrant-for-Murder-Suspect-in-Minot-Fatal-Shooting-360487911.html||http://www.kxnet.com/story/30660346/arrests-made-investigation-continues-for-shooter||http://www.grandforksherald.com/news/crime-and-courts/3895916-minot-shooting-was-gang-related-police-say||http://www.kfyrtv.com/content/news/Man-wanted-for-Minot-murder-arrested-in-Las-Vegas-385271691.html", - "state_house_district": 5.0, - "state_senate_district": 5.0 - } - }, - { - "scenario_id": "161", - "features": { - "incident_id": 1052188, - "date": "2018-02-17", - "state": "Indiana", - "city_or_county": "South Bend", - "address": "4300 block of Silver Ln", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/1052188", - "source_url": "http://wsbt.com/news/local/police-respond-to-shooting-in-south-bend", - "incident_url_fields_missing": "False", - "congressional_district": 2.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", - "latitude": 41.6627, - "location_description": null, - "longitude": -86.3075, - "n_guns_involved": 1.0, - "notes": "near Gladstone", - "participant_age": "0::17", - "participant_age_group": "0::Teen 12-17", - "participant_gender": "0::Male", - "participant_name": "0::Daekwon Tobar", - "participant_relationship": null, - "participant_status": "0::Killed", - "participant_type": "0::Victim", - "sources": "http://wsbt.com/news/local/police-respond-to-shooting-in-south-bend", - "state_house_district": 7.0, - "state_senate_district": 10.0 - } - }, - { - "scenario_id": "165", - "features": { - "incident_id": 533170, - "date": "2016-03-31", - "state": "Missouri", - "city_or_county": "Saint Louis", - "address": "1700 block of Andros Ct", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/533170", - "source_url": "http://www.kmov.com/story/31622905/man-fatally-shot-in-ferguson-late-thursday-night-police-say", - "incident_url_fields_missing": "False", - "congressional_district": 1.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", - "latitude": 38.7399, - "location_description": null, - "longitude": -90.2709, - "n_guns_involved": 1.0, - "notes": "38.739926, -90.270946", - "participant_age": "0::31", - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Killed", - "participant_type": "0::Victim", - "sources": "http://www.kmov.com/story/31622905/man-fatally-shot-in-ferguson-late-thursday-night-police-say", - "state_house_district": 75.0, - "state_senate_district": 14.0 - } - }, - { - "scenario_id": "172", - "features": { - "incident_id": 459184, - "date": "2015-12-04", - "state": "Louisiana", - "city_or_county": "Baton Rouge", - "address": "8300 block of Amy Drive", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/459184", - "source_url": "http://www.wbrz.com/news/husband-charged-in-wife-s-murder", - "incident_url_fields_missing": "False", - "congressional_district": 6.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Domestic Violence", - "latitude": 30.3527, - "location_description": null, - "longitude": -91.1317, - "n_guns_involved": null, - "notes": "Woman shot and killed in incident of domestic violence. Husband charged", - "participant_age": "0::27||1::27", - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Female||1::Male", - "participant_name": "0::Louisa Garcia||1::Juan Angel Delarca Martinez", - "participant_relationship": "1::Significant others - current or former", - "participant_status": "0::Killed||1::Arrested", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://www.ksla.com/story/30669337/deputies-investigate-shooting-on-amy-drive-in-baton-rouge||http://www.wbrz.com/news/husband-charged-in-wife-s-murder", - "state_house_district": 70.0, - "state_senate_district": 14.0 - } - }, - { - "scenario_id": "174", - "features": { - "incident_id": 217607, - "date": "2014-11-08", - "state": "Massachusetts", - "city_or_county": "Boston", - "address": "45 Bowdoin St", - "n_killed": 2, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/217607", - "source_url": "http://boston.homicidewatch.org/2014/11/21/audilia-daveiga-33-shot-and-killed-in-dorchester/", - "incident_url_fields_missing": "False", - "congressional_district": 8.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Suicide^||Murder/Suicide||Domestic Violence", - "latitude": 42.3597, - "location_description": null, - "longitude": -71.0631, - "n_guns_involved": null, - "notes": "1fvk", - "participant_age": "0::33||1::30", - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Female||1::Male", - "participant_name": "0::Audilia DaVeiga||1::Paulo Rosa", - "participant_relationship": "1::Significant others - current or former", - "participant_status": "0::Killed||1::Killed", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://www.boston.com/news/local/massachusetts/2014/11/08/woman-shot-death-dorchester-police-ask-for-public-help/0ilvz2JHLfCCvP2w1jcTWL/story.html||http://boston.homicidewatch.org/2014/11/21/audilia-daveiga-33-shot-and-killed-in-dorchester/", - "state_house_district": null, - "state_senate_district": null - } - }, - { - "scenario_id": "175", - "features": { - "incident_id": 1015232, - "date": "2017-11-21", - "state": "California", - "city_or_county": "Los Angeles", - "address": "625 S San Pedro St", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/1015232", - "source_url": "http://homicide.latimes.com/post/deshon-marquise-tave-riggins/", - "incident_url_fields_missing": "False", - "congressional_district": 34.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", - "latitude": 34.042, - "location_description": null, - "longitude": -118.246, - "n_guns_involved": 1.0, - "notes": null, - "participant_age": "0::22", - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": "0::Deshon Marquise Tave Riggins", - "participant_relationship": null, - "participant_status": "0::Killed", - "participant_type": "0::Victim", - "sources": "http://homicide.latimes.com/post/deshon-marquise-tave-riggins/", - "state_house_district": 53.0, - "state_senate_district": 30.0 - } - }, - { - "scenario_id": "178", - "features": { - "incident_id": 551162, - "date": "2016-05-01", - "state": "Missouri", - "city_or_county": "Saint Louis", - "address": "6000 block of W Florissant Ave", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/551162", - "source_url": "http://www.stltoday.com/news/local/crime-and-courts/man-fatally-shot-on-street-in-walnut-park-neighborhood/article_7c151041-e3cc-5a2b-9eb7-08a9feadac98.html", - "incident_url_fields_missing": "False", - "congressional_district": 1.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", - "latitude": 38.7087, - "location_description": null, - "longitude": -90.2515, - "n_guns_involved": 1.0, - "notes": "38.708687, -90.251536", - "participant_age": "0::33", - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": "0::Kendall White", - "participant_relationship": null, - "participant_status": "0::Killed", - "participant_type": "0::Victim", - "sources": "http://www.kmov.com/story/31859156/1-person-multiple-vehicles-hit-in-north-st-louis-shooting||http://www.kmov.com/story/31859781/homicide-investigation-conducted-after-a-man-is-shot-dead-in-north-st-louis||http://www.stltoday.com/news/local/crime-and-courts/man-fatally-shot-on-street-in-walnut-park-neighborhood/article_7c151041-e3cc-5a2b-9eb7-08a9feadac98.html", - "state_house_district": 76.0, - "state_senate_district": 4.0 - } - }, - { - "scenario_id": "180", - "features": { - "incident_id": 829331, - "date": "2017-04-25", - "state": "New Jersey", - "city_or_county": "Newark", - "address": "S Orange Ave and S 20th St", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/829331", - "source_url": "http://www.nj.com/essex/index.ssf/2017/04/23-year-old_killed_by_newark_detectives_identified.html#incart_river_index", - "incident_url_fields_missing": "False", - "congressional_district": 10.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Officer Involved Incident||Officer Involved Shooting - subject/suspect/perpetrator killed||Brandishing/flourishing/open carry/lost/found", - "latitude": 40.7435, - "location_description": null, - "longitude": -74.2092, - "n_guns_involved": 1.0, - "notes": "officer shot male in car", - "participant_age": "0::23", - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": "0::Hakeem McNair", - "participant_relationship": null, - "participant_status": "0::Killed", - "participant_type": "0::Subject-Suspect", - "sources": "http://abc7ny.com/news/1-dead-in-police-involved-shooting-in-newark-/1917603/||http://www.nj.com/essex/index.ssf/2017/04/23-year-old_killed_by_newark_detectives_identified.html#incart_river_index", - "state_house_district": 28.0, - "state_senate_district": 28.0 - } - }, - { - "scenario_id": "188", - "features": { - "incident_id": 435706, - "date": "2015-10-21", - "state": "South Carolina", - "city_or_county": "Charleston", - "address": "William Kennerty Drive", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/435706", - "source_url": "http://www.postandcourier.com/article/20151023/PC16/151029665", - "incident_url_fields_missing": "False", - "congressional_district": 1.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Domestic Violence", - "latitude": 32.8128, - "location_description": "Moss Creek Apartments", - "longitude": -80.0265, - "n_guns_involved": null, - "notes": null, - "participant_age": "0::22||1::20", - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Male||1::Female", - "participant_name": "0::Parrish Quantize Greene||1::Teonna Cooper", - "participant_relationship": "1::Significant others - current or former", - "participant_status": "0::Killed||1::Unharmed", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://www.postandcourier.com/article/20151023/PC16/151029665", - "state_house_district": 114.0, - "state_senate_district": 41.0 - } - }, - { - "scenario_id": "189", - "features": { - "incident_id": 983062, - "date": "2017-11-07", - "state": "District of Columbia", - "city_or_county": "Washington", - "address": "1300 block of Rittenhouse St", - "n_killed": 1, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/983062", - "source_url": "https://www.washingtonpost.com/local/public-safety/police-arrest-second-suspect-in-killing-of-teenager-on-rittenhouse-street/2017/12/01/4ba9c9ee-d692-11e7-a986-d0a9770d9a3e_story.html?utm_term=.e620d8836434", - "incident_url_fields_missing": "False", - "congressional_district": null, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Wounded/Injured||Shot - Dead (murder, accidental, suicide)||Drive-by (car to street, car to car)", - "latitude": 38.9654, - "location_description": null, - "longitude": -77.0297, - "n_guns_involved": 1.0, - "notes": "Approx 8pm, lookout for red Mustang", - "participant_age": "0::16||2::20||3::17", - "participant_age_group": "0::Teen 12-17||1::Adult 18+||2::Adult 18+||3::Teen 12-17", - "participant_gender": "0::Male||1::Male||2::Male||3::Male", - "participant_name": "0::Yoselis Regino Barrios||2::Kevin Sorto||3::Victor Hernandez", - "participant_relationship": null, - "participant_status": "0::Killed||1::Injured||2::Unharmed, Arrested||3::Unharmed, Arrested", - "participant_type": "0::Victim||1::Victim||2::Subject-Suspect||3::Subject-Suspect", - "sources": "https://twitter.com/DCPoliceDept/status/928074129080836096||https://www.washingtonpost.com/local/public-safety/man-is-gravely-wounded-in-double-shooting-in-the-district/2017/11/07/28de3670-c42c-11e7-afe9-4f60b5a6c4a0_story.html?utm_term=.906b4dc763c2||https://www.washingtonpost.com/local/public-safety/man-killed-another-wounded-in-drive-by-shooting-in-northwest-washington/2017/11/08/af546f8e-c47d-11e7-aae0-cb18a8c29c65_story.html?utm_term=.81971a2fdb7f||https://www.washingtonpost.com/local/public-safety/police-arrest-20-year-old-man-for-killing-district-teen/2017/11/22/1e825e40-cfe4-11e7-a1a3-0d1e45a6de3d_story.html?utm_term=.c1d47de9ddc6||http://wjla.com/news/local/wanted-dc-police-search-for-second-suspect-in-nw-homicide||https://www.washingtonpost.com/local/public-safety/police-arrest-second-suspect-in-killing-of-teenager-on-rittenhouse-street/2017/12/01/4ba9c9ee-d692-11e7-a986-d0a9770d9a3e_story.html?utm_term=.e620d8836434", - "state_house_district": null, - "state_senate_district": null - } - }, - { - "scenario_id": "190", - "features": { - "incident_id": 218138, - "date": "2014-11-09", - "state": "Texas", - "city_or_county": "Irving", - "address": "Airport Freeway", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/218138", - "source_url": "http://www.nbcdfw.com/news/local/Irving-Police-Kill-Man-in-Hotel-Parking-Lot-282095011.html", - "incident_url_fields_missing": "False", - "congressional_district": 24.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Officer Involved Incident||Officer Involved Shooting - subject/suspect/perpetrator killed", - "latitude": 32.8371, - "location_description": null, - "longitude": -96.9683, - "n_guns_involved": null, - "notes": "perp rammed a van and police shot him; Irving police investigating", - "participant_age": "0::38", - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": "0::Ashif Anwar", - "participant_relationship": null, - "participant_status": "0::Killed", - "participant_type": "0::Subject-Suspect", - "sources": "http://www.ksat.com/content/pns/ksat/news/2014/11/10/tx-police-shooting-irving.html||http://dfw.cbslocal.com/2014/11/10/irving-police-shoot-kill-man-wanted-on-felony-warrants/||http://www.nbcdfw.com/news/local/Irving-Police-Kill-Man-in-Hotel-Parking-Lot-282095011.html", - "state_house_district": 105.0, - "state_senate_district": 16.0 - } - }, - { - "scenario_id": "200", - "features": { - "incident_id": 158453, - "date": "2014-06-30", - "state": "Illinois", - "city_or_county": "Danville", - "address": "1200 block of Marion Street", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/158453", - "source_url": "http://www.illinoishomepage.net/story/d/story/2-shootings-investigated/32506/AwJGx4dtj0SdIWuaf_ZRhQ", - "incident_url_fields_missing": "False", - "congressional_district": 15.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", - "latitude": 40.1097, - "location_description": null, - "longitude": -87.6066, - "n_guns_involved": null, - "notes": "1mvk leg", - "participant_age": "0::30", - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": "0::Calvin Cowen", - "participant_relationship": null, - "participant_status": "0::Killed", - "participant_type": "0::Victim", - "sources": "http://www.illinoishomepage.net/story/d/story/2-shootings-investigated/32506/AwJGx4dtj0SdIWuaf_ZRhQ", - "state_house_district": 104.0, - "state_senate_district": 52.0 - } - } - ], - "target_column": "n_killed", - "task_sub_type": "regression", - "ranking_ground_truth": { - "top_k_ids": [ - "002", - "003", - "004", - "006", - "007", - "008", - "009", - "013", - "014", - "015", - "017", - "018", - "020", - "021", - "022" - ] - }, - "sort_direction": "high" - }, - "response": "", - "evaluation_score": {} -} \ No newline at end of file + "sort_direction": "low" + }, + "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/daily/regression/Gun_Violence_Data_B4/Gun_Violence_Data_B4_005/Gun_Violence_Data_B4_005_current.json b/ranking_and_filtering/daily/regression/Gun_Violence_Data_B4/Gun_Violence_Data_B4_005/Gun_Violence_Data_B4_005_current.json index 2148f679e009bedf6183cc0bc40e4599d712c43a..27f456fd11ee7f0dbd73dc831539e875ff5e6572 100644 --- a/ranking_and_filtering/daily/regression/Gun_Violence_Data_B4/Gun_Violence_Data_B4_005/Gun_Violence_Data_B4_005_current.json +++ b/ranking_and_filtering/daily/regression/Gun_Violence_Data_B4/Gun_Violence_Data_B4_005/Gun_Violence_Data_B4_005_current.json @@ -1,6599 +1,6599 @@ { - "id": "005", - "task_type": "B4", - "subtask_type": "filter", - "perspective": "data_holder", - "dataset_name": "Gun Violence Data", - "table_path": "output/Gun Violence Data_B4/Gun Violence Data_B4_005/test.csv", - "query": "Our legislative team is drafting targeted policy for state senate district 15. Using the historical logs I provided, can you identify the 3 upcoming incidents within that district in the current batch you project will have the lowest number of fatalities? We need to understand the baseline for successful interventions.", - "meta_info": { - "domain": "daily" + "id": "005", + "task_type": "B4", + "subtask_type": "filter", + "perspective": "data_holder", + "dataset_name": "Gun Violence Data", + "table_path": "output/Gun Violence Data_B4/Gun Violence Data_B4_005/test.csv", + "query": "Our legislative team is drafting targeted policy for state senate district 15. Using the historical logs I provided, can you identify the 3 upcoming incidents within that district in the current batch you project will have the lowest number of fatalities? We need to understand the baseline for successful interventions.", + "meta_info": { + "domain": "daily" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "incident_id": 136154, + "date": "2014-05-18", + "state": "South Carolina", + "city_or_county": "Columbia", + "address": "7400 block of Garners Ferry Road", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/136154", + "source_url": "http://www.wistv.com/story/25547556/columbia-police-investigating-second-homicide-in-two-weeks", + "incident_url_fields_missing": "False", + "congressional_district": 2.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Accidental Shooting||Accidental/Negligent Discharge", + "latitude": 33.9693, + "location_description": null, + "longitude": -80.9464, + "n_guns_involved": null, + "notes": null, + "participant_age": "1::19", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": "1::Nicholas Reed Moses", + "participant_relationship": null, + "participant_status": "0::Killed||1::Unharmed", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://www.wistv.com/story/25547556/columbia-police-investigating-second-homicide-in-two-weeks", + "state_house_district": 70.0, + "state_senate_district": 21.0 + } + }, + { + "scenario_id": "002", + "features": { + "incident_id": 894730, + "date": "2017-07-19", + "state": "Illinois", + "city_or_county": "Chicago", + "address": "5300 block of S Winchester", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/894730", + "source_url": "http://chicago.suntimes.com/news/17-year-old-boy-shot-in-back-of-the-yards-2/", + "incident_url_fields_missing": "False", + "congressional_district": 7.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Wounded/Injured", + "latitude": 41.7975, + "location_description": "Back of the Yards neighborhood", + "longitude": -87.6735, + "n_guns_involved": 1.0, + "notes": "1 of 2 walk-up attackers fires on teen; leg, stable;", + "participant_age": "0::17", + "participant_age_group": "0::Teen 12-17", + "participant_gender": "0::Male||1::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured||1::Unharmed", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://chicago.suntimes.com/news/17-year-old-boy-shot-in-back-of-the-yards-2/", + "state_house_district": 6.0, + "state_senate_district": 3.0 + } + }, + { + "scenario_id": "003", + "features": { + "incident_id": 995340, + "date": "2017-11-24", + "state": "Tennessee", + "city_or_county": "Chattanooga (East Ridge)", + "address": "Edwin Ln", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/995340", + "source_url": "http://www.wrcbtv.com/story/36923104/east-ridge-shooting-suspect-at-large", + "incident_url_fields_missing": "False", + "congressional_district": 3.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Wounded/Injured", + "latitude": 34.9999, + "location_description": null, + "longitude": -85.2628, + "n_guns_involved": 1.0, + "notes": "one shot", + "participant_age": null, + "participant_age_group": null, + "participant_gender": null, + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured", + "participant_type": "0::Victim", + "sources": "http://www.wrcbtv.com/story/36923104/east-ridge-shooting-suspect-at-large", + "state_house_district": 30.0, + "state_senate_district": 10.0 + } + }, + { + "scenario_id": "004", + "features": { + "incident_id": 812123, + "date": "2017-03-30", + "state": "Texas", + "city_or_county": "Lubbock", + "address": "1700 block of Ave R", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/812123", + "source_url": "http://www.kcbd.com/story/35059618/large-quantity-of-meth-and-illegal-firearms-discovered-at-two-lubbock-homes", + "incident_url_fields_missing": "False", + "congressional_district": 19.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Non-Shooting Incident||Drug involvement||ATF/LE Confiscation/Raid/Arrest||Possession (gun(s) found during commission of other crimes)||Possession of gun by felon or prohibited person", + "latitude": 33.5597, + "location_description": null, + "longitude": -101.856, + "n_guns_involved": 1.0, + "notes": "Drug involvement. Many illegal firearms found. Drugs and guns also found at 5300 block of 31st St. in Lubbock, TX on Mar. 30, 2017.", + "participant_age": "0::38", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Daniel Botello", + "participant_relationship": null, + "participant_status": "0::Unharmed, Arrested", + "participant_type": "0::Subject-Suspect", + "sources": "http://www.kcbd.com/story/35059618/large-quantity-of-meth-and-illegal-firearms-discovered-at-two-lubbock-homes", + "state_house_district": null, + "state_senate_district": 28.0 + } + }, + { + "scenario_id": "005", + "features": { + "incident_id": 943278, + "date": "2017-09-26", + "state": "Florida", + "city_or_county": "Boca Raton", + "address": "1400 Block of W Glades Rd", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/943278", + "source_url": "http://www.palmbeachpost.com/news/crime--law/south-blotter-police-put-wrinkle-woman-attempt-steal-234-worth-face-cream/YnVkFBW99WewDGXTeo3VjP/", + "incident_url_fields_missing": "False", + "congressional_district": null, + "gun_stolen": "0::Stolen", + "gun_type": "0::Unknown", + "incident_characteristics": "Non-Shooting Incident||Gun(s) stolen from owner", + "latitude": 26.3659, + "location_description": null, + "longitude": -80.1123, + "n_guns_involved": 1.0, + "notes": "unclear date; blotter", + "participant_age": null, + "participant_age_group": null, + "participant_gender": null, + "participant_name": null, + "participant_relationship": null, + "participant_status": null, + "participant_type": null, + "sources": "http://www.palmbeachpost.com/news/crime--law/south-blotter-police-put-wrinkle-woman-attempt-steal-234-worth-face-cream/YnVkFBW99WewDGXTeo3VjP/", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "006", + "features": { + "incident_id": 770294, + "date": "2017-02-07", + "state": "Texas", + "city_or_county": "Edna", + "address": "200 block of E Church St", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/770294", + "source_url": "https://www.victoriaadvocate.com/news/2017/may/26/attorneys-analyze-charges-against-edna-woman/", + "incident_url_fields_missing": "False", + "congressional_district": 27.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::40 SW", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Domestic Violence", + "latitude": 28.9774, + "location_description": null, + "longitude": -96.6437, + "n_guns_involved": 1.0, + "notes": "shot boyfriend in chest, self-def claim", + "participant_age": "0::33||1::35", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Female", + "participant_name": "0::Jarrett Parker||1::Amber Nicole Sorensen", + "participant_relationship": "1::Significant others - current or former", + "participant_status": "0::Killed||1::Unharmed, Arrested", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://www.crossroadstoday.com/story/34450748/edna-pd-edna-man-shot-and-killed-by-girlfriend||https://www.victoriaadvocate.com/news/2017/may/26/attorneys-analyze-charges-against-edna-woman/", + "state_house_district": null, + "state_senate_district": 18.0 + } + }, + { + "scenario_id": "007", + "features": { + "incident_id": 574315, + "date": "2016-06-07", + "state": "Texas", + "city_or_county": "Richmond", + "address": "800 block of Maiden Lane", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/574315", + "source_url": "http://www.khou.com/news/crime/1-dead-another-arrested-in-fatal-shooting-in-richmond/235578672", + "incident_url_fields_missing": "False", + "congressional_district": 22.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::223 Rem [AR-15]", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 29.5852, + "location_description": null, + "longitude": -95.7673, + "n_guns_involved": 1.0, + "notes": "1 killed.", + "participant_age": "1::25", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": "1::Travion Depaul Fountain", + "participant_relationship": null, + "participant_status": "0::Killed||1::Unharmed, Arrested", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://thepolicenews.net/default.aspx?act=Newsletter.aspx&category=News+1-2&newsletterid=61382&menugroup=Home||http://www.khou.com/news/crime/1-dead-another-arrested-in-fatal-shooting-in-richmond/235578672", + "state_house_district": 85.0, + "state_senate_district": 18.0 + } + }, + { + "scenario_id": "008", + "features": { + "incident_id": 1041154, + "date": "2018-01-13", + "state": "District of Columbia", + "city_or_county": "Washington", + "address": "4700 block of S Capitol St SE", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/1041154", + "source_url": "https://mpdc.dc.gov/release/suspect-sought-armed-carjacking-gun-offense-4700-block-south-capitol-street-southeast", + "incident_url_fields_missing": "False", + "congressional_district": null, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Non-Shooting Incident||Car-jacking||Brandishing/flourishing/open carry/lost/found", + "latitude": 38.8218, + "location_description": null, + "longitude": -77.0012, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": null, + "participant_age_group": null, + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Unharmed", + "participant_type": "0::Subject-Suspect", + "sources": "https://mpdc.dc.gov/release/suspect-sought-armed-carjacking-gun-offense-4700-block-south-capitol-street-southeast", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "009", + "features": { + "incident_id": 165402, + "date": "2014-01-17", + "state": "Texas", + "city_or_county": "Corpus Christi", + "address": "802 Barry St.", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/165402", + "source_url": "http://ccpdblotter.com/2014/01/17/patrol-report-january-17-2014/", + "incident_url_fields_missing": "False", + "congressional_district": 27.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Armed robbery with injury/death and/or evidence of DGU found", + "latitude": 27.7297, + "location_description": null, + "longitude": -97.3786, + "n_guns_involved": null, + "notes": "ar, Corpus Christi police investigating", + "participant_age": "0::21", + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+", + "participant_gender": "0::Female||1::Male||2::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Unharmed||1::Unharmed||2::Unharmed", + "participant_type": "0::Victim||1::Subject-Suspect||2::Subject-Suspect", + "sources": "http://ccpdblotter.com/2014/01/17/patrol-report-january-17-2014/", + "state_house_district": 34.0, + "state_senate_district": 20.0 + } + }, + { + "scenario_id": "010", + "features": { + "incident_id": 194534, + "date": "2014-09-20", + "state": "Louisiana", + "city_or_county": "New Orleans", + "address": "7200 block of Lake Willow Drive ", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/194534", + "source_url": "http://www.nola.com/crime/index.ssf/2014/09/2_women_robbed_at_gunpoint_in.html#incart_river", + "incident_url_fields_missing": "False", + "congressional_district": 2.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Armed robbery with injury/death and/or evidence of DGU found", + "latitude": 30.0326, + "location_description": null, + "longitude": -90.0013, + "n_guns_involved": null, + "notes": "2 women robbed at gunpoint", + "participant_age": "0::29||1::30", + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+", + "participant_gender": "0::Female||1::Female||2::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Unharmed||1::Unharmed||2::Unharmed", + "participant_type": "0::Victim||1::Victim||2::Subject-Suspect", + "sources": "http://www.nola.com/crime/index.ssf/2014/09/2_women_robbed_at_gunpoint_in.html#incart_river", + "state_house_district": 99.0, + "state_senate_district": 4.0 + } + }, + { + "scenario_id": "011", + "features": { + "incident_id": 804280, + "date": "2017-03-27", + "state": "South Carolina", + "city_or_county": "Anderson", + "address": "Cloverhill Drive", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/804280", + "source_url": "http://www.live5news.com/story/35005647/coroner-responding-to-anderson-co-shooting", + "incident_url_fields_missing": "False", + "congressional_district": 3.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Domestic Violence", + "latitude": 34.447, + "location_description": null, + "longitude": -82.6299, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": "0::23", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male||1::Female", + "participant_name": "0::Otto Thomas Mahsetky Jr.", + "participant_relationship": "1::Family", + "participant_status": "0::Killed", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://www.wtvm.com/story/35005647/coroner-responding-to-anderson-co-shooting||http://www.live5news.com/story/35005647/coroner-responding-to-anderson-co-shooting", + "state_house_district": 7.0, + "state_senate_district": 4.0 + } + }, + { + "scenario_id": "012", + "features": { + "incident_id": 138536, + "date": "2014-05-24", + "state": "Illinois", + "city_or_county": "Chicago", + "address": "500 North Laramie Avenue", + "n_killed": 0, + "n_injured": 2, + "incident_url": "http://www.gunviolencearchive.org/incident/138536", + "source_url": "http://www.dnainfo.com/chicago/20140525/austin/one-dead-seven-wounded-overnight-shootings", + "incident_url_fields_missing": "False", + "congressional_district": 7.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured||Possession (gun(s) found during commission of other crimes)", + "latitude": 41.8894, + "location_description": null, + "longitude": -87.7555, + "n_guns_involved": null, + "notes": "2 shot/injured by walk-up gunman.", + "participant_age": "0::19||1::20", + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+", + "participant_gender": "0::Male||1::Male||2::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured||1::Injured||2::Unharmed", + "participant_type": "0::Victim||1::Victim||2::Subject-Suspect", + "sources": "http://www.dnainfo.com/chicago/20140525/austin/one-dead-seven-wounded-overnight-shootings", + "state_house_district": 8.0, + "state_senate_district": 4.0 + } + }, + { + "scenario_id": "013", + "features": { + "incident_id": 525944, + "date": "2016-03-19", + "state": "Texas", + "city_or_county": "Palestine", + "address": "300 block of Anderson County Road 1370", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/525944", + "source_url": "http://thepolicenews.net/default.aspx?act=Newsletter.aspx&category=News+1-2&newsletterid=60061&menugroup=Home", + "incident_url_fields_missing": "False", + "congressional_district": 5.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Rifle", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Non-Aggression Incident||Accidental Shooting||Accidental Shooting - Death||Accidental/Negligent Discharge||Child Involved Incident||Child picked up & fired gun", + "latitude": 31.7482, + "location_description": null, + "longitude": -95.6811, + "n_guns_involved": 1.0, + "notes": "Child picked up a rifle while in the room with an adult relative who was cleaning weapons. The gun discharged through door, fatally struck adult female.", + "participant_age": "0::23||1::8", + "participant_age_group": "0::Adult 18+||1::Child 0-11", + "participant_gender": "0::Female||1::Male", + "participant_name": "0::Carmen Danielle Morris", + "participant_relationship": "1::Family", + "participant_status": "0::Killed||1::Unharmed", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://www.palestineherald.com/news/child-accidentally-shoots-relative/article_7fc4acb4-ef97-11e5-b045-b715027a6a24.html||http://www.palestineherald.com/news/year-old-mother-dies-in-accidental-shooting/article_19fc8d50-efc6-11e5-b598-9fcdf6d034bd.html||http://thepolicenews.net/default.aspx?act=Newsletter.aspx&category=News+1-2&newsletterid=60061&menugroup=Home", + "state_house_district": 8.0, + "state_senate_district": 3.0 + } + }, + { + "scenario_id": "014", + "features": { + "incident_id": 800576, + "date": "2017-03-22", + "state": "Ohio", + "city_or_county": "Cincinnati (Lockland)", + "address": "619 W Wyoming Ave", + "n_killed": 1, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/800576", + "source_url": "http://local12.com/news/local/investigators-arrest-lockland-murder-suspect-in-mississippi", + "incident_url_fields_missing": "False", + "congressional_district": null, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Wounded/Injured||Shot - Dead (murder, accidental, suicide)", + "latitude": 39.2269, + "location_description": null, + "longitude": -84.4619, + "n_guns_involved": 1.0, + "notes": "Man killed, 2nd victim wounded; 2 men being questioned; dead man's best friend killed at same address in 2014; Myles arrested in MS 3/19/18", + "participant_age": "0::33||1::22", + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+||3::Adult 18+", + "participant_gender": "0::Male||1::Male||2::Male||3::Male", + "participant_name": "0::Ricky Julius Peeper||1::Tony Rembert Jr.||2::Gerry Myles", + "participant_relationship": null, + "participant_status": "0::Killed||1::Injured||2::Unharmed, Arrested||3::Unharmed", + "participant_type": "0::Victim||1::Victim||2::Subject-Suspect||3::Subject-Suspect", + "sources": "http://www.wlwt.com/article/police-respond-to-reports-of-2-shot-in-lockland/9170182||http://www.fox19.com/story/34975947/police-respond-to-lockland-shooting||http://www.wlwt.com/article/police-respond-to-reports-of-2-shot-in-lockland/9170182||http://www.wcpo.com/news/crime/pd-double-shooting-in-lockland-kills-man-injures-another||http://local12.com/news/local/investigators-arrest-lockland-murder-suspect-in-mississippi", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "015", + "features": { + "incident_id": 376789, + "date": "2015-07-17", + "state": "Massachusetts", + "city_or_county": "Boston", + "address": "Normandy and Stanwood Street", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/376789", + "source_url": "http://bpdnews.com/news/2015/7/18/one-less-gun-officers-recover-loaded-firearm-from-suspect-during-search-warrant-in-roxbury", + "incident_url_fields_missing": "False", + "congressional_district": 7.0, + "gun_stolen": "0::Stolen", + "gun_type": "0::9mm", + "incident_characteristics": "Non-Shooting Incident||Drug involvement||ATF/LE Confiscation/Raid/Arrest||Possession (gun(s) found during commission of other crimes)||Stolen/Illegally owned gun{s} recovered during arrest/warrant", + "latitude": 42.3093, + "location_description": null, + "longitude": -71.0803, + "n_guns_involved": 1.0, + "notes": "Roxbury, drugs and S&W M9 9mm stolen in 2013", + "participant_age": "0::24", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Christopher Bonaventure", + "participant_relationship": null, + "participant_status": "0::Unharmed, Arrested", + "participant_type": "0::Subject-Suspect", + "sources": "http://bpdnews.com/news/2015/7/18/one-less-gun-officers-recover-loaded-firearm-from-suspect-during-search-warrant-in-roxbury", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "016", + "features": { + "incident_id": 1053532, + "date": "2018-01-13", + "state": "Colorado", + "city_or_county": "Grand Junction", + "address": "400 block of Chatfield Ln", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/1053532", + "source_url": "https://www.gjsentinel.com/news/blotter/blotter-feb/article_eeaffd78-1479-11e8-a373-10604b9f6eda.html", + "incident_url_fields_missing": "False", + "congressional_district": 3.0, + "gun_stolen": "0::Stolen||1::Stolen", + "gun_type": "0::Unknown||1::223 Rem [AR-15]", + "incident_characteristics": "Non-Shooting Incident||Unlawful purchase/sale||Gun(s) stolen from owner||Stolen/Illegally owned gun{s} recovered during arrest/warrant||Assault weapon (AR-15, AK-47, and ALL variants defined by law enforcement)", + "latitude": 39.0706, + "location_description": null, + "longitude": -108.466, + "n_guns_involved": 2.0, + "notes": "Guns stolen from vehicle; gun illegally sold; original owner recovered rifle.", + "participant_age": "0::19", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Michael Alexander Stewart", + "participant_relationship": null, + "participant_status": "0::Unharmed, Arrested", + "participant_type": "0::Subject-Suspect", + "sources": "https://www.gjsentinel.com/news/blotter/blotter-feb/article_eeaffd78-1479-11e8-a373-10604b9f6eda.html", + "state_house_district": 55.0, + "state_senate_district": 7.0 + } + }, + { + "scenario_id": "017", + "features": { + "incident_id": 710315, + "date": "2016-11-23", + "state": "New York", + "city_or_county": "Brooklyn", + "address": "Williams Avenue", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/710315", + "source_url": "http://www.nydailynews.com/new-york/nyc-crime/man-shot-dead-bronx-wounded-brooklyn-article-1.2886178", + "incident_url_fields_missing": "False", + "congressional_district": 8.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Wounded/Injured", + "latitude": 40.6627, + "location_description": "East New York", + "longitude": -73.8974, + "n_guns_involved": 1.0, + "notes": "near Livonia Ave, shot in ankle", + "participant_age": "0::19", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured", + "participant_type": "0::Victim", + "sources": "http://www.nydailynews.com/new-york/nyc-crime/man-shot-dead-bronx-wounded-brooklyn-article-1.2886178", + "state_house_district": 60.0, + "state_senate_district": 19.0 + } + }, + { + "scenario_id": "019", + "features": { + "incident_id": 724991, + "date": "2016-12-12", + "state": "California", + "city_or_county": "Ventura", + "address": "500 E Harbor Blvd", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/724991", + "source_url": "http://www.vcstar.com/story/news/local/communities/ventura/2016/12/13/homeless-man-arrested-after-shots-fired-ventura/95365066/", + "incident_url_fields_missing": "False", + "congressional_district": 24.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shots Fired - No Injuries", + "latitude": 34.2765, + "location_description": null, + "longitude": -119.291, + "n_guns_involved": 1.0, + "notes": "34.276899, -119.293099", + "participant_age": "0::45", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::John Eldridge", + "participant_relationship": null, + "participant_status": "0::Unharmed, Arrested", + "participant_type": "0::Subject-Suspect", + "sources": "http://www.vcstar.com/story/news/local/communities/ventura/2016/12/13/homeless-man-arrested-after-shots-fired-ventura/95365066/", + "state_house_district": 37.0, + "state_senate_district": 19.0 + } + }, + { + "scenario_id": "020", + "features": { + "incident_id": 648515, + "date": "2016-08-31", + "state": "Tennessee", + "city_or_county": "Athens", + "address": "Cook Drive", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/648515", + "source_url": "http://www.dailypostathenian.com/news/article_96395653-1c82-55c4-8c8b-2b65a3d16e7c.html", + "incident_url_fields_missing": "False", + "congressional_district": 3.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shots Fired - No Injuries", + "latitude": 35.4434, + "location_description": "Cook Park", + "longitude": -84.6022, + "n_guns_involved": 1.0, + "notes": "shots fired", + "participant_age": null, + "participant_age_group": null, + "participant_gender": null, + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Unharmed, Arrested||1::Unharmed, Arrested", + "participant_type": "0::Subject-Suspect||1::Subject-Suspect", + "sources": "http://www.dailypostathenian.com/news/article_96395653-1c82-55c4-8c8b-2b65a3d16e7c.html", + "state_house_district": 23.0, + "state_senate_district": 9.0 + } + }, + { + "scenario_id": "021", + "features": { + "incident_id": 179749, + "date": "2014-08-21", + "state": "Arkansas", + "city_or_county": "Yellville", + "address": "102 Highway 62 E", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/179749", + "source_url": "http://www.ky3.com/news/local/three-arrested-after-armed-robbery-in-marion-county/21048998_27661014", + "incident_url_fields_missing": "False", + "congressional_district": 3.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Armed robbery with injury/death and/or evidence of DGU found||Possession (gun(s) found during commission of other crimes)||Institution/Group/Business", + "latitude": 36.226, + "location_description": null, + "longitude": -92.6828, + "n_guns_involved": null, + "notes": null, + "participant_age": "1::19||2::19||3::21", + "participant_age_group": "1::Adult 18+||2::Adult 18+||3::Adult 18+", + "participant_gender": "2::Male", + "participant_name": "1::Teal Hampton||2::David Wilson||3::Houston Dunsing", + "participant_relationship": null, + "participant_status": "1::Unharmed||2::Unharmed||3::Unharmed", + "participant_type": "0::Victim||1::Subject-Suspect||2::Subject-Suspect||3::Subject-Suspect", + "sources": "http://www.ky3.com/news/local/three-arrested-after-armed-robbery-in-marion-county/21048998_27661014", + "state_house_district": 99.0, + "state_senate_district": 17.0 + } + }, + { + "scenario_id": "022", + "features": { + "incident_id": 612782, + "date": "2016-07-24", + "state": "Virginia", + "city_or_county": "Danville", + "address": "500 block of Henry Street", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/612782", + "source_url": "http://www.wdbj7.com/content/news/Danville-man--388177842.html", + "incident_url_fields_missing": "False", + "congressional_district": 5.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured", + "latitude": 36.596, + "location_description": null, + "longitude": -79.392, + "n_guns_involved": null, + "notes": null, + "participant_age": "0::23", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured", + "participant_type": "0::Victim", + "sources": "http://www.wdbj7.com/content/news/Danville-man--388177842.html", + "state_house_district": 14.0, + "state_senate_district": 20.0 + } + }, + { + "scenario_id": "023", + "features": { + "incident_id": 827827, + "date": "2017-04-24", + "state": "New York", + "city_or_county": "Rochester", + "address": "Maple Street", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/827827", + "source_url": "http://www.whec.com/news/man-shot-rochester-west-side/4462291/?cat=565", + "incident_url_fields_missing": "False", + "congressional_district": 25.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Wounded/Injured", + "latitude": 43.1552, + "location_description": null, + "longitude": -77.644, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": "0::28", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured", + "participant_type": "0::Victim", + "sources": "http://www.whec.com/news/man-shot-rochester-west-side/4462291/?cat=565", + "state_house_district": 137.0, + "state_senate_district": 56.0 + } + }, + { + "scenario_id": "024", + "features": { + "incident_id": 420052, + "date": "2015-09-23", + "state": "Indiana", + "city_or_county": "Charlestown", + "address": "Marshall Drive", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/420052", + "source_url": "http://www.newsandtribune.com/news/charlestown-man-in-jail-for-allegedly-test-firing-gun/article_0bfa5bb2-639c-11e5-aad3-ef3a5c645ff0.html", + "incident_url_fields_missing": "False", + "congressional_district": 9.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Handgun", + "incident_characteristics": "Shots Fired - No Injuries||Officer Involved Incident||Officer Involved Incident - Weapon involved but no shots fired||Possession (gun(s) found during commission of other crimes)", + "latitude": 38.4464, + "location_description": null, + "longitude": -85.6716, + "n_guns_involved": 1.0, + "notes": "Man held w/o bond on charge of firing gun behind home where children lived; gun, magazine recovered", + "participant_age": "0::19", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Diaundre Newby", + "participant_relationship": null, + "participant_status": "0::Unharmed, Arrested", + "participant_type": "0::Subject-Suspect", + "sources": "http://www.newsandtribune.com/news/charlestown-man-in-jail-for-allegedly-test-firing-gun/article_0bfa5bb2-639c-11e5-aad3-ef3a5c645ff0.html", + "state_house_district": 66.0, + "state_senate_district": 45.0 + } + }, + { + "scenario_id": "026", + "features": { + "incident_id": 942114, + "date": "2017-09-25", + "state": "Illinois", + "city_or_county": "Chicago", + "address": "1100 S Troy St", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/942114", + "source_url": "http://chicago.suntimes.com/news/man-hurt-in-lawndale-shooting-3/", + "incident_url_fields_missing": "False", + "congressional_district": null, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Wounded/Injured", + "latitude": null, + "location_description": "(Lawndale)", + "longitude": null, + "n_guns_involved": 1.0, + "notes": "11:09 Walk-up shooter hits man in parked car, both legs, serious;", + "participant_age": "0::32", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured||1::Unharmed", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://chicago.suntimes.com/news/man-hurt-in-lawndale-shooting-3/", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "027", + "features": { + "incident_id": 434070, + "date": "2015-10-16", + "state": "Louisiana", + "city_or_county": "Shreveport", + "address": "8400 block of Linwood Avenue", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/434070", + "source_url": "http://www.ksla.com/story/30309853/services-set-this-week-for-slain-security-guard", + "incident_url_fields_missing": "False", + "congressional_district": 4.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Institution/Group/Business||Officer Involved Incident||Officer Involved Shooting - Officer killed||Armed robbery with injury/death and/or evidence of DGU found", + "latitude": 32.4272, + "location_description": "Thrifty Liquor", + "longitude": -93.7634, + "n_guns_involved": null, + "notes": "Man enters store where victim was working security and shoots him.", + "participant_age": "0::56", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": "0::Michael Wayne Henry", + "participant_relationship": "1::Armed Robbery", + "participant_status": "0::Killed||1::Unharmed", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://www.ksla.com/story/30309853/services-set-this-week-for-slain-security-guard", + "state_house_district": 3.0, + "state_senate_district": 39.0 + } + }, + { + "scenario_id": "028", + "features": { + "incident_id": 141973, + "date": "2014-06-01", + "state": "Iowa", + "city_or_county": "Coalville", + "address": "Old Mill Road", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/141973", + "source_url": "http://www.kcci.com/news/two-men-seriously-injured-after-attempted-robbery/26280058#!TFoVI", + "incident_url_fields_missing": "False", + "congressional_district": 4.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured||Home Invasion||Home Invasion - subject/suspect/perpetrator injured||Defensive Use", + "latitude": 42.4452, + "location_description": null, + "longitude": -94.1258, + "n_guns_involved": null, + "notes": "hi 1mpi; Victim was stabbed", + "participant_age": null, + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Unharmed||1::Injured", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://www.kcci.com/news/two-men-seriously-injured-after-attempted-robbery/26280058#!TSaGt||http://www.kcci.com/news/two-men-seriously-injured-after-attempted-robbery/26280058#!TFoVI", + "state_house_district": 48.0, + "state_senate_district": 24.0 + } + }, + { + "scenario_id": "029", + "features": { + "incident_id": 999338, + "date": "2017-12-01", + "state": "Tennessee", + "city_or_county": "Chattanooga", + "address": "3900 block of Dodds Ave", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/999338", + "source_url": "http://www.wrcbtv.com/story/36974869/shooting-victim-shows-up-at-local-hospital-cpd-investigating", + "incident_url_fields_missing": "False", + "congressional_district": 3.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Wounded/Injured", + "latitude": 34.9974, + "location_description": null, + "longitude": -85.2832, + "n_guns_involved": 1.0, + "notes": "person shot showed up at local hospital", + "participant_age": null, + "participant_age_group": null, + "participant_gender": null, + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured", + "participant_type": "0::Victim", + "sources": "http://www.wrcbtv.com/story/36974869/shooting-victim-shows-up-at-local-hospital-cpd-investigating", + "state_house_district": 30.0, + "state_senate_district": 10.0 + } + }, + { + "scenario_id": "030", + "features": { + "incident_id": 587716, + "date": "2016-06-24", + "state": "Massachusetts", + "city_or_county": "Boston", + "address": "17 Doone Avenue", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/587716", + "source_url": null, + "incident_url_fields_missing": "False", + "congressional_district": 7.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Domestic Violence||Possession of gun by felon or prohibited person", + "latitude": 42.2779, + "location_description": null, + "longitude": -71.0827, + "n_guns_involved": 1.0, + "notes": "Mattapan, dv, shot multiple times, arrest Sept 2016", + "participant_age": "0::43||1::51", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": "0::Jermaine Good||1::Rogers Jordan", + "participant_relationship": null, + "participant_status": "0::Killed||1::Unharmed, Arrested", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://www.necn.com/news/new-england/Boston-Police-Mattapan-Shooting-384307711.html||http://www.wcvb.com/news/man-shot-in-boston/40211788||http://bpdnews.com/news/2016/7/6/update-victim-identified-in-death-investigation-in-the-area-of-17-doone-ave-mattapan||http://bpdnews.com/news/2016/9/27/update-arrest-made-in-connection-to-death-investigation-in-the-area-of-17-doone-avenue-in-mattapan", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "031", + "features": { + "incident_id": 674603, + "date": "2016-10-12", + "state": "Louisiana", + "city_or_county": "Harvey", + "address": "2200 block of Alex Kornman Boulevard", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/674603", + "source_url": "http://www.nola.com/crime/index.ssf/2016/10/caron_white_javon_lacrosse_har.html#incart_most-read_crime_article", + "incident_url_fields_missing": "False", + "congressional_district": null, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Drive-by (car to street, car to car)", + "latitude": null, + "location_description": null, + "longitude": null, + "n_guns_involved": 1.0, + "notes": "LB1SHOT, Jefferson Parish", + "participant_age": "0::22||1::17||2::18||3::20", + "participant_age_group": "0::Adult 18+||1::Teen 12-17||2::Adult 18+||3::Adult 18+||4::Adult 18+", + "participant_gender": "0::Male||1::Male||2::Male||3::Female||4::Male", + "participant_name": "0::Caron White||1::Jyrell Poole||2::Dequincy Batiste||3::Kendra Dupre||4::Javon \"Little Red\" Lacrosse", + "participant_relationship": null, + "participant_status": "0::Killed||1::Unharmed, Arrested||2::Unharmed, Arrested||3::Unharmed, Arrested||4::Unharmed", + "participant_type": "0::Victim||1::Subject-Suspect||2::Subject-Suspect||3::Subject-Suspect||4::Subject-Suspect", + "sources": "http://www.nola.com/crime/index.ssf/2016/10/man_shot_in_harvey_2.html#incart_river_index||http://www.nola.com/crime/index.ssf/2016/10/1_man_dead_following_harvey_sh.html||http://www.nola.com/crime/index.ssf/2016/10/victim_identified_in_wednesday.html#incart_river_index||http://www.nola.com/crime/index.ssf/2016/10/caron_white_javon_lacrosse_har.html#incart_most-read_crime_article", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "032", + "features": { + "incident_id": 427275, + "date": "2015-10-07", + "state": "District of Columbia", + "city_or_county": "Washington", + "address": "Martin Luther King Jr. Ave and Malcolm X Ave, SE", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/427275", + "source_url": "https://twitter.com/DCPoliceDept/status/651957167206043652", + "incident_url_fields_missing": "False", + "congressional_district": 1.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Non-Shooting Incident||Car-jacking", + "latitude": 38.8451, + "location_description": null, + "longitude": -76.9974, + "n_guns_involved": null, + "notes": null, + "participant_age": null, + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+", + "participant_gender": "1::Male||2::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Unharmed", + "participant_type": "0::Victim||1::Subject-Suspect||2::Subject-Suspect", + "sources": "https://twitter.com/DCPoliceDept/status/651957167206043652", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "033", + "features": { + "incident_id": 110779, + "date": "2014-02-28", + "state": "Florida", + "city_or_county": "Brevard County", + "address": null, + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/110779", + "source_url": "http://www.orlandosentinel.com/news/local/breakingnews/os-armed-man-brevard-courthouse-20140228,0,4008034.story", + "incident_url_fields_missing": "False", + "congressional_district": 8.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Officer Involved Incident||Officer Involved Shooting - subject/suspect/perpetrator killed", + "latitude": 28.2639, + "location_description": null, + "longitude": -80.7214, + "n_guns_involved": null, + "notes": null, + "participant_age": null, + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Robert Striffler", + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Victim", + "sources": "http://www.orlandosentinel.com/news/local/breakingnews/os-armed-man-brevard-courthouse-20140228,0,4008034.story||http://www.orlandosentinel.com/news/local/breakingnews/os-armed-man-brevard-courthouse-20140228,0,4008034.story", + "state_house_district": 52.0, + "state_senate_district": 16.0 + } + }, + { + "scenario_id": "034", + "features": { + "incident_id": 599772, + "date": "2016-07-07", + "state": "Nebraska", + "city_or_county": "Lincoln", + "address": "2940 N. 14th Street", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/599772", + "source_url": "http://www.1011now.com/content/news/Police-expected-to-provide-update-in-Kwik-Shop-Murder-Case--Watch-briefing-live-393541721.html", + "incident_url_fields_missing": "False", + "congressional_district": null, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Institution/Group/Business||Armed robbery with injury/death and/or evidence of DGU found", + "latitude": null, + "location_description": "Kwik Shop", + "longitude": null, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": "0::51||1::18", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": "0::Robert L. Hanna||1::Rodzela J. Jones", + "participant_relationship": "1::Armed Robbery", + "participant_status": "0::Killed||1::Unharmed", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://journalstar.com/news/local/911/shooting-victim-was-a-big-brother-to-many-family-says/article_b620c951-d912-553d-af1d-6d6e764fcfcd.html||http://www.ketv.com/news/police-clerk-shot-killed-during-lincoln-robbery/40397006||http://www.1011now.com/content/news/Police-expected-to-provide-update-in-Kwik-Shop-Murder-Case--Watch-briefing-live-393541721.html", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "035", + "features": { + "incident_id": 587635, + "date": "2016-06-23", + "state": "Pennsylvania", + "city_or_county": "Uniontown", + "address": "Millview Street", + "n_killed": 2, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/587635", + "source_url": "http://pittsburgh.cbslocal.com/2016/06/24/men-killed-in-uniontown-shooting-identified/", + "incident_url_fields_missing": "False", + "congressional_district": 9.0, + "gun_stolen": "0::Unknown||1::Unknown", + "gun_type": "0::Handgun||1::Handgun", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 39.9062, + "location_description": null, + "longitude": -79.7192, + "n_guns_involved": 2.0, + "notes": "poss multiple perps, 2 handguns recover", + "participant_age": "0::37||1::25", + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+", + "participant_gender": "0::Male||1::Male||2::Male", + "participant_name": "0::Corey Johnson||1::Meshach \"Shack\" Punter", + "participant_relationship": null, + "participant_status": "0::Killed||1::Killed||2::Unharmed", + "participant_type": "0::Victim||1::Victim||2::Subject-Suspect", + "sources": "http://pittsburgh.cbslocal.com/2016/06/24/men-killed-in-uniontown-shooting-identified/", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "036", + "features": { + "incident_id": 163683, + "date": "2014-07-24", + "state": "Florida", + "city_or_county": "Tampa", + "address": "Hanley Road and Jackson Springs Road", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/163683", + "source_url": "http://tbo.com/news/crime/deputies-seeking-suspects-in-armed-robbery-on-tampa-street-20140724/", + "incident_url_fields_missing": "False", + "congressional_district": 14.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Armed robbery with injury/death and/or evidence of DGU found", + "latitude": 28.0074, + "location_description": null, + "longitude": -82.5626, + "n_guns_involved": null, + "notes": "Armed men robbed multiple people at gunpoint", + "participant_age": null, + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Unharmed||1::Unharmed", + "participant_type": "0::Subject-Suspect||1::Subject-Suspect", + "sources": "http://tbo.com/news/crime/deputies-seeking-suspects-in-armed-robbery-on-tampa-street-20140724/||http://tbo.com/news/crime/deputies-seeking-suspects-in-armed-robbery-on-tampa-street-20140724/", + "state_house_district": 62.0, + "state_senate_district": 19.0 + } + }, + { + "scenario_id": "037", + "features": { + "incident_id": 178777, + "date": "2014-05-11", + "state": "Ohio", + "city_or_county": "Geneva", + "address": "OH-307 and Barnum Rd", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/178777", + "source_url": "http://www.starbeacon.com/news/man-gets-years-in-fatal-shooting/article_b8271960-a67e-11e4-9dcf-2f1a53602b05.html", + "incident_url_fields_missing": "False", + "congressional_district": 14.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 41.7631, + "location_description": null, + "longitude": -80.9215, + "n_guns_involved": 1.0, + "notes": "Man found shot, killed in ditch. Suspect in custody.", + "participant_age": "0::43||1::62", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": "0::Eugene A. Cray||1::Gertis J. Perry", + "participant_relationship": null, + "participant_status": "0::Killed||1::Unharmed", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://www.cleveland19.com/story/25503626/body-found-in-ashtabula-county-ditch-has-been-identified||http://www.starbeacon.com/news/man-gets-years-in-fatal-shooting/article_b8271960-a67e-11e4-9dcf-2f1a53602b05.html", + "state_house_district": 99.0, + "state_senate_district": 32.0 + } + }, + { + "scenario_id": "038", + "features": { + "incident_id": 552698, + "date": "2016-05-03", + "state": "Pennsylvania", + "city_or_county": "Philadelphia", + "address": "1300 block of Rush Street", + "n_killed": 0, + "n_injured": 2, + "incident_url": "http://www.gunviolencearchive.org/incident/552698", + "source_url": "http://6abc.com/news/police-cousins-were-selling-drugs-when-shot-in-north-philly/1319791/", + "incident_url_fields_missing": "False", + "congressional_district": 2.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured||Drug involvement", + "latitude": 39.9956, + "location_description": null, + "longitude": -75.1521, + "n_guns_involved": null, + "notes": "It is believed that the 2 cousins were selling drugs when shot.", + "participant_age": "0::16||1::20", + "participant_age_group": "0::Teen 12-17||1::Adult 18+", + "participant_gender": "0::Male||1::Female", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured||1::Injured", + "participant_type": "0::Victim||1::Victim", + "sources": "http://6abc.com/news/police-cousins-were-selling-drugs-when-shot-in-north-philly/1319791/", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "039", + "features": { + "incident_id": 1028728, + "date": "2018-01-16", + "state": "Missouri", + "city_or_county": "Springfield", + "address": "S Missouri Ave and W Elm St", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/1028728", + "source_url": "http://www.ky3.com/content/news/Attempted-burglary--469500093.html", + "incident_url_fields_missing": "False", + "congressional_district": null, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shots Fired - No Injuries||Defensive Use||Defensive Use - Victim stops crime||Defensive Use - Stand Your Ground/Castle Doctrine established||Defensive Use - Shots fired, no injury/death", + "latitude": 37.2052, + "location_description": null, + "longitude": -93.302, + "n_guns_involved": 1.0, + "notes": "Defensive shots fired during burglary.", + "participant_age": null, + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "1::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Unharmed||1::Unharmed, Arrested", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://www.ky3.com/content/news/Attempted-burglary--469500093.html", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "041", + "features": { + "incident_id": 500305, + "date": "2016-02-05", + "state": "Pennsylvania", + "city_or_county": "York", + "address": "US-30 and Memory Lane", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/500305", + "source_url": "http://www.pennlive.com/news/2016/02/police_search_for_suspect_who.html", + "incident_url_fields_missing": "False", + "congressional_district": 4.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shots Fired - No Injuries||Drive-by (car to street, car to car)", + "latitude": 39.9835, + "location_description": null, + "longitude": -76.6842, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": null, + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Unharmed", + "participant_type": "0::Subject-Suspect", + "sources": "http://www.ydr.com/story/news/crime/2016/02/05/police-car-shot-springettsbury-township/79904016/||http://www.pennlive.com/news/2016/02/police_search_for_suspect_who.html", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "042", + "features": { + "incident_id": 291662, + "date": "2015-02-09", + "state": "Wisconsin", + "city_or_county": "Mazomanie", + "address": "10359 County Road Y", + "n_killed": 2, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/291662", + "source_url": "http://host.madison.com/wsj/news/local/crime_and_courts/year-old-charged-with-mazomanie-murders-allegedly-admits-shootings/article_45c42722-625b-5b55-a015-b892c2c40246.html", + "incident_url_fields_missing": "False", + "congressional_district": 2.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Handgun", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 43.2324, + "location_description": null, + "longitude": -89.7835, + "n_guns_involved": 1.0, + "notes": "1 m vic 1 f vic killed ex-gf's sis and stepfather, poss upset over breakup", + "participant_age": "0::16||1::39||2::17", + "participant_age_group": "0::Teen 12-17||1::Adult 18+||2::Teen 12-17", + "participant_gender": "0::Female||1::Male||2::Male", + "participant_name": "0::Ariyl Brady||1::Chris Schwichtenberg||2::Dean M. Sutcliffe", + "participant_relationship": null, + "participant_status": "0::Killed||1::Killed||2::Unharmed, Arrested", + "participant_type": "0::Victim||1::Victim||2::Subject-Suspect", + "sources": "http://www.channel3000.com/news/police-2-shot-in-mazomanie/31187398||http://host.madison.com/wsj/news/local/crime_and_courts/mom-of-victim-ex-boyfriend-shot-and-killed-two-in/article_f1b59ad6-905e-5223-9703-c360ca6975ab.html||http://host.madison.com/wsj/news/local/crime_and_courts/year-old-charged-with-mazomanie-murders-allegedly-admits-shootings/article_45c42722-625b-5b55-a015-b892c2c40246.html", + "state_house_district": 81.0, + "state_senate_district": 27.0 + } + }, + { + "scenario_id": "043", + "features": { + "incident_id": 537352, + "date": "2016-04-09", + "state": "Ohio", + "city_or_county": "Dayton", + "address": "Interstate 75 and Edwin C. Moses Boulevard", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/537352", + "source_url": "http://www.whio.com/news/news/crime-law/police-man-hurt-in-crash-leaves-gun-behind/nq3J8/", + "incident_url_fields_missing": "False", + "congressional_district": 10.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Handgun", + "incident_characteristics": "Non-Shooting Incident||Officer Involved Incident||Officer Involved Incident - Weapon involved but no shots fired||Possession (gun(s) found during commission of other crimes)", + "latitude": 39.7568, + "location_description": null, + "longitude": -84.2069, + "n_guns_involved": 1.0, + "notes": "Man fleeing police after car crash left loaded handgun in vehicle;", + "participant_age": "0::24", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Stephan Nared-Marshall", + "participant_relationship": null, + "participant_status": "0::Unharmed, Arrested", + "participant_type": "0::Subject-Suspect", + "sources": "http://www.whio.com/news/news/crime-law/police-man-hurt-in-crash-leaves-gun-behind/nq3J8/", + "state_house_district": 39.0, + "state_senate_district": 5.0 + } + }, + { + "scenario_id": "044", + "features": { + "incident_id": 343127, + "date": "2015-05-18", + "state": "New Jersey", + "city_or_county": "Elizabeth", + "address": "Parker Road", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/343127", + "source_url": "http://newyork.cbslocal.com/2016/12/29/pennsylvania-new-jersey-murder-suspect/", + "incident_url_fields_missing": "False", + "congressional_district": 8.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::38 Spl", + "incident_characteristics": "Shot - 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No death or injury||Brandishing/flourishing/open carry/lost/found", + "latitude": 42.948, + "location_description": null, + "longitude": -71.902, + "n_guns_involved": 1.0, + "notes": "hi, threat with handgun and robbed at gunpoint, robbed Dublin home prior night", + "participant_age": "2::7||3::8||4::33", + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Child 0-11||3::Child 0-11||4::Adult 18+", + "participant_gender": "0::Female||1::Female||2::Female||3::Female||4::Male", + "participant_name": "4::Jason Eddy", + "participant_relationship": null, + "participant_status": "0::Unharmed||1::Unharmed||2::Unharmed||3::Unharmed||4::Unharmed, Arrested", + "participant_type": "0::Victim||1::Victim||2::Victim||3::Victim||4::Subject-Suspect", + "sources": "http://www.sentinelsource.com/news/local/man-arrested-in-connection-to-greenfield-home-invasion/article_9d03557f-5f61-5731-ae7e-e076761b8a52.html||http://www.sentinelsource.com/news/local/dublin-man-accused-of-robbing-two-homes-at-gunpoint/article_c8e112ed-3b4d-5b67-8dc0-fe7ae35534df.html||http://www.sentinelsource.com/news/local/affidavit-provides-details-on-greenfield-home-invasion/article_38d46623-a7e8-575e-b9ad-2af6647bed1d.html", + "state_house_district": null, + "state_senate_district": 9.0 + } + }, + { + "scenario_id": "049", + "features": { + "incident_id": 643243, + "date": "2016-08-30", + "state": "Kentucky", + "city_or_county": "Lexington", + "address": "175 North Locust Hill Dr", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/643243", + "source_url": "http://www.wkyt.com/content/news/391815791.html", + "incident_url_fields_missing": "False", + "congressional_district": 6.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - 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Unknown yet if connected", + "participant_age": null, + "participant_age_group": null, + "participant_gender": null, + "participant_name": null, + "participant_relationship": null, + "participant_status": null, + "participant_type": null, + "sources": "http://www.heraldnet.com/news/teen-injured-in-1-of-2-monday-night-shootings-in-everett/", + "state_house_district": 38.0, + "state_senate_district": 38.0 + } + }, + { + "scenario_id": "052", + "features": { + "incident_id": 404284, + "date": "2015-07-28", + "state": "Pennsylvania", + "city_or_county": "State College", + "address": "2535 Fox Hill Rd", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/404284", + "source_url": "http://blog.tsa.gov", + "incident_url_fields_missing": "False", + "congressional_district": 5.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::380 Auto", + "incident_characteristics": "Non-Shooting Incident||TSA Action", + "latitude": 40.8609, + "location_description": "UNV", + "longitude": -77.8364, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": null, + "participant_age_group": null, + "participant_gender": null, + "participant_name": null, + "participant_relationship": null, + "participant_status": null, + "participant_type": null, + "sources": "http://blog.tsa.gov", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "053", + "features": { + "incident_id": 569729, + "date": "2016-06-01", + "state": "Georgia", + "city_or_county": "Jeffersonville", + "address": "375 Watson Drive", + "n_killed": 2, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/569729", + "source_url": "http://www.northwestgeorgianews.com/associated_press/news/national/teenagers-found-shot-to-death-behind-twiggs-county-school/article_48bf32de-28c2-11e6-8666-2f6036162ee4.html", + "incident_url_fields_missing": "False", + "congressional_district": 8.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Suicide^||Murder/Suicide", + "latitude": 32.6872, + "location_description": "Twiggs County Comprehensive Middle-High School", + "longitude": -83.3472, + "n_guns_involved": 1.0, + "notes": "On school property late night, school out for summer - not school schooting", + "participant_age": "0::19||1::18", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Female||1::Male", + "participant_name": "0::Alexia D. Jackson||1::Knickolus B. Jones", + "participant_relationship": null, + "participant_status": "0::Killed||1::Killed", + "participant_type": "0::Victim||1::Victim", + "sources": "http://www.macon.com/news/local/crime/article81112092.html||http://www.northwestgeorgianews.com/associated_press/news/national/teenagers-found-shot-to-death-behind-twiggs-county-school/article_48bf32de-28c2-11e6-8666-2f6036162ee4.html", + "state_house_district": 144.0, + "state_senate_district": 26.0 + } + }, + { + "scenario_id": "054", + "features": { + "incident_id": 556580, + "date": "2016-05-09", + "state": "Wisconsin", + "city_or_county": "Milwaukee", + "address": "48th and Center", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/556580", + "source_url": "http://fox6now.com/2016/05/09/police-man-shot-wounded-near-48th-and-center-in-milwaukee/", + "incident_url_fields_missing": "False", + "congressional_district": 4.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Wounded/Injured", + "latitude": 43.0679, + "location_description": null, + "longitude": -87.9737, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": "0::22", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured||1::Unharmed", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://fox6now.com/2016/05/09/police-man-shot-wounded-near-48th-and-center-in-milwaukee/", + "state_house_district": 18.0, + "state_senate_district": 6.0 + } + }, + { + "scenario_id": "055", + "features": { + "incident_id": 568509, + "date": "2016-05-28", + "state": "Texas", + "city_or_county": "Lubbock", + "address": "300 block of Waco", + "n_killed": 0, + "n_injured": 2, + "incident_url": "http://www.gunviolencearchive.org/incident/568509", + "source_url": "http://www.everythinglubbock.com/news/local-news/mother-her-boyfriend-arrested-after-2-children-shot-with-bb-gun", + "incident_url_fields_missing": "False", + "congressional_district": 19.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured||Child Involved Incident||Child injured (not child shooter)||Domestic Violence||BB/Pellet/Replica gun", + "latitude": 33.5942, + "location_description": null, + "longitude": -101.867, + "n_guns_involved": null, + "notes": "Mother's BF arrested 4 shooting 11mo old & older child w/ BBs. Older child required hand surgery to remove BB. Mother arrested for failure 2 report felony", + "participant_age": "0::0||2::28", + "participant_age_group": "0::Child 0-11||1::Child 0-11||2::Adult 18+", + "participant_gender": "2::Male", + "participant_name": "2::Adrian Garcia-Guevara", + "participant_relationship": "2::Family", + "participant_status": "0::Injured||1::Injured||2::Arrested", + "participant_type": "0::Victim||1::Victim||2::Subject-Suspect", + "sources": "http://www.everythinglubbock.com/news/local-news/mother-her-boyfriend-arrested-after-2-children-shot-with-bb-gun", + "state_house_district": 84.0, + "state_senate_district": 28.0 + } + }, + { + "scenario_id": "056", + "features": { + "incident_id": 1072736, + "date": "2018-03-18", + "state": "New York", + "city_or_county": "Brooklyn", + "address": "E 98th St", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/1072736", + "source_url": "http://brooklyn.news12.com/story/37750466/police-probe-2-separate-overnight-shooting-in-brooklyn", + "incident_url_fields_missing": "False", + "congressional_district": null, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Wounded/Injured", + "latitude": null, + "location_description": null, + "longitude": null, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": null, + "participant_age_group": "0::Adult 18+", + "participant_gender": null, + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured", + "participant_type": "0::Victim", + "sources": "http://brooklyn.news12.com/story/37750466/police-probe-2-separate-overnight-shooting-in-brooklyn", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "057", + "features": { + "incident_id": 105214, + "date": "2014-02-08", + "state": "Illinois", + "city_or_county": "Chicago", + "address": " 7100 block of South South Shore Dr", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/105214", + "source_url": "http://www.chicagotribune.com/news/local/breaking/chi-man-shot-in-south-shore-neighborhood-20140208,0,7590447.story", + "incident_url_fields_missing": "False", + "congressional_district": 2.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured||Brandishing/flourishing/open carry/lost/found", + "latitude": 41.7662, + "location_description": null, + "longitude": -87.5629, + "n_guns_involved": null, + "notes": "Man in doorway shot by 2 men.", + "participant_age": "0::22", + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+", + "participant_gender": "0::Male||1::Male||2::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured||1::Unharmed||2::Unharmed", + "participant_type": "0::Victim||1::Subject-Suspect||2::Subject-Suspect", + "sources": "http://www.chicagotribune.com/news/local/breaking/chi-man-shot-in-south-shore-neighborhood-20140208,0,7590447.story", + "state_house_district": 25.0, + "state_senate_district": 13.0 + } + }, + { + "scenario_id": "058", + "features": { + "incident_id": 779134, + "date": "2017-02-20", + "state": "Texas", + "city_or_county": "Dallas", + "address": "9633 Ferris Branch Blvd", + "n_killed": 1, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/779134", + "source_url": "http://www.kjrh.com/news/local-news/dallas-murder-suspect-latwon-goff-arrested-in-tulsa", + "incident_url_fields_missing": "False", + "congressional_district": 32.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Wounded/Injured||Shot - Dead (murder, accidental, suicide)||Home Invasion||Home Invasion - Resident killed||Home Invasion - Resident injured||Armed robbery with injury/death and/or evidence of DGU found", + "latitude": 32.9038, + "location_description": "Wyndham on the Creek apartments", + "longitude": -96.7258, + "n_guns_involved": 1.0, + "notes": "Lake Highlands. vic shot in leg.", + "participant_age": "0::39||2::18", + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+", + "participant_gender": "0::Male||1::Female||2::Male", + "participant_name": "0::Jason Ali Edwards||2::Latwon Latrell Goff", + "participant_relationship": "2::Armed Robbery", + "participant_status": "0::Killed||1::Injured||2::Unharmed, Arrested", + "participant_type": "0::Victim||1::Victim||2::Subject-Suspect||3::Subject-Suspect", + "sources": "http://dfw.cbslocal.com/2017/02/20/1-dead-in-dallas-home-invasion/||http://www.dallasnews.com/news/lake-highlands/2017/02/20/1-dead-1-wounded-lake-highlands-home-invasion-shooting||http://www.nbcdfw.com/news/local/Teen-Suspected-Of-Deadly-Shooting-In-Lake-Highlands-Police-414797463.html||http://www.kjrh.com/news/local-news/dallas-murder-suspect-latwon-goff-arrested-in-tulsa", + "state_house_district": null, + "state_senate_district": 16.0 + } + }, + { + "scenario_id": "059", + "features": { + "incident_id": 977518, + "date": "2017-10-30", + "state": "Ohio", + "city_or_county": "Cleveland", + "address": "Rosedale Ave", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/977518", + "source_url": "http://www.wkyc.com/news/local/cuyahoga-county/teen-shot-dead-in-east-cleveland-monday-night/487529874", + "incident_url_fields_missing": "False", + "congressional_district": 11.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 41.5433, + "location_description": null, + "longitude": -81.5704, + "n_guns_involved": 1.0, + "notes": "approx 8:15 PM", + "participant_age": "0::18", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Steve Anthony Swain", + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Victim", + "sources": "http://www.wkyc.com/news/local/cuyahoga-county/teen-shot-dead-in-east-cleveland-monday-night/487529874", + "state_house_district": 8.0, + "state_senate_district": 25.0 + } + }, + { + "scenario_id": "060", + "features": { + "incident_id": 301532, + "date": "2015-03-05", + "state": "Michigan", + "city_or_county": "Flint", + "address": "210 E 5th St", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/301532", + "source_url": "http://www.mlive.com/news/flint/index.ssf/2015/03/flint_police_department_missin.html#incart_2box", + "incident_url_fields_missing": "False", + "congressional_district": 5.0, + "gun_stolen": "0::Unknown||1::Unknown||2::Unknown||3::Unknown||4::Unknown||5::Unknown||6::Unknown||7::Unknown||8::Unknown||9::Unknown||10::Unknown||11::Unknown||12::Unknown||13::Unknown||14::Unknown||15::Unknown||16::Unknown||17::Unknown||18::Unknown||19::Unknown||20::Unknown||21::Unknown||22::Unknown||23::Unknown||24::Unknown||25::Unknown||26::Unknown||27::Unknown||28::Unknown||29::Unknown||30::Unknown||31::Unknown||32::Unknown||33::Unknown||34::Unknown||35::Unknown||36::Unknown||37::Unknown||38::Unknown", + "gun_type": "0::Unknown||1::Unknown||2::Unknown||3::Unknown||4::Unknown||5::Unknown||6::Unknown||7::Unknown||8::Unknown||9::Unknown||10::Unknown||11::Unknown||12::Unknown||13::Unknown||14::Unknown||15::Unknown||16::Unknown||17::Unknown||18::Unknown||19::Unknown||20::Unknown||21::Unknown||22::Unknown||23::Unknown||24::Unknown||25::Unknown||26::Unknown||27::Unknown||28::Unknown||29::Unknown||30::Unknown||31::Unknown||32::Unknown||33::Unknown||34::Unknown||35::Unknown||36::Unknown||37::Unknown||38::Unknown", + "incident_characteristics": "Non-Shooting Incident||ATF/LE Confiscation/Raid/Arrest", + "latitude": 43.0119, + "location_description": null, + "longitude": -83.6862, + "n_guns_involved": 39.0, + "notes": "Flint Police Department missing weapons audit. Report published March 5", + "participant_age": null, + "participant_age_group": null, + "participant_gender": null, + "participant_name": null, + "participant_relationship": null, + "participant_status": null, + "participant_type": null, + "sources": "http://www.mlive.com/news/flint/index.ssf/2015/03/flint_police_department_missin.html#incart_2box", + "state_house_district": 49.0, + "state_senate_district": 27.0 + } + }, + { + "scenario_id": "061", + "features": { + "incident_id": 188069, + "date": "2014-09-02", + "state": "Indiana", + "city_or_county": "East Chicago", + "address": "1200 block of West 150th Street", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/188069", + "source_url": "http://www.nwitimes.com/news/local/trio-accused-of-robbing-e-c-residents-at-gunpoint/article_3e0b0584-f312-5cd3-ac1c-7beb70c5d7cb.html", + "incident_url_fields_missing": "False", + "congressional_district": 1.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Drug involvement||Home Invasion||Home Invasion - Resident injured||Armed robbery with injury/death and/or evidence of DGU found||Pistol-whipping", + "latitude": 41.6232, + "location_description": null, + "longitude": -87.4863, + "n_guns_involved": null, + "notes": "3 home invaders pistol-whip one, rob residents of apartment.", + "participant_age": "1::20||2::19||3::18", + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+||3::Adult 18+", + "participant_gender": "1::Female||2::Male||3::Male", + "participant_name": "1::Amanda Velasquez||2::Elijah Solis||3::Xavier Solis", + "participant_relationship": null, + "participant_status": "0::Injured||1::Unharmed||2::Unharmed||3::Unharmed", + "participant_type": "0::Victim||1::Subject-Suspect||2::Subject-Suspect||3::Subject-Suspect", + "sources": "http://www.nwitimes.com/news/local/trio-accused-of-robbing-e-c-residents-at-gunpoint/article_3e0b0584-f312-5cd3-ac1c-7beb70c5d7cb.html", + "state_house_district": 2.0, + "state_senate_district": 2.0 + } + }, + { + "scenario_id": "062", + "features": { + "incident_id": 270627, + "date": "2014-12-28", + "state": "Maryland", + "city_or_county": "Baltimore", + "address": "3000 block of E. Monument St.", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/270627", + "source_url": "http://www.baltimoresun.com/news/maryland/baltimore-city/bs-md-ci-police-involved-shooting-20141228-story.html", + "incident_url_fields_missing": "False", + "congressional_district": 7.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured||Officer Involved Incident||Officer Involved Shooting - subject/suspect/perpetrator shot", + "latitude": 39.2993, + "location_description": null, + "longitude": -76.5751, + "n_guns_involved": null, + "notes": null, + "participant_age": "0::45", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured", + "participant_type": "0::Subject-Suspect", + "sources": "http://www.baltimoresun.com/news/maryland/baltimore-city/bs-md-ci-police-involved-shooting-20141228-story.html", + "state_house_district": 45.0, + "state_senate_district": 45.0 + } + }, + { + "scenario_id": "063", + "features": { + "incident_id": 952498, + "date": "2017-10-04", + "state": "New Jersey", + "city_or_county": "Trenton", + "address": "400 block of Walnut Avenue", + "n_killed": 0, + "n_injured": 2, + "incident_url": "http://www.gunviolencearchive.org/incident/952498", + "source_url": "http://www.trentonian.com/general-news/20171004/gun-arrest-robbery-and-shooting-october-4-2017-police-blotter", + "incident_url_fields_missing": "False", + "congressional_district": 12.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Wounded/Injured", + "latitude": 40.2239, + "location_description": null, + "longitude": -74.7424, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": null, + "participant_age_group": null, + "participant_gender": "0::Male||1::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured||1::Injured", + "participant_type": "0::Victim||1::Victim", + "sources": "http://www.trentonian.com/general-news/20171004/gun-arrest-robbery-and-shooting-october-4-2017-police-blotter", + "state_house_district": 15.0, + "state_senate_district": 15.0 + } + }, + { + "scenario_id": "064", + "features": { + "incident_id": 980119, + "date": "2017-11-04", + "state": "Louisiana", + "city_or_county": "Marrero", + "address": "6500 Block of Lapalco Blvd", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/980119", + "source_url": "http://www.nola.com/crime/index.ssf/2017/11/man_shot_in_marrero_early_satu.html#incart_river_index", + "incident_url_fields_missing": "False", + "congressional_district": 2.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Wounded/Injured", + "latitude": 29.8784, + "location_description": null, + "longitude": -90.1205, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": null, + "participant_age_group": null, + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured", + "participant_type": "0::Victim", + "sources": "http://www.nola.com/crime/index.ssf/2017/11/man_shot_in_marrero_early_satu.html#incart_river_index", + "state_house_district": 83.0, + "state_senate_district": 3.0 + } + }, + { + "scenario_id": "065", + "features": { + "incident_id": 766475, + "date": "2017-02-03", + "state": "Alabama", + "city_or_county": "Birmingham", + "address": "Interstate 59", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/766475", + "source_url": "http://www.wbrc.com/story/34419223/man-found-shot-to-death-in-car-on-i-59", + "incident_url_fields_missing": "False", + "congressional_district": 7.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 33.5252, + "location_description": null, + "longitude": -86.8064, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": "0::27", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Charles Lee Brown", + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Victim", + "sources": "http://www.wbrc.com/story/34419223/man-found-shot-to-death-in-car-on-i-59", + "state_house_district": 54.0, + "state_senate_district": 18.0 + } + }, + { + "scenario_id": "066", + "features": { + "incident_id": 145971, + "date": "2014-06-03", + "state": "Pennsylvania", + "city_or_county": "Lancaster", + "address": "312 W. Walnut St", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/145971", + "source_url": "http://local21news.com/news/features/top-stories/stories/lancaster-police-investigating-shots-fired-at-shamrock-bar-9664.shtml", + "incident_url_fields_missing": "False", + "congressional_district": 16.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shots Fired - No Injuries||Bar/club incident - in or around establishment", + "latitude": 40.042, + "location_description": null, + "longitude": -76.3119, + "n_guns_involved": null, + "notes": "bar shots fired, no injury", + "participant_age": "1::26", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": "1::Sabron Jemal Blackman", + "participant_relationship": null, + "participant_status": "0::Unharmed||1::Unharmed", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://lancasteronline.com/news/local/armed-robber-strikes-store-and-man-fires-gun-outside-bar/article_b86a2ade-eb08-11e3-9b01-0||http://local21news.com/news/features/top-stories/stories/lancaster-police-investigating-shots-fired-at-shamrock-bar-9664.shtml", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "067", + "features": { + "incident_id": 363945, + "date": "2015-06-25", + "state": "Michigan", + "city_or_county": "Sterling Heights", + "address": "3000 block of Polo Circle", + "n_killed": 2, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/363945", + "source_url": "http://www.freep.com/story/news/2015/06/29/sterling-heights-murder-suicide-couple-identified/29477869/", + "incident_url_fields_missing": "False", + "congressional_district": 10.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Suicide^||Murder/Suicide||Home Invasion||Home Invasion - Resident killed||Home Invasion - subject/suspect/perpetrator killed||Domestic Violence", + "latitude": 42.6154, + "location_description": "Polo Green Apartments", + "longitude": -82.9901, + "n_guns_involved": 1.0, + "notes": "Man breaks into home, kills wife, self, w/ 2 young-adult children in home", + "participant_age": "0::44||1::44", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Female||1::Male", + "participant_name": "0::Brigita Karamatic||1::Sebastijan Karamatic", + "participant_relationship": "1::Family", + "participant_status": "0::Killed||1::Killed", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://www.macombdaily.com/general-news/20150625/sterling-heights-couple-found-dead-in-murder-suicide||http://www.freep.com/story/news/2015/06/29/sterling-heights-murder-suicide-couple-identified/29477869/", + "state_house_district": 30.0, + "state_senate_district": 10.0 + } + }, + { + "scenario_id": "068", + "features": { + "incident_id": 836478, + "date": "2017-05-03", + "state": "Washington", + "city_or_county": "Seattle", + "address": "Rainier Avenue and S Orcas Street", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/836478", + "source_url": "http://www.king5.com/news/crime/police-search-for-man-who-pulled-gun-on-pedestrians-in-south-seattle/436890736", + "incident_url_fields_missing": "False", + "congressional_district": 9.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Non-Shooting Incident||Road rage||Brandishing/flourishing/open carry/lost/found", + "latitude": 47.5512, + "location_description": null, + "longitude": -122.278, + "n_guns_involved": 1.0, + "notes": "Man threw beer bottle and then returned and brandished gun and threatened people who walked in front of his car at intersection", + "participant_age": null, + "participant_age_group": "0::Adult 18+", + "participant_gender": null, + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Unharmed", + "participant_type": "0::Subject-Suspect", + "sources": "http://www.king5.com/news/crime/police-search-for-man-who-pulled-gun-on-pedestrians-in-south-seattle/436890736", + "state_house_district": 37.0, + "state_senate_district": 37.0 + } + }, + { + "scenario_id": "069", + "features": { + "incident_id": 142979, + "date": "2014-06-04", + "state": "Arizona", + "city_or_county": "Phoenix", + "address": "16th and Union ", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/142979", + "source_url": "http://www.azcentral.com/videos/news/local/phoenix/2014/06/05/10007467/", + "incident_url_fields_missing": "False", + "congressional_district": 6.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shots fired, no action (reported, no evidence found)", + "latitude": 33.6549, + "location_description": null, + "longitude": -112.048, + "n_guns_involved": null, + "notes": null, + "participant_age": null, + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Unharmed", + "participant_type": "0::Subject-Suspect", + "sources": "http://www.azcentral.com/videos/news/local/phoenix/2014/06/05/10007467/", + "state_house_district": 20.0, + "state_senate_district": 20.0 + } + }, + { + "scenario_id": "070", + "features": { + "incident_id": 533698, + "date": "2016-04-01", + "state": "North Carolina", + "city_or_county": "Union (county)", + "address": "6200 block of Old Pageland Marshville Road", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/533698", + "source_url": "http://www.wbtv.com/story/31625114/woman-turns-self-in-wanted-in-fatal-shooting-of-brother-in-union-county", + "incident_url_fields_missing": "False", + "congressional_district": 8.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 34.8381, + "location_description": null, + "longitude": -80.4178, + "n_guns_involved": null, + "notes": null, + "participant_age": "0::21", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male||1::Female", + "participant_name": "0::Elijah James Moore||1::Breanna Moore", + "participant_relationship": "1::Family", + "participant_status": "0::Killed||1::Unharmed, Arrested", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://www.wbtv.com/story/31625114/woman-turns-self-in-wanted-in-fatal-shooting-of-brother-in-union-county", + "state_house_district": 55.0, + "state_senate_district": 35.0 + } + }, + { + "scenario_id": "071", + "features": { + "incident_id": 444098, + "date": "2015-11-06", + "state": "Georgia", + "city_or_county": "Athens", + "address": "Norwood Lane", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/444098", + "source_url": "http://onlineathens.com/blotter/2015-11-07/report-discharged-weapons-house-athens", + "incident_url_fields_missing": "False", + "congressional_district": 9.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shots Fired - No Injuries||Drive-by (car to street, car to car)", + "latitude": 34.0285, + "location_description": null, + "longitude": -83.3281, + "n_guns_involved": null, + "notes": "shots fired, brass casings found", + "participant_age": null, + "participant_age_group": null, + "participant_gender": null, + "participant_name": null, + "participant_relationship": null, + "participant_status": null, + "participant_type": null, + "sources": "http://onlineathens.com/blotter/2015-11-07/report-discharged-weapons-house-athens", + "state_house_district": 118.0, + "state_senate_district": 47.0 + } + }, + { + "scenario_id": "072", + "features": { + "incident_id": 316651, + "date": "2015-04-01", + "state": "Wisconsin", + "city_or_county": "Milwaukee", + "address": "14th Street and Finn Place", + "n_killed": 0, + "n_injured": 3, + "incident_url": "http://www.gunviolencearchive.org/incident/316651", + "source_url": "http://fox6now.com/2015/04/02/they-dont-know-how-to-respect-life-three-wounded-in-shooting-on-milwaukees-north-side/", + "incident_url_fields_missing": "False", + "congressional_district": 4.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured||Domestic Violence", + "latitude": 43.0834, + "location_description": null, + "longitude": -87.9285, + "n_guns_involved": null, + "notes": "shot in family dispute, poss single shooter", + "participant_age": "0::33||1::40||2::54", + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+", + "participant_gender": "0::Male||1::Male||2::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured||1::Injured||2::Injured", + "participant_type": "0::Victim||1::Victim||2::Victim", + "sources": "http://www.cbs58.com/story/28700399/milwaukee-police-investigate-triple-shooting||http://fox6now.com/2015/04/02/they-dont-know-how-to-respect-life-three-wounded-in-shooting-on-milwaukees-north-side/", + "state_house_district": 10.0, + "state_senate_district": 4.0 + } + }, + { + "scenario_id": "073", + "features": { + "incident_id": 765597, + "date": "2017-02-01", + "state": "Florida", + "city_or_county": "Palm Bay", + "address": "Palm Bay Rd and Hollywood Blvd", + "n_killed": 0, + "n_injured": 2, + "incident_url": "http://www.gunviolencearchive.org/incident/765597", + "source_url": "http://www.floridatoday.com/story/news/crime/2017/02/01/palm-bay-drive--shooting--95-overpass-wounds-two/97350870/", + "incident_url_fields_missing": "False", + "congressional_district": 8.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Wounded/Injured||Drive-by (car to street, car to car)", + "latitude": 28.0352, + "location_description": null, + "longitude": -80.6499, + "n_guns_involved": 1.0, + "notes": "near I 95 overpass, approx location, stem from altercation at Pinups", + "participant_age": null, + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+", + "participant_gender": "0::Male||1::Male||2::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured||1::Injured||2::Unharmed", + "participant_type": "0::Victim||1::Victim||2::Subject-Suspect", + "sources": "http://www.clickorlando.com/news/2-shot-in-car-after-leaving-palm-bay-strip-club||http://www.floridatoday.com/story/news/crime/2017/02/01/palm-bay-drive--shooting--95-overpass-wounds-two/97350870/", + "state_house_district": 53.0, + "state_senate_district": 16.0 + } + }, + { + "scenario_id": "074", + "features": { + "incident_id": 506236, + "date": "2016-02-16", + "state": "Georgia", + "city_or_county": "Augusta", + "address": "3417 Wrightsboro Road", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/506236", + "source_url": "http://wjbf.com/2016/02/17/arrests-made-in-augusta-murder-case/", + "incident_url_fields_missing": "False", + "congressional_district": 12.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Possession (gun(s) found during commission of other crimes)||Possession of gun by felon or prohibited person", + "latitude": 33.468, + "location_description": "Waffle House", + "longitude": -82.0779, + "n_guns_involved": null, + "notes": "Richmond Co", + "participant_age": "0::26||1::35||2::20||3::24", + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+||3::Adult 18+", + "participant_gender": "0::Male||1::Male||2::Male||3::Male", + "participant_name": "0::Terrance Terrell Baker||1::Elijah Bernard Washington||2::Brandon Antonio Carter||3::Shawncy Barrett", + "participant_relationship": null, + "participant_status": "0::Killed||1::Unharmed, Arrested||2::Unharmed, Arrested||3::Unharmed, Arrested", + "participant_type": "0::Victim||1::Subject-Suspect||2::Subject-Suspect||3::Subject-Suspect", + "sources": "http://chronicle.augusta.com/news/crime-courts/2016-02-16/update-slaying-victim-identified||http://wjbf.com/2016/02/17/arrests-made-in-augusta-murder-case/", + "state_house_district": 124.0, + "state_senate_district": 22.0 + } + }, + { + "scenario_id": "075", + "features": { + "incident_id": 490547, + "date": "2016-01-22", + "state": "Pennsylvania", + "city_or_county": "Philadelphia", + "address": "3000 block of North Eighth Street", + "n_killed": 0, + "n_injured": 2, + "incident_url": "http://www.gunviolencearchive.org/incident/490547", + "source_url": "http://www.philly.com/philly/news/20160123_Two_men_shot_in_Fairhill.html", + "incident_url_fields_missing": "False", + "congressional_district": 2.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured", + "latitude": 39.9973, + "location_description": null, + "longitude": -75.1441, + "n_guns_involved": null, + "notes": "2 inj, 1 critical and 1 stable.", + "participant_age": "0::58||1::52", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured||1::Injured", + "participant_type": "0::Victim||1::Victim", + "sources": "http://www.philly.com/philly/news/20160123_Two_men_shot_in_Fairhill.html", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "076", + "features": { + "incident_id": 907022, + "date": "2017-08-04", + "state": "Arizona", + "city_or_county": "Flagstaff", + "address": "1900 block of N 1st St", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/907022", + "source_url": "http://azdailysun.com/news/swat-responds-to-shooting-incident-in-sunnyside/article_6dfb3a17-2b96-52eb-8a20-8f9ce8176a85.html", + "incident_url_fields_missing": "False", + "congressional_district": 1.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shots Fired - No Injuries||Officer Involved Incident||Officer Involved Incident - Weapon involved but no shots fired||Officer Involved Shooting - subject/suspect/perpetrator surrender at standoff||Under the influence of alcohol or drugs (only applies to the subject/suspect/perpetrator )", + "latitude": 35.2064, + "location_description": null, + "longitude": -111.617, + "n_guns_involved": 1.0, + "notes": "suspect arrested after falling asleep on couch", + "participant_age": "0::21", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Horacio Leal", + "participant_relationship": null, + "participant_status": "0::Unharmed, Arrested", + "participant_type": "0::Subject-Suspect", + "sources": "http://azdailysun.com/news/swat-responds-to-shooting-incident-in-sunnyside/article_6dfb3a17-2b96-52eb-8a20-8f9ce8176a85.html", + "state_house_district": 6.0, + "state_senate_district": 6.0 + } + }, + { + "scenario_id": "077", + "features": { + "incident_id": 111691, + "date": "2014-02-25", + "state": "Washington", + "city_or_county": "Lake Stevens", + "address": "7000 block of 126th Drive North", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/111691", + "source_url": "http://www.kirotv.com/news/news/second-arrest-made-attempted-home-invasion-lake-st/nd44r/", + "incident_url_fields_missing": "False", + "congressional_district": 1.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured||Home Invasion||Home Invasion - subject/suspect/perpetrator injured", + "latitude": 48.0593, + "location_description": null, + "longitude": -122.06, + "n_guns_involved": null, + "notes": null, + "participant_age": "2::32||3::33", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Male||2::Male||3::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Unharmed||1::Unharmed||2::Unharmed||3::Injured", + "participant_type": "0::Victim||1::Victim||2::Subject-Suspect||3::Subject-Suspect", + "sources": "http://www.kirotv.com/news/news/second-arrest-made-attempted-home-invasion-lake-st/nd44r/", + "state_house_district": 39.0, + "state_senate_district": 39.0 + } + }, + { + "scenario_id": "078", + "features": { + "incident_id": 294623, + "date": "2015-02-18", + "state": "New York", + "city_or_county": "Manhattan", + "address": "17th Street", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/294623", + "source_url": "http://newyork.cbslocal.com/2015/02/18/23-year-old-man-shot-wounded-in-chelsea/", + "incident_url_fields_missing": "False", + "congressional_district": 12.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured", + "latitude": 40.7355, + "location_description": null, + "longitude": -73.9866, + "n_guns_involved": null, + "notes": "Chelsea, near Ninth Ave, shot in stomach", + "participant_age": "0::23", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured", + "participant_type": "0::Victim", + "sources": "http://newyork.cbslocal.com/2015/02/18/23-year-old-man-shot-wounded-in-chelsea/", + "state_house_district": 74.0, + "state_senate_district": 28.0 + } + }, + { + "scenario_id": "079", + "features": { + "incident_id": 498363, + "date": "2016-02-02", + "state": "Texas", + "city_or_county": "Houston", + "address": "8600 Broadway", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/498363", + "source_url": "http://www.chron.com/houston/article/Shooting-leaves-man-wounded-in-back-in-SE-Houston-6800418.php", + "incident_url_fields_missing": "False", + "congressional_district": 9.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured", + "latitude": 29.6655, + "location_description": null, + "longitude": -95.277, + "n_guns_involved": null, + "notes": "1 inj. This story is developing.", + "participant_age": null, + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured||1::Arrested", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://www.chron.com/houston/article/Shooting-leaves-man-wounded-in-back-in-SE-Houston-6800418.php", + "state_house_district": 147.0, + "state_senate_district": 6.0 + } + }, + { + "scenario_id": "080", + "features": { + "incident_id": 996852, + "date": "2017-11-26", + "state": "Nebraska", + "city_or_county": "Lincoln", + "address": "3636 N 52nd St", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/996852", + "source_url": "http://journalstar.com/news/local/911/man-fired-gun-through-doorway-of-lincoln-apartment-with-people/article_57eb7b48-275e-5398-9332-d1161c64fed3.html", + "incident_url_fields_missing": "False", + "congressional_district": null, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shots Fired - No Injuries||Possession of gun by felon or prohibited person", + "latitude": null, + "location_description": null, + "longitude": null, + "n_guns_involved": 1.0, + "notes": "Suspect fired gun through apartment door.", + "participant_age": "0::29", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Salvador Orozco", + "participant_relationship": null, + "participant_status": "0::Unharmed, Arrested", + "participant_type": "0::Subject-Suspect", + "sources": "http://journalstar.com/news/local/911/man-fired-gun-through-doorway-of-lincoln-apartment-with-people/article_57eb7b48-275e-5398-9332-d1161c64fed3.html", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "081", + "features": { + "incident_id": 301254, + "date": "2015-03-02", + "state": "South Carolina", + "city_or_county": "Westminster", + "address": "Jeremy Drive", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/301254", + "source_url": "http://www.foxcarolina.com/story/28254902/deputies-man-held-wife-father-hostage-threatened-to-kill-them", + "incident_url_fields_missing": "False", + "congressional_district": 3.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Handgun", + "incident_characteristics": "Non-Shooting Incident||Domestic Violence||Kidnapping/abductions/hostage||Pistol-whipping||Brandishing/flourishing/open carry/lost/found", + "latitude": 34.6523, + "location_description": null, + "longitude": -83.1374, + "n_guns_involved": 1.0, + "notes": "drove back from GA to SC, pw father, held him & wife hostage at gunpoint with loaded handgun, threatened to kill if cops called, no shots fired", + "participant_age": "2::25", + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+", + "participant_gender": "0::Male||1::Female||2::Male", + "participant_name": "2::Joshua Brock", + "participant_relationship": "2::Family", + "participant_status": "0::Injured||1::Unharmed||2::Unharmed, Arrested", + "participant_type": "0::Victim||1::Victim||2::Subject-Suspect", + "sources": "http://www.foxcarolina.com/story/28254902/deputies-man-held-wife-father-hostage-threatened-to-kill-them", + "state_house_district": 1.0, + "state_senate_district": 1.0 + } + }, + { + "scenario_id": "082", + "features": { + "incident_id": 767704, + "date": "2017-01-09", + "state": "Missouri", + "city_or_county": "High Ridge", + "address": "4100 block of Schumacher Rd", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/767704", + "source_url": "http://www.stltoday.com/news/local/crime-and-courts/high-ridge-woman-charged-with-killing-boyfriend-dumping-body-in/article_ae35caa6-544f-5f99-b233-c650e6e0d414.html", + "incident_url_fields_missing": "False", + "congressional_district": 3.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Domestic Violence", + "latitude": 38.4791, + "location_description": null, + "longitude": -90.4854, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": "0::50||1::51", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Female", + "participant_name": "0::Vincent White||1::Donna Tooley", + "participant_relationship": "1::Significant others - current or former", + "participant_status": "0::Killed||1::Unharmed, Arrested", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://www.stltoday.com/news/local/crime-and-courts/high-ridge-woman-charged-with-killing-boyfriend-dumping-body-in/article_ae35caa6-544f-5f99-b233-c650e6e0d414.html", + "state_house_district": 112.0, + "state_senate_district": 22.0 + } + }, + { + "scenario_id": "083", + "features": { + "incident_id": 657074, + "date": "2016-09-17", + "state": "Ohio", + "city_or_county": "Toledo", + "address": "North Detroit and Highland", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/657074", + "source_url": "http://www.wtol.com/story/33393824/toledo-police-arrest-2-suspects-in-separate-september-murders", + "incident_url_fields_missing": "False", + "congressional_district": null, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": null, + "location_description": null, + "longitude": null, + "n_guns_involved": 1.0, + "notes": "found at Collingwood and Central", + "participant_age": "0::28||1::26", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": "0::Bobby Gittens||1::Darnell Walker", + "participant_relationship": null, + "participant_status": "0::Killed||1::Unharmed, Arrested", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://www.toledoblade.com/Police-Fire/2016/09/17/Man-dies-after-shooting-in-central-Toledo.html||http://www.wtol.com/story/33393824/toledo-police-arrest-2-suspects-in-separate-september-murders", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "084", + "features": { + "incident_id": 116468, + "date": "2014-03-18", + "state": "Oregon", + "city_or_county": "Hillsboro", + "address": " Northwest West Union Road and Dick Road", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/116468", + "source_url": "http://www.oregonlive.com/north-of-26/index.ssf/2014/03/hillsboro-area_man_killed_neig.html", + "incident_url_fields_missing": "False", + "congressional_district": 1.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Animal shot/killed", + "latitude": 45.5735, + "location_description": null, + "longitude": -122.907, + "n_guns_involved": null, + "notes": "dog shot in yard", + "participant_age": null, + "participant_age_group": null, + "participant_gender": null, + "participant_name": null, + "participant_relationship": null, + "participant_status": null, + "participant_type": null, + "sources": "http://www.oregonlive.com/north-of-26/index.ssf/2014/03/hillsboro-area_man_killed_neig.html", + "state_house_district": 30.0, + "state_senate_district": 15.0 + } + }, + { + "scenario_id": "085", + "features": { + "incident_id": 217293, + "date": "2014-01-12", + "state": "Georgia", + "city_or_county": "Atlanta", + "address": "6000 N Terminal Pkwy", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/217293", + "source_url": "http://blog.tsa.gov/2014/01/tsa-week-in-review-42-firearms.html", + "incident_url_fields_missing": "False", + "congressional_district": 5.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "TSA Action", + "latitude": 33.6356, + "location_description": null, + "longitude": -84.4333, + "n_guns_involved": null, + "notes": "Hartsfield-Jackson Atlanta International Airport", + "participant_age": null, + "participant_age_group": null, + "participant_gender": null, + "participant_name": null, + "participant_relationship": null, + "participant_status": null, + "participant_type": null, + "sources": "http://blog.tsa.gov/2014/01/tsa-week-in-review-42-firearms.html", + "state_house_district": 60.0, + "state_senate_district": 34.0 + } + }, + { + "scenario_id": "086", + "features": { + "incident_id": 320162, + "date": "2015-04-01", + "state": "Wisconsin", + "city_or_county": "Milwaukee", + "address": "5300 S Howell Ave", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/320162", + "source_url": "http://blog.tsa.gov/", + "incident_url_fields_missing": "False", + "congressional_district": 4.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::25 Auto", + "incident_characteristics": "Non-Shooting Incident||TSA Action", + "latitude": 42.9478, + "location_description": null, + "longitude": -87.9096, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": null, + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Unharmed", + "participant_type": "0::Subject-Suspect", + "sources": "http://blog.tsa.gov/", + "state_house_district": 20.0, + "state_senate_district": 7.0 + } + }, + { + "scenario_id": "087", + "features": { + "incident_id": 1029348, + "date": "2018-01-16", + "state": "Louisiana", + "city_or_county": "Bossier City", + "address": "Airline Drive", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/1029348", + "source_url": "http://www.ksla.com/story/37275136/at-least-1-injured-in-bossier-city-shooting", + "incident_url_fields_missing": "False", + "congressional_district": 4.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Wounded/Injured", + "latitude": 32.5693, + "location_description": null, + "longitude": -93.7121, + "n_guns_involved": 1.0, + "notes": "possible suicide attempt", + "participant_age": null, + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured", + "participant_type": "0::Victim", + "sources": "http://www.ksla.com/story/37275136/at-least-1-injured-in-bossier-city-shooting", + "state_house_district": 8.0, + "state_senate_district": 37.0 + } + }, + { + "scenario_id": "088", + "features": { + "incident_id": 624456, + "date": "2016-08-08", + "state": "Pennsylvania", + "city_or_county": "Philadelphia", + "address": "60th Street and Woodland Avenue", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/624456", + "source_url": "http://www.philly.com/philly/news/20160809_Man_shot_to_death_in_South_Philadelphia.html", + "incident_url_fields_missing": "False", + "congressional_district": 1.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured||Armed robbery with injury/death and/or evidence of DGU found||Car-jacking", + "latitude": 39.9304, + "location_description": null, + "longitude": -75.2276, + "n_guns_involved": null, + "notes": "shot during attempt cj", + "participant_age": "0::31", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured||1::Unharmed", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://www.philly.com/philly/news/20160809_Man_shot_to_death_in_South_Philadelphia.html", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "089", + "features": { + "incident_id": 900888, + "date": "2017-07-29", + "state": "Illinois", + "city_or_county": "Chicago", + "address": "5000 block of W Madison", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/900888", + "source_url": "http://chicago.suntimes.com/news/man-shows-up-at-hospital-after-west-side-shooting/amp/", + "incident_url_fields_missing": "False", + "congressional_district": 7.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Wounded/Injured", + "latitude": 41.8807, + "location_description": "Austin neighborhood", + "longitude": -87.7507, + "n_guns_involved": 1.0, + "notes": "Shoulder, while walking, stable;", + "participant_age": "0::23", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured", + "participant_type": "0::Victim", + "sources": "http://chicago.suntimes.com/news/man-shows-up-at-hospital-after-west-side-shooting/amp/", + "state_house_district": 8.0, + "state_senate_district": 4.0 + } + }, + { + "scenario_id": "090", + "features": { + "incident_id": 400876, + "date": "2015-08-24", + "state": "Massachusetts", + "city_or_county": "Boston", + "address": "150 Dudley Street", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/400876", + "source_url": "http://www.necn.com/news/new-england/Police-Investigate-Overnight-Shooting-in-Boston-322658132.html", + "incident_url_fields_missing": "False", + "congressional_district": 7.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured", + "latitude": 42.3287, + "location_description": null, + "longitude": -71.0833, + "n_guns_involved": null, + "notes": "Roxbury", + "participant_age": null, + "participant_age_group": null, + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured", + "participant_type": "0::Victim", + "sources": "http://www.necn.com/news/new-england/Police-Investigate-Overnight-Shooting-in-Boston-322658132.html", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "091", + "features": { + "incident_id": 504596, + "date": "2016-02-08", + "state": "Minnesota", + "city_or_county": "Rochester", + "address": "851 21st Avenue S.E.", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/504596", + "source_url": "http://kimt.com/2016/02/16/rochester-police-arrest-suspects-in-armed-home-invasion/", + "incident_url_fields_missing": "False", + "congressional_district": 1.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Handgun", + "incident_characteristics": "Non-Shooting Incident||Home Invasion||Home Invasion - No death or injury", + "latitude": 44.0142, + "location_description": null, + "longitude": -92.4329, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": "0::20||1::19", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": "0::Adrick Mims||1::Yasin Susi", + "participant_relationship": "0::Home Invasion - Perp Does Not Know Victim||1::Home Invasion - Perp Does Not Know Victim", + "participant_status": "0::Unharmed, Arrested||1::Unharmed, Arrested", + "participant_type": "0::Subject-Suspect||1::Subject-Suspect", + "sources": "http://kimt.com/2016/02/10/3-rochester-students-victims-of-home-invasion/||http://kimt.com/2016/02/16/rochester-police-arrest-suspects-in-armed-home-invasion/", + "state_house_district": null, + "state_senate_district": 26.0 + } + }, + { + "scenario_id": "092", + "features": { + "incident_id": 716163, + "date": "2016-12-01", + "state": "Kentucky", + "city_or_county": "Louisville", + "address": "14417 Aiken Rd", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/716163", + "source_url": "http://www.wlky.com/article/elementary-student-to-be-disciplined-after-bringing-gun-to-school/8413912", + "incident_url_fields_missing": "False", + "congressional_district": 3.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Non-Shooting Incident||School Incident||Gun at school, no death/injury - elementary/secondary school", + "latitude": 38.2687, + "location_description": "Stopher Elementary School", + "longitude": -85.4856, + "n_guns_involved": 1.0, + "notes": "fifth grader brought gun to school", + "participant_age": null, + "participant_age_group": "0::Child 0-11", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Unharmed", + "participant_type": "0::Subject-Suspect", + "sources": "http://www.wlky.com/article/elementary-student-to-be-disciplined-after-bringing-gun-to-school/8413912", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "093", + "features": { + "incident_id": 264314, + "date": "2014-12-16", + "state": "Washington", + "city_or_county": "Lynnwood", + "address": "19300 block of Locust Way", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/264314", + "source_url": "http://blogs.seattletimes.com/today/2014/12/shots-fired-at-would-be-intruder-in-lynnwood/", + "incident_url_fields_missing": "False", + "congressional_district": 2.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shots Fired - No Injuries||Non-Shooting Incident||Home Invasion||Home Invasion - No death or injury||Defensive Use||Defensive Use - Victim stops crime||Defensive Use - Shots fired, no injury/death", + "latitude": 47.7961, + "location_description": null, + "longitude": -122.258, + "n_guns_involved": null, + "notes": null, + "participant_age": "0::26", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Unharmed, Arrested", + "participant_type": "0::Subject-Suspect", + "sources": "http://blogs.seattletimes.com/today/2014/12/shots-fired-at-would-be-intruder-in-lynnwood/", + "state_house_district": 1.0, + "state_senate_district": 1.0 + } + }, + { + "scenario_id": "094", + "features": { + "incident_id": 312412, + "date": "2015-03-24", + "state": "Louisiana", + "city_or_county": "Shreveport", + "address": "Pleasant Drive", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/312412", + "source_url": "http://www.ksla.com/story/28598268/shreveport-police-id-woman-injured-in-early-morning-shooting", + "incident_url_fields_missing": "False", + "congressional_district": 4.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured", + "latitude": 32.4598, + "location_description": null, + "longitude": -93.7944, + "n_guns_involved": null, + "notes": null, + "participant_age": "0::24", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Female", + "participant_name": "0::Breanna Johnson", + "participant_relationship": null, + "participant_status": "0::Injured", + "participant_type": "0::Victim", + "sources": "http://www.ksla.com/story/28598268/shreveport-police-id-woman-injured-in-early-morning-shooting", + "state_house_district": 3.0, + "state_senate_district": 39.0 + } + }, + { + "scenario_id": "095", + "features": { + "incident_id": 587289, + "date": "2016-06-19", + "state": "New York", + "city_or_county": "Brooklyn", + "address": "405 Lefferts Avenue", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/587289", + "source_url": "https://www.dnainfo.com/new-york/20160623/crown-heights/crime-blotter-73-year-old-man-assaulted-robbed-by-woman-asking-for-1", + "incident_url_fields_missing": "False", + "congressional_district": 9.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shots Fired - No Injuries", + "latitude": 40.6626, + "location_description": null, + "longitude": -73.9478, + "n_guns_involved": null, + "notes": "near New York Ave, shots fired into home, no inj report", + "participant_age": null, + "participant_age_group": null, + "participant_gender": null, + "participant_name": null, + "participant_relationship": null, + "participant_status": null, + "participant_type": null, + "sources": "https://www.dnainfo.com/new-york/20160623/crown-heights/crime-blotter-73-year-old-man-assaulted-robbed-by-woman-asking-for-1", + "state_house_district": 43.0, + "state_senate_district": 20.0 + } + }, + { + "scenario_id": "096", + "features": { + "incident_id": 291657, + "date": "2015-02-10", + "state": "California", + "city_or_county": "San Jose", + "address": "2300 block of Senter Road", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/291657", + "source_url": "http://www.times-standard.com/general-news/20150210/san-jose-woman-wounded-in-tuesday-night-shooting", + "incident_url_fields_missing": "False", + "congressional_district": 19.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured", + "latitude": 37.3093, + "location_description": null, + "longitude": -121.849, + "n_guns_involved": null, + "notes": null, + "participant_age": null, + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Female", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured", + "participant_type": "0::Victim", + "sources": "http://www.times-standard.com/general-news/20150210/san-jose-woman-wounded-in-tuesday-night-shooting", + "state_house_district": 27.0, + "state_senate_district": 15.0 + } + }, + { + "scenario_id": "097", + "features": { + "incident_id": 216733, + "date": "2014-11-05", + "state": "Tennessee", + "city_or_county": "Memphis", + "address": "Interstate 40", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/216733", + "source_url": "http://www.walb.com/story/27298916/heavy-police-presence-near-pyramid", + "incident_url_fields_missing": "False", + "congressional_district": 9.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Car-jacking", + "latitude": 35.1788, + "location_description": null, + "longitude": -89.8281, + "n_guns_involved": null, + "notes": "Downtown, woman carjacked near National Civil Rights Museum, suspects crashed near I-40 bridge during police chase, one arrested had shotgun", + "participant_age": null, + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+||3::Adult 18+", + "participant_gender": "0::Female||1::Male||2::Male||3::Male", + "participant_name": "1::Brandon Burnes", + "participant_relationship": null, + "participant_status": "0::Unharmed||1::Unharmed||2::Unharmed||3::Unharmed", + "participant_type": "0::Victim||1::Subject-Suspect||2::Subject-Suspect||3::Subject-Suspect", + "sources": "http://www.walb.com/story/27298916/heavy-police-presence-near-pyramid", + "state_house_district": 97.0, + "state_senate_district": 31.0 + } + }, + { + "scenario_id": "099", + "features": { + "incident_id": 620467, + "date": "2016-08-03", + "state": "Michigan", + "city_or_county": "Detroit", + "address": "24500 block of Grand River", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/620467", + "source_url": "http://www.wxyz.com/news/detroit-police-ask-for-publics-help-identifying-man-in-connection-to-early-morning-carjacking", + "incident_url_fields_missing": "False", + "congressional_district": 13.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Non-Shooting Incident||Car-jacking", + "latitude": 42.4269, + "location_description": null, + "longitude": -83.283, + "n_guns_involved": null, + "notes": "Car-jacking.", + "participant_age": "0::24", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Female||1::Male", + "participant_name": null, + "participant_relationship": "1::Armed Robbery", + "participant_status": "0::Unharmed||1::Unharmed", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://www.wxyz.com/news/detroit-police-ask-for-publics-help-identifying-man-in-connection-to-early-morning-carjacking", + "state_house_district": 10.0, + "state_senate_district": 3.0 + } + }, + { + "scenario_id": "100", + "features": { + "incident_id": 523587, + "date": "2016-03-16", + "state": "Texas", + "city_or_county": "Pilot Point", + "address": "300 block of South Church Street", + "n_killed": 2, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/523587", + "source_url": "http://dallas.suntimes.com/obituaries/7/75/242401/e22586a07232714b8f5aebb2f23e36b6", + "incident_url_fields_missing": "False", + "congressional_district": 26.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Handgun", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Suicide^||Murder/Suicide||Domestic Violence", + "latitude": 33.3945, + "location_description": null, + "longitude": -96.9591, + "n_guns_involved": 1.0, + "notes": "2 killed by gunfire in murder suicide, two others hit by car", + "participant_age": "0::51||1::50", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Female||1::Male", + "participant_name": "0::Rhonda Holmes||1::Wilford \"Nemo\" Holmes", + "participant_relationship": "1::Significant others - current or former", + "participant_status": "0::Killed||1::Killed", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://www.nbcdfw.com/news/local/Three-Shot-in-Denton-County-Neighborhood-Police-372297442.html||http://www.wfaa.com/news/crime/four-injured-amid-confusion-at-pilot-point-crime-scene/86058459||http://dallas.suntimes.com/obituaries/7/75/242401/e22586a07232714b8f5aebb2f23e36b6", + "state_house_district": 106.0, + "state_senate_district": 30.0 + } + }, + { + "scenario_id": "101", + "features": { + "incident_id": 125047, + "date": "2014-04-09", + "state": "California", + "city_or_county": "Soledad", + "address": "Walker and Metz ", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/125047", + "source_url": "http://www.thecalifornian.com/article/20140414/NEWS09/304140030/Reports-lead-Soledad-police-hunt-shooting-victim", + "incident_url_fields_missing": "False", + "congressional_district": 20.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured", + "latitude": 36.4276, + "location_description": null, + "longitude": -121.321, + "n_guns_involved": null, + "notes": null, + "participant_age": "0::40", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured", + "participant_type": "0::Victim", + "sources": "http://www.thecalifornian.com/article/20140414/NEWS09/304140030/Reports-lead-Soledad-police-hunt-shooting-victim", + "state_house_district": 30.0, + "state_senate_district": 12.0 + } + }, + { + "scenario_id": "102", + "features": { + "incident_id": 531895, + "date": "2016-03-31", + "state": "Texas", + "city_or_county": "Dallas", + "address": "3300 block of South Vernon Avenue", + "n_killed": 1, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/531895", + "source_url": "http://www.wfaa.com/news/crime/suspect-in-overnight-murder-arrested/113124740", + "incident_url_fields_missing": "False", + "congressional_district": 33.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured||Shot - Dead (murder, accidental, suicide)", + "latitude": 32.7042, + "location_description": "Vernon Oaks Apartments", + "longitude": -96.8359, + "n_guns_involved": null, + "notes": "1 killed, 1 inj. This story is developing.", + "participant_age": "0::33||1::31||2::29", + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+", + "participant_gender": "0::Male||1::Male||2::Male", + "participant_name": "0::David Sanchez||1::Louis Serena||2::Cortez Bowser", + "participant_relationship": null, + "participant_status": "0::Killed||1::Injured||2::Unharmed, Arrested", + "participant_type": "0::Victim||1::Victim||2::Subject-Suspect", + "sources": "http://dfw.cbslocal.com/2016/03/31/one-dead-another-wounded-in-overnight-oak-cliff-shooting/||http://www.wfaa.com/news/crime/suspect-in-overnight-murder-arrested/113124740", + "state_house_district": 104.0, + "state_senate_district": 23.0 + } + }, + { + "scenario_id": "103", + "features": { + "incident_id": 433138, + "date": "2015-10-16", + "state": "South Carolina", + "city_or_county": "Union", + "address": "Broyce St.", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/433138", + "source_url": "http://wspa.com/2015/10/19/man-shot-during-robbery-in-union/", + "incident_url_fields_missing": "False", + "congressional_district": 5.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - 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No Injuries", + "latitude": 40.6957, + "location_description": null, + "longitude": -89.5835, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": null, + "participant_age_group": null, + "participant_gender": null, + "participant_name": null, + "participant_relationship": null, + "participant_status": null, + "participant_type": null, + "sources": "https://spotcrime.com/crime/93142292-ce3927b160d5678a41c781ef9344153f", + "state_house_district": 92.0, + "state_senate_district": 46.0 + } + }, + { + "scenario_id": "109", + "features": { + "incident_id": 914620, + "date": "2017-07-29", + "state": "District of Columbia", + "city_or_county": "Washington", + "address": "2400 block of Alabama Avenue, Southeast.", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/914620", + "source_url": "https://mpdc.dc.gov/release/mpd%E2%80%99s-weekly-firearm-recoveries-july-24th-july-31st", + "incident_url_fields_missing": "False", + "congressional_district": 1.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::9mm", + "incident_characteristics": "Non-Shooting Incident||ATF/LE Confiscation/Raid/Arrest||Brandishing/flourishing/open carry/lost/found", + "latitude": 38.8517, + "location_description": null, + "longitude": -76.9719, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": null, + "participant_age_group": null, + "participant_gender": null, + "participant_name": null, + "participant_relationship": null, + "participant_status": null, + "participant_type": null, + "sources": "https://mpdc.dc.gov/release/mpd%E2%80%99s-weekly-firearm-recoveries-july-24th-july-31st", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "110", + "features": { + "incident_id": 143419, + "date": "2014-05-29", + "state": "Georgia", + "city_or_county": "Warner Robins", + "address": "Utah Avenue", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/143419", + "source_url": "http://www.macon.com/2014/08/27/3271763_houston-grand-jury-indicts-macon.html", + "incident_url_fields_missing": "False", + "congressional_district": 8.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - 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Wounded/Injured||Shot - Dead (murder, accidental, suicide)||Attempted Murder/Suicide (one variable unsuccessful)||Domestic Violence", + "latitude": 48.0614, + "location_description": null, + "longitude": -106.042, + "n_guns_involved": null, + "notes": "Shot and injured daughter, then fatally shot self.", + "participant_age": "0::16", + "participant_age_group": "0::Teen 12-17||1::Adult 18+", + "participant_gender": "0::Female||1::Male", + "participant_name": "0::Rebecca Rahn||1::Raymond Rahn", + "participant_relationship": "1::Family", + "participant_status": "0::Injured||1::Killed", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://www.kxly.com/news/northwest/teen-hospitalized-in-good-condition-after-weekend-shooting/33223832", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "113", + "features": { + "incident_id": 353414, + "date": "2015-06-07", + "state": "Michigan", + "city_or_county": "Saginaw", + "address": "2400 block of Schaefer Street", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/353414", + "source_url": "http://www.mlive.com/news/saginaw/index.ssf/2015/06/22-year-old_charged_in_robbery.html?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+saginaw_news+%28Saginaw+News+-+MLive.com%29", + "incident_url_fields_missing": "False", + "congressional_district": 5.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - 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"state_senate_district": 46.0 + } + }, + { + "scenario_id": "121", + "features": { + "incident_id": 1008575, + "date": "2017-12-16", + "state": "Louisiana", + "city_or_county": "Prairieville", + "address": "Mission St", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/1008575", + "source_url": "http://www.wafb.com/story/37082187/detectives-investigating-pajama-party-turned-deadly", + "incident_url_fields_missing": "False", + "congressional_district": 6.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 30.2676, + "location_description": null, + "longitude": -90.9891, + "n_guns_involved": 1.0, + "notes": "pajama party", + "participant_age": "0::30", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Acklin Jackson", + "participant_relationship": null, + "participant_status": "0::Killed", + 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"incident_url": "http://www.gunviolencearchive.org/incident/102592", + "source_url": "http://www.oregonlive.com/pacific-northwest-news/index.ssf/2014/01/jogger_fights_back_probably_in.html", + "incident_url_fields_missing": "False", + "congressional_district": 1.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Armed robbery with injury/death and/or evidence of DGU found", + "latitude": 45.2205, + "location_description": null, + "longitude": -123.207, + "n_guns_involved": null, + "notes": null, + "participant_age": "0::18", + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+||3::Adult 18+", + "participant_gender": "0::Male||1::Male||2::Male||3::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Unharmed||1::Unharmed||2::Unharmed||3::Unharmed", + "participant_type": "0::Victim||1::Subject-Suspect||2::Subject-Suspect||3::Subject-Suspect", + "sources": 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null, + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured", + "participant_type": "0::Victim", + "sources": "http://savannahnow.com/crime-courts-news/2016-06-03/update-police-seek-suspects-savannah-home-invasion", + "state_house_district": 165.0, + "state_senate_district": 2.0 + } + }, + { + "scenario_id": "126", + "features": { + "incident_id": 935754, + "date": "2017-09-15", + "state": "Missouri", + "city_or_county": "Saint Louis", + "address": null, + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/935754", + "source_url": "https://twitter.com/SLMPD/status/908937558779924480", + "incident_url_fields_missing": "False", + "congressional_district": 1.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Handgun", + "incident_characteristics": "Non-Shooting Incident||ATF/LE Confiscation/Raid/Arrest||Possession (gun(s) found during commission of other crimes)", + "latitude": 38.6303, + "location_description": "\"Central West End\"", + "longitude": -90.1936, + "n_guns_involved": 1.0, + "notes": "Firearm recovered during Stockley verdict protests in Central West End.", + "participant_age": null, + "participant_age_group": null, + "participant_gender": null, + "participant_name": null, + "participant_relationship": null, + "participant_status": null, + "participant_type": null, + "sources": "https://twitter.com/SLMPD/status/908937558779924480", + "state_house_district": 78.0, + "state_senate_district": 5.0 + } + }, + { + "scenario_id": "127", + "features": { + "incident_id": 563601, + "date": "2016-05-18", + "state": "Mississippi", + "city_or_county": "Jackson", + "address": "Beasley Road", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/563601", + "source_url": "http://wjtv.com/2016/05/19/woman-speaks-out-about-robbing-and-kidnapping/", + "incident_url_fields_missing": "False", + "congressional_district": 2.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Non-Shooting Incident||Sex crime involving firearm||Kidnapping/abductions/hostage||Brandishing/flourishing/open carry/lost/found", + "latitude": 32.385, + "location_description": null, + "longitude": -90.1876, + "n_guns_involved": null, + "notes": null, + "participant_age": null, + "participant_age_group": null, + "participant_gender": "0::Female||1::Male||2::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Unharmed||1::Unharmed||2::Unharmed", + "participant_type": "0::Victim||1::Subject-Suspect||2::Subject-Suspect", + "sources": "http://wjtv.com/2016/05/19/woman-speaks-out-about-robbing-and-kidnapping/", + "state_house_district": 72.0, + "state_senate_district": 26.0 + } + }, + { + "scenario_id": "128", + "features": { + "incident_id": 411470, + "date": "2015-09-08", + "state": "Washington", + "city_or_county": "Seattle", + "address": "400 block of 26 Ave.", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/411470", + "source_url": "http://spdblotter.seattle.gov/2015/09/09/central-district-arrest-leads-to-narcotics-and-stolen-gun/", + "incident_url_fields_missing": "False", + "congressional_district": 9.0, + "gun_stolen": "0::Stolen", + "gun_type": "0::40 SW", + "incident_characteristics": "Non-Shooting Incident||Drug involvement||ATF/LE Confiscation/Raid/Arrest||Possession (gun(s) found during commission of other crimes)||Possession of gun by felon or prohibited person||Stolen/Illegally owned gun{s} recovered during arrest/warrant", + "latitude": 47.6061, + "location_description": null, + "longitude": -122.299, + "n_guns_involved": 1.0, + "notes": ".40 cal handgun, stolen from a Tacoma home a few days previous.", + "participant_age": "0::24", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Unharmed, Arrested", + "participant_type": "0::Subject-Suspect", + "sources": "http://spdblotter.seattle.gov/2015/09/09/central-district-arrest-leads-to-narcotics-and-stolen-gun/", + "state_house_district": 37.0, + "state_senate_district": 37.0 + } + }, + { + "scenario_id": "129", + "features": { + "incident_id": 913505, + "date": "2017-08-14", + "state": "Kansas", + "city_or_county": "Topeka", + "address": "2300 block of SE California Ave", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/913505", + "source_url": "http://www.wibw.com/content/news/Chase-suspect-apprehended-in-Oakland-Neighborhood--440437623.html", + "incident_url_fields_missing": "False", + "congressional_district": 2.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Non-Shooting Incident||ATF/LE Confiscation/Raid/Arrest||Possession (gun(s) found during commission of other crimes)", + "latitude": 39.0256, + "location_description": null, + "longitude": -95.6513, + "n_guns_involved": 1.0, + "notes": "Gun recovered following pursuit.", + "participant_age": "0::37", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Unharmed, Arrested", + "participant_type": "0::Subject-Suspect", + "sources": "http://www.wibw.com/content/news/Chase-suspect-apprehended-in-Oakland-Neighborhood--440437623.html", + "state_house_district": 58.0, + "state_senate_district": 19.0 + } + }, + { + "scenario_id": "130", + "features": { + "incident_id": 544316, + "date": "2016-03-09", + "state": "Louisiana", + "city_or_county": "Metairie", + "address": "1000 block of Aurora Avenue", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/544316", + "source_url": "http://www.nola.com/crime/index.ssf/2016/04/suspect_in_metairie_mandeville.html#incart_river_index", + "incident_url_fields_missing": "False", + "congressional_district": 1.0, + "gun_stolen": "0::Stolen||1::Stolen||2::Stolen||3::Stolen||4::Stolen||5::Stolen||6::Stolen||7::Stolen||8::Stolen||9::Stolen||10::Stolen||11::Stolen||12::Stolen||13::Stolen", + "gun_type": "0::223 Rem [AR-15]||1::Shotgun||2::Shotgun||3::Shotgun||4::Rifle||5::Rifle||6::Rifle||7::Rifle||8::Rifle||9::Rifle||10::Handgun||11::Handgun||12::Handgun||13::Handgun", + "incident_characteristics": "Non-Shooting Incident||Possession of gun by felon or prohibited person||Gun(s) stolen from owner", + "latitude": 30.0067, + "location_description": null, + "longitude": -90.14, + "n_guns_involved": 14.0, + "notes": "14 guns stolen from police officer's house, including an AR-15 assault rifle, 6 other rifles & 3 shotguns. Three guns have been recovered as well as others.", + "participant_age": "0::30||1::30", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": "0::Brian Ernst||1::Raymond Kimble III", + "participant_relationship": null, + "participant_status": "0::Unharmed, Arrested||1::Unharmed, Arrested", + "participant_type": "0::Subject-Suspect||1::Subject-Suspect", + "sources": "http://www.nola.com/crime/index.ssf/2016/04/14_guns_electronics_stolen_in.html#incart_river_index||http://www.nola.com/crime/index.ssf/2016/04/suspect_in_metairie_mandeville.html#incart_river_index", + "state_house_district": 80.0, + "state_senate_district": 9.0 + } + }, + { + "scenario_id": "131", + "features": { + "incident_id": 699226, + "date": "2016-11-11", + "state": "Georgia", + "city_or_county": "Dekalb (county)", + "address": "County Road 9055", + "n_killed": 0, + "n_injured": 2, + "incident_url": "http://www.gunviolencearchive.org/incident/699226", + "source_url": "http://www.northwestgeorgianews.com/cherokee_county/two-shot-and-two-stabbed-in-dekalb-county/article_588b8090-a915-11e6-881c-bbf52c818c67.html", + "incident_url_fields_missing": "False", + "congressional_district": 4.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Wounded/Injured||Drive-by (car to street, car to car)", + "latitude": 33.7956, + "location_description": null, + "longitude": -84.2279, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": null, + "participant_age_group": "0::Adult 18+||1::Teen 12-17", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured||1::Injured", + "participant_type": "0::Victim||1::Victim", + "sources": "http://www.northwestgeorgianews.com/cherokee_county/two-shot-and-two-stabbed-in-dekalb-county/article_588b8090-a915-11e6-881c-bbf52c818c67.html", + "state_house_district": 86.0, + "state_senate_district": 41.0 + } + }, + { + "scenario_id": "132", + "features": { + "incident_id": 872756, + "date": "2017-06-16", + "state": "Virginia", + "city_or_county": "Fredericksburg", + "address": "Altoona Dr", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/872756", + "source_url": "http://www.fredericksburg.com/news/crime_courts/fredericksburg-police-investigating-teen-s-accidental-shooting/article_5183701f-d369-5bbb-a412-3c23392e22ca.html", + "incident_url_fields_missing": "False", + "congressional_district": 1.0, + "gun_stolen": "0::Unknown||1::Unknown", + "gun_type": "0::Handgun||1::Handgun", + "incident_characteristics": "Shot - Wounded/Injured||Non-Aggression Incident||Accidental Shooting||Accidental Shooting - Injury||Accidental/Negligent Discharge||Playing with gun", + "latitude": 38.2932, + "location_description": null, + "longitude": -77.4979, + "n_guns_involved": 2.0, + "notes": "AD shot cousin in neck with revolver, 2 handguns recover from home", + "participant_age": "0::13||1::16", + "participant_age_group": "0::Teen 12-17||1::Teen 12-17", + "participant_gender": "0::Male||1::Male", + "participant_name": null, + "participant_relationship": "1::Family", + "participant_status": "0::Injured||1::Unharmed", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://www.fredericksburg.com/news/crime_courts/fredericksburg-police-investigating-teen-s-accidental-shooting/article_5183701f-d369-5bbb-a412-3c23392e22ca.html", + "state_house_district": 88.0, + "state_senate_district": 17.0 + } + }, + { + "scenario_id": "133", + "features": { + "incident_id": 1011299, + "date": "2017-12-20", + "state": "Florida", + "city_or_county": "Jacksonville", + "address": "1591 Lane Ave S", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/1011299", + "source_url": "https://www.news4jax.com/news/local/jacksonville/jacksonville-shot-dead-at-westside-apartment-complex", + "incident_url_fields_missing": "False", + "congressional_district": 5.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - 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Wounded/Injured||Accidental Shooting||Accidental Shooting - Injury||Accidental/Negligent Discharge||Playing with gun", + "latitude": 34.9115, + "location_description": null, + "longitude": -81.049, + "n_guns_involved": null, + "notes": "party, large group of people playing with gun, dropped accidentally shot vic in foot", + "participant_age": "0::17", + "participant_age_group": "0::Teen 12-17", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured", + "participant_type": "0::Victim", + "sources": "http://www.heraldonline.com/news/local/crime/article22360197.html", + "state_house_district": 49.0, + "state_senate_district": 17.0 + } + }, + { + "scenario_id": "135", + "features": { + "incident_id": 915104, + "date": "2017-08-17", + "state": "Arizona", + "city_or_county": "Phoenix", + "address": "2601 E Roosevelt St", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/915104", + "source_url": "http://www.azcentral.com/story/news/local/phoenix/2017/08/18/man-shot-dead-police-after-wielding-knives-hospital-idd/580661001/", + "incident_url_fields_missing": "False", + "congressional_district": 7.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Institution/Group/Business||Officer Involved Incident||Officer Involved Shooting - subject/suspect/perpetrator killed||Officer Involved Shooting - subject/suspect/perpetrator unarmed", + "latitude": 33.4576, + "location_description": "Maricopa Medical Center", + "longitude": -112.026, + "n_guns_involved": 1.0, + "notes": "subject waving knife", + "participant_age": "0::28", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Edgar Ramirez Carreto", + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Subject-Suspect", + "sources": "http://www.azcentral.com/story/news/local/phoenix-breaking/2017/08/17/phoenix-police-shooting-injure-suspect/576002001/||http://www.azcentral.com/story/news/local/phoenix/2017/08/18/man-shot-dead-police-after-wielding-knives-hospital-idd/580661001/", + "state_house_district": 27.0, + "state_senate_district": 27.0 + } + }, + { + "scenario_id": "136", + "features": { + "incident_id": 122091, + "date": "2014-04-02", + "state": "Louisiana", + "city_or_county": "Houma", + "address": "3600 block of Thomas Drive ", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/122091", + "source_url": "http://www.wwltv.com/news/lafourche-terrebonne/One-arrested-one-sought-in-home-invasion-that-left-man-critical-253945841.html", + "incident_url_fields_missing": "False", + "congressional_district": 1.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured||Home Invasion||Home Invasion - Resident injured||Armed robbery with injury/death and/or evidence of DGU found", + "latitude": 29.5951, + "location_description": null, + "longitude": -90.6706, + "n_guns_involved": null, + "notes": null, + "participant_age": "2::22||3::30", + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+||3::Adult 18+", + "participant_gender": "0::Male||1::Female||2::Male||3::Male", + "participant_name": "2::Joseph Hayes||3::John Ross Harold", + "participant_relationship": null, + "participant_status": "0::Injured||1::Unharmed||2::Unharmed||3::Unharmed", + "participant_type": "0::Victim||1::Victim||2::Subject-Suspect||3::Subject-Suspect", + "sources": "http://www.wwltv.com/news/lafourche-terrebonne/One-arrested-one-sought-in-home-invasion-that-left-man-critical-253945841.html", + "state_house_district": 53.0, + "state_senate_district": 20.0 + } + }, + { + "scenario_id": "137", + "features": { + "incident_id": 385348, + "date": "2015-07-30", + "state": "New York", + "city_or_county": "Brooklyn", + "address": "Cornelia Street and Bushwick Avenue", + "n_killed": 0, + "n_injured": 3, + "incident_url": "http://www.gunviolencearchive.org/incident/385348", + "source_url": "http://www.nydailynews.com/new-york/brooklyn-triple-shooting-gang-related-cops-article-1.2310595", + "incident_url_fields_missing": "False", + "congressional_district": 8.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured||Gang involvement", + "latitude": 40.6888, + "location_description": null, + "longitude": -73.9169, + "n_guns_involved": null, + "notes": "Bushwick, playing Skelly, cards, 4 perps total, 1 shooter, poss related to Patchen Ave shooting", + "participant_age": "0::16||1::18||2::23", + "participant_age_group": "0::Teen 12-17||1::Adult 18+||2::Adult 18+||3::Adult 18+", + "participant_gender": "0::Male||1::Male||2::Male||3::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured||1::Injured||2::Injured||3::Unharmed", + "participant_type": "0::Victim||1::Victim||2::Victim||3::Subject-Suspect", + "sources": "http://www.dnainfo.com/new-york/20150731/bushwick/3-men-shot-while-playing-street-game-bushwick-police-say||http://www.nydailynews.com/new-york/brooklyn-triple-shooting-gang-related-cops-article-1.2310595", + "state_house_district": 54.0, + "state_senate_district": 18.0 + } + }, + { + "scenario_id": "138", + "features": { + "incident_id": 270706, + "date": "2014-12-28", + "state": "Pennsylvania", + "city_or_county": "New Castle", + "address": null, + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/270706", + "source_url": "http://www.nbcphiladelphia.com/news/local/Police-Questioning-Several-After-Young-Man-Found-Shot-in-Home-287009081.html", + "incident_url_fields_missing": "False", + "congressional_district": 3.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 41.0503, + "location_description": "Neshannock Township", + "longitude": -80.3626, + "n_guns_involved": 1.0, + "notes": "41.050313, -80.362614", + "participant_age": "0::20", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Victim", + "sources": "http://www.nbcphiladelphia.com/news/local/Police-Questioning-Several-After-Young-Man-Found-Shot-in-Home-287009081.html", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "139", + "features": { + "incident_id": 854201, + "date": "2016-09-01", + "state": "Oklahoma", + "city_or_county": "Creek (county)", + "address": null, + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/854201", + "source_url": "http://www.tulsaworld.com/homepagelatest/suspect-in-shooting-father-allegedly-place-pipe-bomb-in-shooting/article_0abaff65-b54c-5c30-885e-a2733e301576.html", + "incident_url_fields_missing": "False", + "congressional_district": 3.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Handgun", + "incident_characteristics": "Shots Fired - No Injuries", + "latitude": 35.9981, + "location_description": null, + "longitude": -96.1245, + "n_guns_involved": 1.0, + "notes": "Suspect fired shots at woman. Incident date unspecified.", + "participant_age": "0::53", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Berry Albert Nichols", + "participant_relationship": null, + "participant_status": "0::Unharmed, Arrested", + "participant_type": "0::Subject-Suspect", + "sources": "http://www.tulsaworld.com/homepagelatest/suspect-in-shooting-father-allegedly-place-pipe-bomb-in-shooting/article_0abaff65-b54c-5c30-885e-a2733e301576.html", + "state_house_district": 30.0, + "state_senate_district": 12.0 + } + }, + { + "scenario_id": "140", + "features": { + "incident_id": 915455, + "date": "2017-08-17", + "state": "Oregon", + "city_or_county": "Klamath Falls", + "address": "Altamont Dr and Boardman Ave", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/915455", + "source_url": "http://www.heraldandnews.com/news/man-arrested-for-shooting-at-robbery-victim/article_049142be-7d57-57eb-b0f0-305e515178b1.html", + "incident_url_fields_missing": "False", + "congressional_district": 2.0, + "gun_stolen": "0::Stolen||1::Stolen", + "gun_type": "0::Handgun||1::Unknown", + "incident_characteristics": "Shots Fired - 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Herrera Kindelan", + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Victim", + "sources": "https://wsvn.com/news/local/man-dies-after-being-shot-in-face-in-little-havana/||https://www.local10.com/news/local/miami/luis-herrera-kindelan-identified-as-victim-killed-in-little-havana-shooting", + "state_house_district": 113.0, + "state_senate_district": 37.0 + } + }, + { + "scenario_id": "146", + "features": { + "incident_id": 792202, + "date": "2017-03-05", + "state": "Illinois", + "city_or_county": "Peoria", + "address": "2100 North North Street", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/792202", + "source_url": "https://spotcrime.com/crime/90236075-45276b6da95e1ec878b7c896b9ab9479", + "incident_url_fields_missing": "False", + "congressional_district": 17.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shots Fired - No Injuries", + "latitude": 40.7128, + "location_description": null, + "longitude": -89.5986, + "n_guns_involved": 1.0, + "notes": "Shotspotter, 1 round;", + "participant_age": null, + "participant_age_group": null, + "participant_gender": null, + "participant_name": null, + "participant_relationship": null, + "participant_status": null, + "participant_type": null, + "sources": "https://spotcrime.com/crime/90236075-45276b6da95e1ec878b7c896b9ab9479", + "state_house_district": 92.0, + "state_senate_district": 46.0 + } + }, + { + "scenario_id": "147", + "features": { + "incident_id": 663880, + "date": "2016-09-23", + "state": "Oklahoma", + "city_or_county": "Sulphur", + "address": "2214 W. 14th Street", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/663880", + "source_url": "http://www.kxii.com/content/news/Sulphur-Police-arrest-man-who-displayed-gun-at-work-394641441.html", + "incident_url_fields_missing": "False", + "congressional_district": 4.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Non-Shooting Incident||Possession of gun by felon or prohibited person||Brandishing/flourishing/open carry/lost/found", + "latitude": 34.5222, + "location_description": "Kerr Machine Corporation", + "longitude": -96.985, + "n_guns_involved": 1.0, + "notes": "Suspect brought gun to workplace.", + "participant_age": null, + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Charles Beaver", + "participant_relationship": "0::Co-worker", + "participant_status": "0::Unharmed, Arrested", + "participant_type": "0::Subject-Suspect", + "sources": "http://www.kxii.com/content/news/Sulphur-Police-arrest-man-who-displayed-gun-at-work-394641441.html", + "state_house_district": 22.0, + "state_senate_district": 14.0 + } + }, + { + "scenario_id": "148", + "features": { + "incident_id": 988907, + "date": "2017-11-11", + "state": "Tennessee", + "city_or_county": "Jackson", + "address": "309 N Highland Ave", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/988907", + "source_url": "http://www.wbbjtv.com/2017/11/15/man-charged-firing-gunshot-downtown-jackson/", + "incident_url_fields_missing": "False", + "congressional_district": 8.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::38 Spl", + "incident_characteristics": "Shots Fired - No Injuries", + "latitude": 35.6199, + "location_description": "Abel’s Way", + "longitude": -88.8194, + "n_guns_involved": 1.0, + "notes": "downtown, man shot at another man in parking lot, .38-caliber revolver", + "participant_age": "0::48", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Alfred Scott", + "participant_relationship": null, + "participant_status": "0::Unharmed, Arrested", + "participant_type": "0::Subject-Suspect", + "sources": "http://www.wbbjtv.com/2017/11/15/man-charged-firing-gunshot-downtown-jackson/", + "state_house_district": 80.0, + "state_senate_district": 27.0 + } + }, + { + "scenario_id": "150", + "features": { + "incident_id": 158870, + "date": "2014-07-15", + "state": "Texas", + "city_or_county": "El Paso", + "address": "5900 Deer Avenue and Pheasant Street", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/158870", + "source_url": "http://www.elpasoproud.com/news/local/el-paso-news/judge-issues-acquittal-for-2014-murder-suspect", + "incident_url_fields_missing": "False", + "congressional_district": null, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": null, + "location_description": null, + "longitude": null, + "n_guns_involved": 1.0, + "notes": "1 killed.", + "participant_age": "0::22", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Dominique Kish", + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Victim", + "sources": "http://www.ktsm.com/news/el-paso-police-investigate-deadly-overnight-shooting||http://www.ktsm.com/news/crimebeat/police-identify-22-year-old-murder-victim||http://www.kfoxtv.com/news/features/top-stories/stories/man-arrested-connection-northeast-el-paso-deadly-shooting-6507.shtml#.VB||http://www.elpasoproud.com/news/local/el-paso-news/judge-issues-acquittal-for-2014-murder-suspect", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "151", + "features": { + "incident_id": 421281, + "date": "2015-09-27", + "state": "Ohio", + "city_or_county": "Akron", + "address": "Cutler Parkway", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/421281", + "source_url": "http://www.cleveland.com/akron/index.ssf/2015/09/armed_robbers_handcuff_akron_m.html", + "incident_url_fields_missing": "False", + "congressional_district": 13.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Non-Shooting Incident||Home Invasion||Home Invasion - No death or injury||Possession (gun(s) found during commission of other crimes)", + "latitude": 41.067, + "location_description": null, + "longitude": -81.471, + "n_guns_involved": null, + "notes": "Man bound, mouth duct-taped shut by 3 armed home invaders; home, other occupants robbed; multiple guns mentioned.", + "participant_age": "0::36", + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+||3::Adult 18+", + "participant_gender": "0::Male||1::Male||2::Male||3::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Unharmed||1::Unharmed||2::Unharmed||3::Unharmed", + "participant_type": "0::Victim||1::Subject-Suspect||2::Subject-Suspect||3::Subject-Suspect", + "sources": "http://www.cleveland.com/akron/index.ssf/2015/09/armed_robbers_handcuff_akron_m.html", + "state_house_district": 35.0, + "state_senate_district": 28.0 + } + }, + { + "scenario_id": "152", + "features": { + "incident_id": 115630, + "date": "2014-03-17", + "state": "Kentucky", + "city_or_county": "Letcher County", + "address": "Boot Hill Road", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/115630", + "source_url": "http://www.wkyt.com/news/state/headlines/One-man-dead-following-shooting-in-Letcher-County-250554311.html", + "incident_url_fields_missing": "False", + "congressional_district": 5.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - 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Wounded/Injured", + "latitude": 34.2264, + "location_description": null, + "longitude": -118.549, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": "0::24||1::18", + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+||3::Adult 18+", + "participant_gender": "0::Male||1::Male||2::Male||3::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured||1::Injured||2::Unharmed||3::Unharmed", + "participant_type": "0::Victim||1::Victim||2::Subject-Suspect||3::Subject-Suspect", + "sources": "http://www.dailynews.com/2017/10/28/2-men-hit-in-northridge-double-shooting/", + "state_house_district": 45.0, + "state_senate_district": 27.0 + } + }, + { + "scenario_id": "154", + "features": { + "incident_id": 809748, + "date": "2017-04-02", + "state": "Massachusetts", + "city_or_county": "Boston", + "address": "22 Fenno Street", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/809748", + "source_url": "http://bpdnews.com/news/2017/4/2/one-less-gun-officers-recover-two-firearms-after-facing-violent-armed-suspects-in-two-separate-incidents", + "incident_url_fields_missing": "False", + "congressional_district": 7.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Handgun", + "incident_characteristics": "Shots Fired - No Injuries||Drug involvement||ShotSpotter||ATF/LE Confiscation/Raid/Arrest||Possession (gun(s) found during commission of other crimes)||Possession of gun by felon or prohibited person", + "latitude": 42.3228, + "location_description": "Roxbury", + "longitude": -71.0845, + "n_guns_involved": 1.0, + "notes": "ShotSpotter activation area of Warren Street; LEO heard gunfire @ 44 Kensington Park; 1 male fled, revolver in waistband, two plastic bags of crack", + "participant_age": "0::24", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Tarell Wheeler", + "participant_relationship": null, + "participant_status": "0::Arrested", + "participant_type": "0::Subject-Suspect", + "sources": "http://bpdnews.com/news/2017/4/2/one-less-gun-officers-recover-two-firearms-after-facing-violent-armed-suspects-in-two-separate-incidents", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "155", + "features": { + "incident_id": 165516, + "date": "2014-07-29", + "state": "Illinois", + "city_or_county": "Danville", + "address": "400 block of Chandler Street", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/165516", + "source_url": "http://www.news-gazette.com/news/local/2014-07-28/two-hospitalized-after-weekend-shootings-danville.html", + "incident_url_fields_missing": "False", + "congressional_district": 15.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured", + "latitude": 40.1306, + "location_description": null, + "longitude": -87.6398, + "n_guns_involved": null, + "notes": "Man found shot, injured; abdomen.", + "participant_age": "0::28", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured", + "participant_type": "0::Victim", + "sources": "http://www.news-gazette.com/news/local/2014-07-28/two-hospitalized-after-weekend-shootings-danville.html", + "state_house_district": 104.0, + "state_senate_district": 52.0 + } + }, + { + "scenario_id": "156", + "features": { + "incident_id": 519699, + "date": "2016-03-10", + "state": "Pennsylvania", + "city_or_county": "York", + "address": "478 Salem Avenue", + "n_killed": 0, + "n_injured": 2, + "incident_url": "http://www.gunviolencearchive.org/incident/519699", + "source_url": "http://fox43.com/2016/03/10/york-city-police-investigate-overnight-shooting/#", + "incident_url_fields_missing": "False", + "congressional_district": 4.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured", + "latitude": 39.9569, + "location_description": null, + "longitude": -76.7374, + "n_guns_involved": null, + "notes": null, + "participant_age": null, + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Female||1::Female", + "participant_name": "0::Latica Chavers||1::Tammy Green", + "participant_relationship": null, + "participant_status": "0::Injured||1::Injured", + "participant_type": "0::Victim||1::Victim", + "sources": "http://www.ydr.com/story/news/local/2016/03/10/one-injured-early-morning-shooting-york-city/81572922/||http://www.ydr.com/videos/news/crime/2016/03/10/81573992/||http://fox43.com/2016/03/10/york-city-police-investigate-overnight-shooting/#", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "157", + "features": { + "incident_id": 918147, + "date": "2017-08-21", + "state": "Indiana", + "city_or_county": "Indianapolis", + "address": "E 42nd St and Mitthoeffer", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/918147", + "source_url": "http://www.wthr.com/article/one-dead-in-shooting-at-42nd-and-mitthoeffer", + "incident_url_fields_missing": "False", + "congressional_district": 7.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 39.8336, + "location_description": null, + "longitude": -85.9912, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": null, + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Killed", + "participant_type": "0::Victim", + "sources": "http://www.wthr.com/article/one-dead-in-shooting-at-42nd-and-mitthoeffer", + "state_house_district": 95.0, + "state_senate_district": 34.0 + } + }, + { + "scenario_id": "158", + "features": { + "incident_id": 500612, + "date": "2016-02-06", + "state": "Illinois", + "city_or_county": "Chicago", + "address": "1800 block of South Central Park", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/500612", + "source_url": "http://chicago.suntimes.com/news/7/71/1307679/man-hurt-lawndale-shooting", + "incident_url_fields_missing": "False", + "congressional_district": 7.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured", + "latitude": 41.857, + "location_description": null, + "longitude": -87.7155, + "n_guns_involved": null, + "notes": "Lawndale - man shot, upper thigh; good;", + "participant_age": "0::24", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured", + "participant_type": "0::Victim", + "sources": "http://chicago.suntimes.com/news/7/71/1307679/man-hurt-lawndale-shooting", + "state_house_district": 9.0, + "state_senate_district": 5.0 + } + }, + { + "scenario_id": "159", + "features": { + "incident_id": 114883, + "date": "2014-03-13", + "state": "Arkansas", + "city_or_county": "Hot Springs", + "address": "515 Higdon Ferry Rd. ", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/114883", + "source_url": "http://www.arkansasmatters.com/story/d/story/womans-body-found-inside-hot-springs-home/35605/Ewjj7NmEn066FLxrR8plFg", + "incident_url_fields_missing": "False", + "congressional_district": 4.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", + "latitude": 34.4789, + "location_description": null, + "longitude": -93.0614, + "n_guns_involved": null, + "notes": null, + "participant_age": null, + "participant_age_group": "0:Adult 18+", + "participant_gender": "0:Female", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0:Killed", + "participant_type": "0:Victim", + "sources": "http://www.arkansasmatters.com/story/d/story/womans-body-found-inside-hot-springs-home/35605/Ewjj7NmEn066FLxrR8plFg", + "state_house_district": 24.0, + "state_senate_district": 14.0 + } + }, + { + "scenario_id": "160", + "features": { + "incident_id": 704055, + "date": "2016-11-17", + "state": "Nevada", + "city_or_county": "West Wendover", + "address": "1870 Plateau Way", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/704055", + "source_url": "http://www.reviewjournal.com/crime/shootings/utah-man-wounded-shootout-nevada-officers-also-had-homemade-bomb", + "incident_url_fields_missing": "False", + "congressional_district": 2.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Wounded/Injured||Institution/Group/Business||Shots Fired - No Injuries||Officer Involved Incident||Officer Involved Shooting - subject/suspect/perpetrator shot||Shootout (where VENN diagram of shooters and victims overlap)||Bar/club incident - in or around establishment||Possession of gun by felon or prohibited person||Pistol-whipping", + "latitude": 40.7392, + "location_description": "Southern X-Posure", + "longitude": -114.078, + "n_guns_involved": 1.0, + "notes": "Suspect fired shots at club, fled police, crashed, shot at officers, and was hit by return fire. several guns and crude explosive found in car.", + "participant_age": "0::30", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Anthony Martinez", + "participant_relationship": null, + "participant_status": "0::Injured", + "participant_type": "0::Subject-Suspect", + "sources": "http://www.reviewjournal.com/crime/shootings/police-elko-county-wound-man-fleeing-after-strip-club-shooting||http://www.deseretnews.com/article/865667514/Man-injured-in-West-Wendover-police-shootout-as-squad-tries-to-dismantle-IED.html||http://www.coyote-tv.com/2016/11/21/update-on-thursday-shooting-explosive-device-in-the-suspect-car/||http://www.reviewjournal.com/crime/shootings/utah-man-wounded-shootout-nevada-officers-also-had-homemade-bomb", + "state_house_district": 33.0, + "state_senate_district": 19.0 + } + }, + { + "scenario_id": "161", + "features": { + "incident_id": 696983, + "date": "2016-11-08", + "state": "Oregon", + "city_or_county": "Springfield", + "address": "1100 block of T Street", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/696983", + "source_url": "http://kval.com/news/local/police-man-fired-shotgun-in-street-to-celebrate-election-results", + "incident_url_fields_missing": "False", + "congressional_district": null, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shots Fired - No Injuries||Non-Aggression Incident||Under the influence of alcohol or drugs (only applies to the subject/suspect/perpetrator )||Brandishing/flourishing/open carry/lost/found", + "latitude": null, + "location_description": null, + "longitude": null, + "n_guns_involved": 1.0, + "notes": "Celebratory gunfire, intox suspect fired shotgun or rifle in air for election results", + "participant_age": "0::55", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Mitchell Bratton", + "participant_relationship": null, + "participant_status": "0::Unharmed, Arrested", + "participant_type": "0::Subject-Suspect", + "sources": "http://www.seattletimes.com/seattle-news/crime/oregon-man-jailed-after-celebrating-trump-win-with-gunfire/?utm_source=RSS&utm_medium=Referral&utm_campaign=RSS_seattle-news||http://kval.com/news/local/police-man-fired-shotgun-in-street-to-celebrate-election-results", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "162", + "features": { + "incident_id": 290517, + "date": "2015-02-06", + "state": "New Mexico", + "city_or_county": "Albuquerque", + "address": "224 Atrisco Vista SW", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/290517", + "source_url": "http://www.koat.com/news/swat-team-called-to-southwest-albuquerque/31144098", + "incident_url_fields_missing": "False", + "congressional_district": 1.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Non-Shooting Incident||Shots fired, no action (reported, no evidence found)", + "latitude": 35.0671, + "location_description": null, + "longitude": -106.767, + "n_guns_involved": null, + "notes": "Witness informed SWAT agents that someone may have been shot, but neither victim nor perpetrator have been located.", + "participant_age": null, + "participant_age_group": null, + "participant_gender": null, + "participant_name": null, + "participant_relationship": null, + "participant_status": null, + "participant_type": null, + "sources": "http://www.koat.com/news/swat-team-called-to-southwest-albuquerque/31144098", + "state_house_district": 26.0, + "state_senate_district": 14.0 + } + }, + { + "scenario_id": "163", + "features": { + "incident_id": 542111, + "date": "2016-04-16", + "state": "Indiana", + "city_or_county": "Indianapolis", + "address": "4400 block of North Campbell Avenue", + "n_killed": 1, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/542111", + "source_url": "http://www.indystar.com/story/news/crime/2016/04/17/police-investigating-fatal-shooting-north-side/83166048/?utm_source=feedblitz&utm_medium=FeedBlitzRss&utm_campaign=FeedBlitzRss&utm_content=Police+investigating+fatal+shooting+on+north+side", + "incident_url_fields_missing": "False", + "congressional_district": 7.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured||Shot - Dead (murder, accidental, suicide)||Domestic Violence", + "latitude": 39.8367, + "location_description": null, + "longitude": -86.0663, + "n_guns_involved": null, + "notes": "One killed, 1 wounded, in unclear incident involving a woman;", + "participant_age": "0::30", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": "0::Charles Turner", + "participant_relationship": null, + "participant_status": "0::Killed||1::Injured", + "participant_type": "0::Victim||1::Victim", + "sources": "http://fox59.com/2016/04/16/one-person-killed-another-in-the-hospital-after-northeast-side-shooting/||http://www.indystar.com/story/news/crime/2016/04/17/police-investigating-fatal-shooting-north-side/83166048/?utm_source=feedblitz&utm_medium=FeedBlitzRss&utm_campaign=FeedBlitzRss&utm_content=Police+investigating+fatal+shooting+on+north+side", + "state_house_district": 95.0, + "state_senate_district": 34.0 + } + }, + { + "scenario_id": "164", + "features": { + "incident_id": 873861, + "date": "2017-06-22", + "state": "South Carolina", + "city_or_county": "North Charleston", + "address": "2000 block of Mott Avenue", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/873861", + "source_url": "http://www.wtvm.com/story/35731164/police-investigating-after-one-shot-in-n-charleston", + "incident_url_fields_missing": "False", + "congressional_district": 6.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Wounded/Injured", + "latitude": 32.8504, + "location_description": null, + "longitude": -79.9785, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": null, + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured", + "participant_type": "0::Victim", + "sources": "http://www.wtvm.com/story/35731164/police-investigating-after-one-shot-in-n-charleston", + "state_house_district": 109.0, + "state_senate_district": 42.0 + } + }, + { + "scenario_id": "165", + "features": { + "incident_id": 758446, + "date": "2017-01-18", + "state": "Georgia", + "city_or_county": "Rome", + "address": "Old Summerville Road", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/758446", + "source_url": "http://www.northwestgeorgianews.com/rome/news/police_fire/man-accused-of-shooting-victim-in-leg/article_292b04bc-de06-11e6-8a50-777ec0d9a978.html", + "incident_url_fields_missing": "False", + "congressional_district": 14.0, + "gun_stolen": "0::Stolen||1::Stolen", + "gun_type": "0::Unknown||1::Unknown", + "incident_characteristics": "Shot - Wounded/Injured||Stolen/Illegally owned gun{s} recovered during arrest/warrant", + "latitude": 34.3279, + "location_description": null, + "longitude": -85.1805, + "n_guns_involved": 2.0, + "notes": null, + "participant_age": "1::22", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": "1::Robert Joe Farmer", + "participant_relationship": null, + "participant_status": "0::Injured||1::Unharmed, Arrested", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://www.northwestgeorgianews.com/rome/news/police_fire/man-accused-of-shooting-victim-in-leg/article_292b04bc-de06-11e6-8a50-777ec0d9a978.html", + "state_house_district": 13.0, + "state_senate_district": 52.0 + } + }, + { + "scenario_id": "166", + "features": { + "incident_id": 202136, + "date": "2014-09-27", + "state": "South Carolina", + "city_or_county": "Beech Island", + "address": "Swamp Road", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/202136", + "source_url": "http://www.aikenstandard.com/article/20140928/AIK0102/140929410/1008/blotter-for-sept-29", + "incident_url_fields_missing": "False", + "congressional_district": 2.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shots Fired - No Injuries", + "latitude": 33.4379, + "location_description": null, + "longitude": -81.8939, + "n_guns_involved": null, + "notes": "multiple perps seen leaving in vehicles after shots hit different homes; no injuries reported", + "participant_age": null, + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Female", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Unharmed||1::Unharmed", + "participant_type": "0::Victim||1::Victim", + "sources": "http://www.aikenstandard.com/article/20140928/AIK0102/140929410/1008/blotter-for-sept-29", + "state_house_district": 84.0, + "state_senate_district": 24.0 + } + }, + { + "scenario_id": "167", + "features": { + "incident_id": 526941, + "date": "2016-03-18", + "state": "Washington", + "city_or_county": "Seattle", + "address": "Rainier and Henderson", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/526941", + "source_url": "http://spdblotter.seattle.gov/2016/03/21/man-arrested-for-casual-disregard-of-traffic-bike-helmet-gun-laws/", + "incident_url_fields_missing": "False", + "congressional_district": 9.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Handgun", + "incident_characteristics": "Non-Shooting Incident||Possession (gun(s) found during commission of other crimes)||Possession of gun by felon or prohibited person", + "latitude": 47.5233, + "location_description": "Rainier Beach neighborhood", + "longitude": -122.27, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": "0::64", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Unharmed, Arrested", + "participant_type": "0::Subject-Suspect", + "sources": "http://spdblotter.seattle.gov/2016/03/21/man-arrested-for-casual-disregard-of-traffic-bike-helmet-gun-laws/", + "state_house_district": 37.0, + "state_senate_district": 37.0 + } + }, + { + "scenario_id": "168", + "features": { + "incident_id": 485152, + "date": "2016-01-10", + "state": "New York", + "city_or_county": "Brooklyn", + "address": "225 Parkside Avenue", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/485152", + "source_url": "http://www.dnainfo.com/new-york/20160114/crown-heights/crime-blotter-23-year-old-woman-mugged-on-busy-crown-heights-street", + "incident_url_fields_missing": "False", + "congressional_district": 9.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shots Fired - No Injuries", + "latitude": 40.6559, + "location_description": null, + "longitude": -73.9604, + "n_guns_involved": null, + "notes": "bullethole in wall, yelling heard, no inj reported", + "participant_age": null, + "participant_age_group": null, + "participant_gender": null, + "participant_name": null, + "participant_relationship": null, + "participant_status": null, + "participant_type": null, + "sources": "http://www.dnainfo.com/new-york/20160114/crown-heights/crime-blotter-23-year-old-woman-mugged-on-busy-crown-heights-street", + "state_house_district": 43.0, + "state_senate_district": 21.0 + } + }, + { + "scenario_id": "169", + "features": { + "incident_id": 276633, + "date": "2015-01-10", + "state": "North Dakota", + "city_or_county": "Minot", + "address": null, + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/276633", + "source_url": "http://www.kfyrtv.com/home/headlines/19-year-old-Arrested-For-Stolen-Firearm-288165421.html", + "incident_url_fields_missing": "False", + "congressional_district": 1.0, + "gun_stolen": "0::Stolen", + "gun_type": "0::Unknown", + "incident_characteristics": "Non-Shooting Incident||Possession (gun(s) found during commission of other crimes)||Stolen/Illegally owned gun{s} recovered during arrest/warrant", + "latitude": 48.2132, + "location_description": null, + "longitude": -101.283, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": "0::19", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Unharmed, Arrested", + "participant_type": "0::Subject-Suspect", + "sources": "http://www.kfyrtv.com/home/headlines/19-year-old-Arrested-For-Stolen-Firearm-288165421.html", + "state_house_district": 5.0, + "state_senate_district": 5.0 + } + }, + { + "scenario_id": "170", + "features": { + "incident_id": 107277, + "date": "2014-01-25", + "state": "South Carolina", + "city_or_county": "West Ashley", + "address": "Mulmar Street", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/107277", + "source_url": "http://www.live5news.com/story/24655457/stray-bullet-fired-into-home-homeowner-wants-more-patrols", + "incident_url_fields_missing": "False", + "congressional_district": 1.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shots fired, no action (reported, no evidence found)", + "latitude": 32.8146, + "location_description": null, + "longitude": -80.0076, + "n_guns_involved": null, + "notes": null, + "participant_age": null, + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Female||1::Male", + "participant_name": "0::Jane Edwards||1::Don Edwards", + "participant_relationship": null, + "participant_status": "0::Unharmed||1::Unharmed", + "participant_type": "0::Victim||1::Victim", + "sources": "http://www.live5news.com/story/24655457/stray-bullet-fired-into-home-homeowner-wants-more-patrols", + "state_house_district": 114.0, + "state_senate_district": 41.0 + } + }, + { + "scenario_id": "171", + "features": { + "incident_id": 567349, + "date": "2016-05-26", + "state": "Louisiana", + "city_or_county": "New Orleans", + "address": "4700 block of Dodt Ave", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/567349", + "source_url": "http://www.nola.com/crime/index.ssf/2016/05/man_wanted_for_producing_gun_d.html", + "incident_url_fields_missing": "False", + "congressional_district": 2.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Non-Shooting Incident||Brandishing/flourishing/open carry/lost/found", + "latitude": 30.0195, + "location_description": null, + "longitude": -89.9918, + "n_guns_involved": 1.0, + "notes": "New Orleans East", + "participant_age": "0::22", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Don Wells", + "participant_relationship": null, + "participant_status": "0::Unharmed", + "participant_type": "0::Subject-Suspect", + "sources": "http://www.nola.com/crime/index.ssf/2016/05/man_wanted_for_producing_gun_d.html", + "state_house_district": 99.0, + "state_senate_district": 4.0 + } + }, + { + "scenario_id": "172", + "features": { + "incident_id": 461773, + "date": "2015-11-13", + "state": "Florida", + "city_or_county": "Palm Beach Gardens (Riviera Beach)", + "address": null, + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/461773", + "source_url": "http://www.chron.com/news/crime/article/Police-Man-went-to-strip-club-after-shooting-6685533.php", + "incident_url_fields_missing": "False", + "congressional_district": 20.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - 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No Injuries||Domestic Violence", + "latitude": null, + "location_description": null, + "longitude": null, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": "0::71", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": "0::Significant others - current or former", + "participant_status": "0::Unharmed, Arrested", + "participant_type": "0::Subject-Suspect", + "sources": "http://www.wmcactionnews5.com/story/37642482/sheriff-road-closed-in-crenshaw-county-after-man-shot-at-wife", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "180", + "features": { + "incident_id": 807988, + "date": "2017-03-30", + "state": "Minnesota", + "city_or_county": "Grand Rapids", + "address": "1300 block of 4th Street Southeast", + "n_killed": 1, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/807988", + "source_url": "http://www.therepublic.com/2017/03/30/mn-shooting-grand-rapids/", + "incident_url_fields_missing": "False", + "congressional_district": 8.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Wounded/Injured||Shot - Dead (murder, accidental, suicide)||Suicide^||Attempted Murder/Suicide (one variable unsuccessful)||Child Involved Incident||Child injured (not child shooter)", + "latitude": 47.2278, + "location_description": null, + "longitude": -93.5098, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": "0::8||1::29", + "participant_age_group": "0::Child 0-11||1::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": "0::Wyatt Krauss||1::David Krauss", + "participant_relationship": null, + "participant_status": "0::Injured||1::Killed", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://kstp.com/news/father-son-shooting-grand-rapids/4441128/||http://www.therepublic.com/2017/03/30/mn-shooting-grand-rapids/", + "state_house_district": null, + "state_senate_district": 5.0 + } + }, + { + "scenario_id": "181", + "features": { + "incident_id": 372179, + "date": "2015-07-10", + "state": "Pennsylvania", + "city_or_county": "Philadelphia", + "address": "500 block of West Hill Creek Drive", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/372179", + "source_url": "http://philadelphia.cbslocal.com/2015/07/10/3-injured-in-philadelphia-shootings/", + "incident_url_fields_missing": "False", + "congressional_district": 13.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - 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No Injuries||Officer Involved Incident||Officer Involved Shooting - Shots fired, no injury||Animal shot/killed", + "latitude": 42.6946, + "location_description": null, + "longitude": -84.4253, + "n_guns_involved": 1.0, + "notes": "Officer shoots, wounds, dog running at him in warrant situation;", + "participant_age": null, + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Unharmed, Arrested", + "participant_type": "0::Subject-Suspect", + "sources": "http://www.lansingstatejournal.com/story/news/local/2015/08/27/man-surrenders-standoff-okemos-home/71289948/", + "state_house_district": 69.0, + "state_senate_district": 23.0 + } + }, + { + "scenario_id": "185", + "features": { + "incident_id": 366298, + "date": "2015-06-26", + "state": "Louisiana", + "city_or_county": "Jennings", + "address": "200 block of South Lake Arthur Ave.", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/366298", + "source_url": "http://www.kplctv.com/story/29420521/jennings-police-looking-into-possible-drive-by-shooting", + "incident_url_fields_missing": "False", + "congressional_district": 3.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shots Fired - No Injuries||Drive-by (car to street, car to car)||Possession of gun by felon or prohibited person", + "latitude": 30.2197, + "location_description": null, + "longitude": -92.6634, + "n_guns_involved": null, + "notes": null, + "participant_age": "0::31", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": "0::Michael Phillips Jr.", + "participant_relationship": null, + "participant_status": "0::Unharmed, Arrested", + "participant_type": "0::Subject-Suspect", + "sources": "http://www.kplctv.com/story/29420521/jennings-police-looking-into-possible-drive-by-shooting", + "state_house_district": 37.0, + "state_senate_district": 25.0 + } + }, + { + "scenario_id": "187", + "features": { + "incident_id": 1055809, + "date": "2018-02-21", + "state": "North Carolina", + "city_or_county": "Greenville", + "address": "2200 University Suites Dr", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/1055809", + "source_url": "http://www.reflector.com/News/2018/03/06/Greenville-Police-arrest-men-in-connection-to-home-invasion.html", + "incident_url_fields_missing": "False", + "congressional_district": null, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Non-Shooting Incident||Home Invasion||Home Invasion - 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No Injuries", + "latitude": 35.177, + "location_description": "Bojangles Famous Chicken n Biscuits", + "longitude": -80.7505, + "n_guns_involved": 1.0, + "notes": "fired shots during ar", + "participant_age": null, + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Unharmed", + "participant_type": "0::Subject-Suspect", + "sources": "http://www.wsoctv.com/news/local/shots-fired-inside-charlotte-bojangles/684231809", + "state_house_district": 100.0, + "state_senate_district": 40.0 + } + }, + { + "scenario_id": "191", + "features": { + "incident_id": 566738, + "date": "2016-05-26", + "state": "California", + "city_or_county": "Oakland", + "address": "1000 block of Webster Street", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/566738", + "source_url": "http://www.eastbaytimes.com/breaking-news/ci_29947312/oakland-man-shot-chinatown", + "incident_url_fields_missing": "False", + "congressional_district": 13.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - 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Wounded/Injured", + "latitude": null, + "location_description": null, + "longitude": null, + "n_guns_involved": 1.0, + "notes": null, + "participant_age": "1::59", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Female", + "participant_name": "0::Craig Cerny||1::Rhonda Cerny", + "participant_relationship": null, + "participant_status": "0::Injured||1::Unharmed, Arrested", + "participant_type": "0::Victim||1::Subject-Suspect", + "sources": "http://www.azcentral.com/story/news/local/scottsdale/2015/07/17/scottsdale-shooting-man-hospitalized/30319399/||http://www.azcentral.com/story/news/local/scottsdale/2017/02/13/scottsdale-woman-rhonda-cerny-sentenced-5-years-shooting-ex-husband/97845074/", + "state_house_district": null, + "state_senate_district": null + } + }, + { + "scenario_id": "196", + "features": { + "incident_id": 122025, + "date": "2014-04-04", + "state": "Alabama", + "city_or_county": "Booth", + "address": "County Road 81 ", + "n_killed": 1, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/122025", + "source_url": "http://www.wsfa.com/story/25164739/intruder-killed-by-autauga-co-homeowner-ided-second-suspect-arrested", + "incident_url_fields_missing": "False", + "congressional_district": 2.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Home Invasion||Home Invasion - subject/suspect/perpetrator killed||Defensive Use", + "latitude": 32.5219, + "location_description": null, + "longitude": -86.5697, + "n_guns_involved": null, + "notes": "shot man in hi def", + "participant_age": "1::18||2::32", + "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+", + "participant_gender": "0::Female||1::Male||2::Male", + "participant_name": "1::Mikel Steven Smith||2::James Bryan Vance", + "participant_relationship": null, + "participant_status": "0::Unharmed||1::Killed||2::Unharmed", + "participant_type": "0::Victim||1::Subject-Suspect||2::Subject-Suspect", + "sources": "http://www.timesdaily.com/news/state/article_0d80f1e3-ec40-523a-8b1d-f5007293e5bc.html||http://www.waka.com/news/montgomery-news/Sheriffs-Officials-Autauga-County-Woman-Shoots-Intruder-253904171.html||http://www.wsfa.com/story/25164739/intruder-killed-by-autauga-co-homeowner-ided-second-suspect-arrested", + "state_house_district": 42.0, + "state_senate_district": 30.0 + } + }, + { + "scenario_id": "197", + "features": { + "incident_id": 1039759, + "date": "2018-01-12", + "state": "Ohio", + "city_or_county": "Independence", + "address": "6020 Quarry Ln", + "n_killed": 0, + "n_injured": 0, + "incident_url": "http://www.gunviolencearchive.org/incident/1039759", + "source_url": "http://www.cleveland.com/independence/index.ssf/2018/01/exuberant_partygoer_cited_for.html#incart_river_index", + "incident_url_fields_missing": "False", + "congressional_district": 14.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Institution/Group/Business||Shots Fired - No Injuries", + "latitude": 41.3956, + "location_description": "Red Roof Inn", + "longitude": -81.6495, + "n_guns_involved": 1.0, + "notes": "celebratory gunfire", + "participant_age": null, + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Female", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Unharmed, Arrested", + "participant_type": "0::Subject-Suspect", + "sources": "http://www.cleveland.com/independence/index.ssf/2018/01/exuberant_partygoer_cited_for.html#incart_river_index", + "state_house_district": 6.0, + "state_senate_district": 24.0 + } + }, + { + "scenario_id": "198", + "features": { + "incident_id": 414689, + "date": "2015-09-14", + "state": "Illinois", + "city_or_county": "Chicago", + "address": "7200 block of South Union Avenue", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/414689", + "source_url": "http://www.dnainfo.com/chicago/20150916/englewood/englewood-shootings-leave-teen-girl-man-wounded", + "incident_url_fields_missing": "False", + "congressional_district": 1.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured||Possession (gun(s) found during commission of other crimes)", + "latitude": 41.7633, + "location_description": null, + "longitude": -87.6419, + "n_guns_involved": null, + "notes": "Englewood - 2 men fire into group on sidewalk, wound 16 y/o girl;", + "participant_age": "0::16", + "participant_age_group": "0::Teen 12-17", + "participant_gender": "0::Female", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured", + "participant_type": "0::Victim", + "sources": "http://www.dnainfo.com/chicago/20150916/englewood/englewood-shootings-leave-teen-girl-man-wounded", + "state_house_district": 31.0, + "state_senate_district": 16.0 + } + }, + { + "scenario_id": "199", + "features": { + "incident_id": 622476, + "date": "2016-08-05", + "state": "Illinois", + "city_or_county": "Chicago", + "address": "1500 block of South Harding Avenue", + "n_killed": 0, + "n_injured": 1, + "incident_url": "http://www.gunviolencearchive.org/incident/622476", + "source_url": "http://www.chicagotribune.com/news/local/breaking/ct-man-wounded-in-grand-crossing-shooting-20160805-story.html", + "incident_url_fields_missing": "False", + "congressional_district": 4.0, + "gun_stolen": "0::Unknown", + "gun_type": "0::Unknown", + "incident_characteristics": "Shot - Wounded/Injured||Non-Aggression Incident||Accidental Shooting||Accidental Shooting - Injury||Accidental/Negligent Discharge||Self-Inflicted (not suicide or suicide attempt - NO PERP)", + "latitude": 41.8006, + "location_description": null, + "longitude": -87.722, + "n_guns_involved": 1.0, + "notes": "Teen grazed leg; believed self-inflicted;", + "participant_age": "0::18", + "participant_age_group": "0::Adult 18+", + "participant_gender": "0::Male", + "participant_name": null, + "participant_relationship": null, + "participant_status": "0::Injured", + "participant_type": "0::Victim", + "sources": "http://www.chicagotribune.com/news/local/breaking/ct-man-wounded-in-grand-crossing-shooting-20160805-story.html", + "state_house_district": 1.0, + "state_senate_district": 1.0 + } + }, + { + "scenario_id": "200", + "features": { + "incident_id": 512461, + "date": "2016-02-24", + "state": "Pennsylvania", + "city_or_county": "Monessen", + "address": "600 block of McMahon Avenue", + "n_killed": 0, + "n_injured": 2, + "incident_url": "http://www.gunviolencearchive.org/incident/512461", + "source_url": "http://www.wpxi.com/videos/news/witnesses-say-victims-beat-before-being-shot-in/vC7z9/", + "incident_url_fields_missing": "False", + "congressional_district": 9.0, + "gun_stolen": null, + "gun_type": null, + "incident_characteristics": "Shot - Wounded/Injured", + "latitude": 40.1574, + "location_description": null, + "longitude": -79.8894, + "n_guns_involved": null, + "notes": null, + "participant_age": "0::24||1::26", + "participant_age_group": "0::Adult 18+||1::Adult 18+", + "participant_gender": "0::Male||1::Male", + "participant_name": "0::Travis Holmes||1::Phalonious Wilkerson", + "participant_relationship": null, + "participant_status": "0::Injured||1::Injured", + "participant_type": "0::Victim||1::Victim", + "sources": "http://www.wpxi.com/news/news/local/2-people-shot-monessen-medical-helicopters-called-/nqYM7/||http://triblive.com/news/westmoreland/10040329-74/911-call-gunshot?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+alltribstories+%28TribLIVE+News%29||http://www.wpxi.com/videos/news/witnesses-say-victims-beat-before-being-shot-in/vC7z9/", + "state_house_district": null, + "state_senate_district": null + } + } + ], + "target_column": "n_killed", + "task_sub_type": "regression", + "ranking_ground_truth": { + "top_k_ids": [ + "063", + "084", + "096" + ] }, - "final_decision": {}, - "what_if": {}, - "ground_truth": { - "extracted_features": [ - { - "scenario_id": "001", - "features": { - "incident_id": 136154, - "date": "2014-05-18", - "state": "South Carolina", - "city_or_county": "Columbia", - "address": "7400 block of Garners Ferry Road", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/136154", - "source_url": "http://www.wistv.com/story/25547556/columbia-police-investigating-second-homicide-in-two-weeks", - "incident_url_fields_missing": "False", - "congressional_district": 2.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Accidental Shooting||Accidental/Negligent Discharge", - "latitude": 33.9693, - "location_description": null, - "longitude": -80.9464, - "n_guns_involved": null, - "notes": null, - "participant_age": "1::19", - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Male||1::Male", - "participant_name": "1::Nicholas Reed Moses", - "participant_relationship": null, - "participant_status": "0::Killed||1::Unharmed", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://www.wistv.com/story/25547556/columbia-police-investigating-second-homicide-in-two-weeks", - "state_house_district": 70.0, - "state_senate_district": 21.0 - } - }, - { - "scenario_id": "002", - "features": { - "incident_id": 894730, - "date": "2017-07-19", - "state": "Illinois", - "city_or_county": "Chicago", - "address": "5300 block of S Winchester", - "n_killed": 0, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/894730", - "source_url": "http://chicago.suntimes.com/news/17-year-old-boy-shot-in-back-of-the-yards-2/", - "incident_url_fields_missing": "False", - "congressional_district": 7.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Wounded/Injured", - "latitude": 41.7975, - "location_description": "Back of the Yards neighborhood", - "longitude": -87.6735, - "n_guns_involved": 1.0, - "notes": "1 of 2 walk-up attackers fires on teen; leg, stable;", - "participant_age": "0::17", - "participant_age_group": "0::Teen 12-17", - "participant_gender": "0::Male||1::Male", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Injured||1::Unharmed", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://chicago.suntimes.com/news/17-year-old-boy-shot-in-back-of-the-yards-2/", - "state_house_district": 6.0, - "state_senate_district": 3.0 - } - }, - { - "scenario_id": "003", - "features": { - "incident_id": 995340, - "date": "2017-11-24", - "state": "Tennessee", - "city_or_county": "Chattanooga (East Ridge)", - "address": "Edwin Ln", - "n_killed": 0, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/995340", - "source_url": "http://www.wrcbtv.com/story/36923104/east-ridge-shooting-suspect-at-large", - "incident_url_fields_missing": "False", - "congressional_district": 3.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Wounded/Injured", - "latitude": 34.9999, - "location_description": null, - "longitude": -85.2628, - "n_guns_involved": 1.0, - "notes": "one shot", - "participant_age": null, - "participant_age_group": null, - "participant_gender": null, - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Injured", - "participant_type": "0::Victim", - "sources": "http://www.wrcbtv.com/story/36923104/east-ridge-shooting-suspect-at-large", - "state_house_district": 30.0, - "state_senate_district": 10.0 - } - }, - { - "scenario_id": "004", - "features": { - "incident_id": 812123, - "date": "2017-03-30", - "state": "Texas", - "city_or_county": "Lubbock", - "address": "1700 block of Ave R", - "n_killed": 0, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/812123", - "source_url": "http://www.kcbd.com/story/35059618/large-quantity-of-meth-and-illegal-firearms-discovered-at-two-lubbock-homes", - "incident_url_fields_missing": "False", - "congressional_district": 19.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Non-Shooting Incident||Drug involvement||ATF/LE Confiscation/Raid/Arrest||Possession (gun(s) found during commission of other crimes)||Possession of gun by felon or prohibited person", - "latitude": 33.5597, - "location_description": null, - "longitude": -101.856, - "n_guns_involved": 1.0, - "notes": "Drug involvement. Many illegal firearms found. Drugs and guns also found at 5300 block of 31st St. in Lubbock, TX on Mar. 30, 2017.", - "participant_age": "0::38", - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": "0::Daniel Botello", - "participant_relationship": null, - "participant_status": "0::Unharmed, Arrested", - "participant_type": "0::Subject-Suspect", - "sources": "http://www.kcbd.com/story/35059618/large-quantity-of-meth-and-illegal-firearms-discovered-at-two-lubbock-homes", - "state_house_district": null, - "state_senate_district": 28.0 - } - }, - { - "scenario_id": "005", - "features": { - "incident_id": 943278, - "date": "2017-09-26", - "state": "Florida", - "city_or_county": "Boca Raton", - "address": "1400 Block of W Glades Rd", - "n_killed": 0, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/943278", - "source_url": "http://www.palmbeachpost.com/news/crime--law/south-blotter-police-put-wrinkle-woman-attempt-steal-234-worth-face-cream/YnVkFBW99WewDGXTeo3VjP/", - "incident_url_fields_missing": "False", - "congressional_district": null, - "gun_stolen": "0::Stolen", - "gun_type": "0::Unknown", - "incident_characteristics": "Non-Shooting Incident||Gun(s) stolen from owner", - "latitude": 26.3659, - "location_description": null, - "longitude": -80.1123, - "n_guns_involved": 1.0, - "notes": "unclear date; blotter", - "participant_age": null, - "participant_age_group": null, - "participant_gender": null, - "participant_name": null, - "participant_relationship": null, - "participant_status": null, - "participant_type": null, - "sources": "http://www.palmbeachpost.com/news/crime--law/south-blotter-police-put-wrinkle-woman-attempt-steal-234-worth-face-cream/YnVkFBW99WewDGXTeo3VjP/", - "state_house_district": null, - "state_senate_district": null - } - }, - { - "scenario_id": "006", - "features": { - "incident_id": 770294, - "date": "2017-02-07", - "state": "Texas", - "city_or_county": "Edna", - "address": "200 block of E Church St", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/770294", - "source_url": "https://www.victoriaadvocate.com/news/2017/may/26/attorneys-analyze-charges-against-edna-woman/", - "incident_url_fields_missing": "False", - "congressional_district": 27.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::40 SW", - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Domestic Violence", - "latitude": 28.9774, - "location_description": null, - "longitude": -96.6437, - "n_guns_involved": 1.0, - "notes": "shot boyfriend in chest, self-def claim", - "participant_age": "0::33||1::35", - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Male||1::Female", - "participant_name": "0::Jarrett Parker||1::Amber Nicole Sorensen", - "participant_relationship": "1::Significant others - current or former", - "participant_status": "0::Killed||1::Unharmed, Arrested", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://www.crossroadstoday.com/story/34450748/edna-pd-edna-man-shot-and-killed-by-girlfriend||https://www.victoriaadvocate.com/news/2017/may/26/attorneys-analyze-charges-against-edna-woman/", - "state_house_district": null, - "state_senate_district": 18.0 - } - }, - { - "scenario_id": "007", - "features": { - "incident_id": 574315, - "date": "2016-06-07", - "state": "Texas", - "city_or_county": "Richmond", - "address": "800 block of Maiden Lane", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/574315", - "source_url": "http://www.khou.com/news/crime/1-dead-another-arrested-in-fatal-shooting-in-richmond/235578672", - "incident_url_fields_missing": "False", - "congressional_district": 22.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::223 Rem [AR-15]", - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", - "latitude": 29.5852, - "location_description": null, - "longitude": -95.7673, - "n_guns_involved": 1.0, - "notes": "1 killed.", - "participant_age": "1::25", - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Male||1::Male", - "participant_name": "1::Travion Depaul Fountain", - "participant_relationship": null, - "participant_status": "0::Killed||1::Unharmed, Arrested", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://thepolicenews.net/default.aspx?act=Newsletter.aspx&category=News+1-2&newsletterid=61382&menugroup=Home||http://www.khou.com/news/crime/1-dead-another-arrested-in-fatal-shooting-in-richmond/235578672", - "state_house_district": 85.0, - "state_senate_district": 18.0 - } - }, - { - "scenario_id": "008", - "features": { - "incident_id": 1041154, - "date": "2018-01-13", - "state": "District of Columbia", - "city_or_county": "Washington", - "address": "4700 block of S Capitol St SE", - "n_killed": 0, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/1041154", - "source_url": "https://mpdc.dc.gov/release/suspect-sought-armed-carjacking-gun-offense-4700-block-south-capitol-street-southeast", - "incident_url_fields_missing": "False", - "congressional_district": null, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Non-Shooting Incident||Car-jacking||Brandishing/flourishing/open carry/lost/found", - "latitude": 38.8218, - "location_description": null, - "longitude": -77.0012, - "n_guns_involved": 1.0, - "notes": null, - "participant_age": null, - "participant_age_group": null, - "participant_gender": "0::Male", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Unharmed", - "participant_type": "0::Subject-Suspect", - "sources": "https://mpdc.dc.gov/release/suspect-sought-armed-carjacking-gun-offense-4700-block-south-capitol-street-southeast", - "state_house_district": null, - "state_senate_district": null - } - }, - { - "scenario_id": "009", - "features": { - "incident_id": 165402, - "date": "2014-01-17", - "state": "Texas", - "city_or_county": "Corpus Christi", - "address": "802 Barry St.", - "n_killed": 0, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/165402", - "source_url": "http://ccpdblotter.com/2014/01/17/patrol-report-january-17-2014/", - "incident_url_fields_missing": "False", - "congressional_district": 27.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Armed robbery with injury/death and/or evidence of DGU found", - "latitude": 27.7297, - "location_description": null, - "longitude": -97.3786, - "n_guns_involved": null, - "notes": "ar, Corpus Christi police investigating", - "participant_age": "0::21", - "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+", - "participant_gender": "0::Female||1::Male||2::Male", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Unharmed||1::Unharmed||2::Unharmed", - "participant_type": "0::Victim||1::Subject-Suspect||2::Subject-Suspect", - "sources": "http://ccpdblotter.com/2014/01/17/patrol-report-january-17-2014/", - "state_house_district": 34.0, - "state_senate_district": 20.0 - } - }, - { - "scenario_id": "010", - "features": { - "incident_id": 194534, - "date": "2014-09-20", - "state": "Louisiana", - "city_or_county": "New Orleans", - "address": "7200 block of Lake Willow Drive ", - "n_killed": 0, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/194534", - "source_url": "http://www.nola.com/crime/index.ssf/2014/09/2_women_robbed_at_gunpoint_in.html#incart_river", - "incident_url_fields_missing": "False", - "congressional_district": 2.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Armed robbery with injury/death and/or evidence of DGU found", - "latitude": 30.0326, - "location_description": null, - "longitude": -90.0013, - "n_guns_involved": null, - "notes": "2 women robbed at gunpoint", - "participant_age": "0::29||1::30", - "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+", - "participant_gender": "0::Female||1::Female||2::Male", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Unharmed||1::Unharmed||2::Unharmed", - "participant_type": "0::Victim||1::Victim||2::Subject-Suspect", - 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The gun discharged through door, fatally struck adult female.", - "participant_age": "0::23||1::8", - "participant_age_group": "0::Adult 18+||1::Child 0-11", - "participant_gender": "0::Female||1::Male", - "participant_name": "0::Carmen Danielle Morris", - "participant_relationship": "1::Family", - "participant_status": "0::Killed||1::Unharmed", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://www.palestineherald.com/news/child-accidentally-shoots-relative/article_7fc4acb4-ef97-11e5-b045-b715027a6a24.html||http://www.palestineherald.com/news/year-old-mother-dies-in-accidental-shooting/article_19fc8d50-efc6-11e5-b598-9fcdf6d034bd.html||http://thepolicenews.net/default.aspx?act=Newsletter.aspx&category=News+1-2&newsletterid=60061&menugroup=Home", - "state_house_district": 8.0, - "state_senate_district": 3.0 - } - }, - { - "scenario_id": "014", - "features": { - "incident_id": 800576, - "date": "2017-03-22", - "state": "Ohio", - "city_or_county": "Cincinnati (Lockland)", - 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"participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://chicago.suntimes.com/news/man-hurt-in-lawndale-shooting-3/", - "state_house_district": null, - "state_senate_district": null - } - }, - { - "scenario_id": "027", - "features": { - "incident_id": 434070, - "date": "2015-10-16", - "state": "Louisiana", - "city_or_county": "Shreveport", - "address": "8400 block of Linwood Avenue", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/434070", - "source_url": "http://www.ksla.com/story/30309853/services-set-this-week-for-slain-security-guard", - "incident_url_fields_missing": "False", - "congressional_district": 4.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Institution/Group/Business||Officer Involved Incident||Officer Involved Shooting - Officer killed||Armed robbery with injury/death and/or evidence of DGU found", - "latitude": 32.4272, - "location_description": "Thrifty Liquor", - "longitude": -93.7634, - "n_guns_involved": null, - "notes": "Man enters store where victim was working security and shoots him.", - "participant_age": "0::56", - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Male||1::Male", - "participant_name": "0::Michael Wayne Henry", - "participant_relationship": "1::Armed Robbery", - "participant_status": "0::Killed||1::Unharmed", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://www.ksla.com/story/30309853/services-set-this-week-for-slain-security-guard", - "state_house_district": 3.0, - "state_senate_district": 39.0 - } - }, - { - "scenario_id": "028", - "features": { - "incident_id": 141973, - "date": "2014-06-01", - "state": "Iowa", - "city_or_county": "Coalville", - "address": "Old Mill Road", - "n_killed": 0, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/141973", - "source_url": "http://www.kcci.com/news/two-men-seriously-injured-after-attempted-robbery/26280058#!TFoVI", - 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"features": { - "incident_id": 999338, - "date": "2017-12-01", - "state": "Tennessee", - "city_or_county": "Chattanooga", - "address": "3900 block of Dodds Ave", - "n_killed": 0, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/999338", - "source_url": "http://www.wrcbtv.com/story/36974869/shooting-victim-shows-up-at-local-hospital-cpd-investigating", - "incident_url_fields_missing": "False", - "congressional_district": 3.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Wounded/Injured", - "latitude": 34.9974, - "location_description": null, - "longitude": -85.2832, - "n_guns_involved": 1.0, - "notes": "person shot showed up at local hospital", - "participant_age": null, - "participant_age_group": null, - "participant_gender": null, - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Injured", - "participant_type": "0::Victim", - "sources": "http://www.wrcbtv.com/story/36974869/shooting-victim-shows-up-at-local-hospital-cpd-investigating", - "state_house_district": 30.0, - "state_senate_district": 10.0 - } - }, - { - "scenario_id": "030", - "features": { - "incident_id": 587716, - "date": "2016-06-24", - "state": "Massachusetts", - "city_or_county": "Boston", - "address": "17 Doone Avenue", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/587716", - "source_url": null, - "incident_url_fields_missing": "False", - "congressional_district": 7.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Domestic Violence||Possession of gun by felon or prohibited person", - "latitude": 42.2779, - "location_description": null, - "longitude": -71.0827, - "n_guns_involved": 1.0, - "notes": "Mattapan, dv, shot multiple times, arrest Sept 2016", - "participant_age": "0::43||1::51", - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Male||1::Male", - "participant_name": "0::Jermaine Good||1::Rogers Jordan", - "participant_relationship": null, - "participant_status": "0::Killed||1::Unharmed, Arrested", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://www.necn.com/news/new-england/Boston-Police-Mattapan-Shooting-384307711.html||http://www.wcvb.com/news/man-shot-in-boston/40211788||http://bpdnews.com/news/2016/7/6/update-victim-identified-in-death-investigation-in-the-area-of-17-doone-ave-mattapan||http://bpdnews.com/news/2016/9/27/update-arrest-made-in-connection-to-death-investigation-in-the-area-of-17-doone-avenue-in-mattapan", - "state_house_district": null, - "state_senate_district": null - } - }, - { - "scenario_id": "031", - "features": { - "incident_id": 674603, - "date": "2016-10-12", - "state": "Louisiana", - "city_or_county": "Harvey", - "address": "2200 block of Alex Kornman Boulevard", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/674603", - "source_url": "http://www.nola.com/crime/index.ssf/2016/10/caron_white_javon_lacrosse_har.html#incart_most-read_crime_article", - 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"sources": "http://www.nola.com/crime/index.ssf/2016/10/man_shot_in_harvey_2.html#incart_river_index||http://www.nola.com/crime/index.ssf/2016/10/1_man_dead_following_harvey_sh.html||http://www.nola.com/crime/index.ssf/2016/10/victim_identified_in_wednesday.html#incart_river_index||http://www.nola.com/crime/index.ssf/2016/10/caron_white_javon_lacrosse_har.html#incart_most-read_crime_article", - "state_house_district": null, - "state_senate_district": null - } - }, - { - "scenario_id": "032", - "features": { - "incident_id": 427275, - "date": "2015-10-07", - "state": "District of Columbia", - "city_or_county": "Washington", - "address": "Martin Luther King Jr. Ave and Malcolm X Ave, SE", - "n_killed": 0, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/427275", - "source_url": "https://twitter.com/DCPoliceDept/status/651957167206043652", - "incident_url_fields_missing": "False", - "congressional_district": 1.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Non-Shooting Incident||Car-jacking", - 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"date": "2016-07-07", - "state": "Nebraska", - "city_or_county": "Lincoln", - "address": "2940 N. 14th Street", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/599772", - "source_url": "http://www.1011now.com/content/news/Police-expected-to-provide-update-in-Kwik-Shop-Murder-Case--Watch-briefing-live-393541721.html", - "incident_url_fields_missing": "False", - "congressional_district": null, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Institution/Group/Business||Armed robbery with injury/death and/or evidence of DGU found", - "latitude": null, - "location_description": "Kwik Shop", - "longitude": null, - "n_guns_involved": 1.0, - "notes": null, - "participant_age": "0::51||1::18", - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Male||1::Male", - "participant_name": "0::Robert L. Hanna||1::Rodzela J. Jones", - "participant_relationship": "1::Armed Robbery", - "participant_status": "0::Killed||1::Unharmed", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://journalstar.com/news/local/911/shooting-victim-was-a-big-brother-to-many-family-says/article_b620c951-d912-553d-af1d-6d6e764fcfcd.html||http://www.ketv.com/news/police-clerk-shot-killed-during-lincoln-robbery/40397006||http://www.1011now.com/content/news/Police-expected-to-provide-update-in-Kwik-Shop-Murder-Case--Watch-briefing-live-393541721.html", - "state_house_district": null, - "state_senate_district": null - } - }, - { - "scenario_id": "035", - "features": { - "incident_id": 587635, - "date": "2016-06-23", - "state": "Pennsylvania", - "city_or_county": "Uniontown", - "address": "Millview Street", - "n_killed": 2, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/587635", - "source_url": "http://pittsburgh.cbslocal.com/2016/06/24/men-killed-in-uniontown-shooting-identified/", - "incident_url_fields_missing": "False", - "congressional_district": 9.0, - "gun_stolen": "0::Unknown||1::Unknown", - "gun_type": "0::Handgun||1::Handgun", - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", - "latitude": 39.9062, - "location_description": null, - "longitude": -79.7192, - "n_guns_involved": 2.0, - "notes": "poss multiple perps, 2 handguns recover", - "participant_age": "0::37||1::25", - "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+", - "participant_gender": "0::Male||1::Male||2::Male", - "participant_name": "0::Corey Johnson||1::Meshach \"Shack\" Punter", - "participant_relationship": null, - "participant_status": "0::Killed||1::Killed||2::Unharmed", - "participant_type": "0::Victim||1::Victim||2::Subject-Suspect", - "sources": "http://pittsburgh.cbslocal.com/2016/06/24/men-killed-in-uniontown-shooting-identified/", - "state_house_district": null, - "state_senate_district": null - } - }, - { - "scenario_id": "036", - "features": { - "incident_id": 163683, - "date": "2014-07-24", - "state": "Florida", - "city_or_county": "Tampa", - "address": "Hanley Road and Jackson Springs Road", - "n_killed": 0, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/163683", - "source_url": "http://tbo.com/news/crime/deputies-seeking-suspects-in-armed-robbery-on-tampa-street-20140724/", - "incident_url_fields_missing": "False", - "congressional_district": 14.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Armed robbery with injury/death and/or evidence of DGU found", - "latitude": 28.0074, - "location_description": null, - "longitude": -82.5626, - "n_guns_involved": null, - "notes": "Armed men robbed multiple people at gunpoint", - "participant_age": null, - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Male||1::Male", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Unharmed||1::Unharmed", - "participant_type": "0::Subject-Suspect||1::Subject-Suspect", - "sources": "http://tbo.com/news/crime/deputies-seeking-suspects-in-armed-robbery-on-tampa-street-20140724/||http://tbo.com/news/crime/deputies-seeking-suspects-in-armed-robbery-on-tampa-street-20140724/", - "state_house_district": 62.0, - "state_senate_district": 19.0 - } - }, - { - "scenario_id": "037", - "features": { - "incident_id": 178777, - "date": "2014-05-11", - "state": "Ohio", - "city_or_county": "Geneva", - "address": "OH-307 and Barnum Rd", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/178777", - "source_url": "http://www.starbeacon.com/news/man-gets-years-in-fatal-shooting/article_b8271960-a67e-11e4-9dcf-2f1a53602b05.html", - "incident_url_fields_missing": "False", - "congressional_district": 14.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", - "latitude": 41.7631, - "location_description": null, - "longitude": -80.9215, - "n_guns_involved": 1.0, - "notes": "Man found shot, killed in ditch. Suspect in custody.", - "participant_age": "0::43||1::62", - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Male||1::Male", - "participant_name": "0::Eugene A. Cray||1::Gertis J. Perry", - "participant_relationship": null, - "participant_status": "0::Killed||1::Unharmed", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://www.cleveland19.com/story/25503626/body-found-in-ashtabula-county-ditch-has-been-identified||http://www.starbeacon.com/news/man-gets-years-in-fatal-shooting/article_b8271960-a67e-11e4-9dcf-2f1a53602b05.html", - "state_house_district": 99.0, - "state_senate_district": 32.0 - } - }, - { - "scenario_id": "038", - "features": { - "incident_id": 552698, - "date": "2016-05-03", - "state": "Pennsylvania", - "city_or_county": "Philadelphia", - "address": "1300 block of Rush Street", - "n_killed": 0, - "n_injured": 2, - "incident_url": "http://www.gunviolencearchive.org/incident/552698", - "source_url": "http://6abc.com/news/police-cousins-were-selling-drugs-when-shot-in-north-philly/1319791/", - "incident_url_fields_missing": "False", - "congressional_district": 2.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Wounded/Injured||Drug involvement", - "latitude": 39.9956, - "location_description": null, - "longitude": -75.1521, - "n_guns_involved": null, - "notes": "It is believed that the 2 cousins were selling drugs when shot.", - "participant_age": "0::16||1::20", - "participant_age_group": "0::Teen 12-17||1::Adult 18+", - "participant_gender": "0::Male||1::Female", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Injured||1::Injured", - "participant_type": "0::Victim||1::Victim", - "sources": "http://6abc.com/news/police-cousins-were-selling-drugs-when-shot-in-north-philly/1319791/", - "state_house_district": null, - "state_senate_district": null - } - }, - { - "scenario_id": "039", - "features": { - "incident_id": 1028728, - "date": "2018-01-16", - "state": "Missouri", - "city_or_county": "Springfield", - "address": "S Missouri Ave and W Elm St", - "n_killed": 0, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/1028728", - 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"state_senate_district": null - } - }, - { - "scenario_id": "041", - "features": { - "incident_id": 500305, - "date": "2016-02-05", - "state": "Pennsylvania", - "city_or_county": "York", - "address": "US-30 and Memory Lane", - "n_killed": 0, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/500305", - "source_url": "http://www.pennlive.com/news/2016/02/police_search_for_suspect_who.html", - "incident_url_fields_missing": "False", - "congressional_district": 4.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shots Fired - No Injuries||Drive-by (car to street, car to car)", - "latitude": 39.9835, - "location_description": null, - "longitude": -76.6842, - "n_guns_involved": 1.0, - "notes": null, - "participant_age": null, - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Unharmed", - "participant_type": "0::Subject-Suspect", - "sources": "http://www.ydr.com/story/news/crime/2016/02/05/police-car-shot-springettsbury-township/79904016/||http://www.pennlive.com/news/2016/02/police_search_for_suspect_who.html", - "state_house_district": null, - "state_senate_district": null - } - }, - { - "scenario_id": "042", - "features": { - "incident_id": 291662, - "date": "2015-02-09", - "state": "Wisconsin", - "city_or_county": "Mazomanie", - "address": "10359 County Road Y", - "n_killed": 2, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/291662", - "source_url": "http://host.madison.com/wsj/news/local/crime_and_courts/year-old-charged-with-mazomanie-murders-allegedly-admits-shootings/article_45c42722-625b-5b55-a015-b892c2c40246.html", - "incident_url_fields_missing": "False", - "congressional_district": 2.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Handgun", - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", - "latitude": 43.2324, - "location_description": null, - "longitude": -89.7835, - "n_guns_involved": 1.0, - "notes": "1 m vic 1 f vic killed ex-gf's sis and stepfather, poss upset over breakup", - "participant_age": "0::16||1::39||2::17", - "participant_age_group": "0::Teen 12-17||1::Adult 18+||2::Teen 12-17", - "participant_gender": "0::Female||1::Male||2::Male", - "participant_name": "0::Ariyl Brady||1::Chris Schwichtenberg||2::Dean M. 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"participant_type": "0::Subject-Suspect", - "sources": "http://www.whio.com/news/news/crime-law/police-man-hurt-in-crash-leaves-gun-behind/nq3J8/", - "state_house_district": 39.0, - "state_senate_district": 5.0 - } - }, - { - "scenario_id": "044", - "features": { - "incident_id": 343127, - "date": "2015-05-18", - "state": "New Jersey", - "city_or_county": "Elizabeth", - "address": "Parker Road", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/343127", - "source_url": "http://newyork.cbslocal.com/2016/12/29/pennsylvania-new-jersey-murder-suspect/", - "incident_url_fields_missing": "False", - "congressional_district": 8.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::38 Spl", - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Spree Shooting (multiple victims, multiple locations)", - "latitude": 40.6748, - "location_description": null, - "longitude": -74.2191, - "n_guns_involved": 1.0, - "notes": null, - "participant_age": "0::29||1::24", - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Male||1::Male", - "participant_name": "0::Michael Thompkins||1::Todd West", - "participant_relationship": null, - "participant_status": "0::Killed||1::Unharmed, Arrested", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://www.northjersey.com/news/prosecutors-man-shot-to-death-at-housing-complex-in-elizabeth-1.1337777||http://www.nj.com/union/index.ssf/2015/05/police_id_29-year-old_shot_dead_in_elizabeth_monda.html#incart_river||http://newyork.cbslocal.com/2016/12/29/pennsylvania-new-jersey-murder-suspect/", - "state_house_district": 20.0, - "state_senate_district": 20.0 - } - }, - { - "scenario_id": "045", - "features": { - "incident_id": 299103, - "date": "2015-02-26", - "state": "Delaware", - "city_or_county": "Wilmington", - "address": "1200 block of Elm St.", - "n_killed": 0, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/299103", - "source_url": "http://www.delawareonline.com/story/news/crime/2015/02/26/boy-arrested-stolen-car-stolen-gun/24069605/", - "incident_url_fields_missing": "False", - "congressional_district": 1.0, - "gun_stolen": "0::Stolen", - "gun_type": "0::380 Auto", - "incident_characteristics": "Non-Shooting Incident||Car-jacking||Possession (gun(s) found during commission of other crimes)||Stolen/Illegally owned gun{s} recovered during arrest/warrant", - "latitude": 39.7411, - "location_description": null, - "longitude": -75.5659, - "n_guns_involved": 1.0, - "notes": null, - "participant_age": "0::15", - "participant_age_group": "0::Teen 12-17", - "participant_gender": "0::Male", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Unharmed, Arrested", - "participant_type": "0::Subject-Suspect", - "sources": "http://www.delawareonline.com/story/news/crime/2015/02/26/boy-arrested-stolen-car-stolen-gun/24069605/", - "state_house_district": 3.0, - "state_senate_district": 3.0 - } - }, - { - "scenario_id": "046", - "features": { - "incident_id": 854252, - "date": "2017-05-27", - "state": "North Carolina", - "city_or_county": "Charlotte", - "address": "5336 Docia Crossing Rd", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/854252", - "source_url": "http://www.wbtv.com/story/35534275/victim-identified-in-homicide-in-charlotte-shopping-center-parking-lot", - "incident_url_fields_missing": "False", - "congressional_district": 9.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", - "latitude": 35.3664, - "location_description": "BI-LO", - "longitude": -80.7872, - "n_guns_involved": 1.0, - "notes": null, - "participant_age": "0::22", - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Male||1::Male", - "participant_name": "0::Anderson David Biggers", - "participant_relationship": "1::Aquaintance", - "participant_status": "0::Killed||1::Unharmed", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://www.wbtv.com/story/35534275/victim-identified-in-homicide-in-charlotte-shopping-center-parking-lot", - "state_house_district": 98.0, - "state_senate_district": 38.0 - } - }, - { - "scenario_id": "047", - "features": { - "incident_id": 594255, - "date": "2016-02-29", - "state": "Texas", - "city_or_county": "San Antonio", - "address": "9800 Airport Blvd.380", - "n_killed": 0, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/594255", - "source_url": "http:// http://blog.tsa.gov/2016_03_04_archive.html", - "incident_url_fields_missing": "False", - "congressional_district": 21.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Non-Shooting Incident||TSA Action", - "latitude": 29.5217, - "location_description": "San Antonio Int'l Airport", - "longitude": -98.4767, - "n_guns_involved": 1.0, - "notes": "380 caliber", - "participant_age": null, - "participant_age_group": null, - "participant_gender": null, - "participant_name": null, - "participant_relationship": null, - "participant_status": null, - "participant_type": null, - "sources": null, - "state_house_district": 121.0, - "state_senate_district": 25.0 - } - }, - { - "scenario_id": "048", - "features": { - "incident_id": 387583, - "date": "2015-08-03", - "state": "New Hampshire", - "city_or_county": "Greenfield", - "address": "Swamp Road", - "n_killed": 0, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/387583", - "source_url": "http://www.sentinelsource.com/news/local/affidavit-provides-details-on-greenfield-home-invasion/article_38d46623-a7e8-575e-b9ad-2af6647bed1d.html", - "incident_url_fields_missing": "False", - "congressional_district": 2.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Handgun", - "incident_characteristics": "Non-Shooting Incident||Home Invasion||Home Invasion - No death or injury||Brandishing/flourishing/open carry/lost/found", - "latitude": 42.948, - "location_description": null, - "longitude": -71.902, - "n_guns_involved": 1.0, - "notes": "hi, threat with handgun and robbed at gunpoint, robbed Dublin home prior night", - "participant_age": "2::7||3::8||4::33", - "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Child 0-11||3::Child 0-11||4::Adult 18+", - "participant_gender": "0::Female||1::Female||2::Female||3::Female||4::Male", - "participant_name": "4::Jason Eddy", - "participant_relationship": null, - "participant_status": "0::Unharmed||1::Unharmed||2::Unharmed||3::Unharmed||4::Unharmed, Arrested", - "participant_type": "0::Victim||1::Victim||2::Victim||3::Victim||4::Subject-Suspect", - "sources": "http://www.sentinelsource.com/news/local/man-arrested-in-connection-to-greenfield-home-invasion/article_9d03557f-5f61-5731-ae7e-e076761b8a52.html||http://www.sentinelsource.com/news/local/dublin-man-accused-of-robbing-two-homes-at-gunpoint/article_c8e112ed-3b4d-5b67-8dc0-fe7ae35534df.html||http://www.sentinelsource.com/news/local/affidavit-provides-details-on-greenfield-home-invasion/article_38d46623-a7e8-575e-b9ad-2af6647bed1d.html", - "state_house_district": null, - "state_senate_district": 9.0 - } - }, - { - "scenario_id": "049", - "features": { - "incident_id": 643243, - "date": "2016-08-30", - "state": "Kentucky", - "city_or_county": "Lexington", - "address": "175 North Locust Hill Dr", - "n_killed": 0, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/643243", - "source_url": "http://www.wkyt.com/content/news/391815791.html", - "incident_url_fields_missing": "False", - "congressional_district": 6.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Wounded/Injured||Non-Aggression Incident||Accidental Shooting||Accidental Shooting - Injury||Accidental/Negligent Discharge||Playing with gun", - "latitude": 38.0053, - "location_description": "Raintree Apartments", - "longitude": -84.4436, - "n_guns_involved": 1.0, - "notes": "Vic shot in neck; stable condition. Group of 3 teens playing with gun.", - "participant_age": "0::14", - "participant_age_group": "0::Teen 12-17||1::Teen 12-17", - "participant_gender": "0::Male", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Injured||1::Unharmed", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://www.wkyt.com/content/news/391815791.html", - "state_house_district": null, - "state_senate_district": null - } - }, - { - "scenario_id": "050", - "features": { - "incident_id": 730990, - "date": "2016-12-21", - "state": "Texas", - "city_or_county": "San Antonio", - "address": "246 Fair Avenue", - "n_killed": 0, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/730990", - "source_url": "http://foxsanantonio.com/news/local/breaking-two-loaded-guns-found-on-playground-of-south-side-daycare", - "incident_url_fields_missing": "False", - "congressional_district": 35.0, - "gun_stolen": "0::Unknown||1::Unknown", - "gun_type": "0::Unknown||1::Unknown", - "incident_characteristics": "Institution/Group/Business||Non-Shooting Incident||Brandishing/flourishing/open carry/lost/found", - "latitude": 29.384, - "location_description": "EduCare", - "longitude": -98.4746, - "n_guns_involved": 2.0, - "notes": "2 guns found on playground by teacher, students", - "participant_age": null, - "participant_age_group": null, - "participant_gender": null, - "participant_name": null, - "participant_relationship": null, - "participant_status": null, - "participant_type": null, - "sources": "http://news4sanantonio.com/news/local/just-in-loaded-guns-found-at-educare-childcare-playground||http://foxsanantonio.com/news/local/breaking-two-loaded-guns-found-on-playground-of-south-side-daycare", - "state_house_district": 119.0, - "state_senate_district": 26.0 - } - }, - { - "scenario_id": "051", - "features": { - "incident_id": 935938, - "date": "2017-09-11", - "state": "Washington", - "city_or_county": "Everett", - "address": "2300 block of W Casino Rd", - "n_killed": 0, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/935938", - "source_url": "http://www.heraldnet.com/news/teen-injured-in-1-of-2-monday-night-shootings-in-everett/", - "incident_url_fields_missing": "False", - "congressional_district": 2.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shots Fired - No Injuries", - "latitude": 47.9193, - "location_description": null, - "longitude": -122.265, - "n_guns_involved": 1.0, - "notes": "10:15 pm Nearby and shortly after another shooting on same rd, same night. Unknown yet if connected", - "participant_age": null, - "participant_age_group": null, - "participant_gender": null, - "participant_name": null, - "participant_relationship": null, - "participant_status": null, - "participant_type": null, - "sources": "http://www.heraldnet.com/news/teen-injured-in-1-of-2-monday-night-shootings-in-everett/", - "state_house_district": 38.0, - "state_senate_district": 38.0 - } - }, - { - "scenario_id": "052", - "features": { - "incident_id": 404284, - "date": "2015-07-28", - "state": "Pennsylvania", - "city_or_county": "State College", - "address": "2535 Fox Hill Rd", - "n_killed": 0, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/404284", - "source_url": "http://blog.tsa.gov", - "incident_url_fields_missing": "False", - "congressional_district": 5.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::380 Auto", - "incident_characteristics": "Non-Shooting Incident||TSA Action", - "latitude": 40.8609, - "location_description": "UNV", - "longitude": -77.8364, - "n_guns_involved": 1.0, - "notes": null, - "participant_age": null, - "participant_age_group": null, - "participant_gender": null, - "participant_name": null, - "participant_relationship": null, - "participant_status": null, - "participant_type": null, - "sources": "http://blog.tsa.gov", - "state_house_district": null, - "state_senate_district": null - } - }, - { - "scenario_id": "053", - "features": { - "incident_id": 569729, - "date": "2016-06-01", - "state": "Georgia", - "city_or_county": "Jeffersonville", - "address": "375 Watson Drive", - "n_killed": 2, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/569729", - "source_url": "http://www.northwestgeorgianews.com/associated_press/news/national/teenagers-found-shot-to-death-behind-twiggs-county-school/article_48bf32de-28c2-11e6-8666-2f6036162ee4.html", - "incident_url_fields_missing": "False", - "congressional_district": 8.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Suicide^||Murder/Suicide", - "latitude": 32.6872, - "location_description": "Twiggs County Comprehensive Middle-High School", - "longitude": -83.3472, - "n_guns_involved": 1.0, - "notes": "On school property late night, school out for summer - not school schooting", - "participant_age": "0::19||1::18", - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Female||1::Male", - "participant_name": "0::Alexia D. Jackson||1::Knickolus B. Jones", - "participant_relationship": null, - "participant_status": "0::Killed||1::Killed", - "participant_type": "0::Victim||1::Victim", - "sources": "http://www.macon.com/news/local/crime/article81112092.html||http://www.northwestgeorgianews.com/associated_press/news/national/teenagers-found-shot-to-death-behind-twiggs-county-school/article_48bf32de-28c2-11e6-8666-2f6036162ee4.html", - "state_house_district": 144.0, - "state_senate_district": 26.0 - } - }, - { - "scenario_id": "054", - "features": { - "incident_id": 556580, - "date": "2016-05-09", - "state": "Wisconsin", - "city_or_county": "Milwaukee", - "address": "48th and Center", - "n_killed": 0, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/556580", - "source_url": "http://fox6now.com/2016/05/09/police-man-shot-wounded-near-48th-and-center-in-milwaukee/", - "incident_url_fields_missing": "False", - "congressional_district": 4.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Wounded/Injured", - "latitude": 43.0679, - "location_description": null, - "longitude": -87.9737, - "n_guns_involved": 1.0, - "notes": null, - "participant_age": "0::22", - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Male||1::Male", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Injured||1::Unharmed", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://fox6now.com/2016/05/09/police-man-shot-wounded-near-48th-and-center-in-milwaukee/", - "state_house_district": 18.0, - "state_senate_district": 6.0 - } - }, - { - "scenario_id": "055", - "features": { - "incident_id": 568509, - "date": "2016-05-28", - "state": "Texas", - "city_or_county": "Lubbock", - "address": "300 block of Waco", - "n_killed": 0, - "n_injured": 2, - "incident_url": "http://www.gunviolencearchive.org/incident/568509", - "source_url": "http://www.everythinglubbock.com/news/local-news/mother-her-boyfriend-arrested-after-2-children-shot-with-bb-gun", - "incident_url_fields_missing": "False", - "congressional_district": 19.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Wounded/Injured||Child Involved Incident||Child injured (not child shooter)||Domestic Violence||BB/Pellet/Replica gun", - "latitude": 33.5942, - "location_description": null, - "longitude": -101.867, - "n_guns_involved": null, - "notes": "Mother's BF arrested 4 shooting 11mo old & older child w/ BBs. Older child required hand surgery to remove BB. Mother arrested for failure 2 report felony", - "participant_age": "0::0||2::28", - "participant_age_group": "0::Child 0-11||1::Child 0-11||2::Adult 18+", - "participant_gender": "2::Male", - "participant_name": "2::Adrian Garcia-Guevara", - "participant_relationship": "2::Family", - "participant_status": "0::Injured||1::Injured||2::Arrested", - "participant_type": "0::Victim||1::Victim||2::Subject-Suspect", - "sources": "http://www.everythinglubbock.com/news/local-news/mother-her-boyfriend-arrested-after-2-children-shot-with-bb-gun", - "state_house_district": 84.0, - "state_senate_district": 28.0 - } - }, - { - "scenario_id": "056", - "features": { - "incident_id": 1072736, - "date": "2018-03-18", - "state": "New York", - "city_or_county": "Brooklyn", - "address": "E 98th St", - "n_killed": 0, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/1072736", - "source_url": "http://brooklyn.news12.com/story/37750466/police-probe-2-separate-overnight-shooting-in-brooklyn", - "incident_url_fields_missing": "False", - "congressional_district": null, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Wounded/Injured", - "latitude": null, - "location_description": null, - "longitude": null, - "n_guns_involved": 1.0, - "notes": null, - "participant_age": null, - "participant_age_group": "0::Adult 18+", - "participant_gender": null, - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Injured", - "participant_type": "0::Victim", - "sources": "http://brooklyn.news12.com/story/37750466/police-probe-2-separate-overnight-shooting-in-brooklyn", - "state_house_district": null, - "state_senate_district": null - } - }, - { - "scenario_id": "057", - "features": { - "incident_id": 105214, - "date": "2014-02-08", - "state": "Illinois", - "city_or_county": "Chicago", - "address": " 7100 block of South South Shore Dr", - "n_killed": 0, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/105214", - "source_url": "http://www.chicagotribune.com/news/local/breaking/chi-man-shot-in-south-shore-neighborhood-20140208,0,7590447.story", - "incident_url_fields_missing": "False", - "congressional_district": 2.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Wounded/Injured||Brandishing/flourishing/open carry/lost/found", - "latitude": 41.7662, - "location_description": null, - "longitude": -87.5629, - "n_guns_involved": null, - "notes": "Man in doorway shot by 2 men.", - "participant_age": "0::22", - "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+", - "participant_gender": "0::Male||1::Male||2::Male", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Injured||1::Unharmed||2::Unharmed", - "participant_type": "0::Victim||1::Subject-Suspect||2::Subject-Suspect", - "sources": "http://www.chicagotribune.com/news/local/breaking/chi-man-shot-in-south-shore-neighborhood-20140208,0,7590447.story", - "state_house_district": 25.0, - "state_senate_district": 13.0 - } - }, - { - "scenario_id": "058", - "features": { - "incident_id": 779134, - "date": "2017-02-20", - "state": "Texas", - "city_or_county": "Dallas", - "address": "9633 Ferris Branch Blvd", - "n_killed": 1, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/779134", - "source_url": "http://www.kjrh.com/news/local-news/dallas-murder-suspect-latwon-goff-arrested-in-tulsa", - "incident_url_fields_missing": "False", - "congressional_district": 32.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Wounded/Injured||Shot - Dead (murder, accidental, suicide)||Home Invasion||Home Invasion - Resident killed||Home Invasion - Resident injured||Armed robbery with injury/death and/or evidence of DGU found", - "latitude": 32.9038, - "location_description": "Wyndham on the Creek apartments", - "longitude": -96.7258, - "n_guns_involved": 1.0, - "notes": "Lake Highlands. vic shot in leg.", - "participant_age": "0::39||2::18", - "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+", - "participant_gender": "0::Male||1::Female||2::Male", - "participant_name": "0::Jason Ali Edwards||2::Latwon Latrell Goff", - "participant_relationship": "2::Armed Robbery", - "participant_status": "0::Killed||1::Injured||2::Unharmed, Arrested", - "participant_type": "0::Victim||1::Victim||2::Subject-Suspect||3::Subject-Suspect", - "sources": "http://dfw.cbslocal.com/2017/02/20/1-dead-in-dallas-home-invasion/||http://www.dallasnews.com/news/lake-highlands/2017/02/20/1-dead-1-wounded-lake-highlands-home-invasion-shooting||http://www.nbcdfw.com/news/local/Teen-Suspected-Of-Deadly-Shooting-In-Lake-Highlands-Police-414797463.html||http://www.kjrh.com/news/local-news/dallas-murder-suspect-latwon-goff-arrested-in-tulsa", - "state_house_district": null, - "state_senate_district": 16.0 - } - }, - { - "scenario_id": "059", - "features": { - "incident_id": 977518, - "date": "2017-10-30", - "state": "Ohio", - "city_or_county": "Cleveland", - "address": "Rosedale Ave", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/977518", - "source_url": "http://www.wkyc.com/news/local/cuyahoga-county/teen-shot-dead-in-east-cleveland-monday-night/487529874", - "incident_url_fields_missing": "False", - "congressional_district": 11.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", - "latitude": 41.5433, - "location_description": null, - "longitude": -81.5704, - "n_guns_involved": 1.0, - "notes": "approx 8:15 PM", - "participant_age": "0::18", - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": "0::Steve Anthony Swain", - "participant_relationship": null, - "participant_status": "0::Killed", - "participant_type": "0::Victim", - "sources": "http://www.wkyc.com/news/local/cuyahoga-county/teen-shot-dead-in-east-cleveland-monday-night/487529874", - "state_house_district": 8.0, - "state_senate_district": 25.0 - } - }, - { - "scenario_id": "060", - "features": { - "incident_id": 301532, - "date": "2015-03-05", - "state": "Michigan", - "city_or_county": "Flint", - "address": "210 E 5th St", - "n_killed": 0, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/301532", - "source_url": "http://www.mlive.com/news/flint/index.ssf/2015/03/flint_police_department_missin.html#incart_2box", - "incident_url_fields_missing": "False", - "congressional_district": 5.0, - "gun_stolen": "0::Unknown||1::Unknown||2::Unknown||3::Unknown||4::Unknown||5::Unknown||6::Unknown||7::Unknown||8::Unknown||9::Unknown||10::Unknown||11::Unknown||12::Unknown||13::Unknown||14::Unknown||15::Unknown||16::Unknown||17::Unknown||18::Unknown||19::Unknown||20::Unknown||21::Unknown||22::Unknown||23::Unknown||24::Unknown||25::Unknown||26::Unknown||27::Unknown||28::Unknown||29::Unknown||30::Unknown||31::Unknown||32::Unknown||33::Unknown||34::Unknown||35::Unknown||36::Unknown||37::Unknown||38::Unknown", - "gun_type": "0::Unknown||1::Unknown||2::Unknown||3::Unknown||4::Unknown||5::Unknown||6::Unknown||7::Unknown||8::Unknown||9::Unknown||10::Unknown||11::Unknown||12::Unknown||13::Unknown||14::Unknown||15::Unknown||16::Unknown||17::Unknown||18::Unknown||19::Unknown||20::Unknown||21::Unknown||22::Unknown||23::Unknown||24::Unknown||25::Unknown||26::Unknown||27::Unknown||28::Unknown||29::Unknown||30::Unknown||31::Unknown||32::Unknown||33::Unknown||34::Unknown||35::Unknown||36::Unknown||37::Unknown||38::Unknown", - "incident_characteristics": "Non-Shooting Incident||ATF/LE Confiscation/Raid/Arrest", - "latitude": 43.0119, - "location_description": null, - "longitude": -83.6862, - "n_guns_involved": 39.0, - "notes": "Flint Police Department missing weapons audit. Report published March 5", - "participant_age": null, - "participant_age_group": null, - "participant_gender": null, - "participant_name": null, - "participant_relationship": null, - "participant_status": null, - "participant_type": null, - "sources": "http://www.mlive.com/news/flint/index.ssf/2015/03/flint_police_department_missin.html#incart_2box", - "state_house_district": 49.0, - "state_senate_district": 27.0 - } - }, - { - "scenario_id": "061", - "features": { - "incident_id": 188069, - "date": "2014-09-02", - "state": "Indiana", - "city_or_county": "East Chicago", - "address": "1200 block of West 150th Street", - "n_killed": 0, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/188069", - "source_url": "http://www.nwitimes.com/news/local/trio-accused-of-robbing-e-c-residents-at-gunpoint/article_3e0b0584-f312-5cd3-ac1c-7beb70c5d7cb.html", - "incident_url_fields_missing": "False", - "congressional_district": 1.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Drug involvement||Home Invasion||Home Invasion - Resident injured||Armed robbery with injury/death and/or evidence of DGU found||Pistol-whipping", - "latitude": 41.6232, - "location_description": null, - "longitude": -87.4863, - "n_guns_involved": null, - "notes": "3 home invaders pistol-whip one, rob residents of apartment.", - "participant_age": "1::20||2::19||3::18", - "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+||3::Adult 18+", - "participant_gender": "1::Female||2::Male||3::Male", - "participant_name": "1::Amanda Velasquez||2::Elijah Solis||3::Xavier Solis", - "participant_relationship": null, - "participant_status": "0::Injured||1::Unharmed||2::Unharmed||3::Unharmed", - "participant_type": "0::Victim||1::Subject-Suspect||2::Subject-Suspect||3::Subject-Suspect", - "sources": "http://www.nwitimes.com/news/local/trio-accused-of-robbing-e-c-residents-at-gunpoint/article_3e0b0584-f312-5cd3-ac1c-7beb70c5d7cb.html", - "state_house_district": 2.0, - "state_senate_district": 2.0 - } - }, - { - "scenario_id": "062", - "features": { - "incident_id": 270627, - "date": "2014-12-28", - "state": "Maryland", - "city_or_county": "Baltimore", - "address": "3000 block of E. Monument St.", - "n_killed": 0, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/270627", - "source_url": "http://www.baltimoresun.com/news/maryland/baltimore-city/bs-md-ci-police-involved-shooting-20141228-story.html", - "incident_url_fields_missing": "False", - "congressional_district": 7.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Wounded/Injured||Officer Involved Incident||Officer Involved Shooting - subject/suspect/perpetrator shot", - "latitude": 39.2993, - "location_description": null, - "longitude": -76.5751, - "n_guns_involved": null, - "notes": null, - "participant_age": "0::45", - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Injured", - "participant_type": "0::Subject-Suspect", - "sources": "http://www.baltimoresun.com/news/maryland/baltimore-city/bs-md-ci-police-involved-shooting-20141228-story.html", - "state_house_district": 45.0, - "state_senate_district": 45.0 - } - }, - { - "scenario_id": "063", - "features": { - "incident_id": 952498, - "date": "2017-10-04", - "state": "New Jersey", - "city_or_county": "Trenton", - "address": "400 block of Walnut Avenue", - "n_killed": 0, - "n_injured": 2, - "incident_url": "http://www.gunviolencearchive.org/incident/952498", - "source_url": "http://www.trentonian.com/general-news/20171004/gun-arrest-robbery-and-shooting-october-4-2017-police-blotter", - "incident_url_fields_missing": "False", - "congressional_district": 12.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Wounded/Injured", - "latitude": 40.2239, - "location_description": null, - "longitude": -74.7424, - "n_guns_involved": 1.0, - "notes": null, - "participant_age": null, - "participant_age_group": null, - "participant_gender": "0::Male||1::Male", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Injured||1::Injured", - "participant_type": "0::Victim||1::Victim", - "sources": "http://www.trentonian.com/general-news/20171004/gun-arrest-robbery-and-shooting-october-4-2017-police-blotter", - "state_house_district": 15.0, - "state_senate_district": 15.0 - } - }, - { - "scenario_id": "064", - "features": { - "incident_id": 980119, - "date": "2017-11-04", - "state": "Louisiana", - "city_or_county": "Marrero", - "address": "6500 Block of Lapalco Blvd", - "n_killed": 0, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/980119", - "source_url": "http://www.nola.com/crime/index.ssf/2017/11/man_shot_in_marrero_early_satu.html#incart_river_index", - "incident_url_fields_missing": "False", - "congressional_district": 2.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Wounded/Injured", - "latitude": 29.8784, - "location_description": null, - "longitude": -90.1205, - "n_guns_involved": 1.0, - "notes": null, - "participant_age": null, - "participant_age_group": null, - "participant_gender": "0::Male", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Injured", - "participant_type": "0::Victim", - "sources": "http://www.nola.com/crime/index.ssf/2017/11/man_shot_in_marrero_early_satu.html#incart_river_index", - "state_house_district": 83.0, - "state_senate_district": 3.0 - } - }, - { - "scenario_id": "065", - "features": { - "incident_id": 766475, - "date": "2017-02-03", - "state": "Alabama", - "city_or_county": "Birmingham", - "address": "Interstate 59", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/766475", - "source_url": "http://www.wbrc.com/story/34419223/man-found-shot-to-death-in-car-on-i-59", - "incident_url_fields_missing": "False", - "congressional_district": 7.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", - "latitude": 33.5252, - "location_description": null, - "longitude": -86.8064, - "n_guns_involved": 1.0, - "notes": null, - "participant_age": "0::27", - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": "0::Charles Lee Brown", - "participant_relationship": null, - "participant_status": "0::Killed", - "participant_type": "0::Victim", - "sources": "http://www.wbrc.com/story/34419223/man-found-shot-to-death-in-car-on-i-59", - "state_house_district": 54.0, - "state_senate_district": 18.0 - } - }, - { - "scenario_id": "066", - "features": { - "incident_id": 145971, - "date": "2014-06-03", - "state": "Pennsylvania", - "city_or_county": "Lancaster", - "address": "312 W. 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"state_house_district": null, - "state_senate_district": null - } - }, - { - "scenario_id": "067", - "features": { - "incident_id": 363945, - "date": "2015-06-25", - "state": "Michigan", - "city_or_county": "Sterling Heights", - "address": "3000 block of Polo Circle", - "n_killed": 2, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/363945", - "source_url": "http://www.freep.com/story/news/2015/06/29/sterling-heights-murder-suicide-couple-identified/29477869/", - "incident_url_fields_missing": "False", - "congressional_district": 10.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Suicide^||Murder/Suicide||Home Invasion||Home Invasion - Resident killed||Home Invasion - subject/suspect/perpetrator killed||Domestic Violence", - "latitude": 42.6154, - "location_description": "Polo Green Apartments", - "longitude": -82.9901, - "n_guns_involved": 1.0, - "notes": "Man breaks into home, kills wife, self, w/ 2 young-adult children in home", - "participant_age": "0::44||1::44", - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Female||1::Male", - "participant_name": "0::Brigita Karamatic||1::Sebastijan Karamatic", - "participant_relationship": "1::Family", - "participant_status": "0::Killed||1::Killed", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://www.macombdaily.com/general-news/20150625/sterling-heights-couple-found-dead-in-murder-suicide||http://www.freep.com/story/news/2015/06/29/sterling-heights-murder-suicide-couple-identified/29477869/", - "state_house_district": 30.0, - "state_senate_district": 10.0 - } - }, - { - "scenario_id": "068", - "features": { - "incident_id": 836478, - "date": "2017-05-03", - "state": "Washington", - "city_or_county": "Seattle", - "address": "Rainier Avenue and S Orcas Street", - "n_killed": 0, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/836478", - "source_url": "http://www.king5.com/news/crime/police-search-for-man-who-pulled-gun-on-pedestrians-in-south-seattle/436890736", - "incident_url_fields_missing": "False", - "congressional_district": 9.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Non-Shooting Incident||Road rage||Brandishing/flourishing/open carry/lost/found", - "latitude": 47.5512, - "location_description": null, - "longitude": -122.278, - "n_guns_involved": 1.0, - "notes": "Man threw beer bottle and then returned and brandished gun and threatened people who walked in front of his car at intersection", - "participant_age": null, - "participant_age_group": "0::Adult 18+", - "participant_gender": null, - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Unharmed", - "participant_type": "0::Subject-Suspect", - "sources": "http://www.king5.com/news/crime/police-search-for-man-who-pulled-gun-on-pedestrians-in-south-seattle/436890736", - "state_house_district": 37.0, - "state_senate_district": 37.0 - } - }, - { - "scenario_id": "069", - "features": { - "incident_id": 142979, - 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"state_senate_district": 20.0 - } - }, - { - "scenario_id": "070", - "features": { - "incident_id": 533698, - "date": "2016-04-01", - "state": "North Carolina", - "city_or_county": "Union (county)", - "address": "6200 block of Old Pageland Marshville Road", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/533698", - "source_url": "http://www.wbtv.com/story/31625114/woman-turns-self-in-wanted-in-fatal-shooting-of-brother-in-union-county", - "incident_url_fields_missing": "False", - "congressional_district": 8.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", - "latitude": 34.8381, - "location_description": null, - "longitude": -80.4178, - "n_guns_involved": null, - "notes": null, - "participant_age": "0::21", - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male||1::Female", - "participant_name": "0::Elijah James Moore||1::Breanna Moore", - "participant_relationship": "1::Family", - "participant_status": "0::Killed||1::Unharmed, Arrested", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://www.wbtv.com/story/31625114/woman-turns-self-in-wanted-in-fatal-shooting-of-brother-in-union-county", - "state_house_district": 55.0, - "state_senate_district": 35.0 - } - }, - { - "scenario_id": "071", - "features": { - "incident_id": 444098, - "date": "2015-11-06", - "state": "Georgia", - "city_or_county": "Athens", - "address": "Norwood Lane", - "n_killed": 0, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/444098", - "source_url": "http://onlineathens.com/blotter/2015-11-07/report-discharged-weapons-house-athens", - "incident_url_fields_missing": "False", - "congressional_district": 9.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shots Fired - No Injuries||Drive-by (car to street, car to car)", - "latitude": 34.0285, - "location_description": null, - "longitude": -83.3281, - "n_guns_involved": null, - 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"city_or_county": "Philadelphia", - "address": "3000 block of North Eighth Street", - "n_killed": 0, - "n_injured": 2, - "incident_url": "http://www.gunviolencearchive.org/incident/490547", - "source_url": "http://www.philly.com/philly/news/20160123_Two_men_shot_in_Fairhill.html", - "incident_url_fields_missing": "False", - "congressional_district": 2.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Wounded/Injured", - "latitude": 39.9973, - "location_description": null, - "longitude": -75.1441, - "n_guns_involved": null, - "notes": "2 inj, 1 critical and 1 stable.", - "participant_age": "0::58||1::52", - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Male||1::Male", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Injured||1::Injured", - "participant_type": "0::Victim||1::Victim", - "sources": "http://www.philly.com/philly/news/20160123_Two_men_shot_in_Fairhill.html", - "state_house_district": null, - "state_senate_district": null - } - }, - { - "scenario_id": "076", - "features": { - "incident_id": 907022, - "date": "2017-08-04", - "state": "Arizona", - "city_or_county": "Flagstaff", - "address": "1900 block of N 1st St", - "n_killed": 0, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/907022", - "source_url": "http://azdailysun.com/news/swat-responds-to-shooting-incident-in-sunnyside/article_6dfb3a17-2b96-52eb-8a20-8f9ce8176a85.html", - "incident_url_fields_missing": "False", - "congressional_district": 1.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shots Fired - No Injuries||Officer Involved Incident||Officer Involved Incident - Weapon involved but no shots fired||Officer Involved Shooting - subject/suspect/perpetrator surrender at standoff||Under the influence of alcohol or drugs (only applies to the subject/suspect/perpetrator )", - "latitude": 35.2064, - "location_description": null, - "longitude": -111.617, - "n_guns_involved": 1.0, - "notes": "suspect arrested after falling asleep on couch", - "participant_age": "0::21", - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": "0::Horacio Leal", - "participant_relationship": null, - "participant_status": "0::Unharmed, Arrested", - "participant_type": "0::Subject-Suspect", - "sources": "http://azdailysun.com/news/swat-responds-to-shooting-incident-in-sunnyside/article_6dfb3a17-2b96-52eb-8a20-8f9ce8176a85.html", - "state_house_district": 6.0, - "state_senate_district": 6.0 - } - }, - { - "scenario_id": "077", - "features": { - "incident_id": 111691, - "date": "2014-02-25", - "state": "Washington", - "city_or_county": "Lake Stevens", - "address": "7000 block of 126th Drive North", - "n_killed": 0, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/111691", - "source_url": "http://www.kirotv.com/news/news/second-arrest-made-attempted-home-invasion-lake-st/nd44r/", - "incident_url_fields_missing": "False", - "congressional_district": 1.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Wounded/Injured||Home Invasion||Home Invasion - subject/suspect/perpetrator injured", - "latitude": 48.0593, - "location_description": null, - "longitude": -122.06, - "n_guns_involved": null, - "notes": null, - "participant_age": "2::32||3::33", - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Male||1::Male||2::Male||3::Male", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Unharmed||1::Unharmed||2::Unharmed||3::Injured", - "participant_type": "0::Victim||1::Victim||2::Subject-Suspect||3::Subject-Suspect", - "sources": "http://www.kirotv.com/news/news/second-arrest-made-attempted-home-invasion-lake-st/nd44r/", - "state_house_district": 39.0, - "state_senate_district": 39.0 - } - }, - { - "scenario_id": "078", - "features": { - "incident_id": 294623, - "date": "2015-02-18", - "state": "New York", - "city_or_county": "Manhattan", - "address": "17th Street", - "n_killed": 0, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/294623", - "source_url": "http://newyork.cbslocal.com/2015/02/18/23-year-old-man-shot-wounded-in-chelsea/", - "incident_url_fields_missing": "False", - "congressional_district": 12.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Wounded/Injured", - "latitude": 40.7355, - "location_description": null, - "longitude": -73.9866, - "n_guns_involved": null, - "notes": "Chelsea, near Ninth Ave, shot in stomach", - "participant_age": "0::23", - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Injured", - "participant_type": "0::Victim", - "sources": "http://newyork.cbslocal.com/2015/02/18/23-year-old-man-shot-wounded-in-chelsea/", - "state_house_district": 74.0, - "state_senate_district": 28.0 - } - }, - { - "scenario_id": "079", - "features": { - "incident_id": 498363, - "date": "2016-02-02", - "state": "Texas", - "city_or_county": "Houston", - "address": "8600 Broadway", - "n_killed": 0, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/498363", - "source_url": "http://www.chron.com/houston/article/Shooting-leaves-man-wounded-in-back-in-SE-Houston-6800418.php", - "incident_url_fields_missing": "False", - "congressional_district": 9.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Wounded/Injured", - "latitude": 29.6655, - "location_description": null, - "longitude": -95.277, - "n_guns_involved": null, - "notes": "1 inj. This story is developing.", - "participant_age": null, - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Injured||1::Arrested", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://www.chron.com/houston/article/Shooting-leaves-man-wounded-in-back-in-SE-Houston-6800418.php", - "state_house_district": 147.0, - "state_senate_district": 6.0 - } - }, - { - "scenario_id": "080", - "features": { - "incident_id": 996852, - "date": "2017-11-26", - "state": "Nebraska", - "city_or_county": "Lincoln", - "address": "3636 N 52nd St", - "n_killed": 0, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/996852", - "source_url": "http://journalstar.com/news/local/911/man-fired-gun-through-doorway-of-lincoln-apartment-with-people/article_57eb7b48-275e-5398-9332-d1161c64fed3.html", - "incident_url_fields_missing": "False", - "congressional_district": null, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shots Fired - No Injuries||Possession of gun by felon or prohibited person", - "latitude": null, - "location_description": null, - "longitude": null, - "n_guns_involved": 1.0, - "notes": "Suspect fired gun through apartment door.", - "participant_age": "0::29", - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": "0::Salvador Orozco", - "participant_relationship": null, - "participant_status": "0::Unharmed, Arrested", - "participant_type": "0::Subject-Suspect", - "sources": "http://journalstar.com/news/local/911/man-fired-gun-through-doorway-of-lincoln-apartment-with-people/article_57eb7b48-275e-5398-9332-d1161c64fed3.html", - "state_house_district": null, - "state_senate_district": null - } - }, - { - "scenario_id": "081", - "features": { - "incident_id": 301254, - "date": "2015-03-02", - "state": "South Carolina", - "city_or_county": "Westminster", - "address": "Jeremy Drive", - 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"participant_status": "0::Injured||1::Unharmed||2::Unharmed, Arrested", - "participant_type": "0::Victim||1::Victim||2::Subject-Suspect", - "sources": "http://www.foxcarolina.com/story/28254902/deputies-man-held-wife-father-hostage-threatened-to-kill-them", - "state_house_district": 1.0, - "state_senate_district": 1.0 - } - }, - { - "scenario_id": "082", - "features": { - "incident_id": 767704, - "date": "2017-01-09", - "state": "Missouri", - "city_or_county": "High Ridge", - "address": "4100 block of Schumacher Rd", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/767704", - "source_url": "http://www.stltoday.com/news/local/crime-and-courts/high-ridge-woman-charged-with-killing-boyfriend-dumping-body-in/article_ae35caa6-544f-5f99-b233-c650e6e0d414.html", - "incident_url_fields_missing": "False", - "congressional_district": 3.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Domestic Violence", - 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"incident_id": 587289, - "date": "2016-06-19", - "state": "New York", - "city_or_county": "Brooklyn", - "address": "405 Lefferts Avenue", - "n_killed": 0, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/587289", - "source_url": "https://www.dnainfo.com/new-york/20160623/crown-heights/crime-blotter-73-year-old-man-assaulted-robbed-by-woman-asking-for-1", - "incident_url_fields_missing": "False", - "congressional_district": 9.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shots Fired - No Injuries", - "latitude": 40.6626, - "location_description": null, - "longitude": -73.9478, - "n_guns_involved": null, - "notes": "near New York Ave, shots fired into home, no inj report", - "participant_age": null, - "participant_age_group": null, - "participant_gender": null, - "participant_name": null, - "participant_relationship": null, - "participant_status": null, - "participant_type": null, - "sources": "https://www.dnainfo.com/new-york/20160623/crown-heights/crime-blotter-73-year-old-man-assaulted-robbed-by-woman-asking-for-1", - 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"n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/523587", - "source_url": "http://dallas.suntimes.com/obituaries/7/75/242401/e22586a07232714b8f5aebb2f23e36b6", - "incident_url_fields_missing": "False", - "congressional_district": 26.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Handgun", - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Suicide^||Murder/Suicide||Domestic Violence", - "latitude": 33.3945, - "location_description": null, - "longitude": -96.9591, - "n_guns_involved": 1.0, - "notes": "2 killed by gunfire in murder suicide, two others hit by car", - "participant_age": "0::51||1::50", - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Female||1::Male", - "participant_name": "0::Rhonda Holmes||1::Wilford \"Nemo\" Holmes", - "participant_relationship": "1::Significant others - current or former", - "participant_status": "0::Killed||1::Killed", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://www.nbcdfw.com/news/local/Three-Shot-in-Denton-County-Neighborhood-Police-372297442.html||http://www.wfaa.com/news/crime/four-injured-amid-confusion-at-pilot-point-crime-scene/86058459||http://dallas.suntimes.com/obituaries/7/75/242401/e22586a07232714b8f5aebb2f23e36b6", - "state_house_district": 106.0, - "state_senate_district": 30.0 - } - }, - { - "scenario_id": "101", - "features": { - "incident_id": 125047, - "date": "2014-04-09", - "state": "California", - "city_or_county": "Soledad", - "address": "Walker and Metz ", - "n_killed": 0, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/125047", - "source_url": "http://www.thecalifornian.com/article/20140414/NEWS09/304140030/Reports-lead-Soledad-police-hunt-shooting-victim", - "incident_url_fields_missing": "False", - "congressional_district": 20.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Wounded/Injured", - "latitude": 36.4276, - "location_description": null, - 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"participant_type": "0::Subject-Suspect", - "sources": "http://www.wibw.com/content/news/Chase-suspect-apprehended-in-Oakland-Neighborhood--440437623.html", - "state_house_district": 58.0, - "state_senate_district": 19.0 - } - }, - { - "scenario_id": "130", - "features": { - "incident_id": 544316, - "date": "2016-03-09", - "state": "Louisiana", - "city_or_county": "Metairie", - "address": "1000 block of Aurora Avenue", - "n_killed": 0, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/544316", - "source_url": "http://www.nola.com/crime/index.ssf/2016/04/suspect_in_metairie_mandeville.html#incart_river_index", - "incident_url_fields_missing": "False", - "congressional_district": 1.0, - "gun_stolen": "0::Stolen||1::Stolen||2::Stolen||3::Stolen||4::Stolen||5::Stolen||6::Stolen||7::Stolen||8::Stolen||9::Stolen||10::Stolen||11::Stolen||12::Stolen||13::Stolen", - "gun_type": "0::223 Rem [AR-15]||1::Shotgun||2::Shotgun||3::Shotgun||4::Rifle||5::Rifle||6::Rifle||7::Rifle||8::Rifle||9::Rifle||10::Handgun||11::Handgun||12::Handgun||13::Handgun", - 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"source_url": "http://www.northwestgeorgianews.com/cherokee_county/two-shot-and-two-stabbed-in-dekalb-county/article_588b8090-a915-11e6-881c-bbf52c818c67.html", - "incident_url_fields_missing": "False", - "congressional_district": 4.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Wounded/Injured||Drive-by (car to street, car to car)", - "latitude": 33.7956, - "location_description": null, - "longitude": -84.2279, - "n_guns_involved": 1.0, - "notes": null, - "participant_age": null, - "participant_age_group": "0::Adult 18+||1::Teen 12-17", - "participant_gender": "0::Male", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Injured||1::Injured", - "participant_type": "0::Victim||1::Victim", - "sources": "http://www.northwestgeorgianews.com/cherokee_county/two-shot-and-two-stabbed-in-dekalb-county/article_588b8090-a915-11e6-881c-bbf52c818c67.html", - "state_house_district": 86.0, - "state_senate_district": 41.0 - } - }, - { - "scenario_id": "132", - "features": { - "incident_id": 872756, - "date": "2017-06-16", - "state": "Virginia", - "city_or_county": "Fredericksburg", - "address": "Altoona Dr", - "n_killed": 0, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/872756", - "source_url": "http://www.fredericksburg.com/news/crime_courts/fredericksburg-police-investigating-teen-s-accidental-shooting/article_5183701f-d369-5bbb-a412-3c23392e22ca.html", - "incident_url_fields_missing": "False", - "congressional_district": 1.0, - "gun_stolen": "0::Unknown||1::Unknown", - "gun_type": "0::Handgun||1::Handgun", - "incident_characteristics": "Shot - Wounded/Injured||Non-Aggression Incident||Accidental Shooting||Accidental Shooting - Injury||Accidental/Negligent Discharge||Playing with gun", - "latitude": 38.2932, - "location_description": null, - "longitude": -77.4979, - "n_guns_involved": 2.0, - "notes": "AD shot cousin in neck with revolver, 2 handguns recover from home", - "participant_age": "0::13||1::16", - "participant_age_group": "0::Teen 12-17||1::Teen 12-17", - "participant_gender": "0::Male||1::Male", - "participant_name": null, - "participant_relationship": "1::Family", - "participant_status": "0::Injured||1::Unharmed", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://www.fredericksburg.com/news/crime_courts/fredericksburg-police-investigating-teen-s-accidental-shooting/article_5183701f-d369-5bbb-a412-3c23392e22ca.html", - "state_house_district": 88.0, - "state_senate_district": 17.0 - } - }, - { - "scenario_id": "133", - "features": { - "incident_id": 1011299, - "date": "2017-12-20", - "state": "Florida", - "city_or_county": "Jacksonville", - "address": "1591 Lane Ave S", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/1011299", - "source_url": "https://www.news4jax.com/news/local/jacksonville/jacksonville-shot-dead-at-westside-apartment-complex", - "incident_url_fields_missing": "False", - 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"address": "Crawford Road", - "n_killed": 0, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/347004", - "source_url": "http://www.heraldonline.com/news/local/crime/article22360197.html", - "incident_url_fields_missing": "False", - "congressional_district": 5.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Wounded/Injured||Accidental Shooting||Accidental Shooting - Injury||Accidental/Negligent Discharge||Playing with gun", - "latitude": 34.9115, - "location_description": null, - "longitude": -81.049, - "n_guns_involved": null, - "notes": "party, large group of people playing with gun, dropped accidentally shot vic in foot", - "participant_age": "0::17", - "participant_age_group": "0::Teen 12-17", - "participant_gender": "0::Male", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Injured", - "participant_type": "0::Victim", - "sources": "http://www.heraldonline.com/news/local/crime/article22360197.html", - 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"source_url": "http://www.tulsaworld.com/homepagelatest/suspect-in-shooting-father-allegedly-place-pipe-bomb-in-shooting/article_0abaff65-b54c-5c30-885e-a2733e301576.html", - "incident_url_fields_missing": "False", - "congressional_district": 3.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Handgun", - "incident_characteristics": "Shots Fired - No Injuries", - "latitude": 35.9981, - "location_description": null, - "longitude": -96.1245, - "n_guns_involved": 1.0, - "notes": "Suspect fired shots at woman. 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", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/114883", - "source_url": "http://www.arkansasmatters.com/story/d/story/womans-body-found-inside-hot-springs-home/35605/Ewjj7NmEn066FLxrR8plFg", - "incident_url_fields_missing": "False", - "congressional_district": 4.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)", - "latitude": 34.4789, - "location_description": null, - "longitude": -93.0614, - "n_guns_involved": null, - "notes": null, - "participant_age": null, - "participant_age_group": "0:Adult 18+", - "participant_gender": "0:Female", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0:Killed", - "participant_type": "0:Victim", - "sources": "http://www.arkansasmatters.com/story/d/story/womans-body-found-inside-hot-springs-home/35605/Ewjj7NmEn066FLxrR8plFg", - "state_house_district": 24.0, - "state_senate_district": 14.0 - } - }, - { - "scenario_id": "160", - "features": { - "incident_id": 704055, - "date": "2016-11-17", - "state": "Nevada", - "city_or_county": "West Wendover", - "address": "1870 Plateau Way", - "n_killed": 0, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/704055", - "source_url": "http://www.reviewjournal.com/crime/shootings/utah-man-wounded-shootout-nevada-officers-also-had-homemade-bomb", - "incident_url_fields_missing": "False", - "congressional_district": 2.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Wounded/Injured||Institution/Group/Business||Shots Fired - No Injuries||Officer Involved Incident||Officer Involved Shooting - subject/suspect/perpetrator shot||Shootout (where VENN diagram of shooters and victims overlap)||Bar/club incident - in or around establishment||Possession of gun by felon or prohibited person||Pistol-whipping", - "latitude": 40.7392, - "location_description": "Southern X-Posure", - "longitude": -114.078, - "n_guns_involved": 1.0, - "notes": "Suspect fired shots at club, fled police, crashed, shot at officers, and was hit by return fire. several guns and crude explosive found in car.", - "participant_age": "0::30", - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": "0::Anthony Martinez", - "participant_relationship": null, - "participant_status": "0::Injured", - "participant_type": "0::Subject-Suspect", - "sources": "http://www.reviewjournal.com/crime/shootings/police-elko-county-wound-man-fleeing-after-strip-club-shooting||http://www.deseretnews.com/article/865667514/Man-injured-in-West-Wendover-police-shootout-as-squad-tries-to-dismantle-IED.html||http://www.coyote-tv.com/2016/11/21/update-on-thursday-shooting-explosive-device-in-the-suspect-car/||http://www.reviewjournal.com/crime/shootings/utah-man-wounded-shootout-nevada-officers-also-had-homemade-bomb", - "state_house_district": 33.0, - "state_senate_district": 19.0 - } - }, - { - "scenario_id": "161", - "features": { - "incident_id": 696983, - "date": "2016-11-08", - "state": "Oregon", - "city_or_county": "Springfield", - "address": "1100 block of T Street", - "n_killed": 0, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/696983", - "source_url": "http://kval.com/news/local/police-man-fired-shotgun-in-street-to-celebrate-election-results", - "incident_url_fields_missing": "False", - "congressional_district": null, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shots Fired - No Injuries||Non-Aggression Incident||Under the influence of alcohol or drugs (only applies to the subject/suspect/perpetrator )||Brandishing/flourishing/open carry/lost/found", - "latitude": null, - "location_description": null, - "longitude": null, - "n_guns_involved": 1.0, - "notes": "Celebratory gunfire, intox suspect fired shotgun or rifle in air for election results", - "participant_age": "0::55", - 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"participant_type": "0::Victim||1::Victim", - "sources": "http://fox59.com/2016/04/16/one-person-killed-another-in-the-hospital-after-northeast-side-shooting/||http://www.indystar.com/story/news/crime/2016/04/17/police-investigating-fatal-shooting-north-side/83166048/?utm_source=feedblitz&utm_medium=FeedBlitzRss&utm_campaign=FeedBlitzRss&utm_content=Police+investigating+fatal+shooting+on+north+side", - "state_house_district": 95.0, - "state_senate_district": 34.0 - } - }, - { - "scenario_id": "164", - "features": { - "incident_id": 873861, - "date": "2017-06-22", - "state": "South Carolina", - "city_or_county": "North Charleston", - "address": "2000 block of Mott Avenue", - "n_killed": 0, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/873861", - "source_url": "http://www.wtvm.com/story/35731164/police-investigating-after-one-shot-in-n-charleston", - "incident_url_fields_missing": "False", - "congressional_district": 6.0, - "gun_stolen": "0::Unknown", - 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"incident_id": 202136, - "date": "2014-09-27", - "state": "South Carolina", - "city_or_county": "Beech Island", - "address": "Swamp Road", - "n_killed": 0, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/202136", - "source_url": "http://www.aikenstandard.com/article/20140928/AIK0102/140929410/1008/blotter-for-sept-29", - "incident_url_fields_missing": "False", - "congressional_district": 2.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shots Fired - No Injuries", - "latitude": 33.4379, - "location_description": null, - "longitude": -81.8939, - "n_guns_involved": null, - "notes": "multiple perps seen leaving in vehicles after shots hit different homes; no injuries reported", - "participant_age": null, - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Male||1::Female", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Unharmed||1::Unharmed", - "participant_type": "0::Victim||1::Victim", - 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"notes": null, - "participant_age": "0::64", - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Unharmed, Arrested", - "participant_type": "0::Subject-Suspect", - "sources": "http://spdblotter.seattle.gov/2016/03/21/man-arrested-for-casual-disregard-of-traffic-bike-helmet-gun-laws/", - "state_house_district": 37.0, - "state_senate_district": 37.0 - } - }, - { - "scenario_id": "168", - "features": { - "incident_id": 485152, - "date": "2016-01-10", - "state": "New York", - "city_or_county": "Brooklyn", - "address": "225 Parkside Avenue", - "n_killed": 0, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/485152", - "source_url": "http://www.dnainfo.com/new-york/20160114/crown-heights/crime-blotter-23-year-old-woman-mugged-on-busy-crown-heights-street", - "incident_url_fields_missing": "False", - "congressional_district": 9.0, - "gun_stolen": null, - 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"city_or_county": "West Ashley", - "address": "Mulmar Street", - "n_killed": 0, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/107277", - "source_url": "http://www.live5news.com/story/24655457/stray-bullet-fired-into-home-homeowner-wants-more-patrols", - "incident_url_fields_missing": "False", - "congressional_district": 1.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shots fired, no action (reported, no evidence found)", - "latitude": 32.8146, - "location_description": null, - "longitude": -80.0076, - "n_guns_involved": null, - "notes": null, - "participant_age": null, - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Female||1::Male", - "participant_name": "0::Jane Edwards||1::Don Edwards", - "participant_relationship": null, - "participant_status": "0::Unharmed||1::Unharmed", - "participant_type": "0::Victim||1::Victim", - "sources": "http://www.live5news.com/story/24655457/stray-bullet-fired-into-home-homeowner-wants-more-patrols", - "state_house_district": 114.0, - "state_senate_district": 41.0 - } - }, - { - "scenario_id": "171", - "features": { - "incident_id": 567349, - "date": "2016-05-26", - "state": "Louisiana", - "city_or_county": "New Orleans", - "address": "4700 block of Dodt Ave", - "n_killed": 0, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/567349", - "source_url": "http://www.nola.com/crime/index.ssf/2016/05/man_wanted_for_producing_gun_d.html", - "incident_url_fields_missing": "False", - "congressional_district": 2.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Non-Shooting Incident||Brandishing/flourishing/open carry/lost/found", - "latitude": 30.0195, - "location_description": null, - "longitude": -89.9918, - "n_guns_involved": 1.0, - "notes": "New Orleans East", - "participant_age": "0::22", - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": "0::Don Wells", - "participant_relationship": null, - 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"date": "2018-01-15", - "state": "North Carolina", - "city_or_county": "Charlotte", - "address": "6120 E Independence Blvd", - "n_killed": 0, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/1027815", - "source_url": "http://www.wsoctv.com/news/local/shots-fired-inside-charlotte-bojangles/684231809", - "incident_url_fields_missing": "False", - "congressional_district": 12.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Institution/Group/Business||Shots Fired - No Injuries", - "latitude": 35.177, - "location_description": "Bojangles Famous Chicken n Biscuits", - "longitude": -80.7505, - "n_guns_involved": 1.0, - "notes": "fired shots during ar", - "participant_age": null, - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Unharmed", - "participant_type": "0::Subject-Suspect", - "sources": "http://www.wsoctv.com/news/local/shots-fired-inside-charlotte-bojangles/684231809", - "state_house_district": 100.0, - "state_senate_district": 40.0 - } - }, - { - "scenario_id": "191", - "features": { - "incident_id": 566738, - "date": "2016-05-26", - "state": "California", - "city_or_county": "Oakland", - "address": "1000 block of Webster Street", - "n_killed": 0, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/566738", - "source_url": "http://www.eastbaytimes.com/breaking-news/ci_29947312/oakland-man-shot-chinatown", - "incident_url_fields_missing": "False", - "congressional_district": 13.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Wounded/Injured", - "latitude": 37.8006, - "location_description": null, - "longitude": -122.27, - "n_guns_involved": null, - "notes": null, - "participant_age": null, - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Injured", - "participant_type": "0::Victim", - "sources": "http://www.eastbaytimes.com/breaking-news/ci_29947312/oakland-man-shot-chinatown", - "state_house_district": 18.0, - "state_senate_district": 9.0 - } - }, - { - "scenario_id": "192", - "features": { - "incident_id": 530253, - "date": "2016-03-24", - "state": "North Carolina", - "city_or_county": "Vass", - "address": "Wright Road", - "n_killed": 0, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/530253", - "source_url": "http://www.wral.com/man-faces-long-list-of-charges-in-moore-county-shooting/15604680/", - "incident_url_fields_missing": "False", - "congressional_district": 2.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Wounded/Injured", - "latitude": 35.1918, - "location_description": null, - "longitude": -79.1595, - "n_guns_involved": null, - "notes": null, - "participant_age": "1::23", - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "1::Male", - "participant_name": "1::Zachary Randall Gautier", - "participant_relationship": null, - "participant_status": "0::Injured||1::Unharmed, Arrested", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://www.wral.com/man-faces-long-list-of-charges-in-moore-county-shooting/15604680/", - "state_house_district": 52.0, - "state_senate_district": 29.0 - } - }, - { - "scenario_id": "193", - "features": { - "incident_id": 411836, - "date": "2015-09-09", - "state": "Connecticut", - "city_or_county": "Hartford", - "address": "68 Pliny Street", - "n_killed": 0, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/411836", - "source_url": "http://wtnh.com/2015/09/10/drug-bust/", - "incident_url_fields_missing": "False", - "congressional_district": 1.0, - "gun_stolen": "0::Unknown||1::Unknown", - "gun_type": "0::7.62 [AK-47]||1::45 Auto", - "incident_characteristics": "Non-Shooting Incident||Drug involvement||ATF/LE Confiscation/Raid/Arrest||Possession (gun(s) found during commission of other crimes)||Possession of gun by felon or prohibited person", - "latitude": 41.7808, - "location_description": null, - "longitude": -72.6827, - "n_guns_involved": 2.0, - "notes": "1 of 2 connected raids with Babcock St, drugs, AK47, 45 cal", - "participant_age": "0::37", - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": "0::Luis Cruis", - "participant_relationship": null, - "participant_status": "0::Unharmed, Arrested", - "participant_type": "0::Subject-Suspect", - "sources": "http://wtnh.com/2015/09/10/drug-bust/", - "state_house_district": 7.0, - "state_senate_district": 2.0 - } - }, - { - "scenario_id": "194", - "features": { - "incident_id": 295990, - "date": "2015-02-21", - "state": "Pennsylvania", - "city_or_county": "Uniontown", - "address": "North Union Township", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/295990", - "source_url": "http://triblive.com/news/westmoreland/10776126-74/grady-walker-testified?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+tribunereview%2Fwestmoreland+%28Westmoreland+News%29", - "incident_url_fields_missing": "False", - "congressional_district": 9.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Domestic Violence", - "latitude": 39.8837, - "location_description": null, - "longitude": -79.7157, - "n_guns_involved": 1.0, - "notes": null, - "participant_age": "0::37||1::45", - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Female||1::Male", - "participant_name": "0::Mya Ann Grady||1::Terry Walker", - "participant_relationship": "1::Significant others - current or former", - "participant_status": "0::Killed||1::Arrested", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://www.post-gazette.com/local/region/2015/02/21/Uniontown-woman-found-shot-to-death-in-North-Union-Township/stories/201502210158||http://www.cbsnews.com/news/ex-boyfriend-wanted-in-shooting-of-pennsylvania-mom-found-dead-in-car/||http://triblive.com/news/westmoreland/10776126-74/grady-walker-testified?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+tribunereview%2Fwestmoreland+%28Westmoreland+News%29", - "state_house_district": null, - "state_senate_district": null - } - }, - { - "scenario_id": "195", - "features": { - "incident_id": 376738, - "date": "2015-07-17", - "state": "Arizona", - "city_or_county": "Scottsdale", - "address": "9700 block of East Happy Valley Road", - "n_killed": 0, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/376738", - "source_url": "http://www.azcentral.com/story/news/local/scottsdale/2017/02/13/scottsdale-woman-rhonda-cerny-sentenced-5-years-shooting-ex-husband/97845074/", - "incident_url_fields_missing": "False", - "congressional_district": null, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Wounded/Injured", - "latitude": null, - "location_description": null, - "longitude": null, - "n_guns_involved": 1.0, - "notes": null, - "participant_age": "1::59", - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Male||1::Female", - "participant_name": "0::Craig Cerny||1::Rhonda Cerny", - "participant_relationship": null, - "participant_status": "0::Injured||1::Unharmed, Arrested", - "participant_type": "0::Victim||1::Subject-Suspect", - "sources": "http://www.azcentral.com/story/news/local/scottsdale/2015/07/17/scottsdale-shooting-man-hospitalized/30319399/||http://www.azcentral.com/story/news/local/scottsdale/2017/02/13/scottsdale-woman-rhonda-cerny-sentenced-5-years-shooting-ex-husband/97845074/", - "state_house_district": null, - "state_senate_district": null - } - }, - { - "scenario_id": "196", - "features": { - "incident_id": 122025, - "date": "2014-04-04", - "state": "Alabama", - "city_or_county": "Booth", - "address": "County Road 81 ", - "n_killed": 1, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/122025", - "source_url": "http://www.wsfa.com/story/25164739/intruder-killed-by-autauga-co-homeowner-ided-second-suspect-arrested", - "incident_url_fields_missing": "False", - "congressional_district": 2.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Dead (murder, accidental, suicide)||Home Invasion||Home Invasion - subject/suspect/perpetrator killed||Defensive Use", - "latitude": 32.5219, - "location_description": null, - "longitude": -86.5697, - "n_guns_involved": null, - "notes": "shot man in hi def", - "participant_age": "1::18||2::32", - "participant_age_group": "0::Adult 18+||1::Adult 18+||2::Adult 18+", - "participant_gender": "0::Female||1::Male||2::Male", - "participant_name": "1::Mikel Steven Smith||2::James Bryan Vance", - "participant_relationship": null, - "participant_status": "0::Unharmed||1::Killed||2::Unharmed", - "participant_type": "0::Victim||1::Subject-Suspect||2::Subject-Suspect", - "sources": "http://www.timesdaily.com/news/state/article_0d80f1e3-ec40-523a-8b1d-f5007293e5bc.html||http://www.waka.com/news/montgomery-news/Sheriffs-Officials-Autauga-County-Woman-Shoots-Intruder-253904171.html||http://www.wsfa.com/story/25164739/intruder-killed-by-autauga-co-homeowner-ided-second-suspect-arrested", - "state_house_district": 42.0, - "state_senate_district": 30.0 - } - }, - { - "scenario_id": "197", - "features": { - "incident_id": 1039759, - "date": "2018-01-12", - "state": "Ohio", - "city_or_county": "Independence", - "address": "6020 Quarry Ln", - "n_killed": 0, - "n_injured": 0, - "incident_url": "http://www.gunviolencearchive.org/incident/1039759", - "source_url": "http://www.cleveland.com/independence/index.ssf/2018/01/exuberant_partygoer_cited_for.html#incart_river_index", - "incident_url_fields_missing": "False", - "congressional_district": 14.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Institution/Group/Business||Shots Fired - No Injuries", - "latitude": 41.3956, - "location_description": "Red Roof Inn", - "longitude": -81.6495, - "n_guns_involved": 1.0, - "notes": "celebratory gunfire", - "participant_age": null, - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Female", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Unharmed, Arrested", - "participant_type": "0::Subject-Suspect", - "sources": "http://www.cleveland.com/independence/index.ssf/2018/01/exuberant_partygoer_cited_for.html#incart_river_index", - "state_house_district": 6.0, - "state_senate_district": 24.0 - } - }, - { - "scenario_id": "198", - "features": { - "incident_id": 414689, - "date": "2015-09-14", - "state": "Illinois", - "city_or_county": "Chicago", - "address": "7200 block of South Union Avenue", - "n_killed": 0, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/414689", - "source_url": "http://www.dnainfo.com/chicago/20150916/englewood/englewood-shootings-leave-teen-girl-man-wounded", - "incident_url_fields_missing": "False", - "congressional_district": 1.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Wounded/Injured||Possession (gun(s) found during commission of other crimes)", - "latitude": 41.7633, - "location_description": null, - "longitude": -87.6419, - "n_guns_involved": null, - "notes": "Englewood - 2 men fire into group on sidewalk, wound 16 y/o girl;", - "participant_age": "0::16", - "participant_age_group": "0::Teen 12-17", - "participant_gender": "0::Female", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Injured", - "participant_type": "0::Victim", - "sources": "http://www.dnainfo.com/chicago/20150916/englewood/englewood-shootings-leave-teen-girl-man-wounded", - "state_house_district": 31.0, - "state_senate_district": 16.0 - } - }, - { - "scenario_id": "199", - "features": { - "incident_id": 622476, - "date": "2016-08-05", - "state": "Illinois", - "city_or_county": "Chicago", - "address": "1500 block of South Harding Avenue", - "n_killed": 0, - "n_injured": 1, - "incident_url": "http://www.gunviolencearchive.org/incident/622476", - "source_url": "http://www.chicagotribune.com/news/local/breaking/ct-man-wounded-in-grand-crossing-shooting-20160805-story.html", - "incident_url_fields_missing": "False", - "congressional_district": 4.0, - "gun_stolen": "0::Unknown", - "gun_type": "0::Unknown", - "incident_characteristics": "Shot - Wounded/Injured||Non-Aggression Incident||Accidental Shooting||Accidental Shooting - Injury||Accidental/Negligent Discharge||Self-Inflicted (not suicide or suicide attempt - NO PERP)", - "latitude": 41.8006, - "location_description": null, - "longitude": -87.722, - "n_guns_involved": 1.0, - "notes": "Teen grazed leg; believed self-inflicted;", - "participant_age": "0::18", - "participant_age_group": "0::Adult 18+", - "participant_gender": "0::Male", - "participant_name": null, - "participant_relationship": null, - "participant_status": "0::Injured", - "participant_type": "0::Victim", - "sources": "http://www.chicagotribune.com/news/local/breaking/ct-man-wounded-in-grand-crossing-shooting-20160805-story.html", - "state_house_district": 1.0, - "state_senate_district": 1.0 - } - }, - { - "scenario_id": "200", - "features": { - "incident_id": 512461, - "date": "2016-02-24", - "state": "Pennsylvania", - "city_or_county": "Monessen", - "address": "600 block of McMahon Avenue", - "n_killed": 0, - "n_injured": 2, - "incident_url": "http://www.gunviolencearchive.org/incident/512461", - "source_url": "http://www.wpxi.com/videos/news/witnesses-say-victims-beat-before-being-shot-in/vC7z9/", - "incident_url_fields_missing": "False", - "congressional_district": 9.0, - "gun_stolen": null, - "gun_type": null, - "incident_characteristics": "Shot - Wounded/Injured", - "latitude": 40.1574, - "location_description": null, - "longitude": -79.8894, - "n_guns_involved": null, - "notes": null, - "participant_age": "0::24||1::26", - "participant_age_group": "0::Adult 18+||1::Adult 18+", - "participant_gender": "0::Male||1::Male", - "participant_name": "0::Travis Holmes||1::Phalonious Wilkerson", - "participant_relationship": null, - "participant_status": "0::Injured||1::Injured", - "participant_type": "0::Victim||1::Victim", - "sources": "http://www.wpxi.com/news/news/local/2-people-shot-monessen-medical-helicopters-called-/nqYM7/||http://triblive.com/news/westmoreland/10040329-74/911-call-gunshot?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+alltribstories+%28TribLIVE+News%29||http://www.wpxi.com/videos/news/witnesses-say-victims-beat-before-being-shot-in/vC7z9/", - "state_house_district": null, - "state_senate_district": null - } - } - ], - "target_column": "n_killed", - "task_sub_type": "regression", - "ranking_ground_truth": { - "top_k_ids": [ - "063", - "084", - "096" - ] - }, - "active_filters": [ - { - "col": "state_senate_district", - "op": "==", - "val": 15 - } - ], - "sort_direction": "high" - }, - "response": "", - "evaluation_score": {} -} \ No newline at end of file + "active_filters": [ + { + "col": "state_senate_district", + "op": "==", + "val": 15 + } + ], + "sort_direction": "low" + }, + "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/finance/regression/Bitcoin_Historical_Data_B4/Bitcoin_Historical_Data_B4_002/Bitcoin_Historical_Data_B4_002_current.json b/ranking_and_filtering/finance/regression/Bitcoin_Historical_Data_B4/Bitcoin_Historical_Data_B4_002/Bitcoin_Historical_Data_B4_002_current.json index 37821c252aed7c334cc7673ba52e1599b0fcd840..4c052044c9f4921c224c34c850aafa116480af54 100644 --- a/ranking_and_filtering/finance/regression/Bitcoin_Historical_Data_B4/Bitcoin_Historical_Data_B4_002/Bitcoin_Historical_Data_B4_002_current.json +++ b/ranking_and_filtering/finance/regression/Bitcoin_Historical_Data_B4/Bitcoin_Historical_Data_B4_002/Bitcoin_Historical_Data_B4_002_current.json @@ -1,1878 +1,1878 @@ { - "id": "002", - "task_type": "B4", - "subtask_type": "topk", - "perspective": "data_holder", - "dataset_name": "Bitcoin Historical Data", - "table_path": "output/Bitcoin Historical Data_B4/Bitcoin Historical Data_B4_002/test.csv", - "query": "Our risk protocol requires us to monitor for anomalously quiet trading intervals that might indicate market issues. From the archive of exchange data I've provided, can you isolate the 3 intervals with the lowest trading volume in the new batch of data?", - "meta_info": { - "domain": "finance" + "id": "002", + "task_type": "B4", + "subtask_type": "topk", + "perspective": "data_holder", + "dataset_name": "Bitcoin Historical Data", + "table_path": "output/Bitcoin Historical Data_B4/Bitcoin Historical Data_B4_002/test.csv", + "query": "Our risk protocol requires us to monitor for anomalously quiet trading intervals that might indicate market issues. From the archive of exchange data I've provided, can you isolate the 3 intervals with the lowest trading volume in the new batch of data?", + "meta_info": { + "domain": "finance" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "Timestamp": 1403957100.0, + "Open": 596.0, + "High": 596.0, + "Low": 596.0, + "Close": 596.0, + "Volume": 0.0 + } + }, + { + "scenario_id": "002", + "features": { + "Timestamp": 1469379540.0, + "Open": 652.49, + "High": 654.39, + "Low": 652.31, + "Close": 654.39, + "Volume": 6.11609531 + } + }, + { + "scenario_id": "003", + "features": { + "Timestamp": 1625985240.0, + "Open": 33608.88, + "High": 33610.94, + "Low": 33596.05, + "Close": 33596.05, + "Volume": 0.43128259 + } + }, + { + "scenario_id": "004", + "features": { + "Timestamp": 1495447500.0, + "Open": 2099.43, + "High": 2099.43, + "Low": 2099.42, + "Close": 2099.42, + "Volume": 0.1529617 + } + }, + { + "scenario_id": 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"task_sub_type": "regression", - "ranking_ground_truth": { - "top_k_ids": [ - "001", - "005", - "014" - ] - }, - "sort_direction": "high" - }, - "response": "", - "evaluation_score": {} -} \ No newline at end of file + "sort_direction": "low" + }, + "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/finance/regression/Bitcoin_Historical_Data_B4/Bitcoin_Historical_Data_B4_007/Bitcoin_Historical_Data_B4_007_current.json b/ranking_and_filtering/finance/regression/Bitcoin_Historical_Data_B4/Bitcoin_Historical_Data_B4_007/Bitcoin_Historical_Data_B4_007_current.json index 3bfaea89334c83db828846cc88bf785ec98050d0..8f13ac68ca14ed767a4b529ea3d256761a7a9093 100644 --- a/ranking_and_filtering/finance/regression/Bitcoin_Historical_Data_B4/Bitcoin_Historical_Data_B4_007/Bitcoin_Historical_Data_B4_007_current.json +++ b/ranking_and_filtering/finance/regression/Bitcoin_Historical_Data_B4/Bitcoin_Historical_Data_B4_007/Bitcoin_Historical_Data_B4_007_current.json @@ -1,2209 +1,2209 @@ { - "id": "007", - "task_type": "B4", - "subtask_type": "filter", - "perspective": "data_holder", - "dataset_name": "Bitcoin Historical Data", - "table_path": "output/Bitcoin Historical Data_B4/Bitcoin Historical Data_B4_007/test.csv", - "query": "We're planning capital allocation for quieter market phases. Using our past logs for context, identify all time windows in the NEW batch where the opening price was under $100. From that subset, which bottom 8 periods have the lowest volume?", - "meta_info": { - "domain": "finance" + "id": "007", + "task_type": "B4", + "subtask_type": "filter", + "perspective": "data_holder", + "dataset_name": "Bitcoin Historical Data", + "table_path": "output/Bitcoin Historical Data_B4/Bitcoin Historical Data_B4_007/test.csv", + "query": "We're planning capital allocation for quieter market phases. Using our past logs for context, identify all time windows in the NEW batch where the opening price was under $100. 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b/ranking_and_filtering/finance/regression/Campus_Recruitment_B4/Campus_Recruitment_B4_001/Campus_Recruitment_B4_001_current.json @@ -1,902 +1,909 @@ { - "id": "001", - "task_type": "B4", - "subtask_type": "topk", - "perspective": "data_holder", - "dataset_name": "Campus Recruitment", - "table_path": "output/finance/regression/Campus Recruitment_B4/Campus Recruitment_B4_001/test.csv", - "query": "Our recruitment budget is tight this quarter and we need to make the most strategic offers. From the new graduate pool, I need you to identify the 15 candidates who are projected to command the highest starting salaries, based on the historical placement data I've provided.", - "meta_info": { - "domain": "finance" + "id": "001", + "task_type": "B4", + "subtask_type": "topk", + "perspective": "data_holder", + "dataset_name": "Campus Recruitment", + "table_path": "output/finance/regression/Campus Recruitment_B4/Campus Recruitment_B4_001/test.csv", + "query": "Our recruitment budget is tight this quarter and we need to make the most strategic offers. Among the new graduates recorded as placed, I need you to identify the 15 candidates who are projected to command the highest starting salaries, based on the historical placement data I've provided.", + "meta_info": { + "domain": "finance" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "sl_no": 175, + "gender": "M", + "ssc_p": 73.24, + "ssc_b": "Others", + "hsc_p": 50.83, + "hsc_b": "Others", + "hsc_s": "Science", + "degree_p": 64.27, + "degree_t": "Sci&Tech", + "workex": "Yes", + "etest_p": 64.0, + "specialisation": "Mkt&Fin", + "mba_p": 66.23, + "status": "Placed", + "salary": 500000.0 + } + }, + { + "scenario_id": "002", + "features": { + "sl_no": 4, + "gender": "M", + "ssc_p": 56.0, + "ssc_b": "Central", + "hsc_p": 52.0, + "hsc_b": "Central", + "hsc_s": "Science", + "degree_p": 52.0, + "degree_t": "Sci&Tech", + "workex": "No", + "etest_p": 66.0, + "specialisation": "Mkt&HR", + "mba_p": 59.43, 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"032" - ] - }, - "sort_direction": "high" - }, - "response": "", - "evaluation_score": {} -} \ No newline at end of file + "sort_direction": "high", + "active_filters": [ + { + "col": "status", + "op": "==", + "val": "Placed" + } + ] + }, + "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/finance/regression/Campus_Recruitment_B4/Campus_Recruitment_B4_002/Campus_Recruitment_B4_002_current.json b/ranking_and_filtering/finance/regression/Campus_Recruitment_B4/Campus_Recruitment_B4_002/Campus_Recruitment_B4_002_current.json index d34f82219a001594c3aa87ffebc3349e1ad0c765..62382b8eab8f8607834a7627618074cfd7e0e2fd 100644 --- a/ranking_and_filtering/finance/regression/Campus_Recruitment_B4/Campus_Recruitment_B4_002/Campus_Recruitment_B4_002_current.json +++ b/ranking_and_filtering/finance/regression/Campus_Recruitment_B4/Campus_Recruitment_B4_002/Campus_Recruitment_B4_002_current.json @@ -1,895 +1,902 @@ { - "id": "002", - "task_type": "B4", - "subtask_type": 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Reviewing the candidate profiles against our past recruitment archives, flag the 8 individuals forecasted to have the most modest salary requirements.", - "meta_info": { - "domain": "finance" + "id": "002", + "task_type": "B4", + "subtask_type": "topk", + "perspective": "data_holder", + "dataset_name": "Campus Recruitment", + "table_path": "output/finance/regression/Campus Recruitment_B4/Campus Recruitment_B4_002/test.csv", + "query": "We're launching a new development program aimed at candidates with high potential but more modest starting salary packages. Reviewing the profiles of candidates recorded as placed against our past recruitment archives, flag the 8 individuals forecasted to have the most modest starting salary packages.", + "meta_info": { + "domain": "finance" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "sl_no": 201, + "gender": "M", + "ssc_p": 69.0, + "ssc_b": "Others", + "hsc_p": 60.0, + "hsc_b": "Others", + "hsc_s": "Commerce", + "degree_p": 65.0, + "degree_t": "Comm&Mgmt", + "workex": "No", + "etest_p": 87.55, + "specialisation": "Mkt&Fin", + "mba_p": 52.81, + "status": "Placed", + "salary": 300000.0 + } + }, + { + "scenario_id": "002", + "features": { + "sl_no": 45, + "gender": "F", + "ssc_p": 77.0, + "ssc_b": "Others", + "hsc_p": 73.0, + "hsc_b": "Others", + "hsc_s": "Commerce", + "degree_p": 81.0, + "degree_t": "Comm&Mgmt", + "workex": "Yes", + "etest_p": 89.0, + "specialisation": "Mkt&Fin", + "mba_p": 69.7, + "status": 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[ + { + "col": "status", + "op": "==", + "val": "Placed" + } + ] + }, + "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/finance/regression/Campus_Recruitment_B4/Campus_Recruitment_B4_003/Campus_Recruitment_B4_003_current.json b/ranking_and_filtering/finance/regression/Campus_Recruitment_B4/Campus_Recruitment_B4_003/Campus_Recruitment_B4_003_current.json index ffafa22e156f8708edf4229b3efe6e3ba785711a..b9e07921b86221cf365e34bb4f4e0fc7cb74e374 100644 --- a/ranking_and_filtering/finance/regression/Campus_Recruitment_B4/Campus_Recruitment_B4_003/Campus_Recruitment_B4_003_current.json +++ b/ranking_and_filtering/finance/regression/Campus_Recruitment_B4/Campus_Recruitment_B4_003/Campus_Recruitment_B4_003_current.json @@ -1,892 +1,899 @@ { - "id": "003", - "task_type": "B4", - "subtask_type": "topk", - "perspective": "data_holder", - "dataset_name": "Campus Recruitment", - "table_path": "output/finance/regression/Campus Recruitment_B4/Campus 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+ "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/finance/regression/Campus_Recruitment_B4/Campus_Recruitment_B4_004/Campus_Recruitment_B4_004_current.json b/ranking_and_filtering/finance/regression/Campus_Recruitment_B4/Campus_Recruitment_B4_004/Campus_Recruitment_B4_004_current.json index d4fcf054f1de5671d154185e7847a474ab2c9140..e1f450e1b5051e2ffb65d182c86f2fa079661b2b 100644 --- a/ranking_and_filtering/finance/regression/Campus_Recruitment_B4/Campus_Recruitment_B4_004/Campus_Recruitment_B4_004_current.json +++ b/ranking_and_filtering/finance/regression/Campus_Recruitment_B4/Campus_Recruitment_B4_004/Campus_Recruitment_B4_004_current.json @@ -1,834 +1,841 @@ { - "id": "004", - "task_type": "B4", - "subtask_type": "topk", - "perspective": "data_holder", - "dataset_name": "Campus Recruitment", - "table_path": "output/finance/regression/Campus Recruitment_B4/Campus Recruitment_B4_004/test.csv", - "query": "Our competitor is snatching up all the top talent. To counter this, I need to personally reach out to the most in-demand prospects. Based on the historical salary data from our campus recruitment that I'm sharing, who are the 7 candidates predicted to get the highest offers?", - "meta_info": { - "domain": "finance" + "id": "004", + "task_type": "B4", + "subtask_type": "topk", + "perspective": "data_holder", + "dataset_name": "Campus Recruitment", + "table_path": "output/finance/regression/Campus Recruitment_B4/Campus Recruitment_B4_004/test.csv", + "query": "Our competitor is snatching up all the top talent. To counter this, I need to personally reach out to the most in-demand prospects. Based on the historical salary data from our campus recruitment that I'm sharing, who are the 7 candidates recorded as placed and predicted to get the highest offers?", + "meta_info": { + "domain": "finance" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "sl_no": 41, + "gender": "F", + "ssc_p": 78.0, + "ssc_b": "Central", + "hsc_p": 77.0, + "hsc_b": "Others", + "hsc_s": "Commerce", + "degree_p": 80.0, + "degree_t": "Comm&Mgmt", + "workex": "No", + "etest_p": 60.0, + "specialisation": "Mkt&Fin", + "mba_p": 66.72, + "status": "Placed", + "salary": 287000.0 + } + }, + { + "scenario_id": "002", + "features": { + "sl_no": 60, + "gender": "M", + "ssc_p": 52.6, + "ssc_b": "Central", + "hsc_p": 65.58, + "hsc_b": "Others", + "hsc_s": "Science", + "degree_p": 72.11, + "degree_t": "Sci&Tech", + "workex": "No", + "etest_p": 57.6, + "specialisation": "Mkt&Fin", + "mba_p": 56.66, + "status": "Placed", + "salary": 265000.0 + } + }, + { + "scenario_id": "003", + "features": { + "sl_no": 159, + "gender": "M", + "ssc_p": 67.0, + "ssc_b": "Others", + "hsc_p": 63.0, + "hsc_b": "Others", + "hsc_s": "Science", + "degree_p": 64.0, + "degree_t": "Sci&Tech", + "workex": "No", + "etest_p": 60.0, + "specialisation": "Mkt&Fin", + "mba_p": 61.87, + "status": "Not Placed", + "salary": null + } + }, + { + "scenario_id": "004", + "features": { + "sl_no": 188, + "gender": "M", + "ssc_p": 78.5, + "ssc_b": "Central", + "hsc_p": 65.5, + "hsc_b": "Central", + "hsc_s": "Science", + "degree_p": 67.0, + "degree_t": "Sci&Tech", + "workex": "Yes", + "etest_p": 95.0, + "specialisation": "Mkt&Fin", + "mba_p": 64.86, + "status": "Placed", + "salary": 280000.0 + } + }, + { + "scenario_id": "005", + "features": { + "sl_no": 162, + "gender": "M", + "ssc_p": 55.6, + "ssc_b": "Others", + "hsc_p": 51.0, + "hsc_b": "Others", + "hsc_s": "Commerce", + "degree_p": 57.5, + "degree_t": "Comm&Mgmt", + "workex": "No", + 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a/ranking_and_filtering/finance/regression/Campus_Recruitment_B4/Campus_Recruitment_B4_005/Campus_Recruitment_B4_005_current.json b/ranking_and_filtering/finance/regression/Campus_Recruitment_B4/Campus_Recruitment_B4_005/Campus_Recruitment_B4_005_current.json index c64ffcaecc307ca79a2b6644a1c86aba5b77c183..585ab2f6b79dccd2ef67ab2daf24691548f98e49 100644 --- a/ranking_and_filtering/finance/regression/Campus_Recruitment_B4/Campus_Recruitment_B4_005/Campus_Recruitment_B4_005_current.json +++ b/ranking_and_filtering/finance/regression/Campus_Recruitment_B4/Campus_Recruitment_B4_005/Campus_Recruitment_B4_005_current.json @@ -1,862 +1,867 @@ { - "id": "005", - "task_type": "B4", - "subtask_type": "filter", - "perspective": "data_holder", - "dataset_name": "Campus Recruitment", - "table_path": "output/finance/regression/Campus Recruitment_B4/Campus Recruitment_B4_005/test.csv", - "query": "Our leadership team is focusing on high-achievers from central boards for our premium management trainee program. From our historical placement records, identify the top 3 candidates who completed their higher secondary education from a central board with a specialization in Commerce. Among these candidates, project which ones are expected to command the highest starting salaries, ranking them accordingly.", - "meta_info": { - "domain": "finance" + "id": "005", + "task_type": "B4", + "subtask_type": "filter", + "perspective": "data_holder", + "dataset_name": "Campus Recruitment", + "table_path": "output/finance/regression/Campus Recruitment_B4/Campus Recruitment_B4_005/test.csv", + "query": "Our leadership team is focusing on high-achievers from central boards for our premium management trainee program. Using our historical placement records, identify the top 3 candidates in the new batch who are recorded as placed and completed their higher secondary education from a central board with a specialization in Commerce. Among these candidates, project which ones are expected to command the highest starting salaries, ranking them accordingly.", + "meta_info": { + "domain": "finance" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "sl_no": 4, + "gender": "M", + "ssc_p": 56.0, + "ssc_b": "Central", + "hsc_p": 52.0, + "hsc_b": "Central", + "hsc_s": "Science", + "degree_p": 52.0, + "degree_t": "Sci&Tech", + "workex": "No", + "etest_p": 66.0, + "specialisation": "Mkt&HR", + "mba_p": 59.43, + "status": "Not Placed", + "salary": null + } + }, + { + "scenario_id": "002", + "features": { + "sl_no": 136, + "gender": "F", + "ssc_p": 72.0, + "ssc_b": "Central", + "hsc_p": 56.0, + "hsc_b": "Others", + "hsc_s": "Science", + "degree_p": 69.0, + "degree_t": "Comm&Mgmt", + "workex": "No", + "etest_p": 55.6, + "specialisation": "Mkt&HR", + "mba_p": 65.63, + "status": "Placed", + "salary": 200000.0 + } + }, + { + "scenario_id": "003", + "features": { + "sl_no": 172, + "gender": "M", + "ssc_p": 80.0, + "ssc_b": "Others", + "hsc_p": 80.0, + "hsc_b": "Others", + "hsc_s": "Commerce", + "degree_p": 72.0, + "degree_t": "Comm&Mgmt", + "workex": "Yes", + "etest_p": 63.79, + "specialisation": "Mkt&Fin", + "mba_p": 66.04, + "status": "Placed", + "salary": 290000.0 + } + }, + { + "scenario_id": "004", + "features": { + "sl_no": 114, + "gender": "F", + "ssc_p": 73.96, + "ssc_b": "Others", + "hsc_p": 79.0, + "hsc_b": "Others", + "hsc_s": "Commerce", + "degree_p": 67.0, + "degree_t": "Comm&Mgmt", + "workex": "No", + "etest_p": 72.15, + "specialisation": "Mkt&Fin", + "mba_p": 63.08, + "status": "Placed", + "salary": 280000.0 + } + }, + { + "scenario_id": "005", + "features": { + "sl_no": 86, + "gender": "F", + "ssc_p": 83.84, + "ssc_b": "Others", + "hsc_p": 89.83, + "hsc_b": "Others", + "hsc_s": "Commerce", + "degree_p": 77.2, + "degree_t": "Comm&Mgmt", + "workex": "Yes", + "etest_p": 78.74, + "specialisation": "Mkt&Fin", + "mba_p": 76.18, + "status": "Placed", + "salary": 400000.0 + } + }, + { + "scenario_id": "006", + "features": { + "sl_no": 39, + "gender": "F", + "ssc_p": 73.0, + "ssc_b": "Others", + "hsc_p": 58.0, + "hsc_b": "Others", + "hsc_s": "Science", + "degree_p": 66.0, + "degree_t": "Comm&Mgmt", + "workex": "No", + "etest_p": 53.7, + "specialisation": "Mkt&HR", + "mba_p": 56.86, + "status": "Placed", + "salary": 240000.0 + } + }, + { + "scenario_id": "007", + "features": { + "sl_no": 99, + "gender": "F", + "ssc_p": 69.0, + "ssc_b": "Central", + "hsc_p": 73.0, + "hsc_b": "Central", + "hsc_s": "Commerce", + "degree_p": 65.0, + "degree_t": "Comm&Mgmt", + "workex": "No", + "etest_p": 70.0, + "specialisation": "Mkt&Fin", + "mba_p": 57.31, + "status": "Placed", + "salary": 220000.0 + } + }, + { + "scenario_id": "008", + "features": { + "sl_no": 109, + "gender": "M", + "ssc_p": 61.0, + "ssc_b": "Central", + "hsc_p": 82.0, + "hsc_b": "Central", + "hsc_s": "Commerce", + "degree_p": 69.0, + "degree_t": 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"degree_p": 77.25, - "degree_t": "Comm&Mgmt", - "workex": "Yes", - "etest_p": 75.2, - "specialisation": "Mkt&Fin", - "mba_p": 66.06, - "status": "Placed", - "salary": 285000.0 - } - }, - { - "scenario_id": "031", - "features": { - "sl_no": 1, - "gender": "M", - "ssc_p": 67.0, - "ssc_b": "Others", - "hsc_p": 91.0, - "hsc_b": "Others", - "hsc_s": "Commerce", - "degree_p": 58.0, - "degree_t": "Sci&Tech", - "workex": "No", - "etest_p": 55.0, - "specialisation": "Mkt&HR", - "mba_p": 58.8, - "status": "Placed", - "salary": 270000.0 - } - }, - { - "scenario_id": "032", - "features": { - "sl_no": 104, - "gender": "M", - "ssc_p": 73.0, - "ssc_b": "Central", - "hsc_p": 78.0, - "hsc_b": "Central", - "hsc_s": "Science", - "degree_p": 73.0, - "degree_t": "Sci&Tech", - "workex": "Yes", - "etest_p": 85.0, - "specialisation": "Mkt&HR", - "mba_p": 65.83, - "status": "Placed", - "salary": 240000.0 - } - }, - { - "scenario_id": "033", - "features": { - "sl_no": 186, - "gender": "F", - "ssc_p": 88.0, - "ssc_b": "Central", - "hsc_p": 72.0, - "hsc_b": "Central", - "hsc_s": "Science", - "degree_p": 78.0, - "degree_t": "Others", - "workex": "No", - "etest_p": 82.0, - "specialisation": "Mkt&HR", - "mba_p": 71.43, - "status": "Placed", - "salary": 252000.0 - } - }, - { - "scenario_id": "034", - "features": { - "sl_no": 88, - "gender": "M", - "ssc_p": 59.6, - "ssc_b": "Central", - "hsc_p": 51.0, - "hsc_b": "Central", - "hsc_s": "Science", - "degree_p": 60.0, - "degree_t": "Others", - "workex": "No", - "etest_p": 75.0, - "specialisation": "Mkt&HR", - "mba_p": 59.08, - "status": "Not Placed", - "salary": null - } - }, - { - "scenario_id": "035", - "features": { - "sl_no": 28, - "gender": "M", - "ssc_p": 63.0, - "ssc_b": "Others", - "hsc_p": 67.0, - "hsc_b": "Others", - "hsc_s": "Commerce", - "degree_p": 66.0, - "degree_t": "Comm&Mgmt", - "workex": "No", - "etest_p": 68.0, - "specialisation": "Mkt&HR", - "mba_p": 57.69, - "status": "Placed", - "salary": 265000.0 - } - }, - { - "scenario_id": "036", - "features": { - "sl_no": 190, - "gender": "F", - "ssc_p": 54.0, - "ssc_b": "Central", - "hsc_p": 77.6, - "hsc_b": "Others", - "hsc_s": "Commerce", - "degree_p": 69.2, - "degree_t": "Comm&Mgmt", - "workex": "No", - "etest_p": 95.65, - "specialisation": "Mkt&Fin", - "mba_p": 66.94, - "status": "Not Placed", - "salary": null - } - }, - { - "scenario_id": "039", - "features": { - "sl_no": 93, - "gender": "F", - "ssc_p": 60.23, - "ssc_b": "Central", - "hsc_p": 69.0, - "hsc_b": "Central", - "hsc_s": "Science", - "degree_p": 66.0, - "degree_t": "Comm&Mgmt", - "workex": "No", - "etest_p": 72.0, - "specialisation": "Mkt&Fin", - "mba_p": 59.47, - "status": "Placed", - "salary": 230000.0 - } - }, - { - "scenario_id": "040", - "features": { - "sl_no": 101, - "gender": "F", - "ssc_p": 45.0, - "ssc_b": "Others", - "hsc_p": 57.0, - "hsc_b": "Others", - "hsc_s": "Commerce", - "degree_p": 58.0, - "degree_t": "Comm&Mgmt", - "workex": "Yes", - "etest_p": 56.39, - "specialisation": "Mkt&HR", - "mba_p": 64.95, - "status": "Not Placed", - "salary": null - } - }, - { - "scenario_id": "041", - "features": { - "sl_no": 77, - "gender": "F", - "ssc_p": 66.5, - "ssc_b": "Others", - "hsc_p": 70.4, - "hsc_b": "Central", - "hsc_s": "Arts", - "degree_p": 71.93, - "degree_t": "Comm&Mgmt", - "workex": "No", - "etest_p": 61.0, - "specialisation": "Mkt&Fin", - "mba_p": 64.27, - "status": "Placed", - "salary": 230000.0 - } - }, - { - "scenario_id": "042", - "features": { - "sl_no": 174, - "gender": "F", - "ssc_p": 52.0, - "ssc_b": "Others", - "hsc_p": 52.0, - "hsc_b": "Others", - "hsc_s": "Science", - "degree_p": 55.0, - "degree_t": "Sci&Tech", - "workex": "No", - "etest_p": 67.0, - "specialisation": "Mkt&HR", - "mba_p": 59.32, - "status": "Not Placed", - "salary": null - } - }, - { - "scenario_id": "043", - "features": { - "sl_no": 211, - "gender": "M", - "ssc_p": 80.6, - "ssc_b": "Others", - "hsc_p": 82.0, - "hsc_b": "Others", - "hsc_s": "Commerce", - "degree_p": 77.6, - "degree_t": "Comm&Mgmt", - "workex": "No", - "etest_p": 91.0, - "specialisation": "Mkt&Fin", - "mba_p": 74.49, - "status": "Placed", - "salary": 400000.0 - } - } - ], - "target_column": "salary", - "task_sub_type": "regression", - "ranking_ground_truth": { - "top_k_ids": [ - "028", - "009", - "008" - ] - }, - "active_filters": [ - { - "col": "hsc_b", - "op": "==", - "val": "Central" - }, - { - "col": "hsc_s", - "op": "==", - "val": "Commerce" - } - ], - "sort_direction": "high" - }, - "response": "", - "evaluation_score": {} -} \ No newline at end of file + "active_filters": [ + { + "col": "status", + "op": "==", + "val": "Placed" + }, + { + "col": "hsc_b", + "op": "==", + "val": "Central" + }, + { + "col": "hsc_s", + "op": "==", + "val": "Commerce" + } + ], + "sort_direction": "high" + }, + "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/finance/regression/Campus_Recruitment_B4/Campus_Recruitment_B4_006/Campus_Recruitment_B4_006_current.json b/ranking_and_filtering/finance/regression/Campus_Recruitment_B4/Campus_Recruitment_B4_006/Campus_Recruitment_B4_006_current.json index 10d2c7fa098cc4162b482dc6e926fc7d16237fba..0c653b410fe11f84e55cdb1770412380243b3d98 100644 --- a/ranking_and_filtering/finance/regression/Campus_Recruitment_B4/Campus_Recruitment_B4_006/Campus_Recruitment_B4_006_current.json +++ b/ranking_and_filtering/finance/regression/Campus_Recruitment_B4/Campus_Recruitment_B4_006/Campus_Recruitment_B4_006_current.json @@ -1,904 +1,909 @@ { - "id": "006", - "task_type": "B4", - "subtask_type": "filter", - "perspective": "data_holder", - "dataset_name": "Campus Recruitment", - "table_path": "output/finance/regression/Campus Recruitment_B4/Campus Recruitment_B4_006/test.csv", - "query": "We need to strategically allocate our limited internship conversion offers. Based on the placement history I provided, please shortlist the bottom 5 candidates from the Science stream in higher secondary who have prior work experience, ranked by their salary (lowest salary first), as we want to review those with the lowest salary potential for additional support.", - "meta_info": { - "domain": "finance" + "id": "006", + "task_type": "B4", + "subtask_type": "filter", + "perspective": "data_holder", + "dataset_name": "Campus Recruitment", + "table_path": "output/finance/regression/Campus Recruitment_B4/Campus Recruitment_B4_006/test.csv", + "query": "We need to strategically allocate our limited internship conversion offers. Based on the placement history I provided, please shortlist the bottom 5 candidates recorded as placed who studied Science in higher secondary and have prior work experience, ranked by their salary (lowest salary first), as we want to review those with the lowest salary potential for additional support.", + "meta_info": { + "domain": "finance" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "sl_no": 24, + "gender": "F", + "ssc_p": 77.4, + "ssc_b": "Others", + "hsc_p": 60.0, + "hsc_b": "Others", + "hsc_s": "Science", + "degree_p": 64.74, + "degree_t": "Sci&Tech", + "workex": "Yes", + "etest_p": 92.0, + "specialisation": "Mkt&Fin", + "mba_p": 63.62, + "status": "Placed", + "salary": 300000.0 + } + }, + { + "scenario_id": "002", + "features": { + "sl_no": 93, + "gender": "F", + "ssc_p": 60.23, + "ssc_b": "Central", + "hsc_p": 69.0, + "hsc_b": "Central", + "hsc_s": "Science", + "degree_p": 66.0, + "degree_t": "Comm&Mgmt", + "workex": "No", + "etest_p": 72.0, + "specialisation": "Mkt&Fin", + "mba_p": 59.47, + "status": "Placed", + "salary": 230000.0 + } + }, + { + "scenario_id": "003", + "features": { + "sl_no": 69, + "gender": "F", + "ssc_p": 69.7, + "ssc_b": "Central", + "hsc_p": 47.0, + "hsc_b": "Central", + "hsc_s": "Commerce", + "degree_p": 72.7, + "degree_t": "Sci&Tech", + "workex": "No", + "etest_p": 79.0, + "specialisation": "Mkt&HR", + "mba_p": 59.24, + "status": "Not Placed", + "salary": null + } + }, + { + "scenario_id": "004", + "features": { + "sl_no": 3, + "gender": "M", + "ssc_p": 65.0, + "ssc_b": "Central", + "hsc_p": 68.0, + "hsc_b": "Central", + "hsc_s": "Arts", + "degree_p": 64.0, + "degree_t": "Comm&Mgmt", + "workex": "No", + "etest_p": 75.0, + "specialisation": "Mkt&Fin", + "mba_p": 57.8, + "status": "Placed", + "salary": 250000.0 + } + }, + { + "scenario_id": "005", + "features": { + "sl_no": 136, + "gender": "F", + "ssc_p": 72.0, + "ssc_b": "Central", + "hsc_p": 56.0, + "hsc_b": "Others", + "hsc_s": "Science", + "degree_p": 69.0, + "degree_t": "Comm&Mgmt", + "workex": "No", + "etest_p": 55.6, + "specialisation": "Mkt&HR", + "mba_p": 65.63, + "status": "Placed", + "salary": 200000.0 + } + }, + { + "scenario_id": "006", + "features": { + "sl_no": 92, + "gender": "M", + "ssc_p": 52.0, + "ssc_b": "Central", + "hsc_p": 57.0, + "hsc_b": "Central", + "hsc_s": "Commerce", + "degree_p": 50.8, + "degree_t": "Comm&Mgmt", + "workex": "No", + "etest_p": 67.0, + "specialisation": "Mkt&HR", + "mba_p": 62.79, + "status": "Not Placed", + "salary": null + } + }, + { + "scenario_id": "007", + "features": { + "sl_no": 18, + "gender": "F", + "ssc_p": 55.0, + "ssc_b": "Central", + "hsc_p": 67.0, + "hsc_b": "Central", + "hsc_s": "Commerce", + "degree_p": 64.0, + "degree_t": "Comm&Mgmt", + "workex": "No", + "etest_p": 60.0, + "specialisation": "Mkt&Fin", + "mba_p": 67.28, + "status": "Not Placed", + "salary": null + } + }, + { + "scenario_id": "008", + "features": { + "sl_no": 78, + "gender": "M", + "ssc_p": 64.0, + "ssc_b": "Others", + "hsc_p": 80.0, + "hsc_b": "Others", + "hsc_s": "Science", + "degree_p": 65.0, + "degree_t": "Sci&Tech", + "workex": "Yes", + "etest_p": 69.0, + "specialisation": "Mkt&Fin", + "mba_p": 57.65, + "status": "Placed", + "salary": 500000.0 + } + }, + { + "scenario_id": "009", + "features": { + "sl_no": 43, + "gender": "M", + "ssc_p": 49.0, + "ssc_b": "Others", + "hsc_p": 39.0, + "hsc_b": "Central", + "hsc_s": "Science", + "degree_p": 65.0, + "degree_t": "Others", + "workex": "No", + "etest_p": 63.0, + "specialisation": "Mkt&Fin", + "mba_p": 51.21, + "status": "Not Placed", + "salary": null + } + }, + { + "scenario_id": "010", + "features": { + "sl_no": 135, + "gender": "F", + "ssc_p": 77.44, + "ssc_b": "Central", + "hsc_p": 92.0, + "hsc_b": "Others", + "hsc_s": "Commerce", + "degree_p": 72.0, + "degree_t": "Comm&Mgmt", + "workex": "Yes", + "etest_p": 94.0, + "specialisation": "Mkt&Fin", + "mba_p": 67.13, + "status": "Placed", + "salary": 250000.0 + } + }, + { + "scenario_id": "011", + "features": { + "sl_no": 199, + "gender": "F", + "ssc_p": 67.0, + "ssc_b": "Central", + "hsc_p": 70.0, + "hsc_b": "Central", + "hsc_s": "Commerce", + "degree_p": 65.0, + "degree_t": "Others", + "workex": "No", + "etest_p": 88.0, + "specialisation": "Mkt&HR", + "mba_p": 71.96, + "status": "Not Placed", + "salary": null + } + }, + { + "scenario_id": "012", + "features": { + "sl_no": 103, + "gender": "F", + "ssc_p": 77.0, + "ssc_b": "Others", + "hsc_p": 61.0, + "hsc_b": "Others", + "hsc_s": "Commerce", + "degree_p": 68.0, + "degree_t": "Comm&Mgmt", + "workex": "Yes", + "etest_p": 57.5, + "specialisation": "Mkt&Fin", + "mba_p": 61.31, + "status": "Placed", + "salary": 300000.0 + } + }, + { + "scenario_id": "013", + "features": { + "sl_no": 124, + "gender": "M", + "ssc_p": 74.0, + "ssc_b": "Others", + "hsc_p": 59.0, + "hsc_b": "Others", + "hsc_s": "Commerce", + "degree_p": 73.0, + "degree_t": "Comm&Mgmt", + "workex": "Yes", + "etest_p": 60.0, + "specialisation": "Mkt&HR", + "mba_p": 56.7, + "status": "Placed", + "salary": 240000.0 + } + }, + { + "scenario_id": "014", + "features": { + "sl_no": 159, + "gender": "M", + "ssc_p": 67.0, + "ssc_b": "Others", + "hsc_p": 63.0, + "hsc_b": "Others", + "hsc_s": "Science", + "degree_p": 64.0, + "degree_t": "Sci&Tech", + "workex": "No", + "etest_p": 60.0, + "specialisation": "Mkt&Fin", + "mba_p": 61.87, + "status": "Not Placed", + "salary": null + } + }, + { + "scenario_id": "015", + "features": { + "sl_no": 106, + "gender": "M", + "ssc_p": 59.0, + "ssc_b": "Central", + "hsc_p": 64.0, + "hsc_b": "Others", + "hsc_s": "Science", + "degree_p": 58.0, + "degree_t": "Sci&Tech", + "workex": "No", + "etest_p": 85.0, + "specialisation": "Mkt&HR", + "mba_p": 55.3, + "status": "Not Placed", + "salary": null + } + }, + { + "scenario_id": "016", + "features": { + "sl_no": 178, + "gender": "F", + "ssc_p": 73.0, + "ssc_b": "Central", + "hsc_p": 97.0, + "hsc_b": 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"op": "==", - "val": "Yes" - } - ], - "sort_direction": "low" - }, - "response": "", - "evaluation_score": {} -} \ No newline at end of file + "active_filters": [ + { + "col": "status", + "op": "==", + "val": "Placed" + }, + { + "col": "hsc_s", + "op": "==", + "val": "Science" + }, + { + "col": "workex", + "op": "==", + "val": "Yes" + } + ], + "sort_direction": "low" + }, + "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/finance/regression/Campus_Recruitment_B4/Campus_Recruitment_B4_007/Campus_Recruitment_B4_007_current.json b/ranking_and_filtering/finance/regression/Campus_Recruitment_B4/Campus_Recruitment_B4_007/Campus_Recruitment_B4_007_current.json index 27df0f5875dc92b481b2fcdce6812bfdfd9b276b..7ad84ae2865c7f0f11f1720837dc68d515bcb0de 100644 --- a/ranking_and_filtering/finance/regression/Campus_Recruitment_B4/Campus_Recruitment_B4_007/Campus_Recruitment_B4_007_current.json +++ b/ranking_and_filtering/finance/regression/Campus_Recruitment_B4/Campus_Recruitment_B4_007/Campus_Recruitment_B4_007_current.json @@ -1,906 +1,911 @@ { - "id": "007", - "task_type": "B4", - "subtask_type": "filter", - "perspective": "data_holder", - "dataset_name": "Campus Recruitment", - "table_path": "output/finance/regression/Campus Recruitment_B4/Campus Recruitment_B4_007/test.csv", - "query": "To strengthen our talent pipeline in marketing and finance, I want to prioritize candidates with an MBA in Mkt&Fin. Using our past recruitment data, rank the top 7 such individuals who also hold an undergraduate degree in Commerce & Management, giving us a list of those anticipated to have the highest earnings.", - "meta_info": { - "domain": "finance" + "id": "007", + "task_type": "B4", + "subtask_type": "filter", + "perspective": "data_holder", + "dataset_name": "Campus Recruitment", + "table_path": "output/finance/regression/Campus Recruitment_B4/Campus Recruitment_B4_007/test.csv", + "query": "To strengthen our talent pipeline in marketing and finance, I want to prioritize candidates recorded as placed who have an MBA in Mkt&Fin. Using our past recruitment data, rank the top 7 such individuals who also hold an undergraduate degree in Commerce & Management, giving us a list of those anticipated to have the highest earnings.", + "meta_info": { + "domain": "finance" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "sl_no": 174, + "gender": "F", + "ssc_p": 52.0, + "ssc_b": "Others", + "hsc_p": 52.0, + "hsc_b": "Others", + "hsc_s": "Science", + "degree_p": 55.0, + "degree_t": "Sci&Tech", + "workex": "No", + "etest_p": 67.0, + "specialisation": "Mkt&HR", + "mba_p": 59.32, + "status": "Not Placed", + "salary": null + } + }, + { + "scenario_id": "002", + "features": { + "sl_no": 91, + "gender": "F", + "ssc_p": 85.0, + "ssc_b": "Others", + "hsc_p": 90.0, + "hsc_b": "Others", + "hsc_s": "Commerce", + "degree_p": 82.0, + "degree_t": "Comm&Mgmt", + "workex": "No", + "etest_p": 92.0, + "specialisation": "Mkt&Fin", + "mba_p": 68.03, + 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a/ranking_and_filtering/finance/regression/Mall_Customer_Segmentation_Data_B4/Mall_Customer_Segmentation_Data_B4_001/Mall_Customer_Segmentation_Data_B4_001_current.json +++ b/ranking_and_filtering/finance/regression/Mall_Customer_Segmentation_Data_B4/Mall_Customer_Segmentation_Data_B4_001/Mall_Customer_Segmentation_Data_B4_001_current.json @@ -1,435 +1,435 @@ { - "id": "001", - "task_type": "B4", - "subtask_type": "topk", - "perspective": "data_holder", - "dataset_name": "Mall Customer Segmentation Data", - "table_path": "output/Mall Customer Segmentation Data_B4/Mall Customer Segmentation Data_B4_001/test.csv", - "query": "For a new budget-conscious product line launch, I need to identify the 8 shoppers we estimate will be the most receptive based on their projected spending patterns, specifically using their Spending Score from our past sales logs. Who are our top candidates?", - "meta_info": { - "domain": "finance" + "id": "001", + "task_type": "B4", + "subtask_type": "topk", + "perspective": "data_holder", + "dataset_name": "Mall Customer Segmentation Data", + "table_path": "output/Mall Customer Segmentation Data_B4/Mall Customer Segmentation Data_B4_001/test.csv", + "query": "For a new budget-conscious product line launch, I need to identify the 8 shoppers expected to have the lowest Spending Scores in the new batch, using the patterns in our past sales logs. Who fits that low-spending segment?", + "meta_info": { + "domain": "finance" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "CustomerID": 7, + "Gender": "Female", + "Age": 35, + "Annual Income (k$)": 18, + "Spending Score (1-100)": 6 + } + }, + { + "scenario_id": "002", + "features": { + "CustomerID": 157, + "Gender": "Male", + "Age": 37, + "Annual Income (k$)": 78, + "Spending Score (1-100)": 1 + } + }, + { + "scenario_id": "003", + "features": { + "CustomerID": 111, + "Gender": "Male", + "Age": 65, + "Annual Income (k$)": 63, + "Spending Score (1-100)": 52 + } + }, + { + "scenario_id": "004", + "features": { + "CustomerID": 103, + "Gender": "Male", + "Age": 67, + "Annual Income (k$)": 62, + "Spending Score (1-100)": 59 + } + }, + { + "scenario_id": "005", + "features": { + "CustomerID": 165, + "Gender": "Male", + "Age": 50, + "Annual Income (k$)": 85, + "Spending Score (1-100)": 26 + } + }, + { + "scenario_id": "006", + "features": { + "CustomerID": 1, + "Gender": "Male", + "Age": 19, + "Annual Income (k$)": 15, + "Spending Score (1-100)": 39 + } + }, + { + "scenario_id": "007", + "features": { + "CustomerID": 162, + "Gender": "Female", + "Age": 29, + "Annual Income (k$)": 79, + "Spending Score (1-100)": 83 + } + }, + { + "scenario_id": "008", + "features": { + "CustomerID": 126, + "Gender": "Female", + "Age": 31, + "Annual Income (k$)": 70, + "Spending Score (1-100)": 77 + } + }, + { + "scenario_id": "009", + "features": { + "CustomerID": 2, + "Gender": "Male", + "Age": 21, + "Annual Income (k$)": 15, + "Spending Score (1-100)": 81 + } + }, + { + "scenario_id": "010", + "features": { + "CustomerID": 159, + "Gender": "Male", + "Age": 34, + "Annual Income (k$)": 78, + "Spending Score (1-100)": 1 + } + }, + { + "scenario_id": "011", + "features": { + "CustomerID": 30, + "Gender": "Female", + "Age": 23, + "Annual Income (k$)": 29, + "Spending Score (1-100)": 87 + } + }, + { + "scenario_id": "012", + "features": { + "CustomerID": 34, + "Gender": "Male", + "Age": 18, + "Annual Income (k$)": 33, + "Spending Score (1-100)": 92 + } + }, + { + "scenario_id": "013", + "features": { + "CustomerID": 46, + "Gender": "Female", + "Age": 24, + "Annual Income (k$)": 39, + "Spending Score (1-100)": 65 + } + }, + { + "scenario_id": "014", + "features": { + "CustomerID": 94, + "Gender": "Female", + "Age": 40, + "Annual Income (k$)": 60, + "Spending Score (1-100)": 40 + } + }, + { + "scenario_id": "015", + "features": { + "CustomerID": 10, + "Gender": "Female", + "Age": 30, + "Annual Income (k$)": 19, + "Spending Score (1-100)": 72 + } + }, + { + "scenario_id": "016", + "features": { + "CustomerID": 73, + "Gender": "Female", + "Age": 60, + "Annual Income (k$)": 50, + "Spending Score (1-100)": 49 + } + }, + { + "scenario_id": "017", + "features": { + "CustomerID": 15, + "Gender": "Male", + "Age": 37, + "Annual Income (k$)": 20, + "Spending Score (1-100)": 13 + } + }, + { + "scenario_id": "018", + "features": { + "CustomerID": 191, + "Gender": "Female", + "Age": 34, + "Annual Income (k$)": 103, + "Spending Score (1-100)": 23 + } + }, + { + "scenario_id": "019", + "features": { + "CustomerID": 169, + "Gender": "Female", + "Age": 36, + "Annual Income (k$)": 87, + "Spending Score (1-100)": 27 + } + }, + { + "scenario_id": "020", + "features": { + "CustomerID": 4, + "Gender": "Female", + "Age": 23, + "Annual Income (k$)": 16, + "Spending Score (1-100)": 77 + } + }, + { + "scenario_id": "021", + "features": { + "CustomerID": 85, + "Gender": "Female", + "Age": 21, + "Annual Income (k$)": 54, + "Spending Score (1-100)": 57 + } + }, + { + "scenario_id": "022", + "features": { + "CustomerID": 109, + "Gender": "Male", + "Age": 68, + "Annual Income (k$)": 63, + "Spending Score (1-100)": 43 + } + }, + { + "scenario_id": "023", + "features": { + "CustomerID": 45, + "Gender": "Female", + "Age": 49, + "Annual Income (k$)": 39, + "Spending Score (1-100)": 28 + } + }, + { + "scenario_id": "024", + "features": { + "CustomerID": 140, + "Gender": "Female", + "Age": 35, + "Annual Income (k$)": 74, + "Spending Score (1-100)": 72 + } + }, + { + "scenario_id": "025", + "features": { + "CustomerID": 166, + "Gender": "Female", + "Age": 36, + "Annual Income (k$)": 85, + "Spending Score (1-100)": 75 + } + }, + { + "scenario_id": "026", + "features": { + "CustomerID": 123, + "Gender": "Female", + "Age": 40, + "Annual Income (k$)": 69, + "Spending Score (1-100)": 58 + } + }, + { + "scenario_id": "027", + "features": { + "CustomerID": 33, + "Gender": "Male", + "Age": 53, + "Annual Income (k$)": 33, + "Spending Score (1-100)": 4 + } + }, + { + "scenario_id": "028", + "features": { + "CustomerID": 76, + "Gender": "Male", + "Age": 26, + "Annual Income (k$)": 54, + "Spending Score (1-100)": 54 + } + }, + { + "scenario_id": "029", + "features": { + "CustomerID": 21, + "Gender": "Male", + "Age": 35, + "Annual Income (k$)": 24, + "Spending Score (1-100)": 35 + } + }, + { + "scenario_id": "030", + "features": { + "CustomerID": 144, + "Gender": "Female", + "Age": 32, + "Annual Income (k$)": 76, + "Spending Score (1-100)": 87 + } + }, + { + "scenario_id": "031", + "features": { + "CustomerID": 22, + "Gender": "Male", + "Age": 25, + "Annual Income (k$)": 24, + "Spending Score (1-100)": 73 + } + }, + { + "scenario_id": "032", + "features": { + "CustomerID": 68, + "Gender": "Female", + "Age": 68, + "Annual Income (k$)": 48, + "Spending Score (1-100)": 48 + } + }, + { + "scenario_id": "033", + "features": { + "CustomerID": 184, + "Gender": "Female", + "Age": 29, + "Annual Income (k$)": 98, + "Spending Score (1-100)": 88 + } + }, + { + "scenario_id": "034", + "features": { + "CustomerID": 167, + "Gender": "Male", + "Age": 42, + "Annual Income (k$)": 86, + "Spending Score (1-100)": 20 + } + }, + { + "scenario_id": "035", + "features": { + "CustomerID": 130, + "Gender": "Male", + "Age": 38, + "Annual Income (k$)": 71, + "Spending Score (1-100)": 75 + } + }, + { + "scenario_id": "036", + "features": { + "CustomerID": 164, + "Gender": "Female", + "Age": 31, + "Annual Income (k$)": 81, + "Spending Score (1-100)": 93 + } + }, + { + "scenario_id": "037", + "features": { + "CustomerID": 86, + "Gender": "Male", + "Age": 48, + "Annual Income (k$)": 54, + "Spending Score (1-100)": 46 + } + }, + { + "scenario_id": "038", + "features": { + "CustomerID": 121, + "Gender": "Male", + "Age": 27, + "Annual Income (k$)": 67, + "Spending Score (1-100)": 56 + } + }, + { + "scenario_id": "039", + "features": { + "CustomerID": 177, + "Gender": "Male", + "Age": 58, + "Annual Income (k$)": 88, + "Spending Score (1-100)": 15 + } + }, + { + "scenario_id": "040", + "features": { + "CustomerID": 43, + "Gender": "Male", + "Age": 48, + "Annual Income (k$)": 39, + "Spending Score (1-100)": 36 + } + } + ], + "target_column": "Spending Score (1-100)", + "task_sub_type": "regression", + "ranking_ground_truth": { + "top_k_ids": [ + "002", + "010", + "027", + "001", + "017", + "039", + "034", + "018" + ] }, - "final_decision": {}, - "what_if": {}, - "ground_truth": { - "extracted_features": [ - { - "scenario_id": "001", - "features": { - "CustomerID": 7, - "Gender": "Female", - "Age": 35, - "Annual Income (k$)": 18, - "Spending Score (1-100)": 6 - } - }, - { - "scenario_id": "002", - "features": { - "CustomerID": 157, - "Gender": "Male", - "Age": 37, - "Annual Income (k$)": 78, - "Spending Score (1-100)": 1 - } - }, - { - "scenario_id": "003", - "features": { - "CustomerID": 111, - "Gender": "Male", - "Age": 65, - "Annual Income (k$)": 63, - "Spending Score (1-100)": 52 - } - }, - { - "scenario_id": "004", - "features": { - "CustomerID": 103, - "Gender": "Male", - "Age": 67, - "Annual Income (k$)": 62, - "Spending Score (1-100)": 59 - } - }, - { - "scenario_id": "005", - "features": { - "CustomerID": 165, - "Gender": "Male", - "Age": 50, - "Annual Income (k$)": 85, - "Spending Score (1-100)": 26 - } - }, - { - "scenario_id": "006", - "features": { - "CustomerID": 1, - "Gender": "Male", - "Age": 19, - "Annual Income (k$)": 15, - "Spending Score (1-100)": 39 - } - }, - { - "scenario_id": "007", - "features": { - "CustomerID": 162, - "Gender": "Female", - "Age": 29, - "Annual Income (k$)": 79, - "Spending Score (1-100)": 83 - } - }, - { - "scenario_id": "008", - "features": { - "CustomerID": 126, - "Gender": "Female", - "Age": 31, - "Annual Income (k$)": 70, - "Spending Score (1-100)": 77 - } - }, - { - "scenario_id": "009", - "features": { - "CustomerID": 2, - "Gender": "Male", - "Age": 21, - "Annual Income (k$)": 15, - "Spending Score (1-100)": 81 - } - }, - { - "scenario_id": "010", - "features": { - "CustomerID": 159, - "Gender": "Male", - "Age": 34, - "Annual Income (k$)": 78, - "Spending Score (1-100)": 1 - } - }, - { - "scenario_id": "011", - "features": { - "CustomerID": 30, - "Gender": "Female", - "Age": 23, - "Annual Income (k$)": 29, - "Spending Score (1-100)": 87 - } - }, - { - "scenario_id": "012", - "features": { - "CustomerID": 34, - "Gender": "Male", - "Age": 18, - "Annual Income (k$)": 33, - "Spending Score (1-100)": 92 - } - }, - { - "scenario_id": "013", - "features": { - "CustomerID": 46, - "Gender": "Female", - "Age": 24, - "Annual Income (k$)": 39, - "Spending Score (1-100)": 65 - } - }, - { - "scenario_id": "014", - "features": { - "CustomerID": 94, - "Gender": "Female", - "Age": 40, - "Annual Income (k$)": 60, - "Spending Score (1-100)": 40 - } - }, - { - "scenario_id": "015", - "features": { - "CustomerID": 10, - "Gender": "Female", - "Age": 30, - "Annual Income (k$)": 19, - "Spending Score (1-100)": 72 - } - }, - { - "scenario_id": "016", - "features": { - "CustomerID": 73, - "Gender": "Female", - "Age": 60, - "Annual Income (k$)": 50, - "Spending Score (1-100)": 49 - } - }, - { - "scenario_id": "017", - "features": { - "CustomerID": 15, - "Gender": "Male", - "Age": 37, - "Annual Income (k$)": 20, - "Spending Score (1-100)": 13 - } - }, - { - "scenario_id": "018", - "features": { - "CustomerID": 191, - "Gender": "Female", - "Age": 34, - "Annual Income (k$)": 103, - "Spending Score (1-100)": 23 - } - }, - { - "scenario_id": "019", - "features": { - "CustomerID": 169, - "Gender": "Female", - "Age": 36, - "Annual Income (k$)": 87, - "Spending Score (1-100)": 27 - } - }, - { - "scenario_id": "020", - "features": { - "CustomerID": 4, - "Gender": "Female", - "Age": 23, - "Annual Income (k$)": 16, - "Spending Score (1-100)": 77 - } - }, - { - "scenario_id": "021", - "features": { - "CustomerID": 85, - "Gender": "Female", - "Age": 21, - "Annual Income (k$)": 54, - "Spending Score (1-100)": 57 - } - }, - { - "scenario_id": "022", - "features": { - "CustomerID": 109, - "Gender": "Male", - "Age": 68, - "Annual Income (k$)": 63, - "Spending Score (1-100)": 43 - } - }, - { - "scenario_id": "023", - "features": { - "CustomerID": 45, - "Gender": "Female", - "Age": 49, - "Annual Income (k$)": 39, - "Spending Score (1-100)": 28 - } - }, - { - "scenario_id": "024", - "features": { - "CustomerID": 140, - "Gender": "Female", - "Age": 35, - "Annual Income (k$)": 74, - "Spending Score (1-100)": 72 - } - }, - { - "scenario_id": "025", - "features": { - "CustomerID": 166, - "Gender": "Female", - "Age": 36, - "Annual Income (k$)": 85, - "Spending Score (1-100)": 75 - } - }, - { - "scenario_id": "026", - "features": { - "CustomerID": 123, - "Gender": "Female", - "Age": 40, - "Annual Income (k$)": 69, - "Spending Score (1-100)": 58 - } - }, - { - "scenario_id": "027", - "features": { - "CustomerID": 33, - "Gender": "Male", - "Age": 53, - "Annual Income (k$)": 33, - "Spending Score (1-100)": 4 - } - }, - { - "scenario_id": "028", - "features": { - "CustomerID": 76, - "Gender": "Male", - "Age": 26, - "Annual Income (k$)": 54, - "Spending Score (1-100)": 54 - } - }, - { - "scenario_id": "029", - "features": { - "CustomerID": 21, - "Gender": "Male", - "Age": 35, - "Annual Income (k$)": 24, - "Spending Score (1-100)": 35 - } - }, - { - "scenario_id": "030", - "features": { - "CustomerID": 144, - "Gender": "Female", - "Age": 32, - "Annual Income (k$)": 76, - "Spending Score (1-100)": 87 - } - }, - { - "scenario_id": "031", - "features": { - "CustomerID": 22, - "Gender": "Male", - "Age": 25, - "Annual Income (k$)": 24, - "Spending Score (1-100)": 73 - } - }, - { - "scenario_id": "032", - "features": { - "CustomerID": 68, - "Gender": "Female", - "Age": 68, - "Annual Income (k$)": 48, - "Spending Score (1-100)": 48 - } - }, - { - "scenario_id": "033", - "features": { - "CustomerID": 184, - "Gender": "Female", - "Age": 29, - "Annual Income (k$)": 98, - "Spending Score (1-100)": 88 - } - }, - { - "scenario_id": "034", - "features": { - "CustomerID": 167, - "Gender": "Male", - "Age": 42, - "Annual Income (k$)": 86, - "Spending Score (1-100)": 20 - } - }, - { - "scenario_id": "035", - "features": { - "CustomerID": 130, - "Gender": "Male", - "Age": 38, - "Annual Income (k$)": 71, - "Spending Score (1-100)": 75 - } - }, - { - "scenario_id": "036", - "features": { - "CustomerID": 164, - "Gender": "Female", - "Age": 31, - "Annual Income (k$)": 81, - "Spending Score (1-100)": 93 - } - }, - { - "scenario_id": "037", - "features": { - "CustomerID": 86, - "Gender": "Male", - "Age": 48, - "Annual Income (k$)": 54, - "Spending Score (1-100)": 46 - } - }, - { - "scenario_id": "038", - "features": { - "CustomerID": 121, - "Gender": "Male", - "Age": 27, - "Annual Income (k$)": 67, - "Spending Score (1-100)": 56 - } - }, - { - "scenario_id": "039", - "features": { - "CustomerID": 177, - "Gender": "Male", - "Age": 58, - "Annual Income (k$)": 88, - "Spending Score (1-100)": 15 - } - }, - { - "scenario_id": "040", - "features": { - "CustomerID": 43, - "Gender": "Male", - "Age": 48, - "Annual Income (k$)": 39, - "Spending Score (1-100)": 36 - } - } - ], - "target_column": "Spending Score (1-100)", - "task_sub_type": "regression", - "ranking_ground_truth": { - "top_k_ids": [ - "002", - "010", - "027", - "001", - "017", - "039", - "034", - "018" - ] - }, - "sort_direction": "low" - }, - "response": "", - "evaluation_score": {} -} \ No newline at end of file + "sort_direction": "low" + }, + "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/finance/regression/Mall_Customer_Segmentation_Data_B4/Mall_Customer_Segmentation_Data_B4_005/Mall_Customer_Segmentation_Data_B4_005_current.json b/ranking_and_filtering/finance/regression/Mall_Customer_Segmentation_Data_B4/Mall_Customer_Segmentation_Data_B4_005/Mall_Customer_Segmentation_Data_B4_005_current.json index d4efb0b02e5474b9654804fdfc14f287eb306dbe..84dcc9efeaa2bc63ddb7e95ddf838d39faaf4d48 100644 --- a/ranking_and_filtering/finance/regression/Mall_Customer_Segmentation_Data_B4/Mall_Customer_Segmentation_Data_B4_005/Mall_Customer_Segmentation_Data_B4_005_current.json +++ b/ranking_and_filtering/finance/regression/Mall_Customer_Segmentation_Data_B4/Mall_Customer_Segmentation_Data_B4_005/Mall_Customer_Segmentation_Data_B4_005_current.json @@ -1,434 +1,434 @@ { - "id": "005", - "task_type": "B4", - "subtask_type": "filter", - "perspective": "data_holder", - "dataset_name": "Mall Customer Segmentation Data", - "table_path": "output/Mall Customer Segmentation Data_B4/Mall Customer Segmentation Data_B4_005/test.csv", - "query": "Our VIP program is focusing on high-earning customers. From the historical customer data I've shared, could you identify the top 10 female clients in the NEW batch of data with the highest projected spending scores to prioritize for our exclusive platinum-tier invitations?", - "meta_info": { - "domain": "finance" + "id": "005", + "task_type": "B4", + "subtask_type": "filter", + "perspective": "data_holder", + "dataset_name": "Mall Customer Segmentation Data", + "table_path": "output/Mall Customer Segmentation Data_B4/Mall Customer Segmentation Data_B4_005/test.csv", + "query": "Our VIP program is focusing on high-spending customers. From the historical customer data I've shared, could you identify the top 10 female clients in the NEW batch of data with the highest projected spending scores to prioritize for our exclusive platinum-tier invitations?", + "meta_info": { + "domain": "finance" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "CustomerID": 75, + "Gender": "Male", + "Age": 59, + "Annual Income (k$)": 54, + "Spending Score (1-100)": 47 + } + }, + { + "scenario_id": "002", + "features": { + "CustomerID": 32, + "Gender": "Female", + "Age": 21, + "Annual Income (k$)": 30, + "Spending Score (1-100)": 73 + } + }, + { + "scenario_id": "003", + "features": { + "CustomerID": 131, + "Gender": "Male", + "Age": 47, + "Annual Income (k$)": 71, + "Spending Score (1-100)": 9 + } + }, + { + "scenario_id": "004", + "features": { + "CustomerID": 53, + "Gender": "Female", + "Age": 31, + "Annual Income (k$)": 43, + "Spending Score (1-100)": 54 + } + }, + { + "scenario_id": "005", + "features": { + "CustomerID": 109, + "Gender": "Male", + "Age": 68, + "Annual Income (k$)": 63, + "Spending Score (1-100)": 43 + } + }, + { + "scenario_id": "006", + "features": { + "CustomerID": 154, + "Gender": "Female", + "Age": 38, + "Annual Income (k$)": 78, + "Spending Score (1-100)": 76 + } + }, + { + "scenario_id": "007", + "features": { + "CustomerID": 135, + "Gender": "Male", + "Age": 20, + "Annual Income (k$)": 73, + "Spending Score (1-100)": 5 + } + }, + { + "scenario_id": "008", + "features": { + "CustomerID": 162, + "Gender": "Female", + "Age": 29, + "Annual Income (k$)": 79, + "Spending Score (1-100)": 83 + } + }, + { + "scenario_id": "009", + "features": { + "CustomerID": 160, + "Gender": "Female", + "Age": 30, + "Annual Income (k$)": 78, + "Spending Score (1-100)": 73 + } + }, + { + "scenario_id": "010", + "features": { + "CustomerID": 169, + "Gender": "Female", + "Age": 36, + "Annual Income (k$)": 87, + "Spending Score (1-100)": 27 + } + }, + { + "scenario_id": "011", + "features": { + "CustomerID": 16, + "Gender": "Male", + "Age": 22, + "Annual Income (k$)": 20, + "Spending Score (1-100)": 79 + } + }, + { + "scenario_id": "012", + "features": { + "CustomerID": 147, + "Gender": "Male", + "Age": 48, + "Annual Income (k$)": 77, + "Spending Score (1-100)": 36 + } + }, + { + "scenario_id": "013", + "features": { + "CustomerID": 8, + "Gender": "Female", + "Age": 23, + "Annual Income (k$)": 18, + "Spending Score (1-100)": 94 + } + }, + { + "scenario_id": "014", + "features": { + "CustomerID": 171, + "Gender": "Male", + "Age": 40, + "Annual Income (k$)": 87, + "Spending Score (1-100)": 13 + } + }, + { + "scenario_id": "015", + "features": { + "CustomerID": 2, + "Gender": "Male", + "Age": 21, + "Annual Income (k$)": 15, + "Spending Score (1-100)": 81 + } + }, + { + "scenario_id": "016", + "features": { + "CustomerID": 161, + "Gender": "Female", + "Age": 56, + "Annual Income (k$)": 79, + "Spending Score (1-100)": 35 + } + }, + { + "scenario_id": "017", + "features": { + "CustomerID": 27, + "Gender": "Female", + "Age": 45, + "Annual Income (k$)": 28, + "Spending Score (1-100)": 32 + } + }, + { + "scenario_id": "018", + "features": { + "CustomerID": 181, + "Gender": "Female", + "Age": 37, + "Annual Income (k$)": 97, + "Spending Score (1-100)": 32 + } + }, + { + "scenario_id": "019", + "features": { + "CustomerID": 11, + "Gender": "Male", + "Age": 67, + "Annual Income (k$)": 19, + "Spending Score (1-100)": 14 + } + }, + { + "scenario_id": "020", + "features": { + "CustomerID": 123, + "Gender": "Female", + "Age": 40, + "Annual Income (k$)": 69, + "Spending Score (1-100)": 58 + } + }, + { + "scenario_id": "021", + "features": { + "CustomerID": 58, + "Gender": "Male", + "Age": 69, + "Annual Income (k$)": 44, + "Spending Score (1-100)": 46 + } + }, + { + "scenario_id": "022", + "features": { + "CustomerID": 34, + "Gender": "Male", + "Age": 18, + "Annual Income (k$)": 33, + "Spending Score (1-100)": 92 + } + }, + { + "scenario_id": "023", + "features": { + "CustomerID": 59, + "Gender": "Female", + "Age": 27, + "Annual Income (k$)": 46, + "Spending Score (1-100)": 51 + } + }, + { + "scenario_id": "024", + "features": { + "CustomerID": 60, + "Gender": "Male", + "Age": 53, + "Annual Income (k$)": 46, + "Spending Score (1-100)": 46 + } + }, + { + "scenario_id": "025", + "features": { + "CustomerID": 159, + "Gender": "Male", + "Age": 34, + "Annual Income (k$)": 78, + "Spending Score (1-100)": 1 + } + }, + { + "scenario_id": "026", + "features": { + "CustomerID": 146, + "Gender": "Male", + "Age": 28, + "Annual Income (k$)": 77, + "Spending Score (1-100)": 97 + } + }, + { + "scenario_id": "027", + "features": { + "CustomerID": 144, + "Gender": "Female", + "Age": 32, + "Annual Income (k$)": 76, + "Spending Score (1-100)": 87 + } + }, + { + "scenario_id": "028", + "features": { + "CustomerID": 165, + "Gender": "Male", + "Age": 50, + "Annual Income (k$)": 85, + "Spending Score 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- "response": "", - "evaluation_score": {} -} \ No newline at end of file + "active_filters": [ + { + "col": "Gender", + "op": "==", + "val": "Female" + } + ], + "sort_direction": "high" + }, + "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_001/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_001_current.json b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_001/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_001_current.json index ff370a686eb67134f1ee7f448b78ebb892d3dc6b..d8c3cd523d2b1860d64f3daba90788f7369a0a89 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_001/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_001_current.json +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_001/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_001_current.json @@ -1,12043 +1,11847 @@ { - "id": "001", - "task_type": "B4", - "subtask_type": "list", - "perspective": "data_holder", - "dataset_name": "Indicators of Heart Disease (2022 UPDATE)", - "table_path": "output/medical/classification/Indicators of Heart Disease (2022 UPDATE)_B4/Indicators of Heart Disease (2022 UPDATE)_B4_001/test.csv", - "query": "I've shared our complete patient health survey archive with you. Can you review our new batch of survey respondents and flag all individuals whose overall health profile historically aligns with those reporting \"Poor\" general health status?", - "meta_info": { - "domain": "medical" + "id": "001", + "task_type": "B4", + "subtask_type": "list", + "perspective": "data_holder", + "dataset_name": "Indicators of Heart Disease (2022 UPDATE)", + "table_path": "output/medical/classification/Indicators of Heart Disease (2022 UPDATE)_B4/Indicators of Heart Disease (2022 UPDATE)_B4_001/test.csv", + "query": "I've shared our complete patient health survey archive with you. Can you review our new batch of survey respondents and flag all individuals whose overall health profile historically aligns with those reporting \"Poor\" general health status?", + "meta_info": { + "domain": "medical" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "002", + "features": { + "HeartDisease": null, + "BMI": 39.86, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 60 to 64", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Tennessee", + "GeneralHealth": "Poor", + "PhysicalHealthDays": 30.0, + "MentalHealthDays": 30.0, + "LastCheckupTime": "Within past 2 years (1 year but less than 2 years ago)", + "PhysicalActivities": "No", + "SleepHours": 8.0, + "RemovedTeeth": "1 to 5", + "HadHeartAttack": "Yes", + "HadAngina": "Yes", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "Yes", + "HadDepressiveDisorder": "Yes", + "HadKidneyDisease": "No", + "HadArthritis": "Yes", + "HadDiabetes": "Yes", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "Yes", + "DifficultyWalking": "Yes", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "Yes", + "SmokerStatus": "Former smoker", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "Yes", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.6, + "WeightInKilograms": 102.06, + "AlcoholDrinkers": "No", + "HIVTesting": "Yes", + "FluVaxLast12": "No", + "PneumoVaxEver": "Yes", + "TetanusLast10Tdap": "Yes, received tetanus shot but not sure what type", + "HighRiskLastYear": "No", + "CovidPos": "Yes" + } + }, + { + "scenario_id": "007", + "features": { + "HeartDisease": null, + "BMI": 37.5, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 65 to 69", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Ohio", + "GeneralHealth": "Good", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 30.0, + "LastCheckupTime": "5 or more years ago", + "PhysicalActivities": "No", + "SleepHours": 5.0, + "RemovedTeeth": "1 to 5", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "Yes", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "No", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.91, + "WeightInKilograms": 136.08, + "AlcoholDrinkers": "No", + "HIVTesting": "No", + "FluVaxLast12": "No", + "PneumoVaxEver": "No", + "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", + "HighRiskLastYear": "No", + "CovidPos": "Yes" + } + }, + { + "scenario_id": "008", + "features": { + "HeartDisease": null, + "BMI": 21.93, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 35 to 39", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Maine", + "GeneralHealth": "Very good", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 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"HighRiskLastYear": "No", + "CovidPos": "Yes" + } + }, + { + "scenario_id": "009", + "features": { + "HeartDisease": null, + "BMI": 35.43, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 55 to 59", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Texas", + "GeneralHealth": "Fair", + "PhysicalHealthDays": 30.0, + "MentalHealthDays": 7.0, + "LastCheckupTime": "Within past 2 years (1 year but less than 2 years ago)", + "PhysicalActivities": "No", + "SleepHours": 8.0, + "RemovedTeeth": "1 to 5", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "Yes", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "Yes", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "No", + "RaceEthnicityCategory": "Hispanic", + "HeightInMeters": 1.6, + "WeightInKilograms": 90.72, + "AlcoholDrinkers": "No", + "HIVTesting": "No", + "FluVaxLast12": "No", + "PneumoVaxEver": "No", + "TetanusLast10Tdap": null, + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "016", + "features": { + "HeartDisease": null, + "BMI": 30.99, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 70 to 74", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Ohio", + "GeneralHealth": "Good", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 7.0, + "RemovedTeeth": "1 to 5", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "Yes", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Not at all (right now)", + "ChestScan": "Yes", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.78, + "WeightInKilograms": 97.98, + "AlcoholDrinkers": "No", + "HIVTesting": "No", + "FluVaxLast12": 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"PneumoVaxEver": "No", + "TetanusLast10Tdap": "Yes, received Tdap", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "022", + "features": { + "HeartDisease": null, + "BMI": 28.13, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 60 to 64", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Puerto Rico", + "GeneralHealth": "Good", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 9.0, + "RemovedTeeth": "6 or more, but not all", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + 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null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 45 to 49", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "North Dakota", + "GeneralHealth": "Excellent", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 7.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Not at all (right now)", + "ChestScan": "No", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.85, + "WeightInKilograms": 113.4, + "AlcoholDrinkers": "Yes", + "HIVTesting": "No", + "FluVaxLast12": "No", + "PneumoVaxEver": "No", + "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "031", + "features": { + "HeartDisease": null, + "BMI": 28.32, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 70 to 74", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Washington", + "GeneralHealth": "Good", + 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tetanus shot, but not Tdap", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "033", + "features": { + "HeartDisease": null, + "BMI": 21.14, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 80 or older", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Alabama", + "GeneralHealth": "Poor", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "No", + "SleepHours": 8.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + 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"PneumoVaxEver": null, + "TetanusLast10Tdap": null, + "HighRiskLastYear": null, + "CovidPos": null + } + }, + { + "scenario_id": "039", + "features": { + "HeartDisease": "Yes", + "BMI": 29.23, + "Smoking": "No", + "AlcoholDrinking": "No", + "Stroke": "Yes", + "PhysicalHealth": 20.0, + "MentalHealth": 20.0, + "DiffWalking": "No", + "Sex": "Male", + "AgeCategory": "75-79", + "Race": "American Indian/Alaskan Native", + "Diabetic": "No", + "PhysicalActivity": "Yes", + "SleepTime": 8.0, + "Asthma": "No", + "KidneyDisease": "Yes", + "SkinCancer": "No", + "State": null, + "GeneralHealth": "Poor", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + "LastCheckupTime": null, + "PhysicalActivities": null, + "SleepHours": null, + "RemovedTeeth": null, + "HadHeartAttack": null, + "HadAngina": null, + "HadStroke": null, + "HadAsthma": null, + "HadSkinCancer": null, + "HadCOPD": null, + "HadDepressiveDisorder": null, + "HadKidneyDisease": null, + "HadArthritis": null, + "HadDiabetes": null, + "DeafOrHardOfHearing": null, + "BlindOrVisionDifficulty": null, + "DifficultyConcentrating": null, + "DifficultyWalking": null, + "DifficultyDressingBathing": null, + "DifficultyErrands": null, + "SmokerStatus": null, + "ECigaretteUsage": null, + "ChestScan": null, + "RaceEthnicityCategory": null, + "HeightInMeters": null, + "WeightInKilograms": null, + "AlcoholDrinkers": null, + "HIVTesting": null, + "FluVaxLast12": null, + "PneumoVaxEver": null, + "TetanusLast10Tdap": null, + "HighRiskLastYear": null, + "CovidPos": null + } + }, + { + "scenario_id": "046", + "features": { + "HeartDisease": null, + "BMI": 38.62, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 30 to 34", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "West Virginia", + 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"TetanusLast10Tdap": "Yes, received tetanus shot, but not Tdap", + "HighRiskLastYear": "Yes", + "CovidPos": "Yes" + } + }, + { + "scenario_id": "054", + "features": { + "HeartDisease": null, + "BMI": 27.44, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 60 to 64", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Wyoming", + "GeneralHealth": "Excellent", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 2.0, + "LastCheckupTime": "5 or more years ago", + "PhysicalActivities": "Yes", + "SleepHours": 4.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": 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"scenario_id": "068", + "features": { + "HeartDisease": "No", + "BMI": 38.26, + "Smoking": "Yes", + "AlcoholDrinking": "No", + "Stroke": "No", + "PhysicalHealth": 0.0, + "MentalHealth": 0.0, + "DiffWalking": "No", + "Sex": "Male", + "AgeCategory": "45-49", + "Race": "White", + "Diabetic": "No", + "PhysicalActivity": "Yes", + "SleepTime": 5.0, + "Asthma": "No", + "KidneyDisease": "No", + "SkinCancer": "No", + "State": null, + "GeneralHealth": "Excellent", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + "LastCheckupTime": null, + "PhysicalActivities": null, + "SleepHours": null, + "RemovedTeeth": null, + "HadHeartAttack": null, + "HadAngina": null, + "HadStroke": null, + "HadAsthma": null, + "HadSkinCancer": null, + "HadCOPD": null, + "HadDepressiveDisorder": null, + "HadKidneyDisease": null, + "HadArthritis": null, + "HadDiabetes": null, + "DeafOrHardOfHearing": null, + "BlindOrVisionDifficulty": null, + "DifficultyConcentrating": null, + "DifficultyWalking": null, + 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"CovidPos": "No" + } + }, + { + "scenario_id": "207", + "features": { + "HeartDisease": "No", + "BMI": 27.26, + "Smoking": "No", + "AlcoholDrinking": "No", + "Stroke": "No", + "PhysicalHealth": 0.0, + "MentalHealth": 0.0, + "DiffWalking": "No", + "Sex": "Male", + "AgeCategory": "50-54", + "Race": "White", + "Diabetic": "No", + "PhysicalActivity": "Yes", + "SleepTime": 8.0, + "Asthma": "No", + "KidneyDisease": "No", + "SkinCancer": "No", + "State": null, + "GeneralHealth": "Very good", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + "LastCheckupTime": null, + "PhysicalActivities": null, + "SleepHours": null, + "RemovedTeeth": null, + "HadHeartAttack": null, + "HadAngina": null, + "HadStroke": null, + "HadAsthma": null, + "HadSkinCancer": null, + "HadCOPD": null, + "HadDepressiveDisorder": null, + "HadKidneyDisease": null, + "HadArthritis": null, + "HadDiabetes": null, + "DeafOrHardOfHearing": null, + "BlindOrVisionDifficulty": null, + "DifficultyConcentrating": null, + 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"No", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "No", + "RaceEthnicityCategory": "Hispanic", + "HeightInMeters": 1.88, + "WeightInKilograms": 127.01, + "AlcoholDrinkers": "Yes", + "HIVTesting": "Yes", + "FluVaxLast12": "No", + "PneumoVaxEver": "No", + "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", + "HighRiskLastYear": "No", + "CovidPos": "Yes" + } + }, + { + "scenario_id": "249", + "features": { + "HeartDisease": null, + "BMI": 28.48, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 18 to 24", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Florida", + "GeneralHealth": "Very good", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 0.0, + 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"Yes", + "CovidPos": "No" + } + }, + { + "scenario_id": "252", + "features": { + "HeartDisease": null, + "BMI": 31.89, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 60 to 64", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Texas", + "GeneralHealth": "Very good", + "PhysicalHealthDays": 4.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 9.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "Yes", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Not at all (right now)", + "ChestScan": "No", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.6, + "WeightInKilograms": 81.65, + "AlcoholDrinkers": "No", + "HIVTesting": "No", + "FluVaxLast12": "No", + "PneumoVaxEver": "No", + "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "253", + "features": { + "HeartDisease": null, + "BMI": 44.93, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 75 to 79", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + 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"No", + "HIVTesting": "Yes", + "FluVaxLast12": "Yes", + "PneumoVaxEver": "Yes", + "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "255", + "features": { + "HeartDisease": null, + "BMI": 48.47, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 50 to 54", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Indiana", + "GeneralHealth": "Poor", + "PhysicalHealthDays": 30.0, + "MentalHealthDays": 10.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "No", + "SleepHours": 10.0, + "RemovedTeeth": "6 or more, but not all", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "Yes", + "HadDiabetes": "Yes", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "Yes", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "No", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.57, + "WeightInKilograms": 120.2, + "AlcoholDrinkers": "No", + "HIVTesting": "Yes", + "FluVaxLast12": "Yes", + "PneumoVaxEver": "Yes", + "TetanusLast10Tdap": "Yes, received tetanus shot but not sure what type", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "262", + "features": { + "HeartDisease": null, + "BMI": 19.97, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 40 to 44", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "New Hampshire", + "GeneralHealth": "Very good", + "PhysicalHealthDays": 10.0, + "MentalHealthDays": 3.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 7.0, + "RemovedTeeth": "1 to 5", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": 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my entire life", + "ChestScan": "No", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.52, + "WeightInKilograms": 68.04, + "AlcoholDrinkers": "Yes", + "HIVTesting": "Yes", + "FluVaxLast12": "No", + "PneumoVaxEver": "No", + "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "497", + "features": { + "HeartDisease": null, + "BMI": 17.36, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 18 to 24", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Illinois", + "GeneralHealth": "Very good", + "PhysicalHealthDays": 7.0, + "MentalHealthDays": 2.0, + "LastCheckupTime": "Within past 2 years (1 year but less than 2 years ago)", 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"500", + "features": { + "HeartDisease": "Yes", + "BMI": 32.55, + "Smoking": "Yes", + "AlcoholDrinking": "No", + "Stroke": "Yes", + "PhysicalHealth": 30.0, + "MentalHealth": 15.0, + "DiffWalking": "Yes", + "Sex": "Male", + "AgeCategory": "70-74", + "Race": "White", + "Diabetic": "No", + "PhysicalActivity": "No", + "SleepTime": 6.0, + "Asthma": "No", + "KidneyDisease": "Yes", + "SkinCancer": "Yes", + "State": null, + "GeneralHealth": "Poor", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + "LastCheckupTime": null, + "PhysicalActivities": null, + "SleepHours": null, + "RemovedTeeth": null, + "HadHeartAttack": null, + "HadAngina": null, + "HadStroke": null, + "HadAsthma": null, + "HadSkinCancer": null, + "HadCOPD": null, + "HadDepressiveDisorder": null, + "HadKidneyDisease": null, + "HadArthritis": null, + "HadDiabetes": null, + "DeafOrHardOfHearing": null, + "BlindOrVisionDifficulty": null, + "DifficultyConcentrating": null, + "DifficultyWalking": null, + 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"RaceEthnicityCategory": "Hispanic", - "HeightInMeters": 1.73, - "WeightInKilograms": 83.91, - "AlcoholDrinkers": "No", - "HIVTesting": "No", - "FluVaxLast12": "No", - "PneumoVaxEver": "No", - "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", - "HighRiskLastYear": "No", - "CovidPos": "No" - } - }, - { - "scenario_id": "023", - "features": { - "HeartDisease": null, - "BMI": 33.01, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Female", - "AgeCategory": "Age 65 to 69", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "South Carolina", - "GeneralHealth": "Good", - "PhysicalHealthDays": 0.0, - "MentalHealthDays": 5.0, - "LastCheckupTime": "Within past year (anytime less than 12 months ago)", - "PhysicalActivities": "Yes", - 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{ - "HeartDisease": null, - "BMI": 25.79, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Female", - "AgeCategory": "Age 55 to 59", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "Nevada", - "GeneralHealth": "Very good", - "PhysicalHealthDays": 0.0, - "MentalHealthDays": 2.0, - "LastCheckupTime": "Within past year (anytime less than 12 months ago)", - "PhysicalActivities": "Yes", - "SleepHours": 7.0, - "RemovedTeeth": "None of them", - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "No", - "HadSkinCancer": "No", - "HadCOPD": "No", - "HadDepressiveDisorder": "No", - "HadKidneyDisease": "No", - "HadArthritis": "No", - "HadDiabetes": "No", - "DeafOrHardOfHearing": "No", - "BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "No", - "DifficultyWalking": "No", - "DifficultyDressingBathing": "No", - "DifficultyErrands": "No", - "SmokerStatus": "Current smoker - now smokes some days", - "ECigaretteUsage": "Never used e-cigarettes in my entire life", - "ChestScan": "Yes", - "RaceEthnicityCategory": "White only, Non-Hispanic", - "HeightInMeters": 1.65, - "WeightInKilograms": 70.31, - "AlcoholDrinkers": "Yes", - "HIVTesting": "No", - "FluVaxLast12": "No", - "PneumoVaxEver": null, - "TetanusLast10Tdap": "Yes, received Tdap", - "HighRiskLastYear": "No", - "CovidPos": "No" - } - }, - { - "scenario_id": "026", - "features": { - "HeartDisease": null, - "BMI": 32.98, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Male", - "AgeCategory": "Age 45 to 49", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": 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"No", - "FluVaxLast12": "No", - "PneumoVaxEver": "No", - "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", - "HighRiskLastYear": "No", - "CovidPos": "No" - } - }, - { - "scenario_id": "031", - "features": { - "HeartDisease": null, - "BMI": 28.32, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Female", - "AgeCategory": "Age 70 to 74", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "Washington", - "GeneralHealth": "Good", - "PhysicalHealthDays": 15.0, - "MentalHealthDays": 0.0, - "LastCheckupTime": "Within past year (anytime less than 12 months ago)", - "PhysicalActivities": "Yes", - "SleepHours": 6.0, - "RemovedTeeth": "1 to 5", - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "No", - "HadSkinCancer": "No", - "HadCOPD": "No", - "HadDepressiveDisorder": "No", - "HadKidneyDisease": "No", - "HadArthritis": "Yes", - "HadDiabetes": "No", - "DeafOrHardOfHearing": "No", - "BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "No", - "DifficultyWalking": "No", - "DifficultyDressingBathing": "No", - "DifficultyErrands": "No", - "SmokerStatus": "Never smoked", - "ECigaretteUsage": "Not at all (right now)", - "ChestScan": "No", - "RaceEthnicityCategory": "White only, Non-Hispanic", - "HeightInMeters": 1.52, - "WeightInKilograms": 65.77, - "AlcoholDrinkers": "No", - "HIVTesting": "No", - "FluVaxLast12": "No", - "PneumoVaxEver": "No", - "TetanusLast10Tdap": "Yes, received tetanus shot, but not Tdap", - "HighRiskLastYear": "No", - "CovidPos": "No" - } - }, - { - "scenario_id": "033", - "features": { - "HeartDisease": null, - "BMI": 21.14, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": 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"CovidPos": "Yes" - } - }, - { - "scenario_id": "249", - "features": { - "HeartDisease": null, - "BMI": 28.48, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Male", - "AgeCategory": "Age 18 to 24", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "Florida", - "GeneralHealth": "Very good", - "PhysicalHealthDays": 0.0, - "MentalHealthDays": 0.0, - "LastCheckupTime": "Within past year (anytime less than 12 months ago)", - "PhysicalActivities": "Yes", - "SleepHours": 5.0, - "RemovedTeeth": "None of them", - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "No", - "HadSkinCancer": "No", - "HadCOPD": "No", - "HadDepressiveDisorder": "No", - "HadKidneyDisease": "No", - "HadArthritis": "No", - "HadDiabetes": "No", - 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"No", - "CovidPos": "No" - } - }, - { - "scenario_id": "356", - "features": { - "HeartDisease": null, - "BMI": 27.44, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Female", - "AgeCategory": "Age 80 or older", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "Minnesota", - "GeneralHealth": "Fair", - "PhysicalHealthDays": 0.0, - "MentalHealthDays": 30.0, - "LastCheckupTime": "Within past year (anytime less than 12 months ago)", - "PhysicalActivities": "No", - "SleepHours": 7.0, - "RemovedTeeth": "1 to 5", - "HadHeartAttack": "No", - "HadAngina": "Yes", - "HadStroke": "No", - "HadAsthma": "No", - "HadSkinCancer": "No", - "HadCOPD": "No", - "HadDepressiveDisorder": "Yes", - "HadKidneyDisease": "No", - "HadArthritis": "Yes", - "HadDiabetes": "No", - 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"scenario_id": "374", - "features": { - "HeartDisease": null, - "BMI": 23.54, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Male", - "AgeCategory": "Age 18 to 24", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "Maryland", - "GeneralHealth": "Excellent", - "PhysicalHealthDays": 0.0, - "MentalHealthDays": 0.0, - "LastCheckupTime": "Within past 2 years (1 year but less than 2 years ago)", - "PhysicalActivities": "Yes", - "SleepHours": 8.0, - "RemovedTeeth": "1 to 5", - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "No", - "HadSkinCancer": "No", - "HadCOPD": "No", - "HadDepressiveDisorder": "No", - "HadKidneyDisease": "No", - "HadArthritis": "No", - "HadDiabetes": "No", - "DeafOrHardOfHearing": "No", - "BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "No", - "DifficultyWalking": "No", - "DifficultyDressingBathing": "No", - "DifficultyErrands": "No", - "SmokerStatus": "Former smoker", - "ECigaretteUsage": "Never used e-cigarettes in my entire life", - "ChestScan": "No", - "RaceEthnicityCategory": "Hispanic", - "HeightInMeters": 1.7, - "WeightInKilograms": 68.04, - "AlcoholDrinkers": "No", - "HIVTesting": "No", - "FluVaxLast12": "No", - "PneumoVaxEver": "No", - "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", - "HighRiskLastYear": "No", - "CovidPos": "No" - } - }, - { - "scenario_id": "378", - "features": { - "HeartDisease": "No", - "BMI": 27.5, - "Smoking": "Yes", - "AlcoholDrinking": "Yes", - "Stroke": "No", - "PhysicalHealth": 0.0, - "MentalHealth": 0.0, - "DiffWalking": "No", - "Sex": "Male", - "AgeCategory": "18-24", - "Race": "White", - "Diabetic": "No", - "PhysicalActivity": "Yes", - "GenHealth": "Excellent", - "SleepTime": 5.0, - 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"HighRiskLastYear": null, - "CovidPos": null - } - }, - { - "scenario_id": "379", - "features": { - "HeartDisease": null, - "BMI": 27.44, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Female", - "AgeCategory": "Age 60 to 64", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "New York", - "GeneralHealth": "Very good", - "PhysicalHealthDays": 0.0, - "MentalHealthDays": 10.0, - "LastCheckupTime": "Within past year (anytime less than 12 months ago)", - "PhysicalActivities": "Yes", - "SleepHours": 7.0, - "RemovedTeeth": "1 to 5", - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "No", - "HadSkinCancer": "No", - "HadCOPD": "No", - "HadDepressiveDisorder": "Yes", - "HadKidneyDisease": "No", - "HadArthritis": "No", - "HadDiabetes": "No", - "DeafOrHardOfHearing": "No", - "BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "Yes", - "DifficultyWalking": "No", - "DifficultyDressingBathing": "No", - "DifficultyErrands": "No", - "SmokerStatus": "Never smoked", - "ECigaretteUsage": "Never used e-cigarettes in my entire life", - "ChestScan": "No", - "RaceEthnicityCategory": "White only, Non-Hispanic", - "HeightInMeters": 1.57, - "WeightInKilograms": 68.04, - "AlcoholDrinkers": "Yes", - "HIVTesting": "No", - "FluVaxLast12": "Yes", - "PneumoVaxEver": "No", - "TetanusLast10Tdap": "Yes, received tetanus shot but not sure what type", - "HighRiskLastYear": "No", - "CovidPos": "No" - } - }, - { - "scenario_id": "381", - "features": { - "HeartDisease": null, - "BMI": 51.49, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Female", - "AgeCategory": "Age 35 to 39", - "Race": null, - "Diabetic": null, - "PhysicalActivity": 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"WeightInKilograms": null, - "AlcoholDrinkers": null, - "HIVTesting": null, - "FluVaxLast12": null, - "PneumoVaxEver": null, - "TetanusLast10Tdap": null, - "HighRiskLastYear": null, - "CovidPos": null - } - }, - { - "scenario_id": "489", - "features": { - "HeartDisease": "No", - "BMI": 33.47, - "Smoking": "No", - "AlcoholDrinking": "No", - "Stroke": "No", - "PhysicalHealth": 0.0, - "MentalHealth": 0.0, - "DiffWalking": "No", - "Sex": "Male", - "AgeCategory": "55-59", - "Race": "White", - "Diabetic": "No", - "PhysicalActivity": "Yes", - "GenHealth": "Good", - "SleepTime": 7.0, - "Asthma": "No", - "KidneyDisease": "No", - "SkinCancer": "No", - "State": null, - "GeneralHealth": null, - "PhysicalHealthDays": null, - "MentalHealthDays": null, - "LastCheckupTime": null, - "PhysicalActivities": null, - "SleepHours": null, - "RemovedTeeth": null, - "HadHeartAttack": null, - "HadAngina": null, - "HadStroke": null, - "HadAsthma": null, - "HadSkinCancer": null, - "HadCOPD": null, - "HadDepressiveDisorder": null, - "HadKidneyDisease": null, - "HadArthritis": null, - "HadDiabetes": null, - "DeafOrHardOfHearing": null, - "BlindOrVisionDifficulty": null, - "DifficultyConcentrating": null, - "DifficultyWalking": null, - "DifficultyDressingBathing": null, - "DifficultyErrands": null, - "SmokerStatus": null, - "ECigaretteUsage": null, - "ChestScan": null, - "RaceEthnicityCategory": null, - "HeightInMeters": null, - "WeightInKilograms": null, - "AlcoholDrinkers": null, - "HIVTesting": null, - "FluVaxLast12": null, - "PneumoVaxEver": null, - "TetanusLast10Tdap": null, - "HighRiskLastYear": null, - "CovidPos": null - } - }, - { - "scenario_id": "490", - "features": { - "HeartDisease": null, - "BMI": 23.91, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Female", - "AgeCategory": "Age 65 to 69", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "Washington", - "GeneralHealth": "Poor", - "PhysicalHealthDays": 30.0, - "MentalHealthDays": 15.0, - "LastCheckupTime": "Within past year (anytime less than 12 months ago)", - "PhysicalActivities": "No", - "SleepHours": 8.0, - "RemovedTeeth": "All", - "HadHeartAttack": "No", - "HadAngina": "Yes", - "HadStroke": "Yes", - "HadAsthma": "Yes", - "HadSkinCancer": "No", - "HadCOPD": "Yes", - "HadDepressiveDisorder": "Yes", - "HadKidneyDisease": "Yes", - "HadArthritis": "Yes", - "HadDiabetes": "No", - "DeafOrHardOfHearing": "No", - "BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "Yes", - "DifficultyWalking": "Yes", - "DifficultyDressingBathing": "Yes", - "DifficultyErrands": "Yes", - "SmokerStatus": "Former smoker", - "ECigaretteUsage": "Use them every day", - "ChestScan": "Yes", - "RaceEthnicityCategory": "White only, Non-Hispanic", - "HeightInMeters": 1.6, - "WeightInKilograms": 61.23, - "AlcoholDrinkers": "No", - "HIVTesting": "Yes", - "FluVaxLast12": "Yes", - "PneumoVaxEver": "Yes", - "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", - "HighRiskLastYear": "No", - "CovidPos": "No" - } - }, - { - "scenario_id": "492", - "features": { - "HeartDisease": null, - "BMI": 22.81, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Male", - "AgeCategory": "Age 18 to 24", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "Kansas", - "GeneralHealth": "Very good", - "PhysicalHealthDays": 0.0, - "MentalHealthDays": 28.0, - "LastCheckupTime": "Within past year (anytime less than 12 months ago)", - "PhysicalActivities": "Yes", - "SleepHours": 6.0, - "RemovedTeeth": "None of them", - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "No", - "HadSkinCancer": "No", - "HadCOPD": "No", - "HadDepressiveDisorder": "No", - "HadKidneyDisease": "No", - "HadArthritis": "No", - "HadDiabetes": "No", - "DeafOrHardOfHearing": "No", - "BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "No", - "DifficultyWalking": "No", - "DifficultyDressingBathing": "No", - "DifficultyErrands": "No", - "SmokerStatus": "Current smoker - now smokes every day", - "ECigaretteUsage": "Not at all (right now)", - "ChestScan": "Yes", - "RaceEthnicityCategory": "White only, Non-Hispanic", - "HeightInMeters": 1.73, - "WeightInKilograms": 68.04, - "AlcoholDrinkers": "Yes", - "HIVTesting": "No", - "FluVaxLast12": "No", - "PneumoVaxEver": "Yes", - "TetanusLast10Tdap": "Yes, received Tdap", - "HighRiskLastYear": "No", - "CovidPos": "Yes" - } - }, - { - "scenario_id": "495", - "features": { - "HeartDisease": "No", - "BMI": 28.19, - "Smoking": "No", - "AlcoholDrinking": "No", - "Stroke": "No", - "PhysicalHealth": 3.0, - "MentalHealth": 4.0, - "DiffWalking": "No", - "Sex": "Female", - "AgeCategory": "55-59", - "Race": "White", - "Diabetic": "No", - "PhysicalActivity": "Yes", - "GenHealth": "Very good", - "SleepTime": 7.0, - "Asthma": "No", - "KidneyDisease": "No", - "SkinCancer": "No", - "State": null, - "GeneralHealth": null, - "PhysicalHealthDays": null, - "MentalHealthDays": null, - "LastCheckupTime": null, - "PhysicalActivities": null, - "SleepHours": null, - "RemovedTeeth": null, - "HadHeartAttack": null, - "HadAngina": null, - "HadStroke": null, - "HadAsthma": null, - "HadSkinCancer": null, - "HadCOPD": null, - "HadDepressiveDisorder": null, - "HadKidneyDisease": null, - "HadArthritis": null, - "HadDiabetes": null, - "DeafOrHardOfHearing": null, - "BlindOrVisionDifficulty": null, - "DifficultyConcentrating": null, - "DifficultyWalking": null, - "DifficultyDressingBathing": null, - "DifficultyErrands": null, - "SmokerStatus": null, - "ECigaretteUsage": null, - "ChestScan": null, - "RaceEthnicityCategory": null, - "HeightInMeters": null, - "WeightInKilograms": null, - "AlcoholDrinkers": null, - "HIVTesting": null, - "FluVaxLast12": null, - "PneumoVaxEver": null, - "TetanusLast10Tdap": null, - "HighRiskLastYear": null, - "CovidPos": null - } - }, - { - "scenario_id": "496", - "features": { - "HeartDisease": null, - "BMI": 29.29, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Female", - "AgeCategory": "Age 70 to 74", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "Ohio", - "GeneralHealth": "Excellent", - "PhysicalHealthDays": 0.0, - "MentalHealthDays": 0.0, - "LastCheckupTime": "Within past year (anytime less than 12 months ago)", - "PhysicalActivities": "Yes", - "SleepHours": 8.0, - "RemovedTeeth": "None of them", - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "No", - "HadSkinCancer": "Yes", - "HadCOPD": "No", - "HadDepressiveDisorder": "No", - "HadKidneyDisease": "No", - "HadArthritis": "No", - "HadDiabetes": "No", - "DeafOrHardOfHearing": "No", - "BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "No", - "DifficultyWalking": "Yes", - "DifficultyDressingBathing": "No", - "DifficultyErrands": "No", - "SmokerStatus": "Former smoker", - "ECigaretteUsage": "Never used e-cigarettes in my entire life", - "ChestScan": "No", - "RaceEthnicityCategory": "White only, Non-Hispanic", - "HeightInMeters": 1.52, - "WeightInKilograms": 68.04, - "AlcoholDrinkers": "Yes", - "HIVTesting": "Yes", - "FluVaxLast12": "No", - "PneumoVaxEver": "No", - "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", - "HighRiskLastYear": "No", - "CovidPos": "No" - } - }, - { - "scenario_id": "497", - "features": { - "HeartDisease": null, - "BMI": 17.36, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Male", - "AgeCategory": "Age 18 to 24", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "Illinois", - "GeneralHealth": "Very good", - "PhysicalHealthDays": 7.0, - "MentalHealthDays": 2.0, - "LastCheckupTime": "Within past 2 years (1 year but less than 2 years ago)", - "PhysicalActivities": "Yes", - "SleepHours": 5.0, - "RemovedTeeth": "None of them", - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "No", - "HadSkinCancer": "No", - "HadCOPD": "No", - "HadDepressiveDisorder": "No", - "HadKidneyDisease": "No", - "HadArthritis": "No", - "HadDiabetes": "No", - "DeafOrHardOfHearing": "No", - "BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "Yes", - "DifficultyWalking": "No", - "DifficultyDressingBathing": "No", - "DifficultyErrands": "No", - "SmokerStatus": "Never smoked", - "ECigaretteUsage": "Not at all (right now)", - "ChestScan": "No", - "RaceEthnicityCategory": "White only, Non-Hispanic", - "HeightInMeters": 1.6, - "WeightInKilograms": 44.45, - "AlcoholDrinkers": "Yes", - "HIVTesting": "Yes", - "FluVaxLast12": "No", - "PneumoVaxEver": "No", - "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", - "HighRiskLastYear": "No", - "CovidPos": "No" - } - }, - { - "scenario_id": "500", - "features": { - "HeartDisease": "Yes", - "BMI": 32.55, - "Smoking": "Yes", - "AlcoholDrinking": "No", - "Stroke": "Yes", - "PhysicalHealth": 30.0, - "MentalHealth": 15.0, - "DiffWalking": "Yes", - "Sex": "Male", - "AgeCategory": "70-74", - "Race": "White", - "Diabetic": "No", - "PhysicalActivity": "No", - "GenHealth": "Poor", - "SleepTime": 6.0, - "Asthma": "No", - "KidneyDisease": "Yes", - "SkinCancer": "Yes", - "State": null, - "GeneralHealth": null, - "PhysicalHealthDays": null, - "MentalHealthDays": null, - "LastCheckupTime": null, - "PhysicalActivities": null, - "SleepHours": null, - "RemovedTeeth": null, - "HadHeartAttack": null, - "HadAngina": null, - "HadStroke": null, - "HadAsthma": null, - "HadSkinCancer": null, - "HadCOPD": null, - "HadDepressiveDisorder": null, - "HadKidneyDisease": null, - "HadArthritis": null, - "HadDiabetes": null, - "DeafOrHardOfHearing": null, - "BlindOrVisionDifficulty": null, - "DifficultyConcentrating": null, - "DifficultyWalking": null, - "DifficultyDressingBathing": null, - "DifficultyErrands": null, - "SmokerStatus": null, - "ECigaretteUsage": null, - "ChestScan": null, - "RaceEthnicityCategory": null, - "HeightInMeters": null, - "WeightInKilograms": null, - "AlcoholDrinkers": null, - "HIVTesting": null, - "FluVaxLast12": null, - "PneumoVaxEver": null, - "TetanusLast10Tdap": null, - "HighRiskLastYear": null, - "CovidPos": null - } - } - ], - "target_column": "GeneralHealth", - "task_sub_type": "classification", - "ranking_ground_truth": { - "top_k_ids": [ - "002", - "033", - "103", - "163", - "226", - "255", - "275", - "297", - "366", - "403", - "421", - "430", - "460", - "470", - "490" - ] - }, - "active_filters": [], - "target_class_value": "poor" - }, - "response": "", - "evaluation_score": {} -} \ No newline at end of file + "active_filters": [], + "target_class_value": "poor" + }, + "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_001/current.csv b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_001/current.csv index 1ff2088a20936fee7e723f3fe9865ab9dadf483b..5c42d3acec4167a64859eb48c2ef97529220db49 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_001/current.csv +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_001/current.csv @@ -1,201 +1,201 @@ -HeartDisease,BMI,Smoking,AlcoholDrinking,Stroke,PhysicalHealth,MentalHealth,DiffWalking,Sex,AgeCategory,Race,Diabetic,PhysicalActivity,GenHealth,SleepTime,Asthma,KidneyDisease,SkinCancer,State,PhysicalHealthDays,MentalHealthDays,LastCheckupTime,PhysicalActivities,SleepHours,RemovedTeeth,HadHeartAttack,HadAngina,HadStroke,HadAsthma,HadSkinCancer,HadCOPD,HadDepressiveDisorder,HadKidneyDisease,HadArthritis,HadDiabetes,DeafOrHardOfHearing,BlindOrVisionDifficulty,DifficultyConcentrating,DifficultyWalking,DifficultyDressingBathing,DifficultyErrands,SmokerStatus,ECigaretteUsage,ChestScan,RaceEthnicityCategory,HeightInMeters,WeightInKilograms,AlcoholDrinkers,HIVTesting,FluVaxLast12,PneumoVaxEver,TetanusLast10Tdap,HighRiskLastYear,CovidPos -,39.86,,,,,,,Female,Age 60 to 64,,,,,,,,,Tennessee,30.0,30.0,Within past 2 years (1 year but less than 2 years ago),No,8.0,1 to 5,Yes,Yes,No,No,No,Yes,Yes,No,Yes,Yes,No,No,Yes,Yes,No,Yes,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,102.06,No,Yes,No,Yes,"Yes, received tetanus shot but not sure what type",No,Yes -,37.5,,,,,,,Male,Age 65 to 69,,,,,,,,,Ohio,0.0,30.0,5 or more years ago,No,5.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.91,136.08,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,21.93,,,,,,,Female,Age 35 to 39,,,,,,,,,Maine,0.0,15.0,Within past 2 years (1 year but less than 2 years ago),Yes,6.0,None of them,No,No,No,No,Yes,No,Yes,No,No,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,63.5,No,No,No,No,"Yes, received Tdap",No,Yes -,35.43,,,,,,,Female,Age 55 to 59,,,,,,,,,Texas,30.0,7.0,Within past 2 years (1 year but less than 2 years ago),No,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,Yes,No,Yes,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.6,90.72,No,No,No,No,,No,No -,30.99,,,,,,,Male,Age 70 to 74,,,,,,,,,Ohio,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.78,97.98,No,No,Yes,Yes,,No,No -No,25.1,No,No,No,2.0,0.0,No,Female,35-39,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,26.63,,,,,,,Female,Age 50 to 54,,,,,,,,,Illinois,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,72.57,No,No,Yes,No,"Yes, received Tdap",No,No -,28.13,,,,,,,Male,Age 60 to 64,,,,,,,,,Puerto Rico,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,Yes,Yes,Yes,No,No,No,No,Former smoker,Not at all (right now),No,Hispanic,1.73,83.91,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,33.01,,,,,,,Female,Age 65 to 69,,,,,,,,,South Carolina,0.0,5.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,Yes,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.52,76.66,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,25.79,,,,,,,Female,Age 55 to 59,,,,,,,,,Nevada,0.0,2.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,70.31,Yes,No,No,,"Yes, received Tdap",No,No -,32.98,,,,,,,Male,Age 45 to 49,,,,,,,,,North Dakota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.85,113.4,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,28.32,,,,,,,Female,Age 70 to 74,,,,,,,,,Washington,15.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.52,65.77,No,No,No,No,"Yes, received tetanus shot, but not Tdap",No,No -,21.14,,,,,,,Female,Age 80 or older,,,,,,,,,Alabama,,,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,Yes,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.7,61.23,No,,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -No,40.89,Yes,No,No,10.0,30.0,Yes,Male,50-54,Black,Yes,No,Fair,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Yes,29.23,No,No,Yes,20.0,20.0,No,Male,75-79,American Indian/Alaskan Native,No,Yes,Poor,8.0,No,Yes,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,38.62,,,,,,,Female,Age 30 to 34,,,,,,,,,West Virginia,3.0,15.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Use them every day,No,"White only, Non-Hispanic",1.63,102.06,Yes,Yes,Yes,No,"Yes, received tetanus shot, but not Tdap",Yes,Yes -,27.44,,,,,,,Female,Age 60 to 64,,,,,,,,,Wyoming,0.0,2.0,5 or more years ago,Yes,4.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,77.11,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,No -,32.88,,,,,,,Female,Age 75 to 79,,,,,,,,,Montana,5.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,Yes,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.55,78.93,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,28.75,,,,,,,Male,Age 40 to 44,,,,,,,,,Nebraska,4.0,30.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,No,No,No,Yes,No,No,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.91,104.33,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",Yes,No -,36.92,,,,,,,Female,Age 50 to 54,,,,,,,,,Nebraska,14.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,Yes,No,No,No,No,No,Yes,No,Yes,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Other race only, Non-Hispanic",1.75,113.4,No,Yes,No,Yes,"Yes, received tetanus shot but not sure what type",No,Yes -,,,,,,,,Male,Age 50 to 54,,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,Yes,No,No,No,No,Yes,No,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, -No,29.13,Yes,No,No,0.0,0.0,No,Male,60-64,White,No,Yes,Excellent,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,27.44,,,,,,,Female,Age 75 to 79,,,,,,,,,Maine,6.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,68.04,Yes,No,No,No,"Yes, received tetanus shot, but not Tdap",No,No -No,38.26,Yes,No,No,0.0,0.0,No,Male,45-49,White,No,Yes,Excellent,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,46.99,,,,,,,Male,Age 60 to 64,,,,,,,,,Washington,3.0,0.0,Within past year (anytime less than 12 months ago),No,5.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,136.08,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,21.41,,,,,,,Male,Age 80 or older,,,,,,,,,Alaska,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,Yes,Yes,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,65.77,No,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,Yes -,21.48,,,,,,,Female,Age 65 to 69,,,,,,,,,Hawaii,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,,,,,,,,,"Other race only, Non-Hispanic",1.52,49.9,,,,,,, -,31.46,,,,,,,Male,Age 40 to 44,,,,,,,,,Indiana,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),Yes,Hispanic,1.74,95.25,Yes,Yes,No,Yes,"Yes, received Tdap",No,No -,31.32,,,,,,,Male,Age 60 to 64,,,,,,,,,New Mexico,0.0,8.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,Hispanic,1.7,90.72,Yes,No,Yes,No,"Yes, received Tdap",No,No -,27.32,,,,,,,Female,Age 35 to 39,,,,,,,,,Colorado,4.0,9.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,Yes,No,No,No,Yes,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,83.91,No,Yes,Yes,No,"Yes, received Tdap",No,Yes -,48.82,,,,,,,Female,Age 55 to 59,,,,,,,,,Arizona,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.52,113.4,No,Yes,Yes,Yes,"Yes, received Tdap",No,No -,32.28,,,,,,,Female,Age 70 to 74,,,,,,,,,Nebraska,0.0,7.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,No,No,No,No,No,Yes,No,Yes,No,Yes,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,90.72,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,27.96,,,,,,,Female,Age 30 to 34,,,,,,,,,Louisiana,5.0,30.0,Within past year (anytime less than 12 months ago),Yes,9.0,None of them,No,No,No,No,No,Yes,Yes,No,Yes,No,No,No,Yes,No,No,No,Former smoker,Not at all (right now),Yes,"Multiracial, Non-Hispanic",1.55,67.13,Yes,,No,No,"Yes, received Tdap",No,Yes -,27.44,,,,,,,Female,Age 65 to 69,,,,,,,,,Minnesota,30.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,Yes,No,Yes,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.57,68.04,Yes,No,Yes,Yes,"Yes, received Tdap",No,No -No,26.63,Yes,No,No,3.0,2.0,No,Male,65-69,White,No,Yes,Good,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,33.45,Yes,No,No,0.0,0.0,No,Male,50-54,White,Yes,Yes,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,23.49,No,Yes,No,0.0,5.0,No,Female,35-39,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,26.54,,,,,,,Male,Age 60 to 64,,,,,,,,,Idaho,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,83.91,No,No,Yes,Yes,"Yes, received Tdap",No,No -No,24.33,No,No,No,0.0,0.0,No,Male,30-34,Hispanic,No,Yes,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,32.12,Yes,No,No,0.0,0.0,No,Female,60-64,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Yes,28.34,No,No,No,0.0,15.0,Yes,Female,80 or older,White,No,No,Fair,8.0,Yes,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,21.3,,,,,,,Female,Age 35 to 39,,,,,,,,,Washington,0.0,4.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Other race only, Non-Hispanic",1.65,58.06,Yes,No,Yes,No,"Yes, received tetanus shot, but not Tdap",No,No -,27.88,,,,,,,Male,Age 35 to 39,,,,,,,,,Minnesota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,80.74,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No -,29.5,,,,,,,Male,Age 70 to 74,,,,,,,,,West Virginia,5.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,All,Yes,Yes,No,No,No,No,No,No,No,Yes,No,No,No,Yes,Yes,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,88.0,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,31.01,,,,,,,Female,Age 50 to 54,,,,,,,,,Maryland,0.0,5.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,1 to 5,No,No,,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,,1.75,95.25,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -,20.37,,,,,,,Female,Age 75 to 79,,,,,,,,,Wisconsin,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,52.16,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes -,,,,,,,,Female,Age 40 to 44,,,,,,,,,Texas,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,"Yes, but only during pregnancy (female)",No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,,63.5,No,No,No,Yes,"Yes, received Tdap",No,No -No,26.78,No,No,No,0.0,0.0,No,Male,70-74,Hispanic,No,No,Good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,26.54,,,,,,,Male,Age 40 to 44,,,,,,,,,Ohio,2.0,10.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Use them some days,No,"White only, Non-Hispanic",1.78,83.91,Yes,Yes,Yes,No,"Yes, received Tdap",No,No -,30.11,,,,,,,Female,Age 55 to 59,,,,,,,,,Maine,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,Yes,"No, pre-diabetes or borderline diabetes",No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,77.11,No,Yes,No,No,"Yes, received Tdap",No,No -,26.63,,,,,,,Male,Age 75 to 79,,,,,,,,,Louisiana,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,None of them,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,74.84,Yes,No,Yes,,,No,No -No,34.2,No,No,No,1.0,0.0,No,Female,80 or older,Black,Yes,No,Good,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,30.7,,,,,,,Female,Age 18 to 24,,,,,,,,,Washington,3.0,20.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.7,88.9,Yes,No,No,,"Yes, received Tdap",No,Yes -No,26.52,No,No,No,0.0,0.0,No,Female,60-64,White,No,Yes,Very good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,30.17,No,No,No,0.0,0.0,No,Male,45-49,White,No,Yes,Excellent,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,41.05,,,,,,,Female,Age 75 to 79,,,,,,,,,Nebraska,15.0,1.0,Within past year (anytime less than 12 months ago),No,9.0,"6 or more, but not all",No,Yes,No,No,No,Yes,No,No,Yes,Yes,Yes,Yes,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,122.47,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes -,21.41,,,,,,,Female,Age 60 to 64,,,,,,,,,Massachusetts,2.0,2.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,65.77,No,No,Yes,No,"Yes, received Tdap",No,No -,24.94,,,,,,,Female,Age 70 to 74,,,,,,,,,Michigan,0.0,6.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,Yes,No,No,Yes,No,Yes,No,Yes,,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,74.39,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,No -,31.0,,,,,,,Male,Age 45 to 49,,,,,,,,,Connecticut,0.0,2.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,106.59,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -No,22.31,No,No,No,0.0,0.0,No,Female,60-64,Black,"No, borderline diabetes",Yes,Very good,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,27.44,,,,,,,Male,Age 25 to 29,,,,,,,,,Connecticut,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),No,5.0,None of them,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),,"White only, Non-Hispanic",1.68,77.11,Yes,No,No,,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,24.21,,,,,,,Female,Age 30 to 34,,,,,,,,,New Jersey,3.0,30.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.68,68.04,Yes,Yes,Yes,No,"Yes, received Tdap",No,Yes -,39.11,,,,,,,Female,Age 40 to 44,,,,,,,,,Tennessee,0.0,2.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),No,Hispanic,1.65,106.59,Yes,Yes,No,No,"Yes, received tetanus shot, but not Tdap",No,Yes -No,30.41,No,No,No,15.0,30.0,No,Male,65-69,Black,No,Yes,Good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,32.22,,,,,,,Female,Age 70 to 74,,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,All,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.52,74.84,No,No,No,Yes,"Yes, received Tdap",No,No -,40.42,,,,,,,Female,Age 60 to 64,,,,,,,,,Montana,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,Yes,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,Yes,"Other race only, Non-Hispanic",1.57,100.24,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,39.46,,,,,,,Male,Age 60 to 64,,,,,,,,,Mississippi,0.0,30.0,Within past 2 years (1 year but less than 2 years ago),Yes,5.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,124.74,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No -,21.46,,,,,,,Female,Age 70 to 74,,,,,,,,,Vermont,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,56.7,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -No,31.17,Yes,No,No,1.0,3.0,No,Male,35-39,White,No,Yes,Very good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,26.54,No,No,No,0.0,0.0,No,Male,75-79,White,Yes,Yes,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,21.18,No,No,No,0.0,2.0,No,Male,18-24,White,No,Yes,Excellent,8.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,,,,,,,,Female,,,,,,,,,,Connecticut,7.0,2.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,Yes,No,Yes,No,No,Yes,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,,,,,,,,,,, -,22.67,,,,,,,Female,Age 70 to 74,,,,,,,,,California,5.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,58.06,Yes,No,Yes,Yes,"Yes, received Tdap",No,No -No,31.46,Yes,No,No,0.0,0.0,No,Male,45-49,White,No,Yes,Good,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,30.13,Yes,No,Yes,20.0,5.0,Yes,Male,55-59,Black,Yes,No,Fair,3.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,,,,,,,,Male,,,,,,,,,,Vermont,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",,,No,,,Yes,"Yes, received tetanus shot but not sure what type",No,No -No,34.3,Yes,No,No,3.0,0.0,No,Male,45-49,Hispanic,No,Yes,Good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,30.13,,,,,,,Male,Age 55 to 59,,,,,,,,,Maryland,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,,,,,,,,"Black only, Non-Hispanic",1.78,95.25,,,,,,, -,27.37,,,,,,,Male,Age 55 to 59,,,,,,,,,Ohio,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),No,6.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,81.65,Yes,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No -No,23.75,No,No,No,0.0,0.0,No,Male,60-64,White,No,Yes,Excellent,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,20.34,,,,,,,Male,Age 70 to 74,,,,,,,,,Texas,,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,Yes,Yes,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.83,68.04,Yes,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,,,,,,,,Female,Age 60 to 64,,,,,,,,,Arkansas,0.0,2.0,Within past 2 years (1 year but less than 2 years ago),No,7.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes every day,Use them every day,Yes,"White only, Non-Hispanic",1.7,,No,No,Yes,Yes,"Yes, received Tdap",No,No -,37.59,,,,,,,Female,Age 30 to 34,,,,,,,,,Minnesota,5.0,5.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"Other race only, Non-Hispanic",1.7,108.86,Yes,Yes,Yes,,"Yes, received tetanus shot but not sure what type",No,Yes -,23.65,,,,,,,Male,Age 75 to 79,,,,,,,,,Wisconsin,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,68.49,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes -No,22.71,No,No,No,0.0,0.0,No,Male,75-79,White,No,Yes,Excellent,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,33.45,,,,,,,Male,Age 45 to 49,,,,,,,,,Massachusetts,0.0,,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,1 to 5,No,No,No,Yes,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,99.79,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -No,27.26,No,No,No,0.0,0.0,No,Male,50-54,White,No,Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,25.84,,,,,,,Male,Age 50 to 54,,,,,,,,,Maryland,0.0,1.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.7,74.84,Yes,No,Yes,No,,No,No -,30.34,,,,,,,Male,Age 40 to 44,,,,,,,,,Maryland,2.0,0.0,Within past year (anytime less than 12 months ago),Yes,4.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes some days,Use them every day,Yes,"White only, Non-Hispanic",1.85,104.33,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No -,24.39,,,,,,,Male,Age 70 to 74,,,,,,,,,Virginia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,77.11,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,,,,,,,,Female,Age 50 to 54,,,,,,,,,Connecticut,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.57,,No,,Yes,Yes,,No,Yes -,16.27,,,,,,,Male,Age 50 to 54,,,,,,,,,Georgia,30.0,20.0,Within past year (anytime less than 12 months ago),No,5.0,1 to 5,No,No,Yes,Yes,No,Yes,Yes,No,No,No,No,Yes,Yes,Yes,Yes,Yes,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.83,54.43,No,Yes,No,Yes,"Yes, received tetanus shot but not sure what type",No,No -,34.97,,,,,,,Male,Age 18 to 24,,,,,,,,,Indiana,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.73,104.33,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,No -,19.37,,,,,,,Female,Age 30 to 34,,,,,,,,,Connecticut,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.68,54.43,,,,,,, -,23.24,,,,,,,Female,Age 75 to 79,,,,,,,,,New York,30.0,0.0,Within past year (anytime less than 12 months ago),No,5.0,None of them,No,No,No,No,No,Yes,No,No,No,Yes,No,No,No,Yes,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.68,65.32,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -No,29.84,Yes,No,No,0.0,0.0,Yes,Male,80 or older,White,Yes,No,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,34.21,Yes,No,No,0.0,0.0,No,Female,40-44,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,23.42,,,,,,,Female,Age 45 to 49,,,,,,,,,Massachusetts,0.0,2.0,Within past 2 years (1 year but less than 2 years ago),Yes,9.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,69.85,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,23.78,,,,,,,Female,Age 25 to 29,,,,,,,,,Missouri,10.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,"Yes, but only during pregnancy (female)",No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.57,58.97,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,24.89,,,,,,,Female,Age 70 to 74,,,,,,,,,Maine,1.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,65.77,Yes,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,28.76,,,,,,,Male,Age 30 to 34,,,,,,,,,Alaska,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,98.88,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -No,22.89,No,No,No,0.0,0.0,No,Male,45-49,White,No,Yes,Excellent,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,35.95,,,,,,,Male,Age 35 to 39,,,,,,,,,California,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.88,127.01,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,28.48,,,,,,,Male,Age 18 to 24,,,,,,,,,Florida,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.83,95.25,Yes,Yes,No,No,"Yes, received tetanus shot, but not Tdap",Yes,No -,31.89,,,,,,,Male,Age 60 to 64,,,,,,,,,Texas,4.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,None of them,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.6,81.65,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,44.93,,,,,,,Female,Age 75 to 79,,,,,,,,,Michigan,2.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,1 to 5,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,122.47,No,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,48.47,,,,,,,Female,Age 50 to 54,,,,,,,,,Indiana,30.0,10.0,Within past year (anytime less than 12 months ago),No,10.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,120.2,No,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,19.97,,,,,,,Female,Age 40 to 44,,,,,,,,,New Hampshire,10.0,3.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,54.43,Yes,No,Yes,No,"Yes, received tetanus shot, but not Tdap",No,Tested positive using home test without a health professional -,36.26,,,,,,,Male,Age 65 to 69,,,,,,,,,Minnesota,4.0,3.0,Within past year (anytime less than 12 months ago),No,7.0,1 to 5,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,117.93,Yes,No,No,Yes,"Yes, received tetanus shot, but not Tdap",No,No -No,50.0,Yes,No,No,30.0,0.0,Yes,Male,50-54,White,No,No,Fair,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,24.27,No,No,No,3.0,0.0,No,Male,80 or older,White,No,Yes,Very good,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,29.84,,,,,,,Male,Age 25 to 29,,,,,,,,,Maine,4.0,3.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,99.79,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,Yes -No,25.85,No,No,No,0.0,5.0,No,Female,18-24,White,No,Yes,Excellent,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,23.75,,,,,,,Male,Age 30 to 34,,,,,,,,,Colorado,30.0,30.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,1 to 5,No,No,No,Yes,No,No,Yes,No,No,"No, pre-diabetes or borderline diabetes",No,No,,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.91,86.18,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -No,20.34,Yes,No,Yes,0.0,0.0,No,Male,60-64,White,No,Yes,Good,6.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,36.05,No,No,No,3.0,0.0,No,Female,80 or older,White,No,Yes,Fair,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,25.82,,,,,,,Male,Age 30 to 34,,,,,,,,,Texas,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,5.0,None of them,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.68,72.57,Yes,No,No,No,"Yes, received tetanus shot, but not Tdap",Yes,Yes -,30.85,,,,,,,Male,Age 65 to 69,,,,,,,,,New Jersey,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,Yes,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,97.52,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,19.05,,,,,,,Female,Age 75 to 79,,,,,,,,,Connecticut,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,50.35,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,25.34,,,,,,,Female,Age 70 to 74,,,,,,,,,South Carolina,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,Yes,No,Yes,No,No,Yes,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,71.21,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -Yes,22.96,No,No,No,0.0,0.0,No,Male,75-79,White,No,Yes,Very good,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,34.21,No,No,No,2.0,5.0,No,Male,60-64,White,No,Yes,Very good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,27.98,,,,,,,Male,Age 55 to 59,,,,,,,,,Connecticut,30.0,30.0,Within past year (anytime less than 12 months ago),Yes,5.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,88.45,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -Yes,30.67,Yes,No,Yes,15.0,30.0,Yes,Female,65-69,White,No,No,Fair,6.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,25.63,No,No,No,2.0,20.0,No,Male,60-64,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,30.54,No,No,No,0.0,0.0,No,Male,75-79,White,Yes,Yes,Very good,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,22.82,,,,,,,Female,Age 65 to 69,,,,,,,,,Mississippi,1.0,0.0,Within past year (anytime less than 12 months ago),Yes,,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.85,78.47,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,,,,,,,,Male,Age 40 to 44,,,,,,,,,Utah,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,All,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,,,,,,,,,,, -,33.66,,,,,,,Female,Age 60 to 64,,,,,,,,,Maryland,25.0,15.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,Yes,No,No,No,No,No,"No, pre-diabetes or borderline diabetes",No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,86.18,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,No -,29.05,,,,,,,Female,Age 25 to 29,,,,,,,,,Michigan,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,10.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,81.65,No,,No,,,, -,19.84,,,,,,,Female,Age 65 to 69,,,,,,,,,Wisconsin,0.0,0.0,Within past year (anytime less than 12 months ago),No,4.0,None of them,No,No,No,No,No,No,No,No,Yes,"Yes, but only during pregnancy (female)",No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.55,47.63,No,No,Yes,No,"Yes, received Tdap",No,No -,28.89,,,,,,,Female,Age 60 to 64,,,,,,,,,Washington,0.0,5.0,Within past 2 years (1 year but less than 2 years ago),Yes,5.0,1 to 5,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),,"White only, Non-Hispanic",1.73,86.18,No,Yes,No,No,,No,No -No,36.96,Yes,No,No,20.0,15.0,Yes,Female,45-49,White,Yes,Yes,Poor,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,34.87,,,,,,,Male,Age 65 to 69,,,,,,,,,Vermont,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,1 to 5,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.8,113.4,Yes,No,No,Yes,"Yes, received tetanus shot but not sure what type",No,No -No,25.73,Yes,No,No,7.0,14.0,No,Male,35-39,White,No,Yes,Very good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Yes,37.51,Yes,No,No,30.0,0.0,Yes,Female,55-59,American Indian/Alaskan Native,Yes,No,Fair,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,27.62,,,,,,,Male,Age 65 to 69,,,,,,,,,Oklahoma,2.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,Yes,No,No,No,No,No,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,89.81,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,41.09,,,,,,,Male,Age 65 to 69,,,,,,,,,Minnesota,20.0,5.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,Yes,No,Yes,Yes,Yes,Yes,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.88,145.15,No,Yes,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -No,35.31,Yes,No,No,0.0,0.0,No,Male,45-49,White,No,Yes,Excellent,6.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,36.61,No,No,No,0.0,0.0,No,Female,50-54,Black,No,Yes,Very good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,22.31,,,,,,,Female,Age 18 to 24,,,,,,,,,Virginia,14.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Use them some days,No,Hispanic,1.63,58.97,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,No -,24.03,,,,,,,Female,Age 40 to 44,,,,,,,,,Vermont,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"Other race only, Non-Hispanic",1.63,63.5,No,,Yes,No,"Yes, received tetanus shot but not sure what type",No,Tested positive using home test without a health professional -,19.77,,,,,,,Female,Age 40 to 44,,,,,,,,,Hawaii,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),No,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.73,58.97,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,27.44,,,,,,,Female,Age 80 or older,,,,,,,,,Minnesota,0.0,30.0,Within past year (anytime less than 12 months ago),No,7.0,1 to 5,No,Yes,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.57,68.04,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,22.46,,,,,,,Female,Age 30 to 34,,,,,,,,,Nebraska,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Not at all (right now),No,"White only, Non-Hispanic",1.65,61.23,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No -No,26.57,No,No,No,0.0,0.0,No,Female,45-49,Hispanic,No,No,Excellent,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,24.21,,,,,,,Female,Age 55 to 59,,,,,,,,,Vermont,10.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,68.04,No,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -No,24.03,No,No,No,0.0,0.0,No,Female,40-44,Hispanic,No,Yes,Good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,29.95,,,,,,,Male,Age 70 to 74,,,,,,,,,Virgin Islands,,20.0,Within past year (anytime less than 12 months ago),Yes,4.0,1 to 5,No,No,No,No,No,No,Yes,No,No,No,Yes,No,Yes,Yes,Yes,Yes,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.65,81.65,No,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,,,,,,,,Female,Age 25 to 29,,,,,,,,,Washington,0.0,20.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,,Yes,Yes,Yes,No,"Yes, received Tdap",No,No -,23.54,,,,,,,Male,Age 18 to 24,,,,,,,,,Maryland,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,Hispanic,1.7,68.04,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -No,27.5,Yes,Yes,No,0.0,0.0,No,Male,18-24,White,No,Yes,Excellent,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,27.44,,,,,,,Female,Age 60 to 64,,,,,,,,,New York,0.0,10.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,Yes,No,No,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,68.04,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -,51.49,,,,,,,Female,Age 35 to 39,,,,,,,,,West Virginia,0.0,3.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Yes,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,136.08,No,Yes,No,No,"Yes, received Tdap",No,No -,36.32,,,,,,,Female,Age 70 to 74,,,,,,,,,Wisconsin,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,Yes,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,102.06,Yes,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No -,25.39,,,,,,,Female,Age 60 to 64,,,,,,,,,Ohio,20.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.52,58.97,No,,No,Yes,"Yes, received Tdap",No,Yes -No,25.02,Yes,No,No,0.0,0.0,No,Male,35-39,White,No,No,Good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,26.43,,,,,,,Female,Age 70 to 74,,,,,,,,,Puerto Rico,0.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Yes,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.63,69.85,No,No,No,Yes,,No,Yes -,27.12,,,,,,,Male,Age 75 to 79,,,,,,,,,Washington,2.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,"6 or more, but not all",Yes,No,No,No,No,No,No,No,Yes,Yes,No,No,No,Yes,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.83,90.72,No,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,,,,,,,,Female,Age 65 to 69,,,,,,,,,South Dakota,3.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,,1.65,,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,24.8,,,,,,,Female,Age 40 to 44,,,,,,,,,Washington,2.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,1 to 5,No,No,No,No,No,No,Yes,Yes,Yes,No,No,No,Yes,No,No,Yes,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.6,63.5,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No -No,21.46,No,No,No,0.0,3.0,No,Female,18-24,White,No,Yes,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,29.53,,,,,,,Female,Age 50 to 54,,,,,,,,,North Dakota,14.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Other race only, Non-Hispanic",1.75,90.72,No,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -No,20.22,No,No,No,0.0,15.0,No,Male,25-29,White,No,Yes,Very good,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,28.06,,,,,,,Male,Age 60 to 64,,,,,,,,,Louisiana,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,Yes,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.75,86.18,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,30.0,,,,,,,Male,Age 80 or older,,,,,,,,,Minnesota,25.0,0.0,,No,12.0,None of them,,No,No,No,Yes,No,No,No,Yes,Yes,Yes,No,Yes,Yes,Yes,Yes,Never smoked,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.91,108.86,No,No,Yes,,"Yes, received Tdap",No,Yes -,31.87,,,,,,,Male,Age 50 to 54,,,,,,,,,Minnesota,0.0,1.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,106.59,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No -No,28.17,Yes,No,No,0.0,0.0,No,Male,80 or older,White,No,Yes,Very good,9.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,37.31,No,No,No,0.0,0.0,No,Male,55-59,White,Yes,Yes,Very good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,23.03,,,,,,,Female,Age 70 to 74,,,,,,,,,Ohio,0.0,10.0,Within past year (anytime less than 12 months ago),Yes,5.0,"6 or more, but not all",No,No,No,No,Yes,No,No,No,Yes,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,58.97,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No, -No,21.93,No,No,No,8.0,1.0,No,Female,60-64,White,No,Yes,Good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,15.41,Yes,Yes,Yes,0.0,30.0,No,Male,18-24,Hispanic,Yes,Yes,Excellent,5.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,52.42,,,,,,,Female,Age 55 to 59,,,,,,,,,Puerto Rico,30.0,20.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,1 to 5,No,No,No,No,No,No,Yes,No,Yes,No,No,Yes,Yes,Yes,Yes,Yes,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.65,142.88,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -No,28.48,No,No,No,0.0,9.0,No,Male,18-24,White,No,Yes,Good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,23.49,No,No,No,0.0,0.0,No,Male,45-49,White,No,Yes,Good,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Yes,35.94,No,No,No,0.0,0.0,No,Male,65-69,White,Yes,No,Good,7.0,Yes,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,24.8,No,No,No,4.0,0.0,Yes,Female,75-79,White,No,No,Poor,9.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,22.24,No,No,No,0.0,0.0,No,Female,65-69,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,45.73,,,,,,,Female,Age 30 to 34,,,,,,,,,California,0.0,3.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.57,113.4,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,35.71,,,,,,,Female,Age 40 to 44,,,,,,,,,Washington,7.0,28.0,Within past year (anytime less than 12 months ago),No,5.0,"6 or more, but not all",No,No,No,Yes,No,No,Yes,No,Yes,No,No,Yes,Yes,Yes,No,Yes,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.55,85.73,No,Yes,No,No,"Yes, received Tdap",No,No -,22.67,,,,,,,Female,Age 70 to 74,,,,,,,,,Washington,4.0,0.0,Within past year (anytime less than 12 months ago),Yes,12.0,None of them,No,No,No,No,No,Yes,Yes,Yes,Yes,No,No,No,No,Yes,No,No,,,,"White only, Non-Hispanic",1.6,58.06,,,,,,, -,31.62,,,,,,,Female,Age 30 to 34,,,,,,,,,South Dakota,0.0,4.0,Within past 2 years (1 year but less than 2 years ago),Yes,6.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.65,86.18,Yes,No,No,No,"Yes, received Tdap",No,Yes -,28.37,,,,,,,Male,Age 25 to 29,,,,,,,,,Kentucky,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,97.52,Yes,No,Yes,No,"Yes, received Tdap",No,No -No,21.16,No,No,No,0.0,0.0,No,Female,50-54,White,No,Yes,Excellent,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,13.95,,,,,,,Male,Age 35 to 39,,,,,,,,,Maine,30.0,30.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,Yes,No,Yes,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",2.13,63.5,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -No,21.03,Yes,No,Yes,7.0,0.0,No,Female,55-59,White,No,Yes,Very good,9.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,40.09,,,,,,,Male,Age 25 to 29,,,,,,,,,New York,,12.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Use them some days,Yes,"Other race only, Non-Hispanic",1.7,116.12,Yes,Yes,Yes,,"Yes, received Tdap",No,Yes -,30.67,,,,,,,Female,Age 65 to 69,,,,,,,,,Georgia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,10.0,1 to 5,No,No,No,No,No,No,No,No,No,"No, pre-diabetes or borderline diabetes",No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.68,86.18,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,55.17,,,,,,,Female,Age 55 to 59,,,,,,,,,Mississippi,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.55,132.45,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,26.63,,,,,,,Male,Age 30 to 34,,,,,,,,,Vermont,2.0,2.0,5 or more years ago,Yes,8.0,"6 or more, but not all",No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes some days,Use them some days,No,"White only, Non-Hispanic",1.65,72.57,Yes,Yes,No,No,"Yes, received tetanus shot, but not Tdap",No,No -,33.28,,,,,,,Male,Age 80 or older,,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),No,9.0,None of them,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Not at all (right now),No,Hispanic,1.65,90.72,No,Yes,No,Yes,"Yes, received Tdap",No,No -,32.89,,,,,,,Female,Age 65 to 69,,,,,,,,,Utah,3.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,"6 or more, but not all",No,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,,,"White only, Non-Hispanic",1.7,95.25,,,,,,, -No,39.68,No,No,No,0.0,0.0,No,Female,55-59,Other,No,Yes,Good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,33.47,No,No,No,0.0,0.0,No,Male,55-59,White,No,Yes,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,23.91,,,,,,,Female,Age 65 to 69,,,,,,,,,Washington,30.0,15.0,Within past year (anytime less than 12 months ago),No,8.0,All,No,Yes,Yes,Yes,No,Yes,Yes,Yes,Yes,No,No,No,Yes,Yes,Yes,Yes,Former smoker,Use them every day,Yes,"White only, Non-Hispanic",1.6,61.23,No,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,22.81,,,,,,,Male,Age 18 to 24,,,,,,,,,Kansas,0.0,28.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.73,68.04,Yes,No,No,Yes,"Yes, received Tdap",No,Yes -No,28.19,No,No,No,3.0,4.0,No,Female,55-59,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,29.29,,,,,,,Female,Age 70 to 74,,,,,,,,,Ohio,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,Yes,No,No,No,No,No,No,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.52,68.04,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,17.36,,,,,,,Male,Age 18 to 24,,,,,,,,,Illinois,7.0,2.0,Within past 2 years (1 year but less than 2 years ago),Yes,5.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.6,44.45,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -Yes,32.55,Yes,No,Yes,30.0,15.0,Yes,Male,70-74,White,No,No,Poor,6.0,No,Yes,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +HeartDisease,BMI,Smoking,AlcoholDrinking,Stroke,PhysicalHealth,MentalHealth,DiffWalking,Sex,AgeCategory,Race,Diabetic,PhysicalActivity,SleepTime,Asthma,KidneyDisease,SkinCancer,State,PhysicalHealthDays,MentalHealthDays,LastCheckupTime,PhysicalActivities,SleepHours,RemovedTeeth,HadHeartAttack,HadAngina,HadStroke,HadAsthma,HadSkinCancer,HadCOPD,HadDepressiveDisorder,HadKidneyDisease,HadArthritis,HadDiabetes,DeafOrHardOfHearing,BlindOrVisionDifficulty,DifficultyConcentrating,DifficultyWalking,DifficultyDressingBathing,DifficultyErrands,SmokerStatus,ECigaretteUsage,ChestScan,RaceEthnicityCategory,HeightInMeters,WeightInKilograms,AlcoholDrinkers,HIVTesting,FluVaxLast12,PneumoVaxEver,TetanusLast10Tdap,HighRiskLastYear,CovidPos +,39.86,,,,,,,Female,Age 60 to 64,,,,,,,,Tennessee,30.0,30.0,Within past 2 years (1 year but less than 2 years ago),No,8.0,1 to 5,Yes,Yes,No,No,No,Yes,Yes,No,Yes,Yes,No,No,Yes,Yes,No,Yes,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,102.06,No,Yes,No,Yes,"Yes, received tetanus shot but not sure what type",No,Yes +,37.5,,,,,,,Male,Age 65 to 69,,,,,,,,Ohio,0.0,30.0,5 or more years ago,No,5.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.91,136.08,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,21.93,,,,,,,Female,Age 35 to 39,,,,,,,,Maine,0.0,15.0,Within past 2 years (1 year but less than 2 years ago),Yes,6.0,None of them,No,No,No,No,Yes,No,Yes,No,No,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,63.5,No,No,No,No,"Yes, received Tdap",No,Yes +,35.43,,,,,,,Female,Age 55 to 59,,,,,,,,Texas,30.0,7.0,Within past 2 years (1 year but less than 2 years ago),No,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,Yes,No,Yes,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.6,90.72,No,No,No,No,,No,No +,30.99,,,,,,,Male,Age 70 to 74,,,,,,,,Ohio,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.78,97.98,No,No,Yes,Yes,,No,No +No,25.1,No,No,No,2.0,0.0,No,Female,35-39,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,26.63,,,,,,,Female,Age 50 to 54,,,,,,,,Illinois,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,72.57,No,No,Yes,No,"Yes, received Tdap",No,No +,28.13,,,,,,,Male,Age 60 to 64,,,,,,,,Puerto Rico,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,Yes,Yes,Yes,No,No,No,No,Former smoker,Not at all (right now),No,Hispanic,1.73,83.91,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,33.01,,,,,,,Female,Age 65 to 69,,,,,,,,South Carolina,0.0,5.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,Yes,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.52,76.66,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,25.79,,,,,,,Female,Age 55 to 59,,,,,,,,Nevada,0.0,2.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,70.31,Yes,No,No,,"Yes, received Tdap",No,No +,32.98,,,,,,,Male,Age 45 to 49,,,,,,,,North Dakota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.85,113.4,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,28.32,,,,,,,Female,Age 70 to 74,,,,,,,,Washington,15.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.52,65.77,No,No,No,No,"Yes, received tetanus shot, but not Tdap",No,No +,21.14,,,,,,,Female,Age 80 or older,,,,,,,,Alabama,,,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,Yes,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.7,61.23,No,,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +No,40.89,Yes,No,No,10.0,30.0,Yes,Male,50-54,Black,Yes,No,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Yes,29.23,No,No,Yes,20.0,20.0,No,Male,75-79,American Indian/Alaskan Native,No,Yes,8.0,No,Yes,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,38.62,,,,,,,Female,Age 30 to 34,,,,,,,,West Virginia,3.0,15.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Use them every day,No,"White only, Non-Hispanic",1.63,102.06,Yes,Yes,Yes,No,"Yes, received tetanus shot, but not Tdap",Yes,Yes +,27.44,,,,,,,Female,Age 60 to 64,,,,,,,,Wyoming,0.0,2.0,5 or more years ago,Yes,4.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,77.11,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,No +,32.88,,,,,,,Female,Age 75 to 79,,,,,,,,Montana,5.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,Yes,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.55,78.93,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,28.75,,,,,,,Male,Age 40 to 44,,,,,,,,Nebraska,4.0,30.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,No,No,No,Yes,No,No,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.91,104.33,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",Yes,No +,36.92,,,,,,,Female,Age 50 to 54,,,,,,,,Nebraska,14.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,Yes,No,No,No,No,No,Yes,No,Yes,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Other race only, Non-Hispanic",1.75,113.4,No,Yes,No,Yes,"Yes, received tetanus shot but not sure what type",No,Yes +,,,,,,,,Male,Age 50 to 54,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,Yes,No,No,No,No,Yes,No,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, +No,29.13,Yes,No,No,0.0,0.0,No,Male,60-64,White,No,Yes,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,27.44,,,,,,,Female,Age 75 to 79,,,,,,,,Maine,6.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,68.04,Yes,No,No,No,"Yes, received tetanus shot, but not Tdap",No,No +No,38.26,Yes,No,No,0.0,0.0,No,Male,45-49,White,No,Yes,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,46.99,,,,,,,Male,Age 60 to 64,,,,,,,,Washington,3.0,0.0,Within past year (anytime less than 12 months ago),No,5.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,136.08,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,21.41,,,,,,,Male,Age 80 or older,,,,,,,,Alaska,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,Yes,Yes,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,65.77,No,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,Yes +,21.48,,,,,,,Female,Age 65 to 69,,,,,,,,Hawaii,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,,,,,,,,,"Other race only, Non-Hispanic",1.52,49.9,,,,,,, +,31.46,,,,,,,Male,Age 40 to 44,,,,,,,,Indiana,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),Yes,Hispanic,1.74,95.25,Yes,Yes,No,Yes,"Yes, received Tdap",No,No +,31.32,,,,,,,Male,Age 60 to 64,,,,,,,,New Mexico,0.0,8.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,Hispanic,1.7,90.72,Yes,No,Yes,No,"Yes, received Tdap",No,No +,27.32,,,,,,,Female,Age 35 to 39,,,,,,,,Colorado,4.0,9.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,Yes,No,No,No,Yes,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,83.91,No,Yes,Yes,No,"Yes, received Tdap",No,Yes +,48.82,,,,,,,Female,Age 55 to 59,,,,,,,,Arizona,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.52,113.4,No,Yes,Yes,Yes,"Yes, received Tdap",No,No +,32.28,,,,,,,Female,Age 70 to 74,,,,,,,,Nebraska,0.0,7.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,No,No,No,No,No,Yes,No,Yes,No,Yes,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,90.72,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,27.96,,,,,,,Female,Age 30 to 34,,,,,,,,Louisiana,5.0,30.0,Within past year (anytime less than 12 months ago),Yes,9.0,None of them,No,No,No,No,No,Yes,Yes,No,Yes,No,No,No,Yes,No,No,No,Former smoker,Not at all (right now),Yes,"Multiracial, Non-Hispanic",1.55,67.13,Yes,,No,No,"Yes, received Tdap",No,Yes +,27.44,,,,,,,Female,Age 65 to 69,,,,,,,,Minnesota,30.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,Yes,No,Yes,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.57,68.04,Yes,No,Yes,Yes,"Yes, received Tdap",No,No +No,26.63,Yes,No,No,3.0,2.0,No,Male,65-69,White,No,Yes,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,33.45,Yes,No,No,0.0,0.0,No,Male,50-54,White,Yes,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,23.49,No,Yes,No,0.0,5.0,No,Female,35-39,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,26.54,,,,,,,Male,Age 60 to 64,,,,,,,,Idaho,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,83.91,No,No,Yes,Yes,"Yes, received Tdap",No,No +No,24.33,No,No,No,0.0,0.0,No,Male,30-34,Hispanic,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,32.12,Yes,No,No,0.0,0.0,No,Female,60-64,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Yes,28.34,No,No,No,0.0,15.0,Yes,Female,80 or older,White,No,No,8.0,Yes,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,21.3,,,,,,,Female,Age 35 to 39,,,,,,,,Washington,0.0,4.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Other race only, Non-Hispanic",1.65,58.06,Yes,No,Yes,No,"Yes, received tetanus shot, but not Tdap",No,No +,27.88,,,,,,,Male,Age 35 to 39,,,,,,,,Minnesota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,80.74,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No +,29.5,,,,,,,Male,Age 70 to 74,,,,,,,,West Virginia,5.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,All,Yes,Yes,No,No,No,No,No,No,No,Yes,No,No,No,Yes,Yes,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,88.0,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,31.01,,,,,,,Female,Age 50 to 54,,,,,,,,Maryland,0.0,5.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,1 to 5,No,No,,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,,1.75,95.25,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +,20.37,,,,,,,Female,Age 75 to 79,,,,,,,,Wisconsin,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,52.16,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes +,,,,,,,,Female,Age 40 to 44,,,,,,,,Texas,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,"Yes, but only during pregnancy (female)",No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,,63.5,No,No,No,Yes,"Yes, received Tdap",No,No +No,26.78,No,No,No,0.0,0.0,No,Male,70-74,Hispanic,No,No,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,26.54,,,,,,,Male,Age 40 to 44,,,,,,,,Ohio,2.0,10.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Use them some days,No,"White only, Non-Hispanic",1.78,83.91,Yes,Yes,Yes,No,"Yes, received Tdap",No,No +,30.11,,,,,,,Female,Age 55 to 59,,,,,,,,Maine,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,Yes,"No, pre-diabetes or borderline diabetes",No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,77.11,No,Yes,No,No,"Yes, received Tdap",No,No +,26.63,,,,,,,Male,Age 75 to 79,,,,,,,,Louisiana,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,None of them,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,74.84,Yes,No,Yes,,,No,No +No,34.2,No,No,No,1.0,0.0,No,Female,80 or older,Black,Yes,No,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,30.7,,,,,,,Female,Age 18 to 24,,,,,,,,Washington,3.0,20.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.7,88.9,Yes,No,No,,"Yes, received Tdap",No,Yes +No,26.52,No,No,No,0.0,0.0,No,Female,60-64,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,30.17,No,No,No,0.0,0.0,No,Male,45-49,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,41.05,,,,,,,Female,Age 75 to 79,,,,,,,,Nebraska,15.0,1.0,Within past year (anytime less than 12 months ago),No,9.0,"6 or more, but not all",No,Yes,No,No,No,Yes,No,No,Yes,Yes,Yes,Yes,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,122.47,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes +,21.41,,,,,,,Female,Age 60 to 64,,,,,,,,Massachusetts,2.0,2.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,65.77,No,No,Yes,No,"Yes, received Tdap",No,No +,24.94,,,,,,,Female,Age 70 to 74,,,,,,,,Michigan,0.0,6.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,Yes,No,No,Yes,No,Yes,No,Yes,,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,74.39,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,No +,31.0,,,,,,,Male,Age 45 to 49,,,,,,,,Connecticut,0.0,2.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,106.59,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +No,22.31,No,No,No,0.0,0.0,No,Female,60-64,Black,"No, borderline diabetes",Yes,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,27.44,,,,,,,Male,Age 25 to 29,,,,,,,,Connecticut,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),No,5.0,None of them,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),,"White only, Non-Hispanic",1.68,77.11,Yes,No,No,,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,24.21,,,,,,,Female,Age 30 to 34,,,,,,,,New Jersey,3.0,30.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.68,68.04,Yes,Yes,Yes,No,"Yes, received Tdap",No,Yes +,39.11,,,,,,,Female,Age 40 to 44,,,,,,,,Tennessee,0.0,2.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),No,Hispanic,1.65,106.59,Yes,Yes,No,No,"Yes, received tetanus shot, but not Tdap",No,Yes +No,30.41,No,No,No,15.0,30.0,No,Male,65-69,Black,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,32.22,,,,,,,Female,Age 70 to 74,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,All,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.52,74.84,No,No,No,Yes,"Yes, received Tdap",No,No +,40.42,,,,,,,Female,Age 60 to 64,,,,,,,,Montana,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,Yes,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,Yes,"Other race only, Non-Hispanic",1.57,100.24,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,39.46,,,,,,,Male,Age 60 to 64,,,,,,,,Mississippi,0.0,30.0,Within past 2 years (1 year but less than 2 years ago),Yes,5.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,124.74,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No +,21.46,,,,,,,Female,Age 70 to 74,,,,,,,,Vermont,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,56.7,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +No,31.17,Yes,No,No,1.0,3.0,No,Male,35-39,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,26.54,No,No,No,0.0,0.0,No,Male,75-79,White,Yes,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,21.18,No,No,No,0.0,2.0,No,Male,18-24,White,No,Yes,8.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,,,,,,,,Female,,,,,,,,,Connecticut,7.0,2.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,Yes,No,Yes,No,No,Yes,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,,,,,,,,,,, +,22.67,,,,,,,Female,Age 70 to 74,,,,,,,,California,5.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,58.06,Yes,No,Yes,Yes,"Yes, received Tdap",No,No +No,31.46,Yes,No,No,0.0,0.0,No,Male,45-49,White,No,Yes,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,30.13,Yes,No,Yes,20.0,5.0,Yes,Male,55-59,Black,Yes,No,3.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,,,,,,,,Male,,,,,,,,,Vermont,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",,,No,,,Yes,"Yes, received tetanus shot but not sure what type",No,No +No,34.3,Yes,No,No,3.0,0.0,No,Male,45-49,Hispanic,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,30.13,,,,,,,Male,Age 55 to 59,,,,,,,,Maryland,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,,,,,,,,"Black only, Non-Hispanic",1.78,95.25,,,,,,, +,27.37,,,,,,,Male,Age 55 to 59,,,,,,,,Ohio,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),No,6.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,81.65,Yes,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No +No,23.75,No,No,No,0.0,0.0,No,Male,60-64,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,20.34,,,,,,,Male,Age 70 to 74,,,,,,,,Texas,,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,Yes,Yes,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.83,68.04,Yes,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,,,,,,,,Female,Age 60 to 64,,,,,,,,Arkansas,0.0,2.0,Within past 2 years (1 year but less than 2 years ago),No,7.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes every day,Use them every day,Yes,"White only, Non-Hispanic",1.7,,No,No,Yes,Yes,"Yes, received Tdap",No,No +,37.59,,,,,,,Female,Age 30 to 34,,,,,,,,Minnesota,5.0,5.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"Other race only, Non-Hispanic",1.7,108.86,Yes,Yes,Yes,,"Yes, received tetanus shot but not sure what type",No,Yes +,23.65,,,,,,,Male,Age 75 to 79,,,,,,,,Wisconsin,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,68.49,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes +No,22.71,No,No,No,0.0,0.0,No,Male,75-79,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,33.45,,,,,,,Male,Age 45 to 49,,,,,,,,Massachusetts,0.0,,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,1 to 5,No,No,No,Yes,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,99.79,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,27.26,No,No,No,0.0,0.0,No,Male,50-54,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,25.84,,,,,,,Male,Age 50 to 54,,,,,,,,Maryland,0.0,1.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.7,74.84,Yes,No,Yes,No,,No,No +,30.34,,,,,,,Male,Age 40 to 44,,,,,,,,Maryland,2.0,0.0,Within past year (anytime less than 12 months ago),Yes,4.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes some days,Use them every day,Yes,"White only, Non-Hispanic",1.85,104.33,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No +,24.39,,,,,,,Male,Age 70 to 74,,,,,,,,Virginia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,77.11,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,,,,,,,,Female,Age 50 to 54,,,,,,,,Connecticut,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.57,,No,,Yes,Yes,,No,Yes +,16.27,,,,,,,Male,Age 50 to 54,,,,,,,,Georgia,30.0,20.0,Within past year (anytime less than 12 months ago),No,5.0,1 to 5,No,No,Yes,Yes,No,Yes,Yes,No,No,No,No,Yes,Yes,Yes,Yes,Yes,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.83,54.43,No,Yes,No,Yes,"Yes, received tetanus shot but not sure what type",No,No +,34.97,,,,,,,Male,Age 18 to 24,,,,,,,,Indiana,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.73,104.33,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,No +,26.52,,,,,,,Male,Age 65 to 69,,,,,,,,Indiana,24.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,Yes,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.57,65.77,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No +,23.24,,,,,,,Female,Age 75 to 79,,,,,,,,New York,30.0,0.0,Within past year (anytime less than 12 months ago),No,5.0,None of them,No,No,No,No,No,Yes,No,No,No,Yes,No,No,No,Yes,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.68,65.32,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +No,29.84,Yes,No,No,0.0,0.0,Yes,Male,80 or older,White,Yes,No,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,34.21,Yes,No,No,0.0,0.0,No,Female,40-44,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,23.42,,,,,,,Female,Age 45 to 49,,,,,,,,Massachusetts,0.0,2.0,Within past 2 years (1 year but less than 2 years ago),Yes,9.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,69.85,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,23.78,,,,,,,Female,Age 25 to 29,,,,,,,,Missouri,10.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,"Yes, but only during pregnancy (female)",No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.57,58.97,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,24.89,,,,,,,Female,Age 70 to 74,,,,,,,,Maine,1.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,65.77,Yes,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,28.76,,,,,,,Male,Age 30 to 34,,,,,,,,Alaska,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,98.88,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,22.89,No,No,No,0.0,0.0,No,Male,45-49,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,35.95,,,,,,,Male,Age 35 to 39,,,,,,,,California,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.88,127.01,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,28.48,,,,,,,Male,Age 18 to 24,,,,,,,,Florida,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.83,95.25,Yes,Yes,No,No,"Yes, received tetanus shot, but not Tdap",Yes,No +,31.89,,,,,,,Male,Age 60 to 64,,,,,,,,Texas,4.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,None of them,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.6,81.65,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,44.93,,,,,,,Female,Age 75 to 79,,,,,,,,Michigan,2.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,1 to 5,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,122.47,No,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,48.47,,,,,,,Female,Age 50 to 54,,,,,,,,Indiana,30.0,10.0,Within past year (anytime less than 12 months ago),No,10.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,120.2,No,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,19.97,,,,,,,Female,Age 40 to 44,,,,,,,,New Hampshire,10.0,3.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,54.43,Yes,No,Yes,No,"Yes, received tetanus shot, but not Tdap",No,Tested positive using home test without a health professional +,36.26,,,,,,,Male,Age 65 to 69,,,,,,,,Minnesota,4.0,3.0,Within past year (anytime less than 12 months ago),No,7.0,1 to 5,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,117.93,Yes,No,No,Yes,"Yes, received tetanus shot, but not Tdap",No,No +No,50.0,Yes,No,No,30.0,0.0,Yes,Male,50-54,White,No,No,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,24.27,No,No,No,3.0,0.0,No,Male,80 or older,White,No,Yes,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,29.84,,,,,,,Male,Age 25 to 29,,,,,,,,Maine,4.0,3.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,99.79,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,Yes +No,25.85,No,No,No,0.0,5.0,No,Female,18-24,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,23.75,,,,,,,Male,Age 30 to 34,,,,,,,,Colorado,30.0,30.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,1 to 5,No,No,No,Yes,No,No,Yes,No,No,"No, pre-diabetes or borderline diabetes",No,No,,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.91,86.18,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,20.34,Yes,No,Yes,0.0,0.0,No,Male,60-64,White,No,Yes,6.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,36.05,No,No,No,3.0,0.0,No,Female,80 or older,White,No,Yes,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,25.82,,,,,,,Male,Age 30 to 34,,,,,,,,Texas,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,5.0,None of them,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.68,72.57,Yes,No,No,No,"Yes, received tetanus shot, but not Tdap",Yes,Yes +,30.85,,,,,,,Male,Age 65 to 69,,,,,,,,New Jersey,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,Yes,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,97.52,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,19.05,,,,,,,Female,Age 75 to 79,,,,,,,,Connecticut,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,50.35,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,25.34,,,,,,,Female,Age 70 to 74,,,,,,,,South Carolina,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,Yes,No,Yes,No,No,Yes,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,71.21,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +Yes,22.96,No,No,No,0.0,0.0,No,Male,75-79,White,No,Yes,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,34.21,No,No,No,2.0,5.0,No,Male,60-64,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,27.98,,,,,,,Male,Age 55 to 59,,,,,,,,Connecticut,30.0,30.0,Within past year (anytime less than 12 months ago),Yes,5.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,88.45,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +Yes,30.67,Yes,No,Yes,15.0,30.0,Yes,Female,65-69,White,No,No,6.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,25.63,No,No,No,2.0,20.0,No,Male,60-64,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,30.54,No,No,No,0.0,0.0,No,Male,75-79,White,Yes,Yes,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,22.82,,,,,,,Female,Age 65 to 69,,,,,,,,Mississippi,1.0,0.0,Within past year (anytime less than 12 months ago),Yes,,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.85,78.47,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,,,,,,,,Male,Age 40 to 44,,,,,,,,Utah,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,All,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,,,,,,,,,,, +,33.66,,,,,,,Female,Age 60 to 64,,,,,,,,Maryland,25.0,15.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,Yes,No,No,No,No,No,"No, pre-diabetes or borderline diabetes",No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,86.18,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,No +,29.05,,,,,,,Female,Age 25 to 29,,,,,,,,Michigan,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,10.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,81.65,No,,No,,,, +,19.84,,,,,,,Female,Age 65 to 69,,,,,,,,Wisconsin,0.0,0.0,Within past year (anytime less than 12 months ago),No,4.0,None of them,No,No,No,No,No,No,No,No,Yes,"Yes, but only during pregnancy (female)",No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.55,47.63,No,No,Yes,No,"Yes, received Tdap",No,No +,28.89,,,,,,,Female,Age 60 to 64,,,,,,,,Washington,0.0,5.0,Within past 2 years (1 year but less than 2 years ago),Yes,5.0,1 to 5,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),,"White only, Non-Hispanic",1.73,86.18,No,Yes,No,No,,No,No +No,36.96,Yes,No,No,20.0,15.0,Yes,Female,45-49,White,Yes,Yes,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,34.87,,,,,,,Male,Age 65 to 69,,,,,,,,Vermont,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,1 to 5,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.8,113.4,Yes,No,No,Yes,"Yes, received tetanus shot but not sure what type",No,No +No,25.73,Yes,No,No,7.0,14.0,No,Male,35-39,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Yes,37.51,Yes,No,No,30.0,0.0,Yes,Female,55-59,American Indian/Alaskan Native,Yes,No,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,27.62,,,,,,,Male,Age 65 to 69,,,,,,,,Oklahoma,2.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,Yes,No,No,No,No,No,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,89.81,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,41.09,,,,,,,Male,Age 65 to 69,,,,,,,,Minnesota,20.0,5.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,Yes,No,Yes,Yes,Yes,Yes,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.88,145.15,No,Yes,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +No,35.31,Yes,No,No,0.0,0.0,No,Male,45-49,White,No,Yes,6.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,36.61,No,No,No,0.0,0.0,No,Female,50-54,Black,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,22.31,,,,,,,Female,Age 18 to 24,,,,,,,,Virginia,14.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Use them some days,No,Hispanic,1.63,58.97,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,No +,24.03,,,,,,,Female,Age 40 to 44,,,,,,,,Vermont,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"Other race only, Non-Hispanic",1.63,63.5,No,,Yes,No,"Yes, received tetanus shot but not sure what type",No,Tested positive using home test without a health professional +,19.77,,,,,,,Female,Age 40 to 44,,,,,,,,Hawaii,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),No,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.73,58.97,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,27.44,,,,,,,Female,Age 80 or older,,,,,,,,Minnesota,0.0,30.0,Within past year (anytime less than 12 months ago),No,7.0,1 to 5,No,Yes,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.57,68.04,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,22.46,,,,,,,Female,Age 30 to 34,,,,,,,,Nebraska,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Not at all (right now),No,"White only, Non-Hispanic",1.65,61.23,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No +No,26.57,No,No,No,0.0,0.0,No,Female,45-49,Hispanic,No,No,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,24.21,,,,,,,Female,Age 55 to 59,,,,,,,,Vermont,10.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,68.04,No,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +No,24.03,No,No,No,0.0,0.0,No,Female,40-44,Hispanic,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,29.95,,,,,,,Male,Age 70 to 74,,,,,,,,Virgin Islands,,20.0,Within past year (anytime less than 12 months ago),Yes,4.0,1 to 5,No,No,No,No,No,No,Yes,No,No,No,Yes,No,Yes,Yes,Yes,Yes,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.65,81.65,No,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,,,,,,,,Female,Age 25 to 29,,,,,,,,Washington,0.0,20.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,,Yes,Yes,Yes,No,"Yes, received Tdap",No,No +,23.54,,,,,,,Male,Age 18 to 24,,,,,,,,Maryland,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,Hispanic,1.7,68.04,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,27.5,Yes,Yes,No,0.0,0.0,No,Male,18-24,White,No,Yes,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,27.44,,,,,,,Female,Age 60 to 64,,,,,,,,New York,0.0,10.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,Yes,No,No,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,68.04,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +,51.49,,,,,,,Female,Age 35 to 39,,,,,,,,West Virginia,0.0,3.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Yes,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,136.08,No,Yes,No,No,"Yes, received Tdap",No,No +,36.32,,,,,,,Female,Age 70 to 74,,,,,,,,Wisconsin,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,Yes,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,102.06,Yes,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No +,25.39,,,,,,,Female,Age 60 to 64,,,,,,,,Ohio,20.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.52,58.97,No,,No,Yes,"Yes, received Tdap",No,Yes +No,25.02,Yes,No,No,0.0,0.0,No,Male,35-39,White,No,No,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,26.43,,,,,,,Female,Age 70 to 74,,,,,,,,Puerto Rico,0.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Yes,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.63,69.85,No,No,No,Yes,,No,Yes +,27.12,,,,,,,Male,Age 75 to 79,,,,,,,,Washington,2.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,"6 or more, but not all",Yes,No,No,No,No,No,No,No,Yes,Yes,No,No,No,Yes,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.83,90.72,No,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,,,,,,,,Female,Age 65 to 69,,,,,,,,South Dakota,3.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,,1.65,,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,24.8,,,,,,,Female,Age 40 to 44,,,,,,,,Washington,2.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,1 to 5,No,No,No,No,No,No,Yes,Yes,Yes,No,No,No,Yes,No,No,Yes,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.6,63.5,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No +No,21.46,No,No,No,0.0,3.0,No,Female,18-24,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,29.53,,,,,,,Female,Age 50 to 54,,,,,,,,North Dakota,14.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Other race only, Non-Hispanic",1.75,90.72,No,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +No,20.22,No,No,No,0.0,15.0,No,Male,25-29,White,No,Yes,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,28.06,,,,,,,Male,Age 60 to 64,,,,,,,,Louisiana,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,Yes,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.75,86.18,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,30.0,,,,,,,Male,Age 80 or older,,,,,,,,Minnesota,25.0,0.0,,No,12.0,None of them,,No,No,No,Yes,No,No,No,Yes,Yes,Yes,No,Yes,Yes,Yes,Yes,Never smoked,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.91,108.86,No,No,Yes,,"Yes, received Tdap",No,Yes +,31.87,,,,,,,Male,Age 50 to 54,,,,,,,,Minnesota,0.0,1.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,106.59,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No +No,28.17,Yes,No,No,0.0,0.0,No,Male,80 or older,White,No,Yes,9.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,37.31,No,No,No,0.0,0.0,No,Male,55-59,White,Yes,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,23.03,,,,,,,Female,Age 70 to 74,,,,,,,,Ohio,0.0,10.0,Within past year (anytime less than 12 months ago),Yes,5.0,"6 or more, but not all",No,No,No,No,Yes,No,No,No,Yes,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,58.97,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No, +No,21.93,No,No,No,8.0,1.0,No,Female,60-64,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,15.41,Yes,Yes,Yes,0.0,30.0,No,Male,18-24,Hispanic,Yes,Yes,5.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,52.42,,,,,,,Female,Age 55 to 59,,,,,,,,Puerto Rico,30.0,20.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,1 to 5,No,No,No,No,No,No,Yes,No,Yes,No,No,Yes,Yes,Yes,Yes,Yes,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.65,142.88,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,28.48,No,No,No,0.0,9.0,No,Male,18-24,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,23.49,No,No,No,0.0,0.0,No,Male,45-49,White,No,Yes,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Yes,35.94,No,No,No,0.0,0.0,No,Male,65-69,White,Yes,No,7.0,Yes,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,24.8,No,No,No,4.0,0.0,Yes,Female,75-79,White,No,No,9.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,22.24,No,No,No,0.0,0.0,No,Female,65-69,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,45.73,,,,,,,Female,Age 30 to 34,,,,,,,,California,0.0,3.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.57,113.4,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,35.71,,,,,,,Female,Age 40 to 44,,,,,,,,Washington,7.0,28.0,Within past year (anytime less than 12 months ago),No,5.0,"6 or more, but not all",No,No,No,Yes,No,No,Yes,No,Yes,No,No,Yes,Yes,Yes,No,Yes,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.55,85.73,No,Yes,No,No,"Yes, received Tdap",No,No +,22.67,,,,,,,Female,Age 70 to 74,,,,,,,,Washington,4.0,0.0,Within past year (anytime less than 12 months ago),Yes,12.0,None of them,No,No,No,No,No,Yes,Yes,Yes,Yes,No,No,No,No,Yes,No,No,,,,"White only, Non-Hispanic",1.6,58.06,,,,,,, +,31.62,,,,,,,Female,Age 30 to 34,,,,,,,,South Dakota,0.0,4.0,Within past 2 years (1 year but less than 2 years ago),Yes,6.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.65,86.18,Yes,No,No,No,"Yes, received Tdap",No,Yes +,28.37,,,,,,,Male,Age 25 to 29,,,,,,,,Kentucky,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,97.52,Yes,No,Yes,No,"Yes, received Tdap",No,No +No,21.16,No,No,No,0.0,0.0,No,Female,50-54,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,13.95,,,,,,,Male,Age 35 to 39,,,,,,,,Maine,30.0,30.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,Yes,No,Yes,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",2.13,63.5,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,21.03,Yes,No,Yes,7.0,0.0,No,Female,55-59,White,No,Yes,9.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,40.09,,,,,,,Male,Age 25 to 29,,,,,,,,New York,,12.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Use them some days,Yes,"Other race only, Non-Hispanic",1.7,116.12,Yes,Yes,Yes,,"Yes, received Tdap",No,Yes +,30.67,,,,,,,Female,Age 65 to 69,,,,,,,,Georgia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,10.0,1 to 5,No,No,No,No,No,No,No,No,No,"No, pre-diabetes or borderline diabetes",No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.68,86.18,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,55.17,,,,,,,Female,Age 55 to 59,,,,,,,,Mississippi,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.55,132.45,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,26.63,,,,,,,Male,Age 30 to 34,,,,,,,,Vermont,2.0,2.0,5 or more years ago,Yes,8.0,"6 or more, but not all",No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes some days,Use them some days,No,"White only, Non-Hispanic",1.65,72.57,Yes,Yes,No,No,"Yes, received tetanus shot, but not Tdap",No,No +,33.28,,,,,,,Male,Age 80 or older,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),No,9.0,None of them,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Not at all (right now),No,Hispanic,1.65,90.72,No,Yes,No,Yes,"Yes, received Tdap",No,No +,32.89,,,,,,,Female,Age 65 to 69,,,,,,,,Utah,3.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,"6 or more, but not all",No,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,,,"White only, Non-Hispanic",1.7,95.25,,,,,,, +No,39.68,No,No,No,0.0,0.0,No,Female,55-59,Other,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,33.47,No,No,No,0.0,0.0,No,Male,55-59,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,23.91,,,,,,,Female,Age 65 to 69,,,,,,,,Washington,30.0,15.0,Within past year (anytime less than 12 months ago),No,8.0,All,No,Yes,Yes,Yes,No,Yes,Yes,Yes,Yes,No,No,No,Yes,Yes,Yes,Yes,Former smoker,Use them every day,Yes,"White only, Non-Hispanic",1.6,61.23,No,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,22.81,,,,,,,Male,Age 18 to 24,,,,,,,,Kansas,0.0,28.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.73,68.04,Yes,No,No,Yes,"Yes, received Tdap",No,Yes +No,28.19,No,No,No,3.0,4.0,No,Female,55-59,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,29.29,,,,,,,Female,Age 70 to 74,,,,,,,,Ohio,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,Yes,No,No,No,No,No,No,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.52,68.04,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,17.36,,,,,,,Male,Age 18 to 24,,,,,,,,Illinois,7.0,2.0,Within past 2 years (1 year but less than 2 years ago),Yes,5.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.6,44.45,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +Yes,32.55,Yes,No,Yes,30.0,15.0,Yes,Male,70-74,White,No,No,6.0,No,Yes,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_001/history.csv b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_001/history.csv index c8febb056b1aa5093071ebdfad5055e6c5bc6459..af26f5b09ec987851015c11c77db886554be1182 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_001/history.csv +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_001/history.csv @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:198e20311a862ea52503f9a980b130af427674cf1475dca7aaf35894a0d0a8c8 -size 271614118 +oid sha256:3c9b02bac584fd919d536725a4b130edcd751b6cdb3f6013d7a3032ecc53a629 +size 270592772 diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_001/info.json b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_001/info.json index 53272e3074451c673817a05ef4da5ee438d07863..c876b7e9aa18ec2175d72a1a6b59bf487c77e98c 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_001/info.json +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_001/info.json @@ -1,401 +1,393 @@ -{ - "name": "Indicators of Heart Disease (2022 UPDATE)", - "source": "https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease/data", - "data_intro": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", - "is_splited": false, - "overall_size": 1010949, - "train_size": 0, - "test_size": 0, - "c_classes": 46, - "n_classes": 9, - "cat_feature_intro": { - "HeartDisease": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", - "Smoking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", - "AlcoholDrinking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", - "Stroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", - "DiffWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", - "Sex": "Sex is a biological factor that can influence the risk of heart disease. Men and women may experience different symptoms and risk factors for heart disease.", - "AgeCategory": "Age is a significant risk factor for heart disease. As people age, their risk of developing heart disease increases. It is important to monitor heart health as individuals get older.", - "Race": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", - "Diabetic": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", - "PhysicalActivity": "Regular physical activity is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", - "GenHealth": "General health status can influence the risk of heart disease. It is important to maintain overall health and address any underlying health issues to reduce the risk of heart-related complications.", - "Asthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", - "KidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", - "SkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", - "State": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", - "GeneralHealth": "General health status can influence the risk of heart disease. It is important to maintain overall health and address any underlying health issues to reduce the risk of heart-related complications.", - "LastCheckupTime": "The time since the last medical checkup can be an important factor in heart health. Regular checkups can help identify and address risk factors for heart disease early.", - "PhysicalActivities": "Engaging in regular physical activities is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", - "RemovedTeeth": "The number of removed teeth can be an indicator of overall health, including heart health. Poor oral health has been linked to an increased risk of heart disease.", - "HadHeartAttack": "As described above, the original dataset of nearly 300 variables was reduced to 40 variables. In addition to classical EDA, this dataset can be used to apply a number of machine learning methods, especially classifier models (logistic regression, SVM, random forest, etc.). You should treat the variable \"HadHeartAttack\" as binary (\"Yes\" - respondent had heart disease; \"No\" - respondent did not have heart disease). Note, however, that the classes are unbalanced, so the classic approach of applying a model is not advisable. Fixing the weights/undersampling should yield much better results. Based on the data set, I built a logistic regression model and embedded it in an application that might inspire you: https://share.streamlit.io/kamilpytlak/heart-condition-checker/main/app.py. Can you indicate which variables have a significant effect on the likelihood of heart disease?", - "HadAngina": "Angina is a type of chest pain caused by reduced blood flow to the heart. It is a symptom of coronary artery disease and can indicate an increased risk of heart disease.", - "HadStroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", - "HadAsthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", - "HadSkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", - "HadCOPD": "Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease that causes obstructed airflow from the lungs. It can increase the risk of heart disease.", - "HadDepressiveDisorder": "Depressive disorders can affect overall health and increase the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", - "HadKidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", - "HadArthritis": "Arthritis is a condition that causes inflammation of the joints. It can affect overall health and increase the risk of heart disease.", - "HadDiabetes": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", - "DeafOrHardOfHearing": "Hearing loss can affect overall health and quality of life. It is important to address and manage hearing issues to maintain overall health and reduce the risk of heart disease.", - "BlindOrVisionDifficulty": "Vision difficulties can affect overall health and quality of life. It is important to address and manage vision issues to maintain overall health and reduce the risk of heart disease.", - "DifficultyConcentrating": "Difficulty concentrating can be a sign of underlying health issues, including heart disease. It is important to address cognitive issues to improve overall health and reduce the risk of heart-related complications.", - "DifficultyWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", - "DifficultyDressingBathing": "Difficulty with daily activities such as dressing and bathing can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", - "DifficultyErrands": "Difficulty running errands can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", - "SmokerStatus": "Smoking is a major risk factor for heart disease. It is important to address and manage smoking habits to reduce the risk of heart-related complications.", - "ECigaretteUsage": "E-cigarette usage can affect heart health. It is important to monitor and manage e-cigarette usage to reduce the risk of heart disease.", - "ChestScan": "Chest scans can help identify underlying health issues, including heart disease. It is important to monitor and address any abnormalities found in chest scans to maintain overall health.", - "RaceEthnicityCategory": "Race and ethnicity can influence the risk of heart disease. It is important to consider these factors when assessing and managing heart health.", - "AlcoholDrinkers": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", - "HIVTesting": "HIV testing is important for overall health. It is important to address and manage HIV status to reduce the risk of heart-related complications.", - "FluVaxLast12": "Receiving a flu vaccine in the last 12 months can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", - "PneumoVaxEver": "Receiving a pneumococcal vaccine can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", - "TetanusLast10Tdap": "Receiving a tetanus vaccine in the last 10 years can help reduce the risk of infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", - "HighRiskLastYear": "Being at high risk for health issues in the last year can affect overall health and increase the risk of heart disease. It is important to address and manage high-risk factors to reduce the risk of heart-related complications.", - "CovidPos": "Testing positive for COVID-19 can affect overall health and increase the risk of heart disease. It is important to monitor and manage COVID-19 symptoms to reduce the risk of heart-related complications." - }, - "num_feature_intro": { - "BMI": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", - "PhysicalHealth": "Physical health is an important factor in overall well-being and can influence the risk of heart disease. It is important to maintain good physical health to reduce the risk of heart-related complications.", - "MentalHealth": "Mental health is an important factor in overall well-being and can influence the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", - "SleepTime": "Sleep time is an important factor in overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", - "PhysicalHealthDays": "The number of days with good physical health can be an indicator of overall well-being. It is important to maintain good physical health to reduce the risk of heart-related complications.", - "MentalHealthDays": "The number of days with good mental health can be an indicator of overall well-being. It is important to address and manage mental health to reduce the risk of heart-related complications.", - "SleepHours": "The number of hours of sleep can affect overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", - "HeightInMeters": "Height can be an indicator of overall health. It is important to monitor and maintain a healthy height-to-weight ratio to reduce the risk of heart disease.", - "WeightInKilograms": "Weight can be an indicator of overall health. It is important to maintain a healthy weight to reduce the risk of heart disease." - }, - "evaluation_metric": "The evaluation metric for this dataset can include accuracy, precision, recall, F1-score, and AUC-ROC. These metrics can help assess the performance of machine learning models in predicting heart disease.", - "task_type": "classification", - "target": "GeneralHealth", - "num_feature_value": { - "BMI": [ - 12.02, - 99.64 - ], - "HeightInMeters": [ - 0.91, - 2.41 - ], - "MentalHealth": [ - 0.0, - 30.0 - ], - "MentalHealthDays": [ - 0.0, - 30.0 - ], - "PhysicalHealth": [ - 0.0, - 30.0 - ], - "PhysicalHealthDays": [ - 0.0, - 30.0 - ], - "SleepHours": [ - 1.0, - 24.0 - ], - "SleepTime": [ - 1.0, - 24.0 - ], - "WeightInKilograms": [ - 22.68, - 292.57 - ] - }, - "cat_feature_value": { - "AgeCategory": [ - "18-24", - "25-29", - "30-34", - "35-39", - "40-44", - "45-49", - "50-54", - "55-59", - "60-64", - "65-69", - "70-74", - "75-79", - "80 or older", - "Age 18 to 24", - "Age 25 to 29", - "Age 30 to 34", - "Age 35 to 39", - "Age 40 to 44", - "Age 45 to 49", - "Age 50 to 54", - "Age 55 to 59", - "Age 60 to 64", - "Age 65 to 69", - "Age 70 to 74", - "Age 75 to 79", - "Age 80 or older" - ], - "AlcoholDrinkers": [ - "No", - "Yes" - ], - "AlcoholDrinking": [ - "No", - "Yes" - ], - "Asthma": [ - "No", - "Yes" - ], - "BlindOrVisionDifficulty": [ - "No", - "Yes" - ], - "ChestScan": [ - "No", - "Yes" - ], - "CovidPos": [ - "No", - "Tested positive using home test without a health professional", - "Yes" - ], - "DeafOrHardOfHearing": [ - "No", - "Yes" - ], - "Diabetic": [ - "No", - "No, borderline diabetes", - "Yes", - "Yes (during pregnancy)" - ], - "DiffWalking": [ - "No", - "Yes" - ], - "DifficultyConcentrating": [ - "No", - "Yes" - ], - "DifficultyDressingBathing": [ - "No", - "Yes" - ], - "DifficultyErrands": [ - "No", - "Yes" - ], - "DifficultyWalking": [ - "No", - "Yes" - ], - "ECigaretteUsage": [ - "Never used e-cigarettes in my entire life", - "Not at all (right now)", - "Use them every day", - "Use them some days" - ], - "FluVaxLast12": [ - "No", - "Yes" - ], - "GenHealth": [ - "Excellent", - "Fair", - "Good", - "Poor", - "Very good" - ], - "GeneralHealth": [ - "Excellent", - "Fair", - "Good", - "Poor", - "Very good" - ], - "HIVTesting": [ - "No", - "Yes" - ], - "HadAngina": [ - "No", - "Yes" - ], - "HadArthritis": [ - "No", - "Yes" - ], - "HadAsthma": [ - "No", - "Yes" - ], - "HadCOPD": [ - "No", - "Yes" - ], - "HadDepressiveDisorder": [ - "No", - "Yes" - ], - "HadDiabetes": [ - "No", - "No, pre-diabetes or borderline diabetes", - "Yes", - "Yes, but only during pregnancy (female)" - ], - "HadHeartAttack": [ - "No", - "Yes" - ], - "HadKidneyDisease": [ - "No", - "Yes" - ], - "HadSkinCancer": [ - "No", - "Yes" - ], - "HadStroke": [ - "No", - "Yes" - ], - "HeartDisease": [ - "No", - "Yes" - ], - "HighRiskLastYear": [ - "No", - "Yes" - ], - "KidneyDisease": [ - "No", - "Yes" - ], - "LastCheckupTime": [ - "5 or more years ago", - "Within past 2 years (1 year but less than 2 years ago)", - "Within past 5 years (2 years but less than 5 years ago)", - "Within past year (anytime less than 12 months ago)" - ], - "PhysicalActivities": [ - "No", - "Yes" - ], - "PhysicalActivity": [ - "No", - "Yes" - ], - "PneumoVaxEver": [ - "No", - "Yes" - ], - "Race": [ - "American Indian/Alaskan Native", - "Asian", - "Black", - "Hispanic", - "Other", - "White" - ], - "RaceEthnicityCategory": [ - "Black only, Non-Hispanic", - "Hispanic", - "Multiracial, Non-Hispanic", - "Other race only, Non-Hispanic", - "White only, Non-Hispanic" - ], - "RemovedTeeth": [ - "1 to 5", - "6 or more, but not all", - "All", - "None of them" - ], - "Sex": [ - "Female", - "Male" - ], - "SkinCancer": [ - "No", - "Yes" - ], - "SmokerStatus": [ - "Current smoker - now smokes every day", - "Current smoker - now smokes some days", - "Former smoker", - "Never smoked" - ], - "Smoking": [ - "No", - "Yes" - ], - "State": [ - "Alabama", - "Alaska", - "Arizona", - "Arkansas", - "California", - "Colorado", - "Connecticut", - "Delaware", - "District of Columbia", - "Florida", - "Georgia", - "Guam", - "Hawaii", - "Idaho", - "Illinois", - "Indiana", - "Iowa", - "Kansas", - "Kentucky", - "Louisiana", - "Maine", - "Maryland", - "Massachusetts", - "Michigan", - "Minnesota", - "Mississippi", - "Missouri", - "Montana", - "Nebraska", - "Nevada", - "New Hampshire", - "New Jersey", - "New Mexico", - "New York", - "North Carolina", - "North Dakota", - "Ohio", - "Oklahoma", - "Oregon", - "Pennsylvania", - "Puerto Rico", - "Rhode Island", - "South Carolina", - "South Dakota", - "Tennessee", - "Texas", - "Utah", - "Vermont", - "Virgin Islands", - "Virginia", - "Washington", - "West Virginia", - "Wisconsin", - "Wyoming" - ], - "Stroke": [ - "No", - "Yes" - ], - "TetanusLast10Tdap": [ - "No, did not receive any tetanus shot in the past 10 years", - "Yes, received Tdap", - "Yes, received tetanus shot but not sure what type", - "Yes, received tetanus shot, but not Tdap" - ] - } -} \ No newline at end of file +{ + "name": "Indicators of Heart Disease (2022 UPDATE)", + "source": "https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease/data", + "data_intro": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", + "is_splited": false, + "overall_size": 1010949, + "train_size": 0, + "test_size": 0, + "c_classes": 45, + "n_classes": 9, + "cat_feature_intro": { + "HeartDisease": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", + "Smoking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", + "AlcoholDrinking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", + "Stroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", + "DiffWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", + "Sex": "Sex is a biological factor that can influence the risk of heart disease. Men and women may experience different symptoms and risk factors for heart disease.", + "AgeCategory": "Age is a significant risk factor for heart disease. As people age, their risk of developing heart disease increases. It is important to monitor heart health as individuals get older.", + "Race": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", + "Diabetic": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", + "PhysicalActivity": "Regular physical activity is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", + "Asthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", + "KidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", + "SkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", + "State": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", + "GeneralHealth": "Self-reported general health from BRFSS GENHLTH: Excellent, Very good, Good, Fair, or Poor.", + "LastCheckupTime": "The time since the last medical checkup can be an important factor in heart health. Regular checkups can help identify and address risk factors for heart disease early.", + "PhysicalActivities": "Engaging in regular physical activities is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", + "RemovedTeeth": "The number of removed teeth can be an indicator of overall health, including heart health. Poor oral health has been linked to an increased risk of heart disease.", + "HadHeartAttack": "As described above, the original dataset of nearly 300 variables was reduced to 40 variables. In addition to classical EDA, this dataset can be used to apply a number of machine learning methods, especially classifier models (logistic regression, SVM, random forest, etc.). You should treat the variable \"HadHeartAttack\" as binary (\"Yes\" - respondent had heart disease; \"No\" - respondent did not have heart disease). Note, however, that the classes are unbalanced, so the classic approach of applying a model is not advisable. Fixing the weights/undersampling should yield much better results. Based on the data set, I built a logistic regression model and embedded it in an application that might inspire you: https://share.streamlit.io/kamilpytlak/heart-condition-checker/main/app.py. Can you indicate which variables have a significant effect on the likelihood of heart disease?", + "HadAngina": "Angina is a type of chest pain caused by reduced blood flow to the heart. It is a symptom of coronary artery disease and can indicate an increased risk of heart disease.", + "HadStroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", + "HadAsthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", + "HadSkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", + "HadCOPD": "Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease that causes obstructed airflow from the lungs. It can increase the risk of heart disease.", + "HadDepressiveDisorder": "Depressive disorders can affect overall health and increase the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", + "HadKidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", + "HadArthritis": "Arthritis is a condition that causes inflammation of the joints. It can affect overall health and increase the risk of heart disease.", + "HadDiabetes": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", + "DeafOrHardOfHearing": "Hearing loss can affect overall health and quality of life. It is important to address and manage hearing issues to maintain overall health and reduce the risk of heart disease.", + "BlindOrVisionDifficulty": "Vision difficulties can affect overall health and quality of life. It is important to address and manage vision issues to maintain overall health and reduce the risk of heart disease.", + "DifficultyConcentrating": "Difficulty concentrating can be a sign of underlying health issues, including heart disease. It is important to address cognitive issues to improve overall health and reduce the risk of heart-related complications.", + "DifficultyWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", + "DifficultyDressingBathing": "Difficulty with daily activities such as dressing and bathing can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", + "DifficultyErrands": "Difficulty running errands can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", + "SmokerStatus": "Smoking is a major risk factor for heart disease. It is important to address and manage smoking habits to reduce the risk of heart-related complications.", + "ECigaretteUsage": "E-cigarette usage can affect heart health. It is important to monitor and manage e-cigarette usage to reduce the risk of heart disease.", + "ChestScan": "Chest scans can help identify underlying health issues, including heart disease. It is important to monitor and address any abnormalities found in chest scans to maintain overall health.", + "RaceEthnicityCategory": "Race and ethnicity can influence the risk of heart disease. It is important to consider these factors when assessing and managing heart health.", + "AlcoholDrinkers": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", + "HIVTesting": "Whether the respondent has ever been tested for HIV (Yes/No), not the result of that test.", + "FluVaxLast12": "Receiving a flu vaccine in the last 12 months can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", + "PneumoVaxEver": "Receiving a pneumococcal vaccine can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", + "TetanusLast10Tdap": "Receiving a tetanus vaccine in the last 10 years can help reduce the risk of infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", + "HighRiskLastYear": "Being at high risk for health issues in the last year can affect overall health and increase the risk of heart disease. It is important to address and manage high-risk factors to reduce the risk of heart-related complications.", + "CovidPos": "Testing positive for COVID-19 can affect overall health and increase the risk of heart disease. It is important to monitor and manage COVID-19 symptoms to reduce the risk of heart-related complications." + }, + "num_feature_intro": { + "BMI": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", + "PhysicalHealth": "Physical health is an important factor in overall well-being and can influence the risk of heart disease. It is important to maintain good physical health to reduce the risk of heart-related complications.", + "MentalHealth": "Mental health is an important factor in overall well-being and can influence the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", + "SleepTime": "Sleep time is an important factor in overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", + "PhysicalHealthDays": "The number of days with good physical health can be an indicator of overall well-being. It is important to maintain good physical health to reduce the risk of heart-related complications.", + "MentalHealthDays": "The number of days with good mental health can be an indicator of overall well-being. It is important to address and manage mental health to reduce the risk of heart-related complications.", + "SleepHours": "The number of hours of sleep can affect overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", + "HeightInMeters": "Height can be an indicator of overall health. It is important to monitor and maintain a healthy height-to-weight ratio to reduce the risk of heart disease.", + "WeightInKilograms": "Weight can be an indicator of overall health. It is important to maintain a healthy weight to reduce the risk of heart disease." + }, + "evaluation_metric": "The evaluation metric for this dataset can include accuracy, precision, recall, F1-score, and AUC-ROC. These metrics can help assess the performance of machine learning models in predicting heart disease.", + "task_type": "classification", + "target": "GeneralHealth", + "num_feature_value": { + "BMI": [ + 12.02, + 99.64 + ], + "HeightInMeters": [ + 0.91, + 2.41 + ], + "MentalHealth": [ + 0.0, + 30.0 + ], + "MentalHealthDays": [ + 0.0, + 30.0 + ], + "PhysicalHealth": [ + 0.0, + 30.0 + ], + "PhysicalHealthDays": [ + 0.0, + 30.0 + ], + "SleepHours": [ + 1.0, + 24.0 + ], + "SleepTime": [ + 1.0, + 24.0 + ], + "WeightInKilograms": [ + 22.68, + 292.57 + ] + }, + "cat_feature_value": { + "AgeCategory": [ + "18-24", + "25-29", + "30-34", + "35-39", + "40-44", + "45-49", + "50-54", + "55-59", + "60-64", + "65-69", + "70-74", + "75-79", + "80 or older", + "Age 18 to 24", + "Age 25 to 29", + "Age 30 to 34", + "Age 35 to 39", + "Age 40 to 44", + "Age 45 to 49", + "Age 50 to 54", + "Age 55 to 59", + "Age 60 to 64", + "Age 65 to 69", + "Age 70 to 74", + "Age 75 to 79", + "Age 80 or older" + ], + "AlcoholDrinkers": [ + "No", + "Yes" + ], + "AlcoholDrinking": [ + "No", + "Yes" + ], + "Asthma": [ + "No", + "Yes" + ], + "BlindOrVisionDifficulty": [ + "No", + "Yes" + ], + "ChestScan": [ + "No", + "Yes" + ], + "CovidPos": [ + "No", + "Tested positive using home test without a health professional", + "Yes" + ], + "DeafOrHardOfHearing": [ + "No", + "Yes" + ], + "Diabetic": [ + "No", + "No, borderline diabetes", + "Yes", + "Yes (during pregnancy)" + ], + "DiffWalking": [ + "No", + "Yes" + ], + "DifficultyConcentrating": [ + "No", + "Yes" + ], + "DifficultyDressingBathing": [ + "No", + "Yes" + ], + "DifficultyErrands": [ + "No", + "Yes" + ], + "DifficultyWalking": [ + "No", + "Yes" + ], + "ECigaretteUsage": [ + "Never used e-cigarettes in my entire life", + "Not at all (right now)", + "Use them every day", + "Use them some days" + ], + "FluVaxLast12": [ + "No", + "Yes" + ], + "GeneralHealth": [ + "Excellent", + "Very good", + "Good", + "Fair", + "Poor" + ], + "HIVTesting": [ + "No", + "Yes" + ], + "HadAngina": [ + "No", + "Yes" + ], + "HadArthritis": [ + "No", + "Yes" + ], + "HadAsthma": [ + "No", + "Yes" + ], + "HadCOPD": [ + "No", + "Yes" + ], + "HadDepressiveDisorder": [ + "No", + "Yes" + ], + "HadDiabetes": [ + "No", + "No, pre-diabetes or borderline diabetes", + "Yes", + "Yes, but only during pregnancy (female)" + ], + "HadHeartAttack": [ + "No", + "Yes" + ], + "HadKidneyDisease": [ + "No", + "Yes" + ], + "HadSkinCancer": [ + "No", + "Yes" + ], + "HadStroke": [ + "No", + "Yes" + ], + "HeartDisease": [ + "No", + "Yes" + ], + "HighRiskLastYear": [ + "No", + "Yes" + ], + "KidneyDisease": [ + "No", + "Yes" + ], + "LastCheckupTime": [ + "5 or more years ago", + "Within past 2 years (1 year but less than 2 years ago)", + "Within past 5 years (2 years but less than 5 years ago)", + "Within past year (anytime less than 12 months ago)" + ], + "PhysicalActivities": [ + "No", + "Yes" + ], + "PhysicalActivity": [ + "No", + "Yes" + ], + "PneumoVaxEver": [ + "No", + "Yes" + ], + "Race": [ + "American Indian/Alaskan Native", + "Asian", + "Black", + "Hispanic", + "Other", + "White" + ], + "RaceEthnicityCategory": [ + "Black only, Non-Hispanic", + "Hispanic", + "Multiracial, Non-Hispanic", + "Other race only, Non-Hispanic", + "White only, Non-Hispanic" + ], + "RemovedTeeth": [ + "1 to 5", + "6 or more, but not all", + "All", + "None of them" + ], + "Sex": [ + "Female", + "Male" + ], + "SkinCancer": [ + "No", + "Yes" + ], + "SmokerStatus": [ + "Current smoker - now smokes every day", + "Current smoker - now smokes some days", + "Former smoker", + "Never smoked" + ], + "Smoking": [ + "No", + "Yes" + ], + "State": [ + "Alabama", + "Alaska", + "Arizona", + "Arkansas", + "California", + "Colorado", + "Connecticut", + "Delaware", + "District of Columbia", + "Florida", + "Georgia", + "Guam", + "Hawaii", + "Idaho", + "Illinois", + "Indiana", + "Iowa", + "Kansas", + "Kentucky", + "Louisiana", + "Maine", + "Maryland", + "Massachusetts", + "Michigan", + "Minnesota", + "Mississippi", + "Missouri", + "Montana", + "Nebraska", + "Nevada", + "New Hampshire", + "New Jersey", + "New Mexico", + "New York", + "North Carolina", + "North Dakota", + "Ohio", + "Oklahoma", + "Oregon", + "Pennsylvania", + "Puerto Rico", + "Rhode Island", + "South Carolina", + "South Dakota", + "Tennessee", + "Texas", + "Utah", + "Vermont", + "Virgin Islands", + "Virginia", + "Washington", + "West Virginia", + "Wisconsin", + "Wyoming" + ], + "Stroke": [ + "No", + "Yes" + ], + "TetanusLast10Tdap": [ + "No, did not receive any tetanus shot in the past 10 years", + "Yes, received Tdap", + "Yes, received tetanus shot but not sure what type", + "Yes, received tetanus shot, but not Tdap" + ] + } +} diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_001/test_current.csv b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_001/test_current.csv index ef84ad92b8d786aaad84a303ef473b015f78eeee..d215c826ab056c31041a8b499da13b244527959a 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_001/test_current.csv +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_001/test_current.csv @@ -1,16 +1,20 @@ -HeartDisease,BMI,Smoking,AlcoholDrinking,Stroke,PhysicalHealth,MentalHealth,DiffWalking,Sex,AgeCategory,Race,Diabetic,PhysicalActivity,GenHealth,SleepTime,Asthma,KidneyDisease,SkinCancer,State,GeneralHealth,PhysicalHealthDays,MentalHealthDays,LastCheckupTime,PhysicalActivities,SleepHours,RemovedTeeth,HadHeartAttack,HadAngina,HadStroke,HadAsthma,HadSkinCancer,HadCOPD,HadDepressiveDisorder,HadKidneyDisease,HadArthritis,HadDiabetes,DeafOrHardOfHearing,BlindOrVisionDifficulty,DifficultyConcentrating,DifficultyWalking,DifficultyDressingBathing,DifficultyErrands,SmokerStatus,ECigaretteUsage,ChestScan,RaceEthnicityCategory,HeightInMeters,WeightInKilograms,AlcoholDrinkers,HIVTesting,FluVaxLast12,PneumoVaxEver,TetanusLast10Tdap,HighRiskLastYear,CovidPos -,39.86,,,,,,,Female,Age 60 to 64,,,,,,,,,Tennessee,Poor,30.0,30.0,Within past 2 years (1 year but less than 2 years ago),No,8.0,1 to 5,Yes,Yes,No,No,No,Yes,Yes,No,Yes,Yes,No,No,Yes,Yes,No,Yes,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,102.06,No,Yes,No,Yes,"Yes, received tetanus shot but not sure what type",No,Yes -,21.14,,,,,,,Female,Age 80 or older,,,,,,,,,Alabama,Poor,,,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,Yes,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.7,61.23,No,,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,29.5,,,,,,,Male,Age 70 to 74,,,,,,,,,West Virginia,Poor,5.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,All,Yes,Yes,No,No,No,No,No,No,No,Yes,No,No,No,Yes,Yes,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,88.0,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,40.42,,,,,,,Female,Age 60 to 64,,,,,,,,,Montana,Poor,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,Yes,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,Yes,"Other race only, Non-Hispanic",1.57,100.24,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,16.27,,,,,,,Male,Age 50 to 54,,,,,,,,,Georgia,Poor,30.0,20.0,Within past year (anytime less than 12 months ago),No,5.0,1 to 5,No,No,Yes,Yes,No,Yes,Yes,No,No,No,No,Yes,Yes,Yes,Yes,Yes,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.83,54.43,No,Yes,No,Yes,"Yes, received tetanus shot but not sure what type",No,No -,48.47,,,,,,,Female,Age 50 to 54,,,,,,,,,Indiana,Poor,30.0,10.0,Within past year (anytime less than 12 months ago),No,10.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,120.2,No,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,23.75,,,,,,,Male,Age 30 to 34,,,,,,,,,Colorado,Poor,30.0,30.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,1 to 5,No,No,No,Yes,No,No,Yes,No,No,"No, pre-diabetes or borderline diabetes",No,No,,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.91,86.18,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,27.98,,,,,,,Male,Age 55 to 59,,,,,,,,,Connecticut,Poor,30.0,30.0,Within past year (anytime less than 12 months ago),Yes,5.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,88.45,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -,29.95,,,,,,,Male,Age 70 to 74,,,,,,,,,Virgin Islands,Poor,,20.0,Within past year (anytime less than 12 months ago),Yes,4.0,1 to 5,No,No,No,No,No,No,Yes,No,No,No,Yes,No,Yes,Yes,Yes,Yes,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.65,81.65,No,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,27.12,,,,,,,Male,Age 75 to 79,,,,,,,,,Washington,Poor,2.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,"6 or more, but not all",Yes,No,No,No,No,No,No,No,Yes,Yes,No,No,No,Yes,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.83,90.72,No,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,30.0,,,,,,,Male,Age 80 or older,,,,,,,,,Minnesota,Poor,25.0,0.0,,No,12.0,None of them,,No,No,No,Yes,No,No,No,Yes,Yes,Yes,No,Yes,Yes,Yes,Yes,Never smoked,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.91,108.86,No,No,Yes,,"Yes, received Tdap",No,Yes -,52.42,,,,,,,Female,Age 55 to 59,,,,,,,,,Puerto Rico,Poor,30.0,20.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,1 to 5,No,No,No,No,No,No,Yes,No,Yes,No,No,Yes,Yes,Yes,Yes,Yes,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.65,142.88,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,22.67,,,,,,,Female,Age 70 to 74,,,,,,,,,Washington,Poor,4.0,0.0,Within past year (anytime less than 12 months ago),Yes,12.0,None of them,No,No,No,No,No,Yes,Yes,Yes,Yes,No,No,No,No,Yes,No,No,,,,"White only, Non-Hispanic",1.6,58.06,,,,,,, -,13.95,,,,,,,Male,Age 35 to 39,,,,,,,,,Maine,Poor,30.0,30.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,Yes,No,Yes,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",2.13,63.5,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,23.91,,,,,,,Female,Age 65 to 69,,,,,,,,,Washington,Poor,30.0,15.0,Within past year (anytime less than 12 months ago),No,8.0,All,No,Yes,Yes,Yes,No,Yes,Yes,Yes,Yes,No,No,No,Yes,Yes,Yes,Yes,Former smoker,Use them every day,Yes,"White only, Non-Hispanic",1.6,61.23,No,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +HeartDisease,BMI,Smoking,AlcoholDrinking,Stroke,PhysicalHealth,MentalHealth,DiffWalking,Sex,AgeCategory,Race,Diabetic,PhysicalActivity,SleepTime,Asthma,KidneyDisease,SkinCancer,State,GeneralHealth,PhysicalHealthDays,MentalHealthDays,LastCheckupTime,PhysicalActivities,SleepHours,RemovedTeeth,HadHeartAttack,HadAngina,HadStroke,HadAsthma,HadSkinCancer,HadCOPD,HadDepressiveDisorder,HadKidneyDisease,HadArthritis,HadDiabetes,DeafOrHardOfHearing,BlindOrVisionDifficulty,DifficultyConcentrating,DifficultyWalking,DifficultyDressingBathing,DifficultyErrands,SmokerStatus,ECigaretteUsage,ChestScan,RaceEthnicityCategory,HeightInMeters,WeightInKilograms,AlcoholDrinkers,HIVTesting,FluVaxLast12,PneumoVaxEver,TetanusLast10Tdap,HighRiskLastYear,CovidPos +,39.86,,,,,,,Female,Age 60 to 64,,,,,,,,Tennessee,Poor,30.0,30.0,Within past 2 years (1 year but less than 2 years ago),No,8.0,1 to 5,Yes,Yes,No,No,No,Yes,Yes,No,Yes,Yes,No,No,Yes,Yes,No,Yes,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,102.06,No,Yes,No,Yes,"Yes, received tetanus shot but not sure what type",No,Yes +,21.14,,,,,,,Female,Age 80 or older,,,,,,,,Alabama,Poor,,,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,Yes,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.7,61.23,No,,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +Yes,29.23,No,No,Yes,20.0,20.0,No,Male,75-79,American Indian/Alaskan Native,No,Yes,8.0,No,Yes,No,,Poor,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,29.5,,,,,,,Male,Age 70 to 74,,,,,,,,West Virginia,Poor,5.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,All,Yes,Yes,No,No,No,No,No,No,No,Yes,No,No,No,Yes,Yes,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,88.0,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,40.42,,,,,,,Female,Age 60 to 64,,,,,,,,Montana,Poor,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,Yes,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,Yes,"Other race only, Non-Hispanic",1.57,100.24,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,16.27,,,,,,,Male,Age 50 to 54,,,,,,,,Georgia,Poor,30.0,20.0,Within past year (anytime less than 12 months ago),No,5.0,1 to 5,No,No,Yes,Yes,No,Yes,Yes,No,No,No,No,Yes,Yes,Yes,Yes,Yes,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.83,54.43,No,Yes,No,Yes,"Yes, received tetanus shot but not sure what type",No,No +,48.47,,,,,,,Female,Age 50 to 54,,,,,,,,Indiana,Poor,30.0,10.0,Within past year (anytime less than 12 months ago),No,10.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,120.2,No,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,23.75,,,,,,,Male,Age 30 to 34,,,,,,,,Colorado,Poor,30.0,30.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,1 to 5,No,No,No,Yes,No,No,Yes,No,No,"No, pre-diabetes or borderline diabetes",No,No,,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.91,86.18,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,27.98,,,,,,,Male,Age 55 to 59,,,,,,,,Connecticut,Poor,30.0,30.0,Within past year (anytime less than 12 months ago),Yes,5.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,88.45,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +No,36.96,Yes,No,No,20.0,15.0,Yes,Female,45-49,White,Yes,Yes,6.0,Yes,No,No,,Poor,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,29.95,,,,,,,Male,Age 70 to 74,,,,,,,,Virgin Islands,Poor,,20.0,Within past year (anytime less than 12 months ago),Yes,4.0,1 to 5,No,No,No,No,No,No,Yes,No,No,No,Yes,No,Yes,Yes,Yes,Yes,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.65,81.65,No,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,27.12,,,,,,,Male,Age 75 to 79,,,,,,,,Washington,Poor,2.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,"6 or more, but not all",Yes,No,No,No,No,No,No,No,Yes,Yes,No,No,No,Yes,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.83,90.72,No,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,30.0,,,,,,,Male,Age 80 or older,,,,,,,,Minnesota,Poor,25.0,0.0,,No,12.0,None of them,,No,No,No,Yes,No,No,No,Yes,Yes,Yes,No,Yes,Yes,Yes,Yes,Never smoked,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.91,108.86,No,No,Yes,,"Yes, received Tdap",No,Yes +,52.42,,,,,,,Female,Age 55 to 59,,,,,,,,Puerto Rico,Poor,30.0,20.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,1 to 5,No,No,No,No,No,No,Yes,No,Yes,No,No,Yes,Yes,Yes,Yes,Yes,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.65,142.88,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,24.8,No,No,No,4.0,0.0,Yes,Female,75-79,White,No,No,9.0,Yes,No,No,,Poor,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,22.67,,,,,,,Female,Age 70 to 74,,,,,,,,Washington,Poor,4.0,0.0,Within past year (anytime less than 12 months ago),Yes,12.0,None of them,No,No,No,No,No,Yes,Yes,Yes,Yes,No,No,No,No,Yes,No,No,,,,"White only, Non-Hispanic",1.6,58.06,,,,,,, +,13.95,,,,,,,Male,Age 35 to 39,,,,,,,,Maine,Poor,30.0,30.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,Yes,No,Yes,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",2.13,63.5,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,23.91,,,,,,,Female,Age 65 to 69,,,,,,,,Washington,Poor,30.0,15.0,Within past year (anytime less than 12 months ago),No,8.0,All,No,Yes,Yes,Yes,No,Yes,Yes,Yes,Yes,No,No,No,Yes,Yes,Yes,Yes,Former smoker,Use them every day,Yes,"White only, Non-Hispanic",1.6,61.23,No,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +Yes,32.55,Yes,No,Yes,30.0,15.0,Yes,Male,70-74,White,No,No,6.0,No,Yes,Yes,,Poor,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_002/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_002_current.json b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_002/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_002_current.json index 0370e4bc62e53f3c78526b8842368d6d76711b9c..49cf78d3727f567a88e58200868d6255dc1393f4 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_002/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_002_current.json +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_002/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_002_current.json @@ -1,12043 +1,11853 @@ { - "id": "002", - "task_type": "B4", - "subtask_type": "list", - "perspective": "data_holder", - "dataset_name": "Indicators of Heart Disease (2022 UPDATE)", - "table_path": "output/medical/classification/Indicators of Heart Disease (2022 UPDATE)_B4/Indicators of Heart Disease (2022 UPDATE)_B4_002/test.csv", - "query": "Our clinic received new annual health assessment forms. Looking at the historical patterns in the data I provided, which new applicants should we prioritize for wellness programs because their profiles most closely match patients who typically report \"Excellent\" health?", - "meta_info": { - "domain": "medical" + "id": "002", + "task_type": "B4", + "subtask_type": "list", + "perspective": "data_holder", + "dataset_name": "Indicators of Heart Disease (2022 UPDATE)", + "table_path": "output/medical/classification/Indicators of Heart Disease (2022 UPDATE)_B4/Indicators of Heart Disease (2022 UPDATE)_B4_002/test.csv", + "query": "Our clinic received new annual health assessment forms. Looking at the historical patterns in the data I provided, which new applicants should we prioritize for wellness programs because their profiles most closely match patients who typically report \"Excellent\" health?", + "meta_info": { + "domain": "medical" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "002", + "features": { + "HeartDisease": null, + "BMI": 33.89, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 35 to 39", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Delaware", + "GeneralHealth": "Very good", + "PhysicalHealthDays": 10.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 6.0, + "RemovedTeeth": "1 to 5", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "Yes", + "HadSkinCancer": "No", + "HadCOPD": "Yes", + "HadDepressiveDisorder": "Yes", + "HadKidneyDisease": "No", + "HadArthritis": "Yes", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "Yes", + "RaceEthnicityCategory": "Black only, Non-Hispanic", + "HeightInMeters": 1.68, + "WeightInKilograms": 95.25, + "AlcoholDrinkers": "Yes", + "HIVTesting": "Yes", + "FluVaxLast12": "No", + "PneumoVaxEver": "Yes", + "TetanusLast10Tdap": "Yes, received tetanus shot, but not Tdap", + "HighRiskLastYear": "No", + "CovidPos": "Yes" + } + }, + { + "scenario_id": "004", + "features": { + "HeartDisease": "No", + "BMI": 30.13, + "Smoking": "No", + "AlcoholDrinking": "No", + "Stroke": "No", + "PhysicalHealth": 0.0, + "MentalHealth": 0.0, + "DiffWalking": "No", + "Sex": "Male", + "AgeCategory": "70-74", + "Race": "White", + "Diabetic": "No", + "PhysicalActivity": "Yes", + "SleepTime": 9.0, + "Asthma": "No", + "KidneyDisease": "No", + "SkinCancer": "No", + "State": null, + "GeneralHealth": "Very good", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + "LastCheckupTime": null, + "PhysicalActivities": null, + "SleepHours": null, + "RemovedTeeth": null, + "HadHeartAttack": null, + "HadAngina": null, + "HadStroke": null, + "HadAsthma": null, + "HadSkinCancer": null, + "HadCOPD": null, + "HadDepressiveDisorder": null, + "HadKidneyDisease": null, + "HadArthritis": null, + "HadDiabetes": null, + "DeafOrHardOfHearing": null, + "BlindOrVisionDifficulty": null, + "DifficultyConcentrating": null, + "DifficultyWalking": null, + "DifficultyDressingBathing": null, + "DifficultyErrands": null, + "SmokerStatus": null, + "ECigaretteUsage": null, + "ChestScan": null, + "RaceEthnicityCategory": null, + "HeightInMeters": null, + "WeightInKilograms": null, + "AlcoholDrinkers": null, + "HIVTesting": null, + "FluVaxLast12": null, + "PneumoVaxEver": null, + "TetanusLast10Tdap": null, + "HighRiskLastYear": null, + "CovidPos": null + } + }, + { + "scenario_id": "007", + "features": { + "HeartDisease": null, + "BMI": 35.4, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 30 to 34", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Minnesota", + "GeneralHealth": "Excellent", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 7.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "No", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.7, + "WeightInKilograms": 102.51, + "AlcoholDrinkers": "Yes", + "HIVTesting": "No", + "FluVaxLast12": "Yes", + "PneumoVaxEver": null, + "TetanusLast10Tdap": "Yes, received tetanus shot but not sure what type", + "HighRiskLastYear": "No", + "CovidPos": "Yes" + } + }, + { + "scenario_id": "010", + "features": { + "HeartDisease": null, + "BMI": 27.26, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 25 to 29", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Tennessee", + "GeneralHealth": "Very good", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 1.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 6.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "Yes", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Current smoker - now smokes some days", + "ECigaretteUsage": "Not at all (right now)", + "ChestScan": "Yes", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.78, + "WeightInKilograms": 86.18, + "AlcoholDrinkers": "Yes", + "HIVTesting": "No", + "FluVaxLast12": "No", + "PneumoVaxEver": "Yes", + "TetanusLast10Tdap": "Yes, received tetanus shot, but not Tdap", + "HighRiskLastYear": "No", + "CovidPos": "Yes" + } + }, + { + "scenario_id": "012", + "features": { + "HeartDisease": "No", + "BMI": 36.81, + "Smoking": "No", + "AlcoholDrinking": "No", + "Stroke": "No", + "PhysicalHealth": 0.0, + "MentalHealth": 3.0, + "DiffWalking": "No", + "Sex": "Female", + "AgeCategory": "70-74", + "Race": "White", + "Diabetic": "No", + "PhysicalActivity": "No", + "SleepTime": 8.0, + "Asthma": "No", + "KidneyDisease": "No", + "SkinCancer": "No", + "State": null, + "GeneralHealth": "Very good", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + "LastCheckupTime": null, + "PhysicalActivities": null, + "SleepHours": null, + "RemovedTeeth": null, + "HadHeartAttack": null, + "HadAngina": null, + "HadStroke": null, + "HadAsthma": null, + "HadSkinCancer": null, + "HadCOPD": null, + "HadDepressiveDisorder": null, + "HadKidneyDisease": null, + "HadArthritis": null, + "HadDiabetes": null, + "DeafOrHardOfHearing": null, + "BlindOrVisionDifficulty": null, + "DifficultyConcentrating": null, + "DifficultyWalking": null, + "DifficultyDressingBathing": null, + "DifficultyErrands": null, + "SmokerStatus": null, + "ECigaretteUsage": null, + "ChestScan": null, + "RaceEthnicityCategory": null, + "HeightInMeters": null, + "WeightInKilograms": null, + "AlcoholDrinkers": null, + "HIVTesting": null, + "FluVaxLast12": null, + "PneumoVaxEver": null, + "TetanusLast10Tdap": null, + "HighRiskLastYear": null, + "CovidPos": null + } + }, + { + "scenario_id": "013", + 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"HighRiskLastYear": "No", + "CovidPos": "Yes" + } + }, + { + "scenario_id": "412", + "features": { + "HeartDisease": null, + "BMI": 32.32, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 60 to 64", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Colorado", + "GeneralHealth": "Good", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 6.0, + "RemovedTeeth": "1 to 5", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "Yes", + "HadDiabetes": "No", + 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"HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "484", + "features": { + "HeartDisease": null, + "BMI": 23.67, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 35 to 39", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Alaska", + "GeneralHealth": "Good", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": null, + "PhysicalActivities": "Yes", + "SleepHours": 6.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Former smoker", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "No", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.78, + "WeightInKilograms": 74.84, + "AlcoholDrinkers": "Yes", + "HIVTesting": "No", + "FluVaxLast12": "No", + "PneumoVaxEver": "No", + "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "489", + "features": { + "HeartDisease": null, + "BMI": 20.98, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 30 to 34", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, 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"HIVTesting": "No", + "FluVaxLast12": "No", + "PneumoVaxEver": "No", + "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "490", + "features": { + "HeartDisease": null, + "BMI": 34.11, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 25 to 29", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Colorado", + "GeneralHealth": "Very good", + "PhysicalHealthDays": 30.0, + "MentalHealthDays": 30.0, + "LastCheckupTime": "5 or more years ago", + "PhysicalActivities": "Yes", + "SleepHours": 8.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + 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"No", - "PneumoVaxEver": "No", - "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", - "HighRiskLastYear": "No", - "CovidPos": "No" - } - }, - { - "scenario_id": "489", - "features": { - "HeartDisease": null, - "BMI": 20.98, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Female", - "AgeCategory": "Age 30 to 34", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "Alaska", - "GeneralHealth": "Very good", - "PhysicalHealthDays": 0.0, - "MentalHealthDays": 0.0, - "LastCheckupTime": "Within past year (anytime less than 12 months ago)", - "PhysicalActivities": "Yes", - "SleepHours": 6.0, - "RemovedTeeth": "None of them", - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "No", - "HadSkinCancer": "No", - "HadCOPD": "No", - "HadDepressiveDisorder": "No", - "HadKidneyDisease": "No", - "HadArthritis": "No", - "HadDiabetes": "No", - "DeafOrHardOfHearing": "No", - "BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "No", - "DifficultyWalking": "No", - "DifficultyDressingBathing": "No", - "DifficultyErrands": "No", - "SmokerStatus": "Never smoked", - "ECigaretteUsage": "Never used e-cigarettes in my entire life", - "ChestScan": "No", - "RaceEthnicityCategory": "White only, Non-Hispanic", - "HeightInMeters": 1.68, - "WeightInKilograms": 58.97, - "AlcoholDrinkers": "Yes", - "HIVTesting": "No", - "FluVaxLast12": "No", - "PneumoVaxEver": "No", - "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", - "HighRiskLastYear": "No", - "CovidPos": "No" - } - }, - { - "scenario_id": "490", - "features": { - "HeartDisease": null, - "BMI": 34.11, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Female", - "AgeCategory": "Age 25 to 29", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "Colorado", - "GeneralHealth": "Very good", - "PhysicalHealthDays": 30.0, - "MentalHealthDays": 30.0, - "LastCheckupTime": "5 or more years ago", - "PhysicalActivities": "Yes", - "SleepHours": 8.0, - "RemovedTeeth": "None of them", - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "No", - "HadSkinCancer": "No", - "HadCOPD": "No", - "HadDepressiveDisorder": "Yes", - "HadKidneyDisease": "No", - "HadArthritis": "No", - "HadDiabetes": "No", - "DeafOrHardOfHearing": "No", - "BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "No", - "DifficultyWalking": "No", - "DifficultyDressingBathing": "No", - "DifficultyErrands": "No", - "SmokerStatus": "Never smoked", - "ECigaretteUsage": "Not at all (right now)", - "ChestScan": "No", - "RaceEthnicityCategory": "White only, Non-Hispanic", - "HeightInMeters": 1.65, - "WeightInKilograms": 92.99, - "AlcoholDrinkers": "Yes", - "HIVTesting": "No", - "FluVaxLast12": "No", - "PneumoVaxEver": "No", - "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", - "HighRiskLastYear": "No", - "CovidPos": "Tested positive using home test without a health professional" - } - }, - { - "scenario_id": "493", - "features": { - "HeartDisease": "No", - "BMI": 25.1, - "Smoking": "No", - "AlcoholDrinking": "No", - "Stroke": "No", - "PhysicalHealth": 0.0, - "MentalHealth": 0.0, - "DiffWalking": "No", - "Sex": "Male", - "AgeCategory": "18-24", - "Race": "White", - "Diabetic": "No", - "PhysicalActivity": "Yes", - "GenHealth": "Very good", - "SleepTime": 6.0, - "Asthma": "No", - "KidneyDisease": "No", - "SkinCancer": "No", - "State": null, - "GeneralHealth": null, - "PhysicalHealthDays": null, - "MentalHealthDays": null, - "LastCheckupTime": null, - "PhysicalActivities": null, - "SleepHours": null, - "RemovedTeeth": null, - "HadHeartAttack": null, - "HadAngina": null, - "HadStroke": null, - "HadAsthma": null, - "HadSkinCancer": null, - "HadCOPD": null, - "HadDepressiveDisorder": null, - "HadKidneyDisease": null, - "HadArthritis": null, - "HadDiabetes": null, - "DeafOrHardOfHearing": null, - "BlindOrVisionDifficulty": null, - "DifficultyConcentrating": null, - "DifficultyWalking": null, - "DifficultyDressingBathing": null, - "DifficultyErrands": null, - "SmokerStatus": null, - "ECigaretteUsage": null, - "ChestScan": null, - "RaceEthnicityCategory": null, - "HeightInMeters": null, - "WeightInKilograms": null, - "AlcoholDrinkers": null, - "HIVTesting": null, - "FluVaxLast12": null, - "PneumoVaxEver": null, - "TetanusLast10Tdap": null, - "HighRiskLastYear": null, - "CovidPos": null - } - }, - { - "scenario_id": "494", - "features": { - "HeartDisease": null, - "BMI": 29.53, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Male", - "AgeCategory": "Age 45 to 49", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "South Dakota", - "GeneralHealth": "Very good", - "PhysicalHealthDays": 30.0, - "MentalHealthDays": 0.0, - "LastCheckupTime": "Within past year (anytime less than 12 months ago)", - "PhysicalActivities": "No", - "SleepHours": 8.0, - "RemovedTeeth": "None of them", - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "No", - "HadSkinCancer": "No", - "HadCOPD": "No", - "HadDepressiveDisorder": "No", - "HadKidneyDisease": "Yes", - "HadArthritis": "No", - "HadDiabetes": "No", - "DeafOrHardOfHearing": "No", - "BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "No", - "DifficultyWalking": "No", - "DifficultyDressingBathing": "No", - "DifficultyErrands": "No", - "SmokerStatus": "Former smoker", - "ECigaretteUsage": "Never used e-cigarettes in my entire life", - "ChestScan": "Yes", - "RaceEthnicityCategory": "White only, Non-Hispanic", - "HeightInMeters": 1.88, - "WeightInKilograms": 104.33, - "AlcoholDrinkers": "No", - "HIVTesting": "Yes", - "FluVaxLast12": "Yes", - "PneumoVaxEver": "No", - "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", - "HighRiskLastYear": "No", - "CovidPos": "No" - } - }, - { - "scenario_id": "496", - "features": { - "HeartDisease": null, - "BMI": 20.92, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Male", - "AgeCategory": "Age 70 to 74", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "Washington", - "GeneralHealth": "Very good", - "PhysicalHealthDays": 3.0, - "MentalHealthDays": 0.0, - "LastCheckupTime": "Within past year (anytime less than 12 months ago)", - "PhysicalActivities": "Yes", - "SleepHours": 6.0, - "RemovedTeeth": "None of them", - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "No", - "HadSkinCancer": "No", - "HadCOPD": "No", - "HadDepressiveDisorder": "Yes", - "HadKidneyDisease": "No", - "HadArthritis": "No", - "HadDiabetes": "No", - "DeafOrHardOfHearing": "No", - "BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "No", - "DifficultyWalking": "No", - "DifficultyDressingBathing": "No", - "DifficultyErrands": "No", - "SmokerStatus": "Never smoked", - "ECigaretteUsage": "Never used e-cigarettes in my entire life", - "ChestScan": "No", - "RaceEthnicityCategory": "White only, Non-Hispanic", - "HeightInMeters": 1.8, - "WeightInKilograms": 68.04, - "AlcoholDrinkers": "Yes", - "HIVTesting": "No", - "FluVaxLast12": "Yes", - "PneumoVaxEver": "Yes", - "TetanusLast10Tdap": "Yes, received tetanus shot but not sure what type", - "HighRiskLastYear": "No", - "CovidPos": "Yes" - } - }, - { - "scenario_id": "497", - "features": { - "HeartDisease": "No", - "BMI": 26.13, - "Smoking": "No", - "AlcoholDrinking": "No", - "Stroke": "No", - "PhysicalHealth": 0.0, - "MentalHealth": 0.0, - "DiffWalking": "No", - "Sex": "Male", - "AgeCategory": "75-79", - "Race": "White", - "Diabetic": "No", - "PhysicalActivity": "No", - "GenHealth": "Good", - "SleepTime": 3.0, - "Asthma": "No", - "KidneyDisease": "No", - "SkinCancer": "Yes", - "State": null, - "GeneralHealth": null, - "PhysicalHealthDays": null, - "MentalHealthDays": null, - "LastCheckupTime": null, - "PhysicalActivities": null, - "SleepHours": null, - "RemovedTeeth": null, - "HadHeartAttack": null, - "HadAngina": null, - "HadStroke": null, - "HadAsthma": null, - "HadSkinCancer": null, - "HadCOPD": null, - "HadDepressiveDisorder": null, - "HadKidneyDisease": null, - "HadArthritis": null, - "HadDiabetes": null, - "DeafOrHardOfHearing": null, - "BlindOrVisionDifficulty": null, - "DifficultyConcentrating": null, - "DifficultyWalking": null, - "DifficultyDressingBathing": null, - "DifficultyErrands": null, - "SmokerStatus": null, - "ECigaretteUsage": null, - "ChestScan": null, - "RaceEthnicityCategory": null, - "HeightInMeters": null, - "WeightInKilograms": null, - "AlcoholDrinkers": null, - "HIVTesting": null, - "FluVaxLast12": null, - "PneumoVaxEver": null, - "TetanusLast10Tdap": null, - "HighRiskLastYear": null, - "CovidPos": null - } - }, - { - "scenario_id": "498", - "features": { - "HeartDisease": null, - "BMI": 23.96, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Male", - "AgeCategory": "Age 80 or older", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "New York", - "GeneralHealth": "Good", - "PhysicalHealthDays": 0.0, - "MentalHealthDays": 0.0, - "LastCheckupTime": "Within past year (anytime less than 12 months ago)", - "PhysicalActivities": "No", - "SleepHours": 8.0, - "RemovedTeeth": "6 or more, but not all", - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "No", - "HadSkinCancer": "No", - "HadCOPD": "No", - "HadDepressiveDisorder": "No", - "HadKidneyDisease": "No", - "HadArthritis": "No", - "HadDiabetes": "No", - "DeafOrHardOfHearing": "No", - "BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "No", - "DifficultyWalking": "No", - "DifficultyDressingBathing": "No", - "DifficultyErrands": "No", - "SmokerStatus": "Never smoked", - "ECigaretteUsage": "Never used e-cigarettes in my entire life", - "ChestScan": "No", - "RaceEthnicityCategory": "White only, Non-Hispanic", - "HeightInMeters": 1.65, - "WeightInKilograms": 65.32, - "AlcoholDrinkers": "No", - "HIVTesting": "No", - "FluVaxLast12": "Yes", - "PneumoVaxEver": "Yes", - "TetanusLast10Tdap": "Yes, received tetanus shot but not sure what type", - "HighRiskLastYear": "No", - "CovidPos": "No" - } - } - ], - "target_column": "GeneralHealth", - "task_sub_type": "classification", - "ranking_ground_truth": { - "top_k_ids": [ - "007", - "026", - "071", - "092", - "167", - "322", - "324", - "362", - "385", - "388", - "404", - "414", - "415", - "431", - "469" - ] - }, - "active_filters": [], - "target_class_value": "excellent" - }, - "response": "", - "evaluation_score": {} -} \ No newline at end of file + "active_filters": [], + "target_class_value": "excellent" + }, + "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_002/current.csv b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_002/current.csv index 8dcaa58517655f098af3dc447b55de96684635f1..d4e6bcd9e8064dc205c41c7f2c332bc0f390af05 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_002/current.csv +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_002/current.csv @@ -1,201 +1,201 @@ -HeartDisease,BMI,Smoking,AlcoholDrinking,Stroke,PhysicalHealth,MentalHealth,DiffWalking,Sex,AgeCategory,Race,Diabetic,PhysicalActivity,GenHealth,SleepTime,Asthma,KidneyDisease,SkinCancer,State,PhysicalHealthDays,MentalHealthDays,LastCheckupTime,PhysicalActivities,SleepHours,RemovedTeeth,HadHeartAttack,HadAngina,HadStroke,HadAsthma,HadSkinCancer,HadCOPD,HadDepressiveDisorder,HadKidneyDisease,HadArthritis,HadDiabetes,DeafOrHardOfHearing,BlindOrVisionDifficulty,DifficultyConcentrating,DifficultyWalking,DifficultyDressingBathing,DifficultyErrands,SmokerStatus,ECigaretteUsage,ChestScan,RaceEthnicityCategory,HeightInMeters,WeightInKilograms,AlcoholDrinkers,HIVTesting,FluVaxLast12,PneumoVaxEver,TetanusLast10Tdap,HighRiskLastYear,CovidPos -,33.89,,,,,,,Female,Age 35 to 39,,,,,,,,,Delaware,10.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,Yes,No,Yes,Yes,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.68,95.25,Yes,Yes,No,Yes,"Yes, received tetanus shot, but not Tdap",No,Yes -No,30.13,No,No,No,0.0,0.0,No,Male,70-74,White,No,Yes,Very good,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,35.4,,,,,,,Male,Age 30 to 34,,,,,,,,,Minnesota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,102.51,Yes,No,Yes,,"Yes, received tetanus shot but not sure what type",No,Yes -,27.26,,,,,,,Male,Age 25 to 29,,,,,,,,,Tennessee,0.0,1.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Not at all (right now),Yes,"White only, Non-Hispanic",1.78,86.18,Yes,No,No,Yes,"Yes, received tetanus shot, but not Tdap",No,Yes -No,36.81,No,No,No,0.0,3.0,No,Female,70-74,White,No,No,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,33.67,,,,,,,Female,Age 50 to 54,,,,,,,,,Arizona,0.0,1.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,97.52,No,Yes,No,No,"Yes, received Tdap",No,No -,39.06,,,,,,,Female,Age 30 to 34,,,,,,,,,Wisconsin,0.0,25.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.52,90.72,Yes,No,Yes,,"Yes, received tetanus shot but not sure what type",No,No -Yes,34.17,No,No,Yes,30.0,0.0,Yes,Male,65-69,White,Yes,No,Poor,6.0,No,Yes,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,22.27,,,,,,,Female,Age 70 to 74,,,,,,,,,Kansas,0.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,1 to 5,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.68,62.6,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No -,23.69,,,,,,,Female,Age 55 to 59,,,,,,,,,Rhode Island,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,62.6,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -,,,,,,,,Female,Age 75 to 79,,,,,,,,,California,2.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,Yes,No,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, -,26.61,,,,,,,Male,Age 80 or older,,,,,,,,,Illinois,7.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.73,79.38,No,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,20.82,,,,,,,Male,Age 18 to 24,,,,,,,,,Connecticut,5.0,15.0,Within past year (anytime less than 12 months ago),Yes,10.0,None of them,No,No,No,Yes,No,No,Yes,No,No,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.68,58.51,No,Yes,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -,23.63,,,,,,,Male,Age 75 to 79,,,,,,,,,Washington,0.0,,Within past year (anytime less than 12 months ago),Yes,10.0,All,No,No,No,No,No,No,No,No,No,No,No,No,No,,No,No,Current smoker - now smokes every day,Not at all (right now),No,"White only, Non-Hispanic",1.75,72.57,Yes,No,Yes,,"Yes, received tetanus shot but not sure what type",No,No -,39.06,,,,,,,Female,Age 65 to 69,,,,,,,,,West Virginia,0.0,0.0,Within past year (anytime less than 12 months ago),No,9.0,"6 or more, but not all",No,No,No,No,No,No,Yes,No,Yes,No,No,No,Yes,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.52,90.72,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No -,27.6,,,,,,,Male,Age 40 to 44,,,,,,,,,Oregon,0.0,2.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.88,97.52,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No -No,31.09,No,Yes,No,0.0,0.0,No,Female,55-59,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,20.36,Yes,No,No,30.0,0.0,No,Male,70-74,White,No,Yes,Very good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,24.56,,,,,,,Female,Age 70 to 74,,,,,,,,,Kansas,30.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,All,No,Yes,Yes,Yes,No,Yes,No,Yes,Yes,No,No,Yes,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.55,58.97,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes -Yes,22.76,No,No,No,0.0,0.0,No,Female,70-74,White,Yes,Yes,Fair,5.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,33.38,,,,,,,Male,Age 50 to 54,,,,,,,,,New York,2.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.88,117.93,Yes,Yes,Yes,,"Yes, received Tdap",No,Yes -,,,,,,,,Male,Age 55 to 59,,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,,Yes,No,No,No,"Yes, received tetanus shot, but not Tdap",No,No -,34.87,,,,,,,Male,Age 25 to 29,,,,,,,,,Ohio,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),No,"White only, Non-Hispanic",1.8,113.4,No,No,No,,"Yes, received tetanus shot but not sure what type",No,No -,23.44,,,,,,,Female,Age 70 to 74,,,,,,,,,Nebraska,1.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.52,54.43,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,36.92,,,,,,,Male,Age 40 to 44,,,,,,,,,Texas,0.0,0.0,5 or more years ago,Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,Hispanic,1.75,113.4,No,Yes,No,No,,No, -No,15.59,Yes,No,No,25.0,5.0,No,Female,45-49,White,"No, borderline diabetes",Yes,Fair,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,21.52,No,No,No,0.0,0.0,No,Male,65-69,White,No,Yes,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,24.8,,,,,,,Female,Age 60 to 64,,,,,,,,,Missouri,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,63.5,Yes,No,No,No,"Yes, received Tdap",No,Tested positive using home test without a health professional -,19.37,,,,,,,Female,Age 40 to 44,,,,,,,,,Virginia,0.0,4.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.68,54.43,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,26.58,,,,,,,Male,Age 65 to 69,,,,,,,,,Utah,2.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,All,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,81.65,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Tested positive using home test without a health professional -,35.15,,,,,,,Female,Age 18 to 24,,,,,,,,,Pennsylvania,4.0,7.0,Within past 2 years (1 year but less than 2 years ago),No,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),Yes,Hispanic,1.52,81.65,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No -,,,,,,,,Male,Age 35 to 39,,,,,,,,,Maryland,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, -,19.77,,,,,,,Female,Age 35 to 39,,,,,,,,,Texas,5.0,7.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Yes,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,58.97,No,Yes,No,No,"Yes, received Tdap",No,No -No,21.26,Yes,No,No,30.0,0.0,Yes,Female,80 or older,White,Yes,No,Poor,7.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,42.07,,,,,,,Female,Age 75 to 79,,,,,,,,,Kansas,,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,None of them,No,No,No,Yes,Yes,No,No,No,Yes,Yes,Yes,Yes,No,Yes,No,Yes,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,104.33,No,No,No,Yes,"Yes, received tetanus shot but not sure what type",No,No -No,24.21,No,No,No,0.0,10.0,No,Female,30-34,Black,No,Yes,Excellent,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,25.42,,,,,,,Male,Age 40 to 44,,,,,,,,,Florida,0.0,0.0,5 or more years ago,Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.98,99.79,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,24.21,,,,,,,Female,Age 65 to 69,,,,,,,,,California,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,68.04,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,No -,27.41,,,,,,,Male,Age 40 to 44,,,,,,,,,Montana,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Multiracial, Non-Hispanic",1.7,79.38,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No -,20.67,,,,,,,Female,Age 70 to 74,,,,,,,,,Maine,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,63.5,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,29.05,,,,,,,Female,Age 70 to 74,,,,,,,,,Pennsylvania,2.0,3.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,81.65,No,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No -,,,,,,,,Female,Age 80 or older,,,,,,,,,Virginia,2.0,2.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,Yes,No,No,Yes,No,Yes,No,No,Yes,No,Yes,Former smoker,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.6,,Yes,No,Yes,Yes,"Yes, received Tdap",No,No -,31.66,,,,,,,Male,Age 55 to 59,,,,,,,,,Pennsylvania,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,Hispanic,1.85,108.86,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -,24.37,,,,,,,Male,Age 30 to 34,,,,,,,,,Maine,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,8.0,None of them,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Use them some days,No,"White only, Non-Hispanic",1.75,74.84,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",Yes,No -,40.18,,,,,,,Male,Age 65 to 69,,,,,,,,,West Virginia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,Yes,Yes,No,No,No,No,No,No,Yes,Yes,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,127.01,No,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -,27.46,,,,,,,Female,Age 70 to 74,,,,,,,,,South Dakota,0.0,7.0,Within past year (anytime less than 12 months ago),Yes,9.0,1 to 5,No,No,No,No,Yes,Yes,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Not at all (right now),Yes,Hispanic,1.65,74.84,Yes,No,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -No,28.06,No,No,No,4.0,15.0,No,Male,25-29,Hispanic,No,Yes,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,27.37,,,,,,,Male,Age 75 to 79,,,,,,,,,Washington,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,None of them,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,81.65,Yes,No,Yes,Yes,"Yes, received Tdap",No,No -No,25.85,Yes,No,No,0.0,0.0,No,Male,60-64,Black,No,Yes,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,,,,,,,,Male,Age 65 to 69,,,,,,,,,Washington,30.0,10.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,Yes,No,Yes,No,No,No,No,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, -,37.93,,,,,,,Female,Age 60 to 64,,,,,,,,,Washington,0.0,30.0,Within past year (anytime less than 12 months ago),No,7.0,1 to 5,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,106.59,No,Yes,,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,28.89,,,,,,,Male,Age 75 to 79,,,,,,,,,Colorado,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,86.18,Yes,No,No,Yes,"Yes, received Tdap",No,Yes -No,23.06,Yes,No,No,3.0,0.0,No,Male,35-39,Other,No,Yes,Excellent,10.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,27.12,,,,,,,Male,Age 80 or older,,,,,,,,,Virginia,2.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,Yes,Yes,Yes,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,90.72,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -No,21.52,No,No,No,0.0,0.0,No,Male,80 or older,White,No,Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,24.21,,,,,,,Male,Age 30 to 34,,,,,,,,,Indiana,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,68.04,Yes,No,Yes,Yes,"Yes, received Tdap",No,No -,32.77,,,,,,,Female,Age 50 to 54,,,,,,,,,Iowa,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,"No, pre-diabetes or borderline diabetes",No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,83.91,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,25.84,,,,,,,Male,Age 18 to 24,,,,,,,,,Montana,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,74.84,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,27.05,,,,,,,Male,Age 60 to 64,,,,,,,,,California,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,92.99,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes -No,26.89,No,No,No,0.0,0.0,No,Female,70-74,White,No,Yes,Very good,9.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,27.06,Yes,No,No,10.0,0.0,No,Male,75-79,White,No,Yes,Very good,10.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,36.02,Yes,No,No,30.0,0.0,Yes,Female,35-39,Other,Yes,Yes,Poor,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,35.07,,,,,,,Female,Age 45 to 49,,,,,,,,,Nebraska,7.0,5.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,89.81,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -No,18.79,No,No,No,0.0,0.0,No,Female,40-44,White,No,Yes,Excellent,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,38.74,,,,,,,Female,Age 25 to 29,,,,,,,,,Nebraska,5.0,3.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,Yes,No,No,Yes,No,No,No,No,No,Yes,No,No,Yes,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.68,108.86,Yes,Yes,Yes,No,"Yes, received Tdap",No,No -,24.51,,,,,,,Female,Age 60 to 64,,,,,,,,,Montana,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,Hispanic,1.57,60.78,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,No -,20.8,,,,,,,Female,Age 80 or older,,,,,,,,,Kentucky,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,Yes,No,No,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),No,"White only, Non-Hispanic",1.65,56.7,No,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,Yes -No,28.8,Yes,No,No,14.0,3.0,No,Male,70-74,American Indian/Alaskan Native,"No, borderline diabetes",Yes,Good,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,39.16,No,No,No,0.0,10.0,No,Female,55-59,Black,No,No,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Yes,33.29,Yes,No,No,0.0,3.0,No,Female,80 or older,White,Yes,No,Good,8.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,34.44,,,,,,,Male,Age 25 to 29,,,,,,,,,Wisconsin,15.0,10.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,108.86,Yes,Yes,Yes,No,"Yes, received Tdap",No,Yes -,28.32,,,,,,,Female,Age 65 to 69,,,,,,,,,South Dakota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,10.0,"6 or more, but not all",Yes,No,No,No,No,No,No,Yes,Yes,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,74.84,No,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,,,,,,,,Male,Age 80 or older,,,,,,,,,Kansas,30.0,20.0,Within past year (anytime less than 12 months ago),No,7.0,All,No,No,No,No,No,No,No,Yes,Yes,No,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, -,,,,,,,,Female,Age 35 to 39,,,,,,,,,Texas,4.0,0.0,Within past year (anytime less than 12 months ago),Yes,4.0,None of them,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,,,,,,,,,, -,24.87,,,,,,,Female,Age 50 to 54,,,,,,,,,Indiana,10.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.57,61.69,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -No,29.95,No,No,No,0.0,15.0,No,Female,30-34,White,No,Yes,Very good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,49.18,,,,,,,Female,Age 60 to 64,,,,,,,,,Illinois,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,142.43,Yes,No,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,27.12,,,,,,,Male,Age 50 to 54,,,,,,,,,Kansas,1.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,90.72,Yes,Yes,Yes,No,"Yes, received Tdap",No,No -,28.34,,,,,,,Female,Age 40 to 44,,,,,,,,,Nevada,0.0,5.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,72.57,Yes,Yes,No,No,"Yes, received Tdap",No,No -,21.03,,,,,,,Female,Age 35 to 39,,,,,,,,,North Carolina,2.0,5.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,52.16,No,Yes,Yes,No,"Yes, received Tdap",No,No -,28.5,,,,,,,Male,Age 80 or older,,,,,,,,,Virginia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,10.0,All,No,No,No,No,No,Yes,No,No,Yes,No,Yes,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.88,100.7,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,30.23,,,,,,,Female,Age 18 to 24,,,,,,,,,Alaska,0.0,,Within past year (anytime less than 12 months ago),Yes,9.0,,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Use them some days,No,"White only, Non-Hispanic",1.55,72.57,,No,No,No,"No, did not receive any tetanus shot in the past 10 years",Yes,Yes -No,22.81,No,No,No,0.0,0.0,No,Female,60-64,White,No,Yes,Excellent,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,24.69,No,No,No,3.0,30.0,No,Female,18-24,White,No,Yes,Good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,25.06,No,No,No,0.0,0.0,No,Female,65-69,White,Yes,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,27.37,No,No,No,2.0,0.0,Yes,Male,75-79,White,No,No,Fair,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,19.64,,,,,,,Female,Age 75 to 79,,,,,,,,,New York,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.65,53.52,No,Yes,Yes,Yes,"Yes, received Tdap",No,No -,24.8,,,,,,,Female,Age 25 to 29,,,,,,,,,Iowa,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.6,63.5,Yes,Yes,Yes,,"Yes, received tetanus shot but not sure what type",No,No -No,33.66,No,No,No,30.0,30.0,Yes,Female,65-69,Black,No,No,Good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Yes,42.27,Yes,No,Yes,0.0,5.0,No,Female,55-59,White,Yes,No,Good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,20.67,Yes,Yes,No,0.0,7.0,Yes,Female,80 or older,Hispanic,No,Yes,Good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,31.09,Yes,No,No,0.0,15.0,No,Female,60-64,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Yes,26.63,No,No,No,5.0,0.0,No,Female,80 or older,White,Yes,Yes,Fair,8.0,No,Yes,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,42.07,,,,,,,Male,Age 25 to 29,,,,,,,,,North Carolina,25.0,0.0,5 or more years ago,Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,Yes,Yes,Yes,Yes,No,No,Former smoker,Use them every day,No,"Other race only, Non-Hispanic",1.57,104.33,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",Yes,No -No,20.34,No,No,No,0.0,2.0,No,Male,45-49,White,No,Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,30.83,,,,,,,Male,Age 70 to 74,,,,,,,,,Minnesota,15.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,All,No,No,No,No,No,Yes,No,No,Yes,No,Yes,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,86.64,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No -,22.15,,,,,,,Female,Age 70 to 74,,,,,,,,,West Virginia,,0.0,Within past year (anytime less than 12 months ago),No,6.0,"6 or more, but not all",No,Yes,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,68.04,No,No,No,Yes,"Yes, received tetanus shot but not sure what type",No,No -,19.2,,,,,,,Male,Age 65 to 69,,,,,,,,,New Jersey,30.0,5.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,58.97,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,22.15,,,,,,,Female,Age 30 to 34,,,,,,,,,Missouri,1.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,68.04,No,No,Yes,Yes,,No,Yes -,,,,,,,,Female,Age 40 to 44,,,,,,,,,Georgia,5.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.73,,No,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -No,36.62,Yes,No,No,0.0,0.0,No,Male,50-54,White,No,Yes,Good,4.0,Yes,Yes,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,,,,,,,,Female,Age 40 to 44,,,,,,,,,Maryland,0.0,2.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.65,136.08,No,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,40.77,,,,,,,Female,Age 30 to 34,,,,,,,,,Nebraska,0.0,3.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,Yes,No,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.65,111.13,Yes,No,Yes,No,"Yes, received Tdap",No,Yes -No,29.29,Yes,No,No,0.0,0.0,No,Female,70-74,White,Yes,Yes,Good,8.0,Yes,Yes,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,28.75,,,,,,,Male,Age 65 to 69,,,,,,,,,Maryland,1.0,2.0,Within past year (anytime less than 12 months ago),Yes,7.0,"6 or more, but not all",No,Yes,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.91,104.33,,,Yes,Yes,"Yes, received tetanus shot but not sure what type",, -,28.06,,,,,,,Male,Age 35 to 39,,,,,,,,,Indiana,30.0,30.0,Within past year (anytime less than 12 months ago),No,12.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,Yes,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.75,86.18,No,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,28.75,,,,,,,Male,Age 60 to 64,,,,,,,,,Pennsylvania,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.91,104.33,No,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -,28.7,,,,,,,Male,Age 25 to 29,,,,,,,,,California,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.78,90.72,Yes,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,21.03,,,,,,,Female,Age 40 to 44,,,,,,,,,Kansas,0.0,5.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,52.16,,,,,,, -No,31.62,Yes,No,No,0.0,0.0,No,Female,70-74,White,No,Yes,Good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,28.13,,,,,,,Male,Age 75 to 79,,,,,,,,,Guam,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,Yes,No,No,Yes,No,No,No,Yes,Yes,No,Yes,No,Yes,No,No,Never smoked,Not at all (right now),No,"Other race only, Non-Hispanic",1.73,83.91,Yes,No,Yes,No,"Yes, received Tdap",No,No -,,,,,,,,Female,Age 30 to 34,,,,,,,,,Delaware,3.0,0.0,Within past 2 years (1 year but less than 2 years ago),No,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,Hispanic,,65.32,No,Yes,No,No,"Yes, received Tdap",No,No -,33.45,,,,,,,Female,Age 50 to 54,,,,,,,,,South Carolina,30.0,10.0,Within past year (anytime less than 12 months ago),No,4.0,"6 or more, but not all",No,No,No,No,No,No,Yes,Yes,Yes,No,No,Yes,Yes,Yes,Yes,Yes,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,99.79,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -,32.01,,,,,,,Female,Age 70 to 74,,,,,,,,,Alaska,0.0,2.0,Within past year (anytime less than 12 months ago),No,5.0,None of them,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.57,79.38,No,No,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,27.67,,,,,,,Male,Age 60 to 64,,,,,,,,,Arkansas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,All,No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,Former smoker,Use them every day,Yes,"White only, Non-Hispanic",1.73,82.55,Yes,No,No,Yes,"Yes, received tetanus shot but not sure what type",No,No -No,43.07,Yes,No,No,0.0,0.0,No,Male,30-34,White,No,Yes,Good,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,23.17,,,,,,,Female,Age 35 to 39,,,,,,,,,Alaska,2.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,8.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,61.23,Yes,No,No,No,"Yes, received tetanus shot, but not Tdap",No,No -,40.6,,,,,,,Female,Age 60 to 64,,,,,,,,,New Jersey,0.0,30.0,Within past year (anytime less than 12 months ago),No,10.0,1 to 5,No,No,No,Yes,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.73,121.11,Yes,No,Yes,Yes,"Yes, received Tdap",No,No -,34.33,,,,,,,Female,Age 35 to 39,,,,,,,,,Washington,3.0,30.0,Within past year (anytime less than 12 months ago),No,7.0,1 to 5,No,No,No,No,No,No,Yes,No,No,No,No,No,Yes,No,No,No,Former smoker,Not at all (right now),No,Hispanic,1.63,90.72,No,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,22.15,,,,,,,Male,Age 30 to 34,,,,,,,,,Indiana,0.0,3.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.75,68.04,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No -No,30.17,Yes,No,No,0.0,0.0,No,Male,60-64,White,Yes,Yes,Fair,7.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,25.8,,,,,,,Male,Age 25 to 29,,,,,,,,,South Dakota,0.0,2.0,,Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.8,83.91,No,,No,,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,38.52,,,,,,,Male,Age 40 to 44,,,,,,,,,Indiana,10.0,25.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,Yes,No,Yes,Yes,No,No,No,Yes,No,No,Current smoker - now smokes every day,Use them some days,Yes,"White only, Non-Hispanic",1.88,136.08,Yes,Yes,No,No,"Yes, received Tdap",Yes,No -Yes,25.82,No,No,Yes,30.0,4.0,Yes,Male,65-69,Other,Yes,Yes,Poor,4.0,No,Yes,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,25.84,No,No,No,0.0,0.0,No,Male,35-39,White,No,No,Excellent,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,22.81,Yes,No,No,0.0,0.0,No,Male,45-49,White,No,Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,21.7,Yes,No,No,0.0,0.0,Yes,Male,65-69,Black,No,Yes,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,28.34,,,,,,,Female,Age 80 or older,,,,,,,,,Michigan,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,72.57,Yes,No,Yes,Yes,"Yes, received Tdap",No,No -,21.93,,,,,,,Male,Age 60 to 64,,,,,,,,,Louisiana,15.0,15.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,Yes,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,63.5,No,Yes,Yes,,"Yes, received tetanus shot but not sure what type",No,No -No,37.44,Yes,No,No,0.0,2.0,No,Female,55-59,White,No,No,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,31.38,,,,,,,Male,Age 40 to 44,,,,,,,,,Guam,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,6.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Not at all (right now),No,"Other race only, Non-Hispanic",1.8,102.06,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -No,27.76,Yes,No,No,30.0,0.0,No,Male,70-74,Asian,No,Yes,Good,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,18.79,No,No,No,28.0,25.0,No,Female,60-64,White,No,Yes,Excellent,8.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,22.96,Yes,No,No,0.0,0.0,No,Male,70-74,White,No,Yes,Good,8.0,Yes,Yes,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Yes,36.58,No,No,No,20.0,0.0,Yes,Female,70-74,White,No,No,Fair,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,31.89,,,,,,,Female,Age 45 to 49,,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,Yes,No,No,No,No,Yes,No,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.6,81.65,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,Yes -,25.61,,,,,,,Female,Age 75 to 79,,,,,,,,,Utah,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,63.5,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,25.85,,,,,,,Female,Age 65 to 69,,,,,,,,,Wisconsin,15.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,4.0,1 to 5,No,No,No,Yes,No,Yes,No,No,Yes,No,No,No,No,Yes,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.73,77.11,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,28.5,,,,,,,Female,Age 30 to 34,,,,,,,,,Washington,1.0,25.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,Yes,No,Yes,Yes,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,82.55,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No -,32.72,,,,,,,Male,Age 60 to 64,,,,,,,,,Illinois,0.0,0.0,Within past year (anytime less than 12 months ago),No,5.0,1 to 5,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,112.49,Yes,No,Yes,Yes,"Yes, received Tdap",No,No -,30.04,,,,,,,Female,Age 18 to 24,,,,,,,,,Alaska,4.0,7.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,79.38,No,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No -No,23.3,No,No,No,0.0,0.0,No,Male,55-59,Other,No,Yes,Excellent,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,29.16,,,,,,,Male,Age 40 to 44,,,,,,,,,Iowa,1.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.83,97.52,No,Yes,Yes,,"Yes, received Tdap",No,Yes -,14.14,,,,,,,Female,Age 35 to 39,,,,,,,,,Connecticut,3.0,12.0,Within past year (anytime less than 12 months ago),No,6.0,"6 or more, but not all",No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.65,38.56,No,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,Yes -,33.91,,,,,,,Male,Age 40 to 44,,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,113.4,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -,26.78,,,,,,,Male,Age 55 to 59,,,,,,,,,New York,1.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"Other race only, Non-Hispanic",1.8,87.09,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,29.23,,,,,,,Female,Age 65 to 69,,,,,,,,,Idaho,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,All,No,No,No,Yes,No,No,No,No,Yes,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,74.84,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,34.33,,,,,,,Female,Age 50 to 54,,,,,,,,,Kentucky,0.0,2.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,90.72,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,18.79,,,,,,,Female,Age 50 to 54,,,,,,,,,Louisiana,3.0,3.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,54.43,Yes,,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,27.46,,,,,,,Male,Age 40 to 44,,,,,,,,,Massachusetts,,,Within past year (anytime less than 12 months ago),No,7.0,None of them,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,,,,,Hispanic,1.65,74.84,Yes,No,No,No,,No,No -,22.8,,,,,,,Female,Age 50 to 54,,,,,,,,,Washington,30.0,5.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,62.14,Yes,Yes,Yes,No,"Yes, received Tdap",No,No -,29.26,,,,,,,Female,Age 80 or older,,,,,,,,,Puerto Rico,30.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,Yes,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.57,72.57,No,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -No,27.96,No,No,No,0.0,7.0,No,Female,80 or older,White,No,Yes,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,28.34,,,,,,,Female,Age 50 to 54,,,,,,,,,Utah,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,"White only, Non-Hispanic",1.6,72.57,,,,,,, -,32.98,,,,,,,Male,Age 60 to 64,,,,,,,,,Utah,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,113.4,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,32.32,,,,,,,Male,Age 60 to 64,,,,,,,,,Colorado,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.85,111.13,Yes,Yes,No,No,"Yes, received Tdap",No,Yes -,29.65,,,,,,,Male,Age 70 to 74,,,,,,,,,Rhode Island,14.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,88.45,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -,23.09,,,,,,,Male,Age 18 to 24,,,,,,,,,Kentucky,0.0,7.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.85,79.38,Yes,No,No,No,"Yes, received Tdap",No,Yes -,21.97,,,,,,,Female,Age 60 to 64,,,,,,,,,Connecticut,0.0,2.0,Within past year (anytime less than 12 months ago),Yes,4.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,58.06,Yes,Yes,Yes,No,"Yes, received Tdap",No,Yes -No,21.73,No,No,No,21.0,0.0,No,Female,50-54,Hispanic,No,Yes,Excellent,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,19.97,No,No,No,0.0,15.0,No,Female,80 or older,White,No,Yes,Excellent,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,21.61,No,No,No,0.0,0.0,No,Male,25-29,White,No,Yes,Very good,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,23.03,,,,,,,Female,Age 80 or older,,,,,,,,,Maine,30.0,0.0,Within past year (anytime less than 12 months ago),No,10.0,None of them,Yes,No,Yes,Yes,No,Yes,No,No,No,No,No,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,58.97,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No -,25.84,,,,,,,Male,Age 50 to 54,,,,,,,,,Florida,3.0,4.0,5 or more years ago,Yes,4.0,None of them,No,No,No,Yes,No,No,No,No,Yes,No,No,Yes,Yes,No,Yes,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,79.38,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,24.79,,,,,,,Female,,,,,,,,,,California,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,67.59,Yes,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -Yes,24.61,Yes,No,No,30.0,1.0,Yes,Female,80 or older,White,No,No,Poor,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,22.5,No,No,No,3.0,0.0,No,Female,65-69,White,No,Yes,Very good,8.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,26.57,No,No,No,0.0,7.0,No,Female,30-34,Black,No,Yes,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Yes,44.1,No,No,No,3.0,0.0,Yes,Female,55-59,White,No,No,Good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,25.1,,,,,,,Male,Age 45 to 49,,,,,,,,,Mississippi,0.0,0.0,5 or more years ago,Yes,10.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.75,77.11,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -No,36.62,Yes,No,No,0.0,0.0,No,Male,55-59,White,No,Yes,Excellent,8.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,21.26,,,,,,,Female,Age 25 to 29,,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.6,54.43,Yes,No,No,No,"Yes, received Tdap",No,No -,22.3,,,,,,,Female,Age 55 to 59,,,,,,,,,New Mexico,30.0,2.0,Within past year (anytime less than 12 months ago),No,10.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Yes,Yes,Never smoked,Never used e-cigarettes in my entire life,Yes,"Other race only, Non-Hispanic",1.65,60.78,No,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -,22.96,,,,,,,Male,Age 80 or older,,,,,,,,,West Virginia,30.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,"6 or more, but not all",No,No,No,No,Yes,No,No,No,Yes,No,Yes,Yes,No,Yes,No,Yes,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,72.57,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -No,22.05,No,No,No,0.0,0.0,No,Male,45-49,Hispanic,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,28.7,No,No,No,2.0,0.0,No,Male,70-74,White,No,Yes,Good,7.0,No,Yes,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,28.06,Yes,No,No,2.0,0.0,No,Male,25-29,White,No,Yes,Fair,4.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,18.6,,,,,,,Female,Age 18 to 24,,,,,,,,,Delaware,7.0,26.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Multiracial, Non-Hispanic",1.6,47.63,Yes,No,Yes,No,"Yes, received Tdap",Yes,No -,26.63,,,,,,,Male,Age 50 to 54,,,,,,,,,California,0.0,0.0,5 or more years ago,No,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.7,77.11,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,19.8,,,,,,,Male,Age 35 to 39,,,,,,,,,Texas,15.0,2.0,Within past 2 years (1 year but less than 2 years ago),No,6.0,1 to 5,No,No,No,No,No,Yes,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,62.6,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -,29.65,,,,,,,Female,Age 65 to 69,,,,,,,,,Maine,14.0,10.0,Within past 5 years (2 years but less than 5 years ago),Yes,,"6 or more, but not all",No,No,No,No,No,No,Yes,No,Yes,No,No,No,Yes,Yes,Yes,No,,Never used e-cigarettes in my entire life,No,,1.73,88.45,No,No,Yes,Yes,,No,No -,23.71,,,,,,,Male,Age 25 to 29,,,,,,,,,Virgin Islands,0.0,2.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,77.11,Yes,Yes,No,No,"Yes, received Tdap",No,Yes -,22.71,,,,,,,Female,Age 18 to 24,,,,,,,,,New York,2.0,3.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.7,65.77,Yes,Yes,No,No,"Yes, received Tdap",No,Yes -,30.65,,,,,,,Male,Age 70 to 74,,,,,,,,,South Carolina,0.0,2.0,Within past year (anytime less than 12 months ago),No,9.0,None of them,No,No,No,No,No,No,No,Yes,Yes,Yes,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.83,102.51,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -No,51.92,Yes,No,No,1.0,15.0,No,Female,18-24,White,Yes,Yes,Good,8.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,39.16,No,No,No,0.0,0.0,No,Female,35-39,White,No,No,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,29.29,,,,,,,Male,Age 18 to 24,,,,,,,,,Georgia,0.0,0.0,5 or more years ago,No,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Use them some days,No,Hispanic,1.52,68.04,Yes,No,No,No,"Yes, received Tdap",No,No -No,21.93,Yes,No,No,0.0,30.0,No,Female,18-24,White,No,Yes,Good,8.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,,,,,,,,Female,Age 50 to 54,,,,,,,,,Ohio,1.0,0.0,,Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,Yes,No,Yes,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",,,No,No,No,No,"Yes, received Tdap",No,Yes -No,47.03,Yes,No,No,30.0,0.0,Yes,Female,60-64,White,No,No,Poor,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,19.37,,,,,,,Female,Age 50 to 54,,,,,,,,,Washington,30.0,20.0,Within past year (anytime less than 12 months ago),No,7.0,"6 or more, but not all",No,Yes,No,Yes,No,Yes,No,No,Yes,No,No,Yes,Yes,Yes,Yes,Yes,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,54.43,Yes,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",Yes,Yes -,24.89,,,,,,,Female,Age 60 to 64,,,,,,,,,California,20.0,25.0,Within past year (anytime less than 12 months ago),Yes,7.0,"6 or more, but not all",No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes some days,Not at all (right now),No,Hispanic,1.63,65.77,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,23.67,,,,,,,Male,Age 35 to 39,,,,,,,,,Alaska,0.0,0.0,,Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,74.84,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,20.98,,,,,,,Female,Age 30 to 34,,,,,,,,,Alaska,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,58.97,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,34.11,,,,,,,Female,Age 25 to 29,,,,,,,,,Colorado,30.0,30.0,5 or more years ago,Yes,8.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.65,92.99,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Tested positive using home test without a health professional -No,25.1,No,No,No,0.0,0.0,No,Male,18-24,White,No,Yes,Very good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,29.53,,,,,,,Male,Age 45 to 49,,,,,,,,,South Dakota,30.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.88,104.33,No,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,20.92,,,,,,,Male,Age 70 to 74,,,,,,,,,Washington,3.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.8,68.04,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes -No,26.13,No,No,No,0.0,0.0,No,Male,75-79,White,No,No,Good,3.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,23.96,,,,,,,Male,Age 80 or older,,,,,,,,,New York,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,65.32,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +HeartDisease,BMI,Smoking,AlcoholDrinking,Stroke,PhysicalHealth,MentalHealth,DiffWalking,Sex,AgeCategory,Race,Diabetic,PhysicalActivity,SleepTime,Asthma,KidneyDisease,SkinCancer,State,PhysicalHealthDays,MentalHealthDays,LastCheckupTime,PhysicalActivities,SleepHours,RemovedTeeth,HadHeartAttack,HadAngina,HadStroke,HadAsthma,HadSkinCancer,HadCOPD,HadDepressiveDisorder,HadKidneyDisease,HadArthritis,HadDiabetes,DeafOrHardOfHearing,BlindOrVisionDifficulty,DifficultyConcentrating,DifficultyWalking,DifficultyDressingBathing,DifficultyErrands,SmokerStatus,ECigaretteUsage,ChestScan,RaceEthnicityCategory,HeightInMeters,WeightInKilograms,AlcoholDrinkers,HIVTesting,FluVaxLast12,PneumoVaxEver,TetanusLast10Tdap,HighRiskLastYear,CovidPos +,33.89,,,,,,,Female,Age 35 to 39,,,,,,,,Delaware,10.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,Yes,No,Yes,Yes,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.68,95.25,Yes,Yes,No,Yes,"Yes, received tetanus shot, but not Tdap",No,Yes +No,30.13,No,No,No,0.0,0.0,No,Male,70-74,White,No,Yes,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,35.4,,,,,,,Male,Age 30 to 34,,,,,,,,Minnesota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,102.51,Yes,No,Yes,,"Yes, received tetanus shot but not sure what type",No,Yes +,27.26,,,,,,,Male,Age 25 to 29,,,,,,,,Tennessee,0.0,1.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Not at all (right now),Yes,"White only, Non-Hispanic",1.78,86.18,Yes,No,No,Yes,"Yes, received tetanus shot, but not Tdap",No,Yes +No,36.81,No,No,No,0.0,3.0,No,Female,70-74,White,No,No,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,33.67,,,,,,,Female,Age 50 to 54,,,,,,,,Arizona,0.0,1.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,97.52,No,Yes,No,No,"Yes, received Tdap",No,No +,39.06,,,,,,,Female,Age 30 to 34,,,,,,,,Wisconsin,0.0,25.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.52,90.72,Yes,No,Yes,,"Yes, received tetanus shot but not sure what type",No,No +Yes,34.17,No,No,Yes,30.0,0.0,Yes,Male,65-69,White,Yes,No,6.0,No,Yes,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,22.27,,,,,,,Female,Age 70 to 74,,,,,,,,Kansas,0.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,1 to 5,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.68,62.6,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No +,23.69,,,,,,,Female,Age 55 to 59,,,,,,,,Rhode Island,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,62.6,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +,,,,,,,,Female,Age 75 to 79,,,,,,,,California,2.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,Yes,No,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, +,26.61,,,,,,,Male,Age 80 or older,,,,,,,,Illinois,7.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.73,79.38,No,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,20.82,,,,,,,Male,Age 18 to 24,,,,,,,,Connecticut,5.0,15.0,Within past year (anytime less than 12 months ago),Yes,10.0,None of them,No,No,No,Yes,No,No,Yes,No,No,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.68,58.51,No,Yes,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +,23.63,,,,,,,Male,Age 75 to 79,,,,,,,,Washington,0.0,,Within past year (anytime less than 12 months ago),Yes,10.0,All,No,No,No,No,No,No,No,No,No,No,No,No,No,,No,No,Current smoker - now smokes every day,Not at all (right now),No,"White only, Non-Hispanic",1.75,72.57,Yes,No,Yes,,"Yes, received tetanus shot but not sure what type",No,No +,39.06,,,,,,,Female,Age 65 to 69,,,,,,,,West Virginia,0.0,0.0,Within past year (anytime less than 12 months ago),No,9.0,"6 or more, but not all",No,No,No,No,No,No,Yes,No,Yes,No,No,No,Yes,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.52,90.72,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No +,27.6,,,,,,,Male,Age 40 to 44,,,,,,,,Oregon,0.0,2.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.88,97.52,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No +No,31.09,No,Yes,No,0.0,0.0,No,Female,55-59,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,20.36,Yes,No,No,30.0,0.0,No,Male,70-74,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,24.56,,,,,,,Female,Age 70 to 74,,,,,,,,Kansas,30.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,All,No,Yes,Yes,Yes,No,Yes,No,Yes,Yes,No,No,Yes,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.55,58.97,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes +Yes,22.76,No,No,No,0.0,0.0,No,Female,70-74,White,Yes,Yes,5.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,33.38,,,,,,,Male,Age 50 to 54,,,,,,,,New York,2.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.88,117.93,Yes,Yes,Yes,,"Yes, received Tdap",No,Yes +,,,,,,,,Male,Age 55 to 59,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,,Yes,No,No,No,"Yes, received tetanus shot, but not Tdap",No,No +,34.87,,,,,,,Male,Age 25 to 29,,,,,,,,Ohio,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),No,"White only, Non-Hispanic",1.8,113.4,No,No,No,,"Yes, received tetanus shot but not sure what type",No,No +,23.44,,,,,,,Female,Age 70 to 74,,,,,,,,Nebraska,1.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.52,54.43,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,36.92,,,,,,,Male,Age 40 to 44,,,,,,,,Texas,0.0,0.0,5 or more years ago,Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,Hispanic,1.75,113.4,No,Yes,No,No,,No, +No,15.59,Yes,No,No,25.0,5.0,No,Female,45-49,White,"No, borderline diabetes",Yes,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,21.52,No,No,No,0.0,0.0,No,Male,65-69,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,24.8,,,,,,,Female,Age 60 to 64,,,,,,,,Missouri,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,63.5,Yes,No,No,No,"Yes, received Tdap",No,Tested positive using home test without a health professional +,19.37,,,,,,,Female,Age 40 to 44,,,,,,,,Virginia,0.0,4.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.68,54.43,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,26.58,,,,,,,Male,Age 65 to 69,,,,,,,,Utah,2.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,All,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,81.65,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Tested positive using home test without a health professional +,35.15,,,,,,,Female,Age 18 to 24,,,,,,,,Pennsylvania,4.0,7.0,Within past 2 years (1 year but less than 2 years ago),No,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),Yes,Hispanic,1.52,81.65,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No +,,,,,,,,Male,Age 35 to 39,,,,,,,,Maryland,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, +,19.77,,,,,,,Female,Age 35 to 39,,,,,,,,Texas,5.0,7.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Yes,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,58.97,No,Yes,No,No,"Yes, received Tdap",No,No +No,21.26,Yes,No,No,30.0,0.0,Yes,Female,80 or older,White,Yes,No,7.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,42.07,,,,,,,Female,Age 75 to 79,,,,,,,,Kansas,,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,None of them,No,No,No,Yes,Yes,No,No,No,Yes,Yes,Yes,Yes,No,Yes,No,Yes,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,104.33,No,No,No,Yes,"Yes, received tetanus shot but not sure what type",No,No +No,24.21,No,No,No,0.0,10.0,No,Female,30-34,Black,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,25.42,,,,,,,Male,Age 40 to 44,,,,,,,,Florida,0.0,0.0,5 or more years ago,Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.98,99.79,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,24.21,,,,,,,Female,Age 65 to 69,,,,,,,,California,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,68.04,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,No +,27.41,,,,,,,Male,Age 40 to 44,,,,,,,,Montana,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Multiracial, Non-Hispanic",1.7,79.38,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No +,20.67,,,,,,,Female,Age 70 to 74,,,,,,,,Maine,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,63.5,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,29.05,,,,,,,Female,Age 70 to 74,,,,,,,,Pennsylvania,2.0,3.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,81.65,No,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No +,,,,,,,,Female,Age 80 or older,,,,,,,,Virginia,2.0,2.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,Yes,No,No,Yes,No,Yes,No,No,Yes,No,Yes,Former smoker,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.6,,Yes,No,Yes,Yes,"Yes, received Tdap",No,No +,31.66,,,,,,,Male,Age 55 to 59,,,,,,,,Pennsylvania,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,Hispanic,1.85,108.86,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +,24.37,,,,,,,Male,Age 30 to 34,,,,,,,,Maine,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,8.0,None of them,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Use them some days,No,"White only, Non-Hispanic",1.75,74.84,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",Yes,No +,40.18,,,,,,,Male,Age 65 to 69,,,,,,,,West Virginia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,Yes,Yes,No,No,No,No,No,No,Yes,Yes,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,127.01,No,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +,27.46,,,,,,,Female,Age 70 to 74,,,,,,,,South Dakota,0.0,7.0,Within past year (anytime less than 12 months ago),Yes,9.0,1 to 5,No,No,No,No,Yes,Yes,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Not at all (right now),Yes,Hispanic,1.65,74.84,Yes,No,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +No,28.06,No,No,No,4.0,15.0,No,Male,25-29,Hispanic,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,27.37,,,,,,,Male,Age 75 to 79,,,,,,,,Washington,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,None of them,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,81.65,Yes,No,Yes,Yes,"Yes, received Tdap",No,No +No,25.85,Yes,No,No,0.0,0.0,No,Male,60-64,Black,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,,,,,,,,Male,Age 65 to 69,,,,,,,,Washington,30.0,10.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,Yes,No,Yes,No,No,No,No,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, +,37.93,,,,,,,Female,Age 60 to 64,,,,,,,,Washington,0.0,30.0,Within past year (anytime less than 12 months ago),No,7.0,1 to 5,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,106.59,No,Yes,,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,28.89,,,,,,,Male,Age 75 to 79,,,,,,,,Colorado,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,86.18,Yes,No,No,Yes,"Yes, received Tdap",No,Yes +No,23.06,Yes,No,No,3.0,0.0,No,Male,35-39,Other,No,Yes,10.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,27.12,,,,,,,Male,Age 80 or older,,,,,,,,Virginia,2.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,Yes,Yes,Yes,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,90.72,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +No,21.52,No,No,No,0.0,0.0,No,Male,80 or older,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,24.21,,,,,,,Male,Age 30 to 34,,,,,,,,Indiana,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,68.04,Yes,No,Yes,Yes,"Yes, received Tdap",No,No +,32.77,,,,,,,Female,Age 50 to 54,,,,,,,,Iowa,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,"No, pre-diabetes or borderline diabetes",No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,83.91,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,25.84,,,,,,,Male,Age 18 to 24,,,,,,,,Montana,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,74.84,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,27.05,,,,,,,Male,Age 60 to 64,,,,,,,,California,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,92.99,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes +No,26.89,No,No,No,0.0,0.0,No,Female,70-74,White,No,Yes,9.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,27.06,Yes,No,No,10.0,0.0,No,Male,75-79,White,No,Yes,10.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,36.02,Yes,No,No,30.0,0.0,Yes,Female,35-39,Other,Yes,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,35.07,,,,,,,Female,Age 45 to 49,,,,,,,,Nebraska,7.0,5.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,89.81,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +No,18.79,No,No,No,0.0,0.0,No,Female,40-44,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,38.74,,,,,,,Female,Age 25 to 29,,,,,,,,Nebraska,5.0,3.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,Yes,No,No,Yes,No,No,No,No,No,Yes,No,No,Yes,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.68,108.86,Yes,Yes,Yes,No,"Yes, received Tdap",No,No +,24.51,,,,,,,Female,Age 60 to 64,,,,,,,,Montana,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,Hispanic,1.57,60.78,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,No +,20.8,,,,,,,Female,Age 80 or older,,,,,,,,Kentucky,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,Yes,No,No,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),No,"White only, Non-Hispanic",1.65,56.7,No,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,Yes +No,28.8,Yes,No,No,14.0,3.0,No,Male,70-74,American Indian/Alaskan Native,"No, borderline diabetes",Yes,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,39.16,No,No,No,0.0,10.0,No,Female,55-59,Black,No,No,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Yes,33.29,Yes,No,No,0.0,3.0,No,Female,80 or older,White,Yes,No,8.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,34.44,,,,,,,Male,Age 25 to 29,,,,,,,,Wisconsin,15.0,10.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,108.86,Yes,Yes,Yes,No,"Yes, received Tdap",No,Yes +,28.32,,,,,,,Female,Age 65 to 69,,,,,,,,South Dakota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,10.0,"6 or more, but not all",Yes,No,No,No,No,No,No,Yes,Yes,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,74.84,No,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,,,,,,,,Male,Age 80 or older,,,,,,,,Kansas,30.0,20.0,Within past year (anytime less than 12 months ago),No,7.0,All,No,No,No,No,No,No,No,Yes,Yes,No,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, +,,,,,,,,Female,Age 35 to 39,,,,,,,,Texas,4.0,0.0,Within past year (anytime less than 12 months ago),Yes,4.0,None of them,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,,,,,,,,,, +,24.87,,,,,,,Female,Age 50 to 54,,,,,,,,Indiana,10.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.57,61.69,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +No,29.95,No,No,No,0.0,15.0,No,Female,30-34,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,49.18,,,,,,,Female,Age 60 to 64,,,,,,,,Illinois,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,142.43,Yes,No,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,27.12,,,,,,,Male,Age 50 to 54,,,,,,,,Kansas,1.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,90.72,Yes,Yes,Yes,No,"Yes, received Tdap",No,No +,28.34,,,,,,,Female,Age 40 to 44,,,,,,,,Nevada,0.0,5.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,72.57,Yes,Yes,No,No,"Yes, received Tdap",No,No +,21.03,,,,,,,Female,Age 35 to 39,,,,,,,,North Carolina,2.0,5.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,52.16,No,Yes,Yes,No,"Yes, received Tdap",No,No +,28.5,,,,,,,Male,Age 80 or older,,,,,,,,Virginia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,10.0,All,No,No,No,No,No,Yes,No,No,Yes,No,Yes,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.88,100.7,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,30.23,,,,,,,Female,Age 18 to 24,,,,,,,,Alaska,0.0,,Within past year (anytime less than 12 months ago),Yes,9.0,,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Use them some days,No,"White only, Non-Hispanic",1.55,72.57,,No,No,No,"No, did not receive any tetanus shot in the past 10 years",Yes,Yes +No,22.81,No,No,No,0.0,0.0,No,Female,60-64,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,24.69,No,No,No,3.0,30.0,No,Female,18-24,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,25.06,No,No,No,0.0,0.0,No,Female,65-69,White,Yes,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,27.37,No,No,No,2.0,0.0,Yes,Male,75-79,White,No,No,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,19.64,,,,,,,Female,Age 75 to 79,,,,,,,,New York,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.65,53.52,No,Yes,Yes,Yes,"Yes, received Tdap",No,No +,24.8,,,,,,,Female,Age 25 to 29,,,,,,,,Iowa,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.6,63.5,Yes,Yes,Yes,,"Yes, received tetanus shot but not sure what type",No,No +No,33.66,No,No,No,30.0,30.0,Yes,Female,65-69,Black,No,No,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Yes,42.27,Yes,No,Yes,0.0,5.0,No,Female,55-59,White,Yes,No,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,20.67,Yes,Yes,No,0.0,7.0,Yes,Female,80 or older,Hispanic,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,31.09,Yes,No,No,0.0,15.0,No,Female,60-64,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Yes,26.63,No,No,No,5.0,0.0,No,Female,80 or older,White,Yes,Yes,8.0,No,Yes,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,42.07,,,,,,,Male,Age 25 to 29,,,,,,,,North Carolina,25.0,0.0,5 or more years ago,Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,Yes,Yes,Yes,Yes,No,No,Former smoker,Use them every day,No,"Other race only, Non-Hispanic",1.57,104.33,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",Yes,No +No,20.34,No,No,No,0.0,2.0,No,Male,45-49,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,30.83,,,,,,,Male,Age 70 to 74,,,,,,,,Minnesota,15.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,All,No,No,No,No,No,Yes,No,No,Yes,No,Yes,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,86.64,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No +,22.15,,,,,,,Female,Age 70 to 74,,,,,,,,West Virginia,,0.0,Within past year (anytime less than 12 months ago),No,6.0,"6 or more, but not all",No,Yes,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,68.04,No,No,No,Yes,"Yes, received tetanus shot but not sure what type",No,No +,19.2,,,,,,,Male,Age 65 to 69,,,,,,,,New Jersey,30.0,5.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,58.97,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,22.15,,,,,,,Female,Age 30 to 34,,,,,,,,Missouri,1.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,68.04,No,No,Yes,Yes,,No,Yes +,,,,,,,,Female,Age 40 to 44,,,,,,,,Georgia,5.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.73,,No,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +No,36.62,Yes,No,No,0.0,0.0,No,Male,50-54,White,No,Yes,4.0,Yes,Yes,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,,,,,,,,Female,Age 40 to 44,,,,,,,,Maryland,0.0,2.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.65,136.08,No,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,40.77,,,,,,,Female,Age 30 to 34,,,,,,,,Nebraska,0.0,3.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,Yes,No,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.65,111.13,Yes,No,Yes,No,"Yes, received Tdap",No,Yes +No,29.29,Yes,No,No,0.0,0.0,No,Female,70-74,White,Yes,Yes,8.0,Yes,Yes,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,28.75,,,,,,,Male,Age 65 to 69,,,,,,,,Maryland,1.0,2.0,Within past year (anytime less than 12 months ago),Yes,7.0,"6 or more, but not all",No,Yes,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.91,104.33,,,Yes,Yes,"Yes, received tetanus shot but not sure what type",, +,28.06,,,,,,,Male,Age 35 to 39,,,,,,,,Indiana,30.0,30.0,Within past year (anytime less than 12 months ago),No,12.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,Yes,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.75,86.18,No,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,28.75,,,,,,,Male,Age 60 to 64,,,,,,,,Pennsylvania,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.91,104.33,No,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +,28.7,,,,,,,Male,Age 25 to 29,,,,,,,,California,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.78,90.72,Yes,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,21.03,,,,,,,Female,Age 40 to 44,,,,,,,,Kansas,0.0,5.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,52.16,,,,,,, +No,31.62,Yes,No,No,0.0,0.0,No,Female,70-74,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,28.13,,,,,,,Male,Age 75 to 79,,,,,,,,Guam,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,Yes,No,No,Yes,No,No,No,Yes,Yes,No,Yes,No,Yes,No,No,Never smoked,Not at all (right now),No,"Other race only, Non-Hispanic",1.73,83.91,Yes,No,Yes,No,"Yes, received Tdap",No,No +,,,,,,,,Female,Age 30 to 34,,,,,,,,Delaware,3.0,0.0,Within past 2 years (1 year but less than 2 years ago),No,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,Hispanic,,65.32,No,Yes,No,No,"Yes, received Tdap",No,No +,33.45,,,,,,,Female,Age 50 to 54,,,,,,,,South Carolina,30.0,10.0,Within past year (anytime less than 12 months ago),No,4.0,"6 or more, but not all",No,No,No,No,No,No,Yes,Yes,Yes,No,No,Yes,Yes,Yes,Yes,Yes,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,99.79,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +,32.01,,,,,,,Female,Age 70 to 74,,,,,,,,Alaska,0.0,2.0,Within past year (anytime less than 12 months ago),No,5.0,None of them,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.57,79.38,No,No,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,27.67,,,,,,,Male,Age 60 to 64,,,,,,,,Arkansas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,All,No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,Former smoker,Use them every day,Yes,"White only, Non-Hispanic",1.73,82.55,Yes,No,No,Yes,"Yes, received tetanus shot but not sure what type",No,No +No,43.07,Yes,No,No,0.0,0.0,No,Male,30-34,White,No,Yes,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,23.17,,,,,,,Female,Age 35 to 39,,,,,,,,Alaska,2.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,8.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,61.23,Yes,No,No,No,"Yes, received tetanus shot, but not Tdap",No,No +,40.6,,,,,,,Female,Age 60 to 64,,,,,,,,New Jersey,0.0,30.0,Within past year (anytime less than 12 months ago),No,10.0,1 to 5,No,No,No,Yes,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.73,121.11,Yes,No,Yes,Yes,"Yes, received Tdap",No,No +,34.33,,,,,,,Female,Age 35 to 39,,,,,,,,Washington,3.0,30.0,Within past year (anytime less than 12 months ago),No,7.0,1 to 5,No,No,No,No,No,No,Yes,No,No,No,No,No,Yes,No,No,No,Former smoker,Not at all (right now),No,Hispanic,1.63,90.72,No,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,22.15,,,,,,,Male,Age 30 to 34,,,,,,,,Indiana,0.0,3.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.75,68.04,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No +No,30.17,Yes,No,No,0.0,0.0,No,Male,60-64,White,Yes,Yes,7.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,25.8,,,,,,,Male,Age 25 to 29,,,,,,,,South Dakota,0.0,2.0,,Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.8,83.91,No,,No,,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,38.52,,,,,,,Male,Age 40 to 44,,,,,,,,Indiana,10.0,25.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,Yes,No,Yes,Yes,No,No,No,Yes,No,No,Current smoker - now smokes every day,Use them some days,Yes,"White only, Non-Hispanic",1.88,136.08,Yes,Yes,No,No,"Yes, received Tdap",Yes,No +Yes,25.82,No,No,Yes,30.0,4.0,Yes,Male,65-69,Other,Yes,Yes,4.0,No,Yes,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,25.84,No,No,No,0.0,0.0,No,Male,35-39,White,No,No,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,22.81,Yes,No,No,0.0,0.0,No,Male,45-49,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,21.7,Yes,No,No,0.0,0.0,Yes,Male,65-69,Black,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,28.34,,,,,,,Female,Age 80 or older,,,,,,,,Michigan,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,72.57,Yes,No,Yes,Yes,"Yes, received Tdap",No,No +,21.93,,,,,,,Male,Age 60 to 64,,,,,,,,Louisiana,15.0,15.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,Yes,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,63.5,No,Yes,Yes,,"Yes, received tetanus shot but not sure what type",No,No +No,37.44,Yes,No,No,0.0,2.0,No,Female,55-59,White,No,No,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,31.38,,,,,,,Male,Age 40 to 44,,,,,,,,Guam,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,6.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Not at all (right now),No,"Other race only, Non-Hispanic",1.8,102.06,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +No,27.76,Yes,No,No,30.0,0.0,No,Male,70-74,Asian,No,Yes,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,18.79,No,No,No,28.0,25.0,No,Female,60-64,White,No,Yes,8.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,22.96,Yes,No,No,0.0,0.0,No,Male,70-74,White,No,Yes,8.0,Yes,Yes,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Yes,36.58,No,No,No,20.0,0.0,Yes,Female,70-74,White,No,No,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,31.89,,,,,,,Female,Age 45 to 49,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,Yes,No,No,No,No,Yes,No,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.6,81.65,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,Yes +,25.61,,,,,,,Female,Age 75 to 79,,,,,,,,Utah,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,63.5,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,25.85,,,,,,,Female,Age 65 to 69,,,,,,,,Wisconsin,15.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,4.0,1 to 5,No,No,No,Yes,No,Yes,No,No,Yes,No,No,No,No,Yes,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.73,77.11,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,28.5,,,,,,,Female,Age 30 to 34,,,,,,,,Washington,1.0,25.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,Yes,No,Yes,Yes,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,82.55,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No +,32.72,,,,,,,Male,Age 60 to 64,,,,,,,,Illinois,0.0,0.0,Within past year (anytime less than 12 months ago),No,5.0,1 to 5,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,112.49,Yes,No,Yes,Yes,"Yes, received Tdap",No,No +,30.04,,,,,,,Female,Age 18 to 24,,,,,,,,Alaska,4.0,7.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,79.38,No,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No +No,23.3,No,No,No,0.0,0.0,No,Male,55-59,Other,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,29.16,,,,,,,Male,Age 40 to 44,,,,,,,,Iowa,1.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.83,97.52,No,Yes,Yes,,"Yes, received Tdap",No,Yes +,14.14,,,,,,,Female,Age 35 to 39,,,,,,,,Connecticut,3.0,12.0,Within past year (anytime less than 12 months ago),No,6.0,"6 or more, but not all",No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.65,38.56,No,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,Yes +,33.91,,,,,,,Male,Age 40 to 44,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,113.4,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +,26.78,,,,,,,Male,Age 55 to 59,,,,,,,,New York,1.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"Other race only, Non-Hispanic",1.8,87.09,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,29.23,,,,,,,Female,Age 65 to 69,,,,,,,,Idaho,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,All,No,No,No,Yes,No,No,No,No,Yes,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,74.84,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,34.33,,,,,,,Female,Age 50 to 54,,,,,,,,Kentucky,0.0,2.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,90.72,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,18.79,,,,,,,Female,Age 50 to 54,,,,,,,,Louisiana,3.0,3.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,54.43,Yes,,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,27.46,,,,,,,Male,Age 40 to 44,,,,,,,,Massachusetts,,,Within past year (anytime less than 12 months ago),No,7.0,None of them,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,,,,,Hispanic,1.65,74.84,Yes,No,No,No,,No,No +,22.8,,,,,,,Female,Age 50 to 54,,,,,,,,Washington,30.0,5.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,62.14,Yes,Yes,Yes,No,"Yes, received Tdap",No,No +,29.26,,,,,,,Female,Age 80 or older,,,,,,,,Puerto Rico,30.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,Yes,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.57,72.57,No,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +No,27.96,No,No,No,0.0,7.0,No,Female,80 or older,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,28.34,,,,,,,Female,Age 50 to 54,,,,,,,,Utah,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,"White only, Non-Hispanic",1.6,72.57,,,,,,, +,32.98,,,,,,,Male,Age 60 to 64,,,,,,,,Utah,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,113.4,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,32.32,,,,,,,Male,Age 60 to 64,,,,,,,,Colorado,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.85,111.13,Yes,Yes,No,No,"Yes, received Tdap",No,Yes +,29.65,,,,,,,Male,Age 70 to 74,,,,,,,,Rhode Island,14.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,88.45,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +,23.09,,,,,,,Male,Age 18 to 24,,,,,,,,Kentucky,0.0,7.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.85,79.38,Yes,No,No,No,"Yes, received Tdap",No,Yes +,21.97,,,,,,,Female,Age 60 to 64,,,,,,,,Connecticut,0.0,2.0,Within past year (anytime less than 12 months ago),Yes,4.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,58.06,Yes,Yes,Yes,No,"Yes, received Tdap",No,Yes +No,21.73,No,No,No,21.0,0.0,No,Female,50-54,Hispanic,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,19.97,No,No,No,0.0,15.0,No,Female,80 or older,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,21.61,No,No,No,0.0,0.0,No,Male,25-29,White,No,Yes,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,23.03,,,,,,,Female,Age 80 or older,,,,,,,,Maine,30.0,0.0,Within past year (anytime less than 12 months ago),No,10.0,None of them,Yes,No,Yes,Yes,No,Yes,No,No,No,No,No,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,58.97,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No +,25.84,,,,,,,Male,Age 50 to 54,,,,,,,,Florida,3.0,4.0,5 or more years ago,Yes,4.0,None of them,No,No,No,Yes,No,No,No,No,Yes,No,No,Yes,Yes,No,Yes,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,79.38,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,24.79,,,,,,,Female,,,,,,,,,California,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,67.59,Yes,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +Yes,24.61,Yes,No,No,30.0,1.0,Yes,Female,80 or older,White,No,No,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,22.5,No,No,No,3.0,0.0,No,Female,65-69,White,No,Yes,8.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,26.57,No,No,No,0.0,7.0,No,Female,30-34,Black,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Yes,44.1,No,No,No,3.0,0.0,Yes,Female,55-59,White,No,No,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,25.1,,,,,,,Male,Age 45 to 49,,,,,,,,Mississippi,0.0,0.0,5 or more years ago,Yes,10.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.75,77.11,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,36.62,Yes,No,No,0.0,0.0,No,Male,55-59,White,No,Yes,8.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,21.26,,,,,,,Female,Age 25 to 29,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.6,54.43,Yes,No,No,No,"Yes, received Tdap",No,No +,22.3,,,,,,,Female,Age 55 to 59,,,,,,,,New Mexico,30.0,2.0,Within past year (anytime less than 12 months ago),No,10.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Yes,Yes,Never smoked,Never used e-cigarettes in my entire life,Yes,"Other race only, Non-Hispanic",1.65,60.78,No,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +,22.96,,,,,,,Male,Age 80 or older,,,,,,,,West Virginia,30.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,"6 or more, but not all",No,No,No,No,Yes,No,No,No,Yes,No,Yes,Yes,No,Yes,No,Yes,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,72.57,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +No,22.05,No,No,No,0.0,0.0,No,Male,45-49,Hispanic,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,28.7,No,No,No,2.0,0.0,No,Male,70-74,White,No,Yes,7.0,No,Yes,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,28.06,Yes,No,No,2.0,0.0,No,Male,25-29,White,No,Yes,4.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,18.6,,,,,,,Female,Age 18 to 24,,,,,,,,Delaware,7.0,26.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Multiracial, Non-Hispanic",1.6,47.63,Yes,No,Yes,No,"Yes, received Tdap",Yes,No +,26.63,,,,,,,Male,Age 50 to 54,,,,,,,,California,0.0,0.0,5 or more years ago,No,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.7,77.11,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,19.8,,,,,,,Male,Age 35 to 39,,,,,,,,Texas,15.0,2.0,Within past 2 years (1 year but less than 2 years ago),No,6.0,1 to 5,No,No,No,No,No,Yes,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,62.6,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +,29.65,,,,,,,Female,Age 65 to 69,,,,,,,,Maine,14.0,10.0,Within past 5 years (2 years but less than 5 years ago),Yes,,"6 or more, but not all",No,No,No,No,No,No,Yes,No,Yes,No,No,No,Yes,Yes,Yes,No,,Never used e-cigarettes in my entire life,No,,1.73,88.45,No,No,Yes,Yes,,No,No +,23.71,,,,,,,Male,Age 25 to 29,,,,,,,,Virgin Islands,0.0,2.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,77.11,Yes,Yes,No,No,"Yes, received Tdap",No,Yes +,22.71,,,,,,,Female,Age 18 to 24,,,,,,,,New York,2.0,3.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.7,65.77,Yes,Yes,No,No,"Yes, received Tdap",No,Yes +,30.65,,,,,,,Male,Age 70 to 74,,,,,,,,South Carolina,0.0,2.0,Within past year (anytime less than 12 months ago),No,9.0,None of them,No,No,No,No,No,No,No,Yes,Yes,Yes,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.83,102.51,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +No,51.92,Yes,No,No,1.0,15.0,No,Female,18-24,White,Yes,Yes,8.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,39.16,No,No,No,0.0,0.0,No,Female,35-39,White,No,No,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,29.29,,,,,,,Male,Age 18 to 24,,,,,,,,Georgia,0.0,0.0,5 or more years ago,No,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Use them some days,No,Hispanic,1.52,68.04,Yes,No,No,No,"Yes, received Tdap",No,No +No,21.93,Yes,No,No,0.0,30.0,No,Female,18-24,White,No,Yes,8.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,,,,,,,,Female,Age 50 to 54,,,,,,,,Ohio,1.0,0.0,,Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,Yes,No,Yes,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",,,No,No,No,No,"Yes, received Tdap",No,Yes +No,47.03,Yes,No,No,30.0,0.0,Yes,Female,60-64,White,No,No,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,19.37,,,,,,,Female,Age 50 to 54,,,,,,,,Washington,30.0,20.0,Within past year (anytime less than 12 months ago),No,7.0,"6 or more, but not all",No,Yes,No,Yes,No,Yes,No,No,Yes,No,No,Yes,Yes,Yes,Yes,Yes,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,54.43,Yes,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",Yes,Yes +,24.89,,,,,,,Female,Age 60 to 64,,,,,,,,California,20.0,25.0,Within past year (anytime less than 12 months ago),Yes,7.0,"6 or more, but not all",No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes some days,Not at all (right now),No,Hispanic,1.63,65.77,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,23.67,,,,,,,Male,Age 35 to 39,,,,,,,,Alaska,0.0,0.0,,Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,74.84,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,20.98,,,,,,,Female,Age 30 to 34,,,,,,,,Alaska,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,58.97,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,34.11,,,,,,,Female,Age 25 to 29,,,,,,,,Colorado,30.0,30.0,5 or more years ago,Yes,8.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.65,92.99,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Tested positive using home test without a health professional +No,25.1,No,No,No,0.0,0.0,No,Male,18-24,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,29.53,,,,,,,Male,Age 45 to 49,,,,,,,,South Dakota,30.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.88,104.33,No,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,20.92,,,,,,,Male,Age 70 to 74,,,,,,,,Washington,3.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.8,68.04,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes +No,26.13,No,No,No,0.0,0.0,No,Male,75-79,White,No,No,3.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,23.96,,,,,,,Male,Age 80 or older,,,,,,,,New York,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,65.32,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_002/history.csv b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_002/history.csv index 1f3bf5697d3afd7390b0692092fd15c81b25de30..283942457e8255c507a6d0451c22a5486afc915c 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_002/history.csv +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_002/history.csv @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:ecf5f7d0d4be8c88c826acd114cc264492c5e74329f517187905dcef76cb5db0 -size 271613271 +oid sha256:952707f474307bee55636a48ef2b8f9f571d02c244d9bf89572c9611d9872d7d +size 270592628 diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_002/info.json b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_002/info.json index 53272e3074451c673817a05ef4da5ee438d07863..c876b7e9aa18ec2175d72a1a6b59bf487c77e98c 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_002/info.json +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_002/info.json @@ -1,401 +1,393 @@ -{ - "name": "Indicators of Heart Disease (2022 UPDATE)", - "source": "https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease/data", - "data_intro": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", - "is_splited": false, - "overall_size": 1010949, - "train_size": 0, - "test_size": 0, - "c_classes": 46, - "n_classes": 9, - "cat_feature_intro": { - "HeartDisease": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", - "Smoking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", - "AlcoholDrinking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", - "Stroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", - "DiffWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", - "Sex": "Sex is a biological factor that can influence the risk of heart disease. Men and women may experience different symptoms and risk factors for heart disease.", - "AgeCategory": "Age is a significant risk factor for heart disease. As people age, their risk of developing heart disease increases. It is important to monitor heart health as individuals get older.", - "Race": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", - "Diabetic": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", - "PhysicalActivity": "Regular physical activity is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", - "GenHealth": "General health status can influence the risk of heart disease. It is important to maintain overall health and address any underlying health issues to reduce the risk of heart-related complications.", - "Asthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", - "KidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", - "SkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", - "State": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", - "GeneralHealth": "General health status can influence the risk of heart disease. It is important to maintain overall health and address any underlying health issues to reduce the risk of heart-related complications.", - "LastCheckupTime": "The time since the last medical checkup can be an important factor in heart health. Regular checkups can help identify and address risk factors for heart disease early.", - "PhysicalActivities": "Engaging in regular physical activities is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", - "RemovedTeeth": "The number of removed teeth can be an indicator of overall health, including heart health. Poor oral health has been linked to an increased risk of heart disease.", - "HadHeartAttack": "As described above, the original dataset of nearly 300 variables was reduced to 40 variables. In addition to classical EDA, this dataset can be used to apply a number of machine learning methods, especially classifier models (logistic regression, SVM, random forest, etc.). You should treat the variable \"HadHeartAttack\" as binary (\"Yes\" - respondent had heart disease; \"No\" - respondent did not have heart disease). Note, however, that the classes are unbalanced, so the classic approach of applying a model is not advisable. Fixing the weights/undersampling should yield much better results. Based on the data set, I built a logistic regression model and embedded it in an application that might inspire you: https://share.streamlit.io/kamilpytlak/heart-condition-checker/main/app.py. Can you indicate which variables have a significant effect on the likelihood of heart disease?", - "HadAngina": "Angina is a type of chest pain caused by reduced blood flow to the heart. It is a symptom of coronary artery disease and can indicate an increased risk of heart disease.", - "HadStroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", - "HadAsthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", - "HadSkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", - "HadCOPD": "Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease that causes obstructed airflow from the lungs. It can increase the risk of heart disease.", - "HadDepressiveDisorder": "Depressive disorders can affect overall health and increase the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", - "HadKidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", - "HadArthritis": "Arthritis is a condition that causes inflammation of the joints. It can affect overall health and increase the risk of heart disease.", - "HadDiabetes": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", - "DeafOrHardOfHearing": "Hearing loss can affect overall health and quality of life. It is important to address and manage hearing issues to maintain overall health and reduce the risk of heart disease.", - "BlindOrVisionDifficulty": "Vision difficulties can affect overall health and quality of life. It is important to address and manage vision issues to maintain overall health and reduce the risk of heart disease.", - "DifficultyConcentrating": "Difficulty concentrating can be a sign of underlying health issues, including heart disease. It is important to address cognitive issues to improve overall health and reduce the risk of heart-related complications.", - "DifficultyWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", - "DifficultyDressingBathing": "Difficulty with daily activities such as dressing and bathing can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", - "DifficultyErrands": "Difficulty running errands can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", - "SmokerStatus": "Smoking is a major risk factor for heart disease. It is important to address and manage smoking habits to reduce the risk of heart-related complications.", - "ECigaretteUsage": "E-cigarette usage can affect heart health. It is important to monitor and manage e-cigarette usage to reduce the risk of heart disease.", - "ChestScan": "Chest scans can help identify underlying health issues, including heart disease. It is important to monitor and address any abnormalities found in chest scans to maintain overall health.", - "RaceEthnicityCategory": "Race and ethnicity can influence the risk of heart disease. It is important to consider these factors when assessing and managing heart health.", - "AlcoholDrinkers": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", - "HIVTesting": "HIV testing is important for overall health. It is important to address and manage HIV status to reduce the risk of heart-related complications.", - "FluVaxLast12": "Receiving a flu vaccine in the last 12 months can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", - "PneumoVaxEver": "Receiving a pneumococcal vaccine can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", - "TetanusLast10Tdap": "Receiving a tetanus vaccine in the last 10 years can help reduce the risk of infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", - "HighRiskLastYear": "Being at high risk for health issues in the last year can affect overall health and increase the risk of heart disease. It is important to address and manage high-risk factors to reduce the risk of heart-related complications.", - "CovidPos": "Testing positive for COVID-19 can affect overall health and increase the risk of heart disease. It is important to monitor and manage COVID-19 symptoms to reduce the risk of heart-related complications." - }, - "num_feature_intro": { - "BMI": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", - "PhysicalHealth": "Physical health is an important factor in overall well-being and can influence the risk of heart disease. It is important to maintain good physical health to reduce the risk of heart-related complications.", - "MentalHealth": "Mental health is an important factor in overall well-being and can influence the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", - "SleepTime": "Sleep time is an important factor in overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", - "PhysicalHealthDays": "The number of days with good physical health can be an indicator of overall well-being. It is important to maintain good physical health to reduce the risk of heart-related complications.", - "MentalHealthDays": "The number of days with good mental health can be an indicator of overall well-being. It is important to address and manage mental health to reduce the risk of heart-related complications.", - "SleepHours": "The number of hours of sleep can affect overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", - "HeightInMeters": "Height can be an indicator of overall health. It is important to monitor and maintain a healthy height-to-weight ratio to reduce the risk of heart disease.", - "WeightInKilograms": "Weight can be an indicator of overall health. It is important to maintain a healthy weight to reduce the risk of heart disease." - }, - "evaluation_metric": "The evaluation metric for this dataset can include accuracy, precision, recall, F1-score, and AUC-ROC. These metrics can help assess the performance of machine learning models in predicting heart disease.", - "task_type": "classification", - "target": "GeneralHealth", - "num_feature_value": { - "BMI": [ - 12.02, - 99.64 - ], - "HeightInMeters": [ - 0.91, - 2.41 - ], - "MentalHealth": [ - 0.0, - 30.0 - ], - "MentalHealthDays": [ - 0.0, - 30.0 - ], - "PhysicalHealth": [ - 0.0, - 30.0 - ], - "PhysicalHealthDays": [ - 0.0, - 30.0 - ], - "SleepHours": [ - 1.0, - 24.0 - ], - "SleepTime": [ - 1.0, - 24.0 - ], - "WeightInKilograms": [ - 22.68, - 292.57 - ] - }, - "cat_feature_value": { - "AgeCategory": [ - "18-24", - "25-29", - "30-34", - "35-39", - "40-44", - "45-49", - "50-54", - "55-59", - "60-64", - "65-69", - "70-74", - "75-79", - "80 or older", - "Age 18 to 24", - "Age 25 to 29", - "Age 30 to 34", - "Age 35 to 39", - "Age 40 to 44", - "Age 45 to 49", - "Age 50 to 54", - "Age 55 to 59", - "Age 60 to 64", - "Age 65 to 69", - "Age 70 to 74", - "Age 75 to 79", - "Age 80 or older" - ], - "AlcoholDrinkers": [ - "No", - "Yes" - ], - "AlcoholDrinking": [ - "No", - "Yes" - ], - "Asthma": [ - "No", - "Yes" - ], - "BlindOrVisionDifficulty": [ - "No", - "Yes" - ], - "ChestScan": [ - "No", - "Yes" - ], - "CovidPos": [ - "No", - "Tested positive using home test without a health professional", - "Yes" - ], - "DeafOrHardOfHearing": [ - "No", - "Yes" - ], - "Diabetic": [ - "No", - "No, borderline diabetes", - "Yes", - "Yes (during pregnancy)" - ], - "DiffWalking": [ - "No", - "Yes" - ], - "DifficultyConcentrating": [ - "No", - "Yes" - ], - "DifficultyDressingBathing": [ - "No", - "Yes" - ], - "DifficultyErrands": [ - "No", - "Yes" - ], - "DifficultyWalking": [ - "No", - "Yes" - ], - "ECigaretteUsage": [ - "Never used e-cigarettes in my entire life", - "Not at all (right now)", - "Use them every day", - "Use them some days" - ], - "FluVaxLast12": [ - "No", - "Yes" - ], - "GenHealth": [ - "Excellent", - "Fair", - "Good", - "Poor", - "Very good" - ], - "GeneralHealth": [ - "Excellent", - "Fair", - "Good", - "Poor", - "Very good" - ], - "HIVTesting": [ - "No", - "Yes" - ], - "HadAngina": [ - "No", - "Yes" - ], - "HadArthritis": [ - "No", - "Yes" - ], - "HadAsthma": [ - "No", - "Yes" - ], - "HadCOPD": [ - "No", - "Yes" - ], - "HadDepressiveDisorder": [ - "No", - "Yes" - ], - "HadDiabetes": [ - "No", - "No, pre-diabetes or borderline diabetes", - "Yes", - "Yes, but only during pregnancy (female)" - ], - "HadHeartAttack": [ - "No", - "Yes" - ], - "HadKidneyDisease": [ - "No", - "Yes" - ], - "HadSkinCancer": [ - "No", - "Yes" - ], - "HadStroke": [ - "No", - "Yes" - ], - "HeartDisease": [ - "No", - "Yes" - ], - "HighRiskLastYear": [ - "No", - "Yes" - ], - "KidneyDisease": [ - "No", - "Yes" - ], - "LastCheckupTime": [ - "5 or more years ago", - "Within past 2 years (1 year but less than 2 years ago)", - "Within past 5 years (2 years but less than 5 years ago)", - "Within past year (anytime less than 12 months ago)" - ], - "PhysicalActivities": [ - "No", - "Yes" - ], - "PhysicalActivity": [ - "No", - "Yes" - ], - "PneumoVaxEver": [ - "No", - "Yes" - ], - "Race": [ - "American Indian/Alaskan Native", - "Asian", - "Black", - "Hispanic", - "Other", - "White" - ], - "RaceEthnicityCategory": [ - "Black only, Non-Hispanic", - "Hispanic", - "Multiracial, Non-Hispanic", - "Other race only, Non-Hispanic", - "White only, Non-Hispanic" - ], - "RemovedTeeth": [ - "1 to 5", - "6 or more, but not all", - "All", - "None of them" - ], - "Sex": [ - "Female", - "Male" - ], - "SkinCancer": [ - "No", - "Yes" - ], - "SmokerStatus": [ - "Current smoker - now smokes every day", - "Current smoker - now smokes some days", - "Former smoker", - "Never smoked" - ], - "Smoking": [ - "No", - "Yes" - ], - "State": [ - "Alabama", - "Alaska", - "Arizona", - "Arkansas", - "California", - "Colorado", - "Connecticut", - "Delaware", - "District of Columbia", - "Florida", - "Georgia", - "Guam", - "Hawaii", - "Idaho", - "Illinois", - "Indiana", - "Iowa", - "Kansas", - "Kentucky", - "Louisiana", - "Maine", - "Maryland", - "Massachusetts", - "Michigan", - "Minnesota", - "Mississippi", - "Missouri", - "Montana", - "Nebraska", - "Nevada", - "New Hampshire", - "New Jersey", - "New Mexico", - "New York", - "North Carolina", - "North Dakota", - "Ohio", - "Oklahoma", - "Oregon", - "Pennsylvania", - "Puerto Rico", - "Rhode Island", - "South Carolina", - "South Dakota", - "Tennessee", - "Texas", - "Utah", - "Vermont", - "Virgin Islands", - "Virginia", - "Washington", - "West Virginia", - "Wisconsin", - "Wyoming" - ], - "Stroke": [ - "No", - "Yes" - ], - "TetanusLast10Tdap": [ - "No, did not receive any tetanus shot in the past 10 years", - "Yes, received Tdap", - "Yes, received tetanus shot but not sure what type", - "Yes, received tetanus shot, but not Tdap" - ] - } -} \ No newline at end of file +{ + "name": "Indicators of Heart Disease (2022 UPDATE)", + "source": "https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease/data", + "data_intro": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", + "is_splited": false, + "overall_size": 1010949, + "train_size": 0, + "test_size": 0, + "c_classes": 45, + "n_classes": 9, + "cat_feature_intro": { + "HeartDisease": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", + "Smoking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", + "AlcoholDrinking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", + "Stroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", + "DiffWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", + "Sex": "Sex is a biological factor that can influence the risk of heart disease. Men and women may experience different symptoms and risk factors for heart disease.", + "AgeCategory": "Age is a significant risk factor for heart disease. As people age, their risk of developing heart disease increases. It is important to monitor heart health as individuals get older.", + "Race": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", + "Diabetic": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", + "PhysicalActivity": "Regular physical activity is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", + "Asthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", + "KidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", + "SkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", + "State": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", + "GeneralHealth": "Self-reported general health from BRFSS GENHLTH: Excellent, Very good, Good, Fair, or Poor.", + "LastCheckupTime": "The time since the last medical checkup can be an important factor in heart health. Regular checkups can help identify and address risk factors for heart disease early.", + "PhysicalActivities": "Engaging in regular physical activities is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", + "RemovedTeeth": "The number of removed teeth can be an indicator of overall health, including heart health. Poor oral health has been linked to an increased risk of heart disease.", + "HadHeartAttack": "As described above, the original dataset of nearly 300 variables was reduced to 40 variables. In addition to classical EDA, this dataset can be used to apply a number of machine learning methods, especially classifier models (logistic regression, SVM, random forest, etc.). You should treat the variable \"HadHeartAttack\" as binary (\"Yes\" - respondent had heart disease; \"No\" - respondent did not have heart disease). Note, however, that the classes are unbalanced, so the classic approach of applying a model is not advisable. Fixing the weights/undersampling should yield much better results. Based on the data set, I built a logistic regression model and embedded it in an application that might inspire you: https://share.streamlit.io/kamilpytlak/heart-condition-checker/main/app.py. Can you indicate which variables have a significant effect on the likelihood of heart disease?", + "HadAngina": "Angina is a type of chest pain caused by reduced blood flow to the heart. It is a symptom of coronary artery disease and can indicate an increased risk of heart disease.", + "HadStroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", + "HadAsthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", + "HadSkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", + "HadCOPD": "Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease that causes obstructed airflow from the lungs. It can increase the risk of heart disease.", + "HadDepressiveDisorder": "Depressive disorders can affect overall health and increase the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", + "HadKidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", + "HadArthritis": "Arthritis is a condition that causes inflammation of the joints. It can affect overall health and increase the risk of heart disease.", + "HadDiabetes": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", + "DeafOrHardOfHearing": "Hearing loss can affect overall health and quality of life. It is important to address and manage hearing issues to maintain overall health and reduce the risk of heart disease.", + "BlindOrVisionDifficulty": "Vision difficulties can affect overall health and quality of life. It is important to address and manage vision issues to maintain overall health and reduce the risk of heart disease.", + "DifficultyConcentrating": "Difficulty concentrating can be a sign of underlying health issues, including heart disease. It is important to address cognitive issues to improve overall health and reduce the risk of heart-related complications.", + "DifficultyWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", + "DifficultyDressingBathing": "Difficulty with daily activities such as dressing and bathing can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", + "DifficultyErrands": "Difficulty running errands can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", + "SmokerStatus": "Smoking is a major risk factor for heart disease. It is important to address and manage smoking habits to reduce the risk of heart-related complications.", + "ECigaretteUsage": "E-cigarette usage can affect heart health. It is important to monitor and manage e-cigarette usage to reduce the risk of heart disease.", + "ChestScan": "Chest scans can help identify underlying health issues, including heart disease. It is important to monitor and address any abnormalities found in chest scans to maintain overall health.", + "RaceEthnicityCategory": "Race and ethnicity can influence the risk of heart disease. It is important to consider these factors when assessing and managing heart health.", + "AlcoholDrinkers": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", + "HIVTesting": "Whether the respondent has ever been tested for HIV (Yes/No), not the result of that test.", + "FluVaxLast12": "Receiving a flu vaccine in the last 12 months can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", + "PneumoVaxEver": "Receiving a pneumococcal vaccine can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", + "TetanusLast10Tdap": "Receiving a tetanus vaccine in the last 10 years can help reduce the risk of infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", + "HighRiskLastYear": "Being at high risk for health issues in the last year can affect overall health and increase the risk of heart disease. It is important to address and manage high-risk factors to reduce the risk of heart-related complications.", + "CovidPos": "Testing positive for COVID-19 can affect overall health and increase the risk of heart disease. It is important to monitor and manage COVID-19 symptoms to reduce the risk of heart-related complications." + }, + "num_feature_intro": { + "BMI": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", + "PhysicalHealth": "Physical health is an important factor in overall well-being and can influence the risk of heart disease. It is important to maintain good physical health to reduce the risk of heart-related complications.", + "MentalHealth": "Mental health is an important factor in overall well-being and can influence the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", + "SleepTime": "Sleep time is an important factor in overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", + "PhysicalHealthDays": "The number of days with good physical health can be an indicator of overall well-being. It is important to maintain good physical health to reduce the risk of heart-related complications.", + "MentalHealthDays": "The number of days with good mental health can be an indicator of overall well-being. It is important to address and manage mental health to reduce the risk of heart-related complications.", + "SleepHours": "The number of hours of sleep can affect overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", + "HeightInMeters": "Height can be an indicator of overall health. It is important to monitor and maintain a healthy height-to-weight ratio to reduce the risk of heart disease.", + "WeightInKilograms": "Weight can be an indicator of overall health. It is important to maintain a healthy weight to reduce the risk of heart disease." + }, + "evaluation_metric": "The evaluation metric for this dataset can include accuracy, precision, recall, F1-score, and AUC-ROC. These metrics can help assess the performance of machine learning models in predicting heart disease.", + "task_type": "classification", + "target": "GeneralHealth", + "num_feature_value": { + "BMI": [ + 12.02, + 99.64 + ], + "HeightInMeters": [ + 0.91, + 2.41 + ], + "MentalHealth": [ + 0.0, + 30.0 + ], + "MentalHealthDays": [ + 0.0, + 30.0 + ], + "PhysicalHealth": [ + 0.0, + 30.0 + ], + "PhysicalHealthDays": [ + 0.0, + 30.0 + ], + "SleepHours": [ + 1.0, + 24.0 + ], + "SleepTime": [ + 1.0, + 24.0 + ], + "WeightInKilograms": [ + 22.68, + 292.57 + ] + }, + "cat_feature_value": { + "AgeCategory": [ + "18-24", + "25-29", + "30-34", + "35-39", + "40-44", + "45-49", + "50-54", + "55-59", + "60-64", + "65-69", + "70-74", + "75-79", + "80 or older", + "Age 18 to 24", + "Age 25 to 29", + "Age 30 to 34", + "Age 35 to 39", + "Age 40 to 44", + "Age 45 to 49", + "Age 50 to 54", + "Age 55 to 59", + "Age 60 to 64", + "Age 65 to 69", + "Age 70 to 74", + "Age 75 to 79", + "Age 80 or older" + ], + "AlcoholDrinkers": [ + "No", + "Yes" + ], + "AlcoholDrinking": [ + "No", + "Yes" + ], + "Asthma": [ + "No", + "Yes" + ], + "BlindOrVisionDifficulty": [ + "No", + "Yes" + ], + "ChestScan": [ + "No", + "Yes" + ], + "CovidPos": [ + "No", + "Tested positive using home test without a health professional", + "Yes" + ], + "DeafOrHardOfHearing": [ + "No", + "Yes" + ], + "Diabetic": [ + "No", + "No, borderline diabetes", + "Yes", + "Yes (during pregnancy)" + ], + "DiffWalking": [ + "No", + "Yes" + ], + "DifficultyConcentrating": [ + "No", + "Yes" + ], + "DifficultyDressingBathing": [ + "No", + "Yes" + ], + "DifficultyErrands": [ + "No", + "Yes" + ], + "DifficultyWalking": [ + "No", + "Yes" + ], + "ECigaretteUsage": [ + "Never used e-cigarettes in my entire life", + "Not at all (right now)", + "Use them every day", + "Use them some days" + ], + "FluVaxLast12": [ + "No", + "Yes" + ], + "GeneralHealth": [ + "Excellent", + "Very good", + "Good", + "Fair", + "Poor" + ], + "HIVTesting": [ + "No", + "Yes" + ], + "HadAngina": [ + "No", + "Yes" + ], + "HadArthritis": [ + "No", + "Yes" + ], + "HadAsthma": [ + "No", + "Yes" + ], + "HadCOPD": [ + "No", + "Yes" + ], + "HadDepressiveDisorder": [ + "No", + "Yes" + ], + "HadDiabetes": [ + "No", + "No, pre-diabetes or borderline diabetes", + "Yes", + "Yes, but only during pregnancy (female)" + ], + "HadHeartAttack": [ + "No", + "Yes" + ], + "HadKidneyDisease": [ + "No", + "Yes" + ], + "HadSkinCancer": [ + "No", + "Yes" + ], + "HadStroke": [ + "No", + "Yes" + ], + "HeartDisease": [ + "No", + "Yes" + ], + "HighRiskLastYear": [ + "No", + "Yes" + ], + "KidneyDisease": [ + "No", + "Yes" + ], + "LastCheckupTime": [ + "5 or more years ago", + "Within past 2 years (1 year but less than 2 years ago)", + "Within past 5 years (2 years but less than 5 years ago)", + "Within past year (anytime less than 12 months ago)" + ], + "PhysicalActivities": [ + "No", + "Yes" + ], + "PhysicalActivity": [ + "No", + "Yes" + ], + "PneumoVaxEver": [ + "No", + "Yes" + ], + "Race": [ + "American Indian/Alaskan Native", + "Asian", + "Black", + "Hispanic", + "Other", + "White" + ], + "RaceEthnicityCategory": [ + "Black only, Non-Hispanic", + "Hispanic", + "Multiracial, Non-Hispanic", + "Other race only, Non-Hispanic", + "White only, Non-Hispanic" + ], + "RemovedTeeth": [ + "1 to 5", + "6 or more, but not all", + "All", + "None of them" + ], + "Sex": [ + "Female", + "Male" + ], + "SkinCancer": [ + "No", + "Yes" + ], + "SmokerStatus": [ + "Current smoker - now smokes every day", + "Current smoker - now smokes some days", + "Former smoker", + "Never smoked" + ], + "Smoking": [ + "No", + "Yes" + ], + "State": [ + "Alabama", + "Alaska", + "Arizona", + "Arkansas", + "California", + "Colorado", + "Connecticut", + "Delaware", + "District of Columbia", + "Florida", + "Georgia", + "Guam", + "Hawaii", + "Idaho", + "Illinois", + "Indiana", + "Iowa", + "Kansas", + "Kentucky", + "Louisiana", + "Maine", + "Maryland", + "Massachusetts", + "Michigan", + "Minnesota", + "Mississippi", + "Missouri", + "Montana", + "Nebraska", + "Nevada", + "New Hampshire", + "New Jersey", + "New Mexico", + "New York", + "North Carolina", + "North Dakota", + "Ohio", + "Oklahoma", + "Oregon", + "Pennsylvania", + "Puerto Rico", + "Rhode Island", + "South Carolina", + "South Dakota", + "Tennessee", + "Texas", + "Utah", + "Vermont", + "Virgin Islands", + "Virginia", + "Washington", + "West Virginia", + "Wisconsin", + "Wyoming" + ], + "Stroke": [ + "No", + "Yes" + ], + "TetanusLast10Tdap": [ + "No, did not receive any tetanus shot in the past 10 years", + "Yes, received Tdap", + "Yes, received tetanus shot but not sure what type", + "Yes, received tetanus shot, but not Tdap" + ] + } +} diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_002/test_current.csv b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_002/test_current.csv index 0e4fe1f3f76b3db0fb9dfd06bacf52fdc6485290..f64ec3b140b7c6a4d7635f091f4c373bec0376df 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_002/test_current.csv +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_002/test_current.csv @@ -1,16 +1,26 @@ -HeartDisease,BMI,Smoking,AlcoholDrinking,Stroke,PhysicalHealth,MentalHealth,DiffWalking,Sex,AgeCategory,Race,Diabetic,PhysicalActivity,GenHealth,SleepTime,Asthma,KidneyDisease,SkinCancer,State,GeneralHealth,PhysicalHealthDays,MentalHealthDays,LastCheckupTime,PhysicalActivities,SleepHours,RemovedTeeth,HadHeartAttack,HadAngina,HadStroke,HadAsthma,HadSkinCancer,HadCOPD,HadDepressiveDisorder,HadKidneyDisease,HadArthritis,HadDiabetes,DeafOrHardOfHearing,BlindOrVisionDifficulty,DifficultyConcentrating,DifficultyWalking,DifficultyDressingBathing,DifficultyErrands,SmokerStatus,ECigaretteUsage,ChestScan,RaceEthnicityCategory,HeightInMeters,WeightInKilograms,AlcoholDrinkers,HIVTesting,FluVaxLast12,PneumoVaxEver,TetanusLast10Tdap,HighRiskLastYear,CovidPos -,35.4,,,,,,,Male,Age 30 to 34,,,,,,,,,Minnesota,Excellent,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,102.51,Yes,No,Yes,,"Yes, received tetanus shot but not sure what type",No,Yes -,26.61,,,,,,,Male,Age 80 or older,,,,,,,,,Illinois,Excellent,7.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.73,79.38,No,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,19.37,,,,,,,Female,Age 40 to 44,,,,,,,,,Virginia,Excellent,0.0,4.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.68,54.43,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,25.42,,,,,,,Male,Age 40 to 44,,,,,,,,,Florida,Excellent,0.0,0.0,5 or more years ago,Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.98,99.79,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,27.05,,,,,,,Male,Age 60 to 64,,,,,,,,,California,Excellent,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,92.99,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes -,22.15,,,,,,,Male,Age 30 to 34,,,,,,,,,Indiana,Excellent,0.0,3.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.75,68.04,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No -,25.8,,,,,,,Male,Age 25 to 29,,,,,,,,,South Dakota,Excellent,0.0,2.0,,Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.8,83.91,No,,No,,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,25.61,,,,,,,Female,Age 75 to 79,,,,,,,,,Utah,Excellent,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,63.5,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,26.78,,,,,,,Male,Age 55 to 59,,,,,,,,,New York,Excellent,1.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"Other race only, Non-Hispanic",1.8,87.09,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,18.79,,,,,,,Female,Age 50 to 54,,,,,,,,,Louisiana,Excellent,3.0,3.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,54.43,Yes,,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,28.34,,,,,,,Female,Age 50 to 54,,,,,,,,,Utah,Excellent,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,"White only, Non-Hispanic",1.6,72.57,,,,,,, -,29.65,,,,,,,Male,Age 70 to 74,,,,,,,,,Rhode Island,Excellent,14.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,88.45,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -,23.09,,,,,,,Male,Age 18 to 24,,,,,,,,,Kentucky,Excellent,0.0,7.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.85,79.38,Yes,No,No,No,"Yes, received Tdap",No,Yes -,24.79,,,,,,,Female,,,,,,,,,,California,Excellent,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,67.59,Yes,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,22.71,,,,,,,Female,Age 18 to 24,,,,,,,,,New York,Excellent,2.0,3.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.7,65.77,Yes,Yes,No,No,"Yes, received Tdap",No,Yes +HeartDisease,BMI,Smoking,AlcoholDrinking,Stroke,PhysicalHealth,MentalHealth,DiffWalking,Sex,AgeCategory,Race,Diabetic,PhysicalActivity,SleepTime,Asthma,KidneyDisease,SkinCancer,State,GeneralHealth,PhysicalHealthDays,MentalHealthDays,LastCheckupTime,PhysicalActivities,SleepHours,RemovedTeeth,HadHeartAttack,HadAngina,HadStroke,HadAsthma,HadSkinCancer,HadCOPD,HadDepressiveDisorder,HadKidneyDisease,HadArthritis,HadDiabetes,DeafOrHardOfHearing,BlindOrVisionDifficulty,DifficultyConcentrating,DifficultyWalking,DifficultyDressingBathing,DifficultyErrands,SmokerStatus,ECigaretteUsage,ChestScan,RaceEthnicityCategory,HeightInMeters,WeightInKilograms,AlcoholDrinkers,HIVTesting,FluVaxLast12,PneumoVaxEver,TetanusLast10Tdap,HighRiskLastYear,CovidPos +,35.4,,,,,,,Male,Age 30 to 34,,,,,,,,Minnesota,Excellent,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,102.51,Yes,No,Yes,,"Yes, received tetanus shot but not sure what type",No,Yes +,26.61,,,,,,,Male,Age 80 or older,,,,,,,,Illinois,Excellent,7.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.73,79.38,No,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,19.37,,,,,,,Female,Age 40 to 44,,,,,,,,Virginia,Excellent,0.0,4.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.68,54.43,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,24.21,No,No,No,0.0,10.0,No,Female,30-34,Black,No,Yes,6.0,No,No,No,,Excellent,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,25.42,,,,,,,Male,Age 40 to 44,,,,,,,,Florida,Excellent,0.0,0.0,5 or more years ago,Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.98,99.79,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,23.06,Yes,No,No,3.0,0.0,No,Male,35-39,Other,No,Yes,10.0,No,No,No,,Excellent,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,27.05,,,,,,,Male,Age 60 to 64,,,,,,,,California,Excellent,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,92.99,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes +No,18.79,No,No,No,0.0,0.0,No,Female,40-44,White,No,Yes,8.0,No,No,No,,Excellent,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,22.81,No,No,No,0.0,0.0,No,Female,60-64,White,No,Yes,8.0,No,No,No,,Excellent,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,22.15,,,,,,,Male,Age 30 to 34,,,,,,,,Indiana,Excellent,0.0,3.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.75,68.04,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No +,25.8,,,,,,,Male,Age 25 to 29,,,,,,,,South Dakota,Excellent,0.0,2.0,,Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.8,83.91,No,,No,,"No, did not receive any tetanus shot in the past 10 years",No,Yes +No,25.84,No,No,No,0.0,0.0,No,Male,35-39,White,No,No,6.0,No,No,No,,Excellent,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,18.79,No,No,No,28.0,25.0,No,Female,60-64,White,No,Yes,8.0,No,No,Yes,,Excellent,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,25.61,,,,,,,Female,Age 75 to 79,,,,,,,,Utah,Excellent,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,63.5,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +No,23.3,No,No,No,0.0,0.0,No,Male,55-59,Other,No,Yes,7.0,No,No,No,,Excellent,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,26.78,,,,,,,Male,Age 55 to 59,,,,,,,,New York,Excellent,1.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"Other race only, Non-Hispanic",1.8,87.09,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,18.79,,,,,,,Female,Age 50 to 54,,,,,,,,Louisiana,Excellent,3.0,3.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,54.43,Yes,,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,28.34,,,,,,,Female,Age 50 to 54,,,,,,,,Utah,Excellent,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,"White only, Non-Hispanic",1.6,72.57,,,,,,, +,29.65,,,,,,,Male,Age 70 to 74,,,,,,,,Rhode Island,Excellent,14.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,88.45,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +,23.09,,,,,,,Male,Age 18 to 24,,,,,,,,Kentucky,Excellent,0.0,7.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.85,79.38,Yes,No,No,No,"Yes, received Tdap",No,Yes +No,21.73,No,No,No,21.0,0.0,No,Female,50-54,Hispanic,No,Yes,7.0,No,No,No,,Excellent,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,19.97,No,No,No,0.0,15.0,No,Female,80 or older,White,No,Yes,6.0,No,No,No,,Excellent,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,24.79,,,,,,,Female,,,,,,,,,California,Excellent,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,67.59,Yes,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,36.62,Yes,No,No,0.0,0.0,No,Male,55-59,White,No,Yes,8.0,No,No,Yes,,Excellent,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,22.71,,,,,,,Female,Age 18 to 24,,,,,,,,New York,Excellent,2.0,3.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.7,65.77,Yes,Yes,No,No,"Yes, received Tdap",No,Yes diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_003/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_003_current.json b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_003/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_003_current.json index 4da0cb85ead7406672ea89545c693e24763bf72d..7015d3e3cceeec6298707f55c4e23286c3663a66 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_003/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_003_current.json +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_003/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_003_current.json @@ -1,12043 +1,11852 @@ { - "id": "003", - "task_type": "B4", - "subtask_type": "list", - "perspective": "data_holder", - "dataset_name": "Indicators of Heart Disease (2022 UPDATE)", - "table_path": "output/medical/classification/Indicators of Heart Disease (2022 UPDATE)_B4/Indicators of Heart Disease (2022 UPDATE)_B4_003/test.csv", - "query": "I'm sending over the latest batch of community health survey entries. Based on the trends in our historical records, identify all people in this new group whose responses indicate they are likely to rate their general health as \"Fair\".", - "meta_info": { - "domain": "medical" + "id": "003", + "task_type": "B4", + "subtask_type": "list", + "perspective": "data_holder", + "dataset_name": "Indicators of Heart Disease (2022 UPDATE)", + "table_path": "output/medical/classification/Indicators of Heart Disease (2022 UPDATE)_B4/Indicators of Heart Disease (2022 UPDATE)_B4_003/test.csv", + "query": "I'm sending over the latest batch of community health survey entries. Based on the trends in our historical records, identify all people in this new group whose responses indicate they are likely to rate their general health as \"Fair\".", + "meta_info": { + "domain": "medical" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "002", + "features": { + "HeartDisease": null, + "BMI": 28.15, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 55 to 59", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Alabama", + "GeneralHealth": "Good", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 7.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "Yes", + "HadAngina": "Yes", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "Yes", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "Yes", + "RaceEthnicityCategory": "Black only, Non-Hispanic", + "HeightInMeters": 1.63, + "WeightInKilograms": 74.39, + "AlcoholDrinkers": "Yes", + "HIVTesting": "Yes", + "FluVaxLast12": "Yes", + "PneumoVaxEver": "No", + "TetanusLast10Tdap": "Yes, received Tdap", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "003", + "features": { + "HeartDisease": null, + "BMI": 26.58, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 80 or older", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Missouri", + "GeneralHealth": "Poor", + "PhysicalHealthDays": 30.0, + "MentalHealthDays": 30.0, + "LastCheckupTime": null, + "PhysicalActivities": "Yes", + "SleepHours": 8.0, + "RemovedTeeth": "6 or more, but not all", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": null, + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "Yes", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "Yes", + "DifficultyWalking": "Yes", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "Yes", + "SmokerStatus": "Former smoker", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "Yes", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.75, + "WeightInKilograms": 81.65, + "AlcoholDrinkers": "Yes", + "HIVTesting": "No", + "FluVaxLast12": "Yes", + "PneumoVaxEver": "Yes", + "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "004", + "features": { + "HeartDisease": "No", + "BMI": 38.02, + "Smoking": "Yes", + "AlcoholDrinking": "No", + "Stroke": "No", + "PhysicalHealth": 5.0, + "MentalHealth": 20.0, + "DiffWalking": "No", + "Sex": "Male", + "AgeCategory": "30-34", + "Race": "White", + "Diabetic": "No", + "PhysicalActivity": "Yes", + "SleepTime": 8.0, + "Asthma": "No", + "KidneyDisease": "No", + "SkinCancer": "No", + "State": null, + "GeneralHealth": "Very good", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + "LastCheckupTime": null, + "PhysicalActivities": null, + "SleepHours": null, + "RemovedTeeth": null, + "HadHeartAttack": null, + "HadAngina": null, + "HadStroke": null, + "HadAsthma": null, + "HadSkinCancer": null, + "HadCOPD": null, + "HadDepressiveDisorder": null, + "HadKidneyDisease": null, + "HadArthritis": null, + "HadDiabetes": null, + "DeafOrHardOfHearing": null, + "BlindOrVisionDifficulty": null, + "DifficultyConcentrating": null, + "DifficultyWalking": null, + "DifficultyDressingBathing": null, + "DifficultyErrands": null, + "SmokerStatus": null, + "ECigaretteUsage": null, + "ChestScan": null, + "RaceEthnicityCategory": null, + "HeightInMeters": null, + "WeightInKilograms": null, + "AlcoholDrinkers": null, + "HIVTesting": null, + "FluVaxLast12": null, + "PneumoVaxEver": null, + "TetanusLast10Tdap": null, + "HighRiskLastYear": null, + "CovidPos": null + } + }, + { + "scenario_id": "005", + "features": { + "HeartDisease": null, + "BMI": 25.11, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 25 to 29", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Maryland", + "GeneralHealth": "Very good", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 15.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 6.0, + "RemovedTeeth": "1 to 5", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "No", + "RaceEthnicityCategory": "Black only, Non-Hispanic", + "HeightInMeters": 1.78, + "WeightInKilograms": 79.38, + "AlcoholDrinkers": "Yes", + "HIVTesting": "Yes", + "FluVaxLast12": "No", + "PneumoVaxEver": "No", + "TetanusLast10Tdap": "Yes, received tetanus shot but not sure what type", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "007", + "features": { + "HeartDisease": null, + "BMI": 24.41, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 55 to 59", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Washington", + "GeneralHealth": "Excellent", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 8.0, + "RemovedTeeth": "1 to 5", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "No", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.85, + "WeightInKilograms": 83.91, + "AlcoholDrinkers": "Yes", + "HIVTesting": "No", + "FluVaxLast12": "No", + "PneumoVaxEver": "No", + "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "009", + "features": { + "HeartDisease": "No", + "BMI": 30.17, + "Smoking": "No", + "AlcoholDrinking": "No", + "Stroke": "No", + "PhysicalHealth": 0.0, + "MentalHealth": 0.0, + "DiffWalking": "No", + "Sex": "Male", + "AgeCategory": "60-64", + "Race": "White", + "Diabetic": "No", + "PhysicalActivity": "Yes", + "SleepTime": 6.0, + "Asthma": "No", + "KidneyDisease": "No", + "SkinCancer": "No", + "State": null, + "GeneralHealth": "Very good", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + "LastCheckupTime": null, + "PhysicalActivities": null, + "SleepHours": null, + "RemovedTeeth": null, + "HadHeartAttack": null, + "HadAngina": null, + "HadStroke": null, + "HadAsthma": null, + "HadSkinCancer": null, + "HadCOPD": null, + "HadDepressiveDisorder": null, + "HadKidneyDisease": null, + "HadArthritis": null, + "HadDiabetes": null, + "DeafOrHardOfHearing": null, + "BlindOrVisionDifficulty": null, + "DifficultyConcentrating": null, + "DifficultyWalking": null, + "DifficultyDressingBathing": null, + "DifficultyErrands": null, + "SmokerStatus": null, + "ECigaretteUsage": null, + "ChestScan": null, + "RaceEthnicityCategory": null, + "HeightInMeters": null, + "WeightInKilograms": null, + "AlcoholDrinkers": null, + "HIVTesting": null, + "FluVaxLast12": null, + "PneumoVaxEver": null, + "TetanusLast10Tdap": null, + "HighRiskLastYear": null, + "CovidPos": null + } + }, + { + "scenario_id": "010", + "features": { + "HeartDisease": null, + "BMI": 28.06, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 75 to 79", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Utah", + "GeneralHealth": "Fair", + "PhysicalHealthDays": null, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 6.0, + "RemovedTeeth": "1 to 5", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "Yes", + "HadSkinCancer": "Yes", + "HadCOPD": "Yes", + "HadDepressiveDisorder": "Yes", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "Yes", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Former smoker", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "Yes", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.75, + "WeightInKilograms": 86.18, + "AlcoholDrinkers": "No", + "HIVTesting": "No", + "FluVaxLast12": "Yes", + "PneumoVaxEver": "Yes", + "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "011", + "features": { + "HeartDisease": null, + "BMI": 23.3, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 80 or older", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Maine", + "GeneralHealth": "Very good", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 6.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "Yes", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Former smoker", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "Yes", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.65, + "WeightInKilograms": 63.5, + "AlcoholDrinkers": "Yes", + "HIVTesting": "No", + "FluVaxLast12": "Yes", + "PneumoVaxEver": "No", + "TetanusLast10Tdap": "Yes, received tetanus shot but not sure what type", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "013", + "features": { + "HeartDisease": null, + "BMI": 27.8, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 80 or older", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Alabama", + "GeneralHealth": "Very good", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 8.0, + "RemovedTeeth": "1 to 5", + "HadHeartAttack": "Yes", + "HadAngina": "Yes", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "Yes", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.83, + "WeightInKilograms": 92.99, + "AlcoholDrinkers": "No", + "HIVTesting": "No", + "FluVaxLast12": "No", + "PneumoVaxEver": "Yes", + "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "016", + "features": { + "HeartDisease": "No", + "BMI": 49.92, + "Smoking": "Yes", + "AlcoholDrinking": "No", + "Stroke": "No", + "PhysicalHealth": 28.0, + "MentalHealth": 0.0, + "DiffWalking": "Yes", + "Sex": "Female", + "AgeCategory": "55-59", + "Race": "White", + "Diabetic": "No", + "PhysicalActivity": "No", + "SleepTime": 9.0, + "Asthma": "No", + "KidneyDisease": "No", + "SkinCancer": "No", + "State": null, + "GeneralHealth": "Fair", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + "LastCheckupTime": null, + "PhysicalActivities": null, + "SleepHours": null, + "RemovedTeeth": null, + "HadHeartAttack": null, + "HadAngina": null, + "HadStroke": null, + "HadAsthma": 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null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Texas", + "GeneralHealth": "Good", + "PhysicalHealthDays": 17.0, + "MentalHealthDays": 1.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 10.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "Yes", + "HadSkinCancer": "No", + "HadCOPD": "Yes", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "Yes", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "Yes", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "No", + "RaceEthnicityCategory": "White only, 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"scenario_id": "066", + "features": { + "HeartDisease": null, + "BMI": 29.08, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 65 to 69", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Puerto Rico", + "GeneralHealth": "Fair", + "PhysicalHealthDays": 3.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 6.0, + "RemovedTeeth": "1 to 5", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "Yes", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "Yes", + 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"CovidPos": null + } + }, + { + "scenario_id": "292", + "features": { + "HeartDisease": null, + "BMI": 28.89, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 70 to 74", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Tennessee", + "GeneralHealth": "Good", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 6.0, + "RemovedTeeth": "6 or more, but not all", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No, pre-diabetes or borderline 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"HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "370", + "features": { + "HeartDisease": null, + "BMI": 22.07, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 60 to 64", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Puerto Rico", + "GeneralHealth": "Excellent", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 8.0, + "RemovedTeeth": "6 or more, but not all", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "Yes", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "Yes", + "HadDiabetes": 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"scenario_id": "382", + "features": { + "HeartDisease": "No", + "BMI": 29.12, + "Smoking": "No", + "AlcoholDrinking": "No", + "Stroke": "No", + "PhysicalHealth": 0.0, + "MentalHealth": 0.0, + "DiffWalking": "No", + "Sex": "Female", + "AgeCategory": "55-59", + "Race": "Black", + "Diabetic": "No", + "PhysicalActivity": "Yes", + "SleepTime": 8.0, + "Asthma": "No", + "KidneyDisease": "No", + "SkinCancer": "No", + "State": null, + "GeneralHealth": "Excellent", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + "LastCheckupTime": null, + "PhysicalActivities": null, + "SleepHours": null, + "RemovedTeeth": null, + "HadHeartAttack": null, + "HadAngina": null, + "HadStroke": null, + "HadAsthma": null, + "HadSkinCancer": null, + "HadCOPD": null, + "HadDepressiveDisorder": null, + "HadKidneyDisease": null, + "HadArthritis": null, + "HadDiabetes": null, + "DeafOrHardOfHearing": null, + "BlindOrVisionDifficulty": null, + "DifficultyConcentrating": null, + "DifficultyWalking": null, + 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+ "CovidPos": "Yes" + } + }, + { + "scenario_id": "398", + "features": { + "HeartDisease": null, + "BMI": 32.93, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 60 to 64", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Washington", + "GeneralHealth": "Fair", + "PhysicalHealthDays": 30.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 9.0, + "RemovedTeeth": "1 to 5", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "Yes", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "Yes", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + 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- "HIVTesting": "No", - "FluVaxLast12": "Yes", - "PneumoVaxEver": "Yes", - "TetanusLast10Tdap": "Yes, received Tdap", - "HighRiskLastYear": "No", - "CovidPos": "No" - } - }, - { - "scenario_id": "500", - "features": { - "HeartDisease": "No", - "BMI": 23.17, - "Smoking": "No", - "AlcoholDrinking": "No", - "Stroke": "No", - "PhysicalHealth": 0.0, - "MentalHealth": 0.0, - "DiffWalking": "No", - "Sex": "Female", - "AgeCategory": "65-69", - "Race": "White", - "Diabetic": "No", - "PhysicalActivity": "Yes", - "GenHealth": "Good", - "SleepTime": 8.0, - "Asthma": "No", - "KidneyDisease": "No", - "SkinCancer": "No", - "State": null, - "GeneralHealth": null, - "PhysicalHealthDays": null, - "MentalHealthDays": null, - "LastCheckupTime": null, - "PhysicalActivities": null, - "SleepHours": null, - "RemovedTeeth": null, - "HadHeartAttack": null, - "HadAngina": null, - "HadStroke": null, - "HadAsthma": null, - "HadSkinCancer": null, - "HadCOPD": null, - "HadDepressiveDisorder": null, - "HadKidneyDisease": null, - "HadArthritis": null, - "HadDiabetes": null, - "DeafOrHardOfHearing": null, - "BlindOrVisionDifficulty": null, - "DifficultyConcentrating": null, - "DifficultyWalking": null, - "DifficultyDressingBathing": null, - "DifficultyErrands": null, - "SmokerStatus": null, - "ECigaretteUsage": null, - "ChestScan": null, - "RaceEthnicityCategory": null, - "HeightInMeters": null, - "WeightInKilograms": null, - "AlcoholDrinkers": null, - "HIVTesting": null, - "FluVaxLast12": null, - "PneumoVaxEver": null, - "TetanusLast10Tdap": null, - "HighRiskLastYear": null, - "CovidPos": null - } - } - ], - "target_column": "GeneralHealth", - "task_sub_type": "classification", - "ranking_ground_truth": { - "top_k_ids": [ - "010", - "035", - "066", - "089", - "174", - "205", - "245", - "295", - "299", - "337", - "340", - "379", - "398", - "467", - "492" - ] - }, - "active_filters": [], - "target_class_value": "fair" - }, - "response": "", - "evaluation_score": {} -} \ No newline at end of file + "active_filters": [], + "target_class_value": "fair" + }, + "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_003/current.csv b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_003/current.csv index 071d5b2b728b6f72f6ff9e249fb7e70bbae8fd45..a8d856cc9ea15cc42511bb8765b13f43f4d1e074 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_003/current.csv +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_003/current.csv @@ -1,201 +1,201 @@ -HeartDisease,BMI,Smoking,AlcoholDrinking,Stroke,PhysicalHealth,MentalHealth,DiffWalking,Sex,AgeCategory,Race,Diabetic,PhysicalActivity,GenHealth,SleepTime,Asthma,KidneyDisease,SkinCancer,State,PhysicalHealthDays,MentalHealthDays,LastCheckupTime,PhysicalActivities,SleepHours,RemovedTeeth,HadHeartAttack,HadAngina,HadStroke,HadAsthma,HadSkinCancer,HadCOPD,HadDepressiveDisorder,HadKidneyDisease,HadArthritis,HadDiabetes,DeafOrHardOfHearing,BlindOrVisionDifficulty,DifficultyConcentrating,DifficultyWalking,DifficultyDressingBathing,DifficultyErrands,SmokerStatus,ECigaretteUsage,ChestScan,RaceEthnicityCategory,HeightInMeters,WeightInKilograms,AlcoholDrinkers,HIVTesting,FluVaxLast12,PneumoVaxEver,TetanusLast10Tdap,HighRiskLastYear,CovidPos -,28.15,,,,,,,Female,Age 55 to 59,,,,,,,,,Alabama,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,Yes,Yes,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.63,74.39,Yes,Yes,Yes,No,"Yes, received Tdap",No,No -,26.58,,,,,,,Male,Age 80 or older,,,,,,,,,Missouri,30.0,30.0,,Yes,8.0,"6 or more, but not all",No,No,No,No,,No,No,No,Yes,No,No,No,Yes,Yes,No,Yes,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,81.65,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -No,38.02,Yes,No,No,5.0,20.0,No,Male,30-34,White,No,Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,25.11,,,,,,,Male,Age 25 to 29,,,,,,,,,Maryland,0.0,15.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.78,79.38,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No -,24.41,,,,,,,Male,Age 55 to 59,,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,83.91,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -No,30.17,No,No,No,0.0,0.0,No,Male,60-64,White,No,Yes,Very good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,28.06,,,,,,,Female,Age 75 to 79,,,,,,,,,Utah,,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,Yes,Yes,Yes,Yes,No,No,No,No,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,86.18,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,23.3,,,,,,,Female,Age 80 or older,,,,,,,,,Maine,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,63.5,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -,27.8,,,,,,,Male,Age 80 or older,,,,,,,,,Alabama,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,Yes,Yes,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.83,92.99,No,No,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -No,49.92,Yes,No,No,28.0,0.0,Yes,Female,55-59,White,No,No,Fair,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,38.74,,,,,,,Female,Age 55 to 59,,,,,,,,,Texas,17.0,1.0,Within past year (anytime less than 12 months ago),Yes,10.0,None of them,No,No,No,Yes,No,Yes,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,108.86,No,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No -No,23.3,No,No,No,0.0,0.0,No,Male,25-29,Black,No,Yes,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,30.67,,,,,,,Female,Age 25 to 29,,,,,,,,,Kansas,0.0,10.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,86.18,No,No,No,,"Yes, received tetanus shot but not sure what type",Yes,Yes -,22.15,,,,,,,Male,Age 70 to 74,,,,,,,,,Alaska,30.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,Yes,No,No,No,Yes,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,68.04,Yes,No,Yes,,"Yes, received tetanus shot but not sure what type",No,No -No,31.71,No,No,No,20.0,0.0,Yes,Female,80 or older,White,Yes,No,Good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,18.33,Yes,No,No,12.0,30.0,No,Female,80 or older,White,No,Yes,Good,8.0,Yes,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,28.34,No,No,No,3.0,2.0,No,Female,50-54,White,No,Yes,Good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,27.71,Yes,No,No,30.0,30.0,No,Male,55-59,White,No,Yes,Poor,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Yes,50.79,Yes,No,No,0.0,2.0,Yes,Male,70-74,White,Yes,No,Good,7.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,45.26,,,,,,,Female,Age 60 to 64,,,,,,,,,Maine,30.0,30.0,Within past year (anytime less than 12 months ago),No,5.0,"6 or more, but not all",No,No,Yes,No,No,No,Yes,No,Yes,No,No,No,No,Yes,No,Yes,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,123.38,No,Yes,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,Yes -,34.67,,,,,,,Male,Age 30 to 34,,,,,,,,,Florida,0.0,14.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,Yes,No,No,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.88,122.47,No,No,No,No,"Yes, received tetanus shot, but not Tdap",No,No -,23.35,,,,,,,Male,Age 45 to 49,,,,,,,,,Idaho,1.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,Yes,No,Yes,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,80.29,No,No,No,No,"Yes, received Tdap",No,No -,32.26,,,,,,,Male,Age 65 to 69,,,,,,,,,Oklahoma,0.0,5.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,Yes,No,No,No,No,Yes,No,No,Yes,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.93,120.2,Yes,No,No,Yes,"Yes, received Tdap",No,Yes -,30.67,,,,,,,Female,Age 45 to 49,,,,,,,,,Idaho,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,,,,"White only, Non-Hispanic",1.68,86.18,,,,,,, -,30.9,,,,,,,Female,Age 50 to 54,,,,,,,,,Maryland,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Multiracial, Non-Hispanic",1.63,81.65,No,Yes,No,Yes,"Yes, received Tdap",No,No -,32.64,,,,,,,Male,Age 60 to 64,,,,,,,,,South Carolina,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.8,106.14,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,34.46,,,,,,,Male,Age 65 to 69,,,,,,,,,New York,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,1 to 5,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,99.79,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -No,33.15,No,No,No,0.0,4.0,No,Female,50-54,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,27.2,,,,,,,Male,Age 70 to 74,,,,,,,,,South Carolina,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,Yes,Yes,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,88.45,No,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,16.13,,,,,,,Female,Age 65 to 69,,,,,,,,,Virginia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,No,Yes,No,Yes,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,42.64,Yes,No,Yes,Yes,"Yes, received Tdap",No,No -Yes,28.25,Yes,No,Yes,30.0,0.0,Yes,Male,75-79,White,No,Yes,Fair,8.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,31.12,No,No,No,14.0,0.0,No,Male,55-59,Hispanic,Yes,No,Good,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,29.08,,,,,,,Female,Age 65 to 69,,,,,,,,,Puerto Rico,3.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,Yes,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,Hispanic,1.57,72.12,No,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes -No,23.11,Yes,No,No,0.0,0.0,No,Male,30-34,White,No,Yes,Excellent,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,32.49,,,,,,,Male,Age 50 to 54,,,,,,,,,Maryland,14.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,9.0,None of them,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,99.79,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No -,34.96,,,,,,,Male,Age 60 to 64,,,,,,,,,Wisconsin,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.85,120.2,No,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -No,29.29,No,No,No,0.0,0.0,No,Female,70-74,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,25.77,,,,,,,Male,Age 40 to 44,,,,,,,,,Michigan,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,86.18,Yes,No,Yes,No,"Yes, received Tdap",No,No -No,26.64,No,No,No,0.0,2.0,No,Female,60-64,White,No,Yes,Very good,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,32.69,Yes,Yes,No,8.0,30.0,No,Female,40-44,White,No,Yes,Good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,27.12,No,No,No,5.0,2.0,No,Male,40-44,White,No,Yes,Fair,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,,,,,,,,Female,Age 60 to 64,,,,,,,,,Florida,15.0,15.0,Within past year (anytime less than 12 months ago),No,3.0,All,No,No,No,No,No,Yes,Yes,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,,No,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No -,25.96,,,,,,,Female,Age 25 to 29,,,,,,,,,Puerto Rico,6.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,70.76,Yes,No,No,No,"Yes, received Tdap",No,No -,35.43,,,,,,,Female,Age 30 to 34,,,,,,,,,Nebraska,7.0,2.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,90.72,Yes,Yes,Yes,No,"Yes, received Tdap",No,Yes -No,29.05,No,No,No,0.0,0.0,No,Female,80 or older,White,Yes,No,Very good,6.0,No,Yes,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,28.19,,,,,,,Female,Age 30 to 34,,,,,,,,,Idaho,7.0,4.0,Within past 5 years (2 years but less than 5 years ago),No,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,81.65,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,29.69,,,,,,,Female,Age 75 to 79,,,,,,,,,Guam,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,Yes,No,No,Yes,No,No,No,Yes,Yes,"No, pre-diabetes or borderline diabetes",Yes,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Other race only, Non-Hispanic",1.52,68.95,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes -,28.8,,,,,,,Male,Age 55 to 59,,,,,,,,,South Carolina,12.0,4.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,88.45,Yes,No,No,No,"Yes, received tetanus shot, but not Tdap",No,Yes -No,27.07,No,No,No,0.0,15.0,No,Female,18-24,American Indian/Alaskan Native,No,Yes,Very good,4.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,32.45,No,No,No,2.0,2.0,No,Female,45-49,Black,No,Yes,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,31.62,,,,,,,Male,Age 70 to 74,,,,,,,,,Ohio,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,Yes,No,Yes,No,No,No,Yes,No,No,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,86.18,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -,26.85,,,,,,,Male,Age 65 to 69,,,,,,,,,Pennsylvania,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,89.81,Yes,No,No,Yes,"Yes, received Tdap",No,No -No,32.89,No,No,No,0.0,0.0,No,Male,55-59,White,Yes,Yes,Good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,27.41,,,,,,,Female,Age 40 to 44,,,,,,,,,North Dakota,6.0,2.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.7,79.38,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,Yes -,23.87,,,,,,,Male,Age 55 to 59,,,,,,,,,Arizona,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,79.83,No,No,No,No,"Yes, received Tdap",No,No -,,,,,,,,Male,,,,,,,,,,Pennsylvania,,0.0,,Yes,,None of them,No,No,No,No,No,No,No,No,No,No,No,,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",,,No,Yes,No,No,,No,No -,42.05,,,,,,,Female,Age 65 to 69,,,,,,,,,Alaska,0.0,3.0,Within past year (anytime less than 12 months ago),No,8.0,All,No,No,No,No,No,No,Yes,No,Yes,Yes,No,No,Yes,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,111.13,No,No,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -No,27.75,No,No,No,0.0,0.0,No,Male,40-44,Hispanic,No,Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,31.28,,,,,,,Female,Age 80 or older,,,,,,,,,Kansas,30.0,5.0,Within past year (anytime less than 12 months ago),Yes,7.0,All,No,Yes,No,No,No,No,No,No,Yes,Yes,Yes,No,No,Yes,No,Yes,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.57,77.56,No,Yes,Yes,Yes,"Yes, received Tdap",No,No -,30.54,,,,,,,Male,Age 60 to 64,,,,,,,,,Georgia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,88.45,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -,,,,,,,,Male,Age 80 or older,,,,,,,,,Minnesota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,Yes,Yes,No,No,No,No,No,No,Yes,Yes,Yes,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,,Yes,,,,,, -No,27.37,No,No,No,0.0,12.0,No,Male,30-34,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,23.03,No,No,No,3.0,30.0,No,Female,65-69,White,No,Yes,Very good,3.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,29.53,,,,,,,Male,Age 65 to 69,,,,,,,,,Idaho,14.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,90.72,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -,28.57,,,,,,,Female,Age 45 to 49,,,,,,,,,Washington,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),No,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,80.29,Yes,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,24.11,,,,,,,Male,Age 80 or older,,,,,,,,,Arizona,21.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,No,No,No,Yes,No,No,No,No,"No, pre-diabetes or borderline diabetes",No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,76.2,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes -No,26.78,Yes,No,No,0.0,0.0,No,Male,65-69,American Indian/Alaskan Native,No,No,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,22.31,No,No,No,30.0,0.0,Yes,Female,55-59,Black,No,Yes,Excellent,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,33.67,,,,,,,Female,Age 40 to 44,,,,,,,,,Maryland,5.0,15.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,97.52,Yes,Yes,Yes,No,"Yes, received Tdap",No,Yes -No,35.51,No,No,No,0.0,0.0,No,Female,18-24,White,No,Yes,Good,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,22.83,,,,,,,Female,Age 60 to 64,,,,,,,,,Indiana,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,Yes,Yes,Yes,Yes,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,60.33,No,Yes,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -,30.12,,,,,,,Female,Age 65 to 69,,,,,,,,,Florida,3.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,All,No,No,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.65,82.1,Yes,Yes,Yes,Yes,,No,No -,,,,,,,,Female,Age 70 to 74,,,,,,,,,Connecticut,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,Hispanic,,,,,,,,, -,26.63,,,,,,,Female,Age 40 to 44,,,,,,,,,Vermont,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,74.84,Yes,,No,No,,, -,30.54,,,,,,,Male,Age 30 to 34,,,,,,,,,Maryland,0.0,20.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,Yes,Yes,No,No,No,Current smoker - now smokes every day,Not at all (right now),No,Hispanic,1.7,88.45,No,Yes,No,No,"Yes, received tetanus shot, but not Tdap",No,No -,41.2,,,,,,,Female,Age 50 to 54,,,,,,,,,Arkansas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,1.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,108.86,No,Yes,Yes,No,"Yes, received Tdap",No,No -,38.74,,,,,,,Male,Age 60 to 64,,,,,,,,,Hawaii,2.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,Yes,Yes,Yes,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Multiracial, Non-Hispanic",1.78,122.47,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -No,24.39,Yes,No,No,0.0,3.0,No,Male,30-34,White,No,Yes,Excellent,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,24.63,,,,,,,Female,Age 70 to 74,,,,,,,,,Alabama,30.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.65,67.13,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,27.32,,,,,,,Male,Age 65 to 69,,,,,,,,,Connecticut,30.0,7.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,83.91,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes -,,,,,,,,Female,Age 70 to 74,,,,,,,,,Florida,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,33.23,,,,,,,Female,Age 80 or older,,,,,,,,,Indiana,5.0,1.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,1 to 5,No,No,Yes,No,No,No,No,No,Yes,Yes,No,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,102.06,No,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -No,27.46,Yes,No,No,0.0,0.0,Yes,Female,80 or older,White,No,No,Good,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,30.68,No,No,No,0.0,2.0,No,Male,45-49,White,No,No,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,24.68,No,No,No,0.0,0.0,No,Male,55-59,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,34.46,No,No,No,15.0,0.0,No,Male,50-54,White,No,Yes,Fair,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,29.05,,,,,,,Female,Age 80 or older,,,,,,,,,Tennessee,7.0,2.0,Within past 2 years (1 year but less than 2 years ago),Yes,10.0,None of them,No,No,No,No,No,No,Yes,No,Yes,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,81.65,Yes,,Yes,Yes,"Yes, received Tdap",No,Yes -,,,,,,,,Male,Age 55 to 59,,,,,,,,,Washington,8.0,3.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, -,29.84,,,,,,,Male,Age 65 to 69,,,,,,,,,New York,0.0,2.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.83,99.79,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -No,29.47,No,No,No,13.0,0.0,No,Male,45-49,White,No,Yes,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,31.12,,,,,,,Female,Age 60 to 64,,,,,,,,,West Virginia,15.0,30.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,Yes,No,No,Yes,No,Yes,Yes,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,84.82,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,21.52,,,,,,,Male,Age 80 or older,,,,,,,,,New York,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,1 to 5,No,,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,68.04,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,23.4,,,,,,,Female,Age 30 to 34,,,,,,,,,Louisiana,0.0,10.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.68,65.77,Yes,Yes,No,Yes,"No, did not receive any tetanus shot in the past 10 years",Yes,No -,24.24,,,,,,,Male,Age 50 to 54,,,,,,,,,Rhode Island,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,Yes,,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,Hispanic,1.5,54.43,No,Yes,No,,,No,Yes -No,38.26,No,No,No,0.0,0.0,No,Male,35-39,White,No,Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,51.49,Yes,No,No,0.0,0.0,Yes,Female,50-54,White,No,Yes,Good,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,,,,,,,,Female,,,,,,,,,,Arizona,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,9.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,,Yes,No,Yes,No,"Yes, received Tdap",No,No -,32.77,,,,,,,Female,Age 45 to 49,,,,,,,,,New Mexico,3.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,Hispanic,1.6,83.91,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Tested positive using home test without a health professional -No,28.19,Yes,No,No,30.0,14.0,Yes,Female,30-34,White,No,No,Fair,5.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,25.1,Yes,No,No,0.0,4.0,No,Male,35-39,White,No,Yes,Excellent,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,22.98,,,,,,,Female,Age 40 to 44,,,,,,,,,New Jersey,,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,,,,Hispanic,1.5,51.71,,,,,,, -,32.69,,,,,,,Male,Age 70 to 74,,,,,,,,,New Jersey,30.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,"6 or more, but not all",Yes,Yes,Yes,No,No,No,No,Yes,No,Yes,No,Yes,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,97.52,Yes,No,No,Yes,"Yes, received tetanus shot but not sure what type",No,Yes -No,26.3,No,No,No,10.0,0.0,Yes,Female,45-49,White,Yes,No,Fair,4.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,26.61,,,,,,,Male,Age 50 to 54,,,,,,,,,Virginia,0.0,0.0,5 or more years ago,Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,79.38,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -No,26.89,No,No,No,3.0,0.0,No,Female,65-69,Black,Yes,Yes,Fair,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,31.47,,,,,,,Female,Age 65 to 69,,,,,,,,,Georgia,0.0,15.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,88.45,Yes,No,Yes,Yes,"Yes, received Tdap",No,No -,25.84,,,,,,,Male,Age 70 to 74,,,,,,,,,Missouri,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,74.84,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",, -,32.28,,,,,,,Male,Age 55 to 59,,,,,,,,,Minnesota,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,1 to 5,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,90.72,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,29.99,,,,,,,Male,Age 50 to 54,,,,,,,,,Idaho,0.0,0.0,5 or more years ago,Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.8,97.52,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -,,,,,,,,Female,Age 65 to 69,,,,,,,,,Florida,30.0,30.0,Within past year (anytime less than 12 months ago),No,10.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,,,,,,,,"Black only, Non-Hispanic",,,,,,,,, -,28.89,,,,,,,Male,Age 70 to 74,,,,,,,,,Tennessee,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,"No, pre-diabetes or borderline diabetes",No,No,No,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.73,86.18,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,39.06,,,,,,,Female,Age 75 to 79,,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,"6 or more, but not all",No,No,Yes,Yes,No,No,No,Yes,Yes,Yes,Yes,No,No,Yes,Yes,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.52,90.72,No,No,Yes,Yes,"Yes, received Tdap",No,No -,,,,,,,,Male,Age 60 to 64,,,,,,,,,Colorado,10.0,5.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,Yes,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.8,,No,,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes -No,24.41,No,No,No,0.0,0.0,No,Male,65-69,White,Yes,Yes,Good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,25.29,Yes,No,No,0.0,0.0,No,Female,70-74,White,No,Yes,Good,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,19.49,,,,,,,Female,Age 65 to 69,,,,,,,,,Washington,3.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,All,No,No,No,No,No,Yes,No,No,No,Yes,No,No,No,No,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,49.9,Yes,,No,Yes,"Yes, received Tdap",No,No -,26.54,,,,,,,Male,Age 75 to 79,,,,,,,,,Texas,3.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,"6 or more, but not all",No,Yes,No,Yes,No,No,No,Yes,Yes,Yes,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,83.91,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,32.95,,,,,,,Female,Age 50 to 54,,,,,,,,,South Dakota,0.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,Hispanic,1.65,89.81,No,Yes,No,No,"Yes, received tetanus shot, but not Tdap",No,No -,22.86,,,,,,,Female,Age 70 to 74,,,,,,,,,Maryland,0.0,1.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,Yes,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.57,56.7,No,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No -,30.0,,,,,,,Male,Age 30 to 34,,,,,,,,,Montana,15.0,22.0,Within past year (anytime less than 12 months ago),Yes,4.0,None of them,No,No,No,No,No,No,Yes,No,Yes,No,No,No,Yes,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.91,108.86,No,No,No,No,"Yes, received Tdap",No,No -No,34.87,No,No,No,1.0,3.0,No,Male,35-39,White,No,Yes,Very good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,35.12,,,,,,,Female,Age 35 to 39,,,,,,,,,South Carolina,7.0,30.0,Within past year (anytime less than 12 months ago),Yes,5.0,"6 or more, but not all",No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"Black only, Non-Hispanic",1.57,87.09,Yes,,,,,, -,27.89,,,,,,,Male,Age 45 to 49,,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,None of them,Yes,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,Hispanic,1.8,90.72,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes -No,26.58,No,No,No,0.0,0.0,Yes,Female,50-54,White,No,No,Very good,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,34.46,,,,,,,Female,Age 45 to 49,,,,,,,,,Louisiana,0.0,5.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,99.79,Yes,Yes,Yes,No,"Yes, received Tdap",No,No -,,,,,,,,Female,Age 75 to 79,,,,,,,,,New Hampshire,2.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,All,No,Yes,No,No,No,Yes,No,No,Yes,No,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, -No,29.23,No,Yes,No,0.0,0.0,No,Female,75-79,White,No,Yes,Very good,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,23.57,Yes,No,No,0.0,0.0,No,Male,70-74,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,27.79,Yes,No,No,6.0,20.0,No,Female,30-34,White,No,No,Fair,3.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,24.33,,,,,,,Male,Age 35 to 39,,,,,,,,,Massachusetts,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.73,72.57,Yes,No,Yes,No,"Yes, received Tdap",, -No,22.14,Yes,No,No,0.0,0.0,No,Female,70-74,Asian,No,Yes,Excellent,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,29.05,,,,,,,Female,Age 70 to 74,,,,,,,,,Minnesota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,81.65,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,23.91,,,,,,,Female,Age 60 to 64,,,,,,,,,Colorado,0.0,28.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,Yes,No,No,Yes,"No, pre-diabetes or borderline diabetes",No,Yes,Yes,No,No,No,Former smoker,Use them every day,Yes,"White only, Non-Hispanic",1.6,61.23,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No -,32.98,,,,,,,Male,Age 50 to 54,,,,,,,,,Kansas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.85,113.4,No,No,Yes,No,"Yes, received Tdap",No,Yes -,27.98,,,,,,,Male,Age 75 to 79,,,,,,,,,Maine,30.0,30.0,Within past year (anytime less than 12 months ago),No,6.0,None of them,No,Yes,No,No,No,No,No,No,Yes,Yes,No,No,No,Yes,Yes,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,83.46,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,21.93,,,,,,,Male,Age 55 to 59,,,,,,,,,Hawaii,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"Other race only, Non-Hispanic",1.7,63.5,No,No,Yes,No,,No,No -No,23.73,Yes,No,No,0.0,30.0,No,Female,70-74,White,Yes,No,Good,8.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,38.27,Yes,No,No,0.0,0.0,No,Female,55-59,White,No,Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,22.14,No,No,No,0.0,0.0,No,Female,30-34,White,No,Yes,Excellent,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,24.81,,,,,,,Male,Age 40 to 44,,,,,,,,,Tennessee,0.0,30.0,5 or more years ago,Yes,5.0,1 to 5,No,No,No,No,No,No,Yes,No,Yes,No,No,Yes,Yes,No,No,Yes,Current smoker - now smokes every day,Not at all (right now),No,"White only, Non-Hispanic",1.75,76.2,No,Yes,No,Yes,"Yes, received tetanus shot but not sure what type",Yes,Yes -,36.85,,,,,,,Female,Age 60 to 64,,,,,,,,,Minnesota,0.0,30.0,Within past year (anytime less than 12 months ago),No,7.0,None of them,No,No,No,No,No,No,Yes,No,Yes,Yes,Yes,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,94.35,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -,27.98,,,,,,,Female,Age 55 to 59,,,,,,,,,Colorado,3.0,10.0,5 or more years ago,Yes,6.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,73.94,Yes,Yes,No,No,"Yes, received Tdap",No,No -,25.82,,,,,,,Female,Age 25 to 29,,,,,,,,,California,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.68,72.57,No,No,Yes,,"No, did not receive any tetanus shot in the past 10 years",No,No -,32.89,,,,,,,Male,Age 18 to 24,,,,,,,,,Connecticut,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.7,95.25,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,22.07,,,,,,,Female,Age 60 to 64,,,,,,,,,Puerto Rico,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,Yes,No,No,No,No,Yes,No,No,Yes,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.52,51.26,No,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -Yes,33.48,No,No,No,0.0,30.0,Yes,Female,70-74,White,No,Yes,Good,8.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,23.91,,,,,,,Female,Age 55 to 59,,,,,,,,,Maine,0.0,1.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,61.23,No,No,Yes,No,"Yes, received Tdap",No,No -,26.63,,,,,,,Male,Age 40 to 44,,,,,,,,,Missouri,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,74.84,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No -,28.29,,,,,,,Female,Age 65 to 69,,,,,,,,,New Mexico,2.0,2.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,None of them,No,No,No,No,Yes,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,77.11,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -,20.37,,,,,,,Female,Age 45 to 49,,,,,,,,,Indiana,8.0,30.0,Within past year (anytime less than 12 months ago),No,9.0,1 to 5,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,No,Yes,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,64.41,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -,28.35,,,,,,,Female,Age 18 to 24,,,,,,,,,California,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.57,70.31,Yes,No,Yes,,,No,Yes -No,29.12,No,No,No,0.0,0.0,No,Female,55-59,Black,No,Yes,Excellent,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,30.54,No,No,No,0.0,0.0,No,Male,35-39,Hispanic,No,No,Excellent,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,18.64,,,,,,,Female,Age 60 to 64,,,,,,,,,New York,0.0,1.0,Within past year (anytime less than 12 months ago),Yes,9.0,None of them,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,50.8,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,27.44,,,,,,,Female,Age 65 to 69,,,,,,,,,Florida,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,None of them,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,77.11,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,No -,29.12,,,,,,,Female,Age 40 to 44,,,,,,,,,North Carolina,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.65,79.38,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -,25.68,,,,,,,Male,Age 65 to 69,,,,,,,,,West Virginia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.88,90.72,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,21.95,,,,,,,Female,Age 70 to 74,,,,,,,,,Florida,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,12.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,54.43,Yes,No,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,22.43,,,,,,,Male,Age 55 to 59,,,,,,,,,Puerto Rico,4.0,5.0,Within past year (anytime less than 12 months ago),No,4.0,None of them,No,No,No,No,No,No,Yes,No,No,Yes,Yes,No,Yes,Yes,Yes,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,Hispanic,1.85,77.11,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,33.28,,,,,,,Male,Age 40 to 44,,,,,,,,,South Carolina,5.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,Hispanic,1.65,90.72,No,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes -,32.93,,,,,,,Female,Age 60 to 64,,,,,,,,,Washington,30.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,1 to 5,No,No,No,No,No,Yes,No,No,Yes,No,No,No,No,Yes,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.68,92.53,Yes,No,Yes,Yes,"Yes, received Tdap",No,Yes -,26.58,,,,,,,Female,Age 45 to 49,,,,,,,,,Hawaii,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,81.65,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -No,25.8,No,No,No,0.0,0.0,No,Male,30-34,White,No,No,Excellent,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,33.66,,,,,,,Female,Age 70 to 74,,,,,,,,,Georgia,0.0,8.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,Yes,No,Yes,"No, pre-diabetes or borderline diabetes",Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,86.18,Yes,No,Yes,Yes,"Yes, received Tdap",No,Yes -,34.26,,,,,,,Male,Age 45 to 49,,,,,,,,,Puerto Rico,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,Hispanic,1.75,105.23,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -,31.75,,,,,,,Female,Age 45 to 49,,,,,,,,,Indiana,2.0,2.0,Within past year (anytime less than 12 months ago),Yes,7.0,,No,No,No,Yes,No,No,Yes,No,Yes,No,No,No,Yes,No,No,No,Current smoker - now smokes every day,Use them some days,No,"White only, Non-Hispanic",1.63,83.91,Yes,Yes,Yes,No,"Yes, received Tdap",No,Yes -,,,,,,,,Female,Age 35 to 39,,,,,,,,,Vermont,0.0,10.0,Within past year (anytime less than 12 months ago),No,7.0,None of them,No,No,No,Yes,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.63,,Yes,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,,,,,,,,Male,Age 35 to 39,,,,,,,,,Washington,5.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,,83.01,Yes,No,No,,"No, did not receive any tetanus shot in the past 10 years",No,No -No,28.29,No,No,No,0.0,0.0,No,Female,80 or older,Black,No,No,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,23.49,,,,,,,Male,Age 35 to 39,,,,,,,,,Iowa,1.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.7,68.04,Yes,,No,No,"Yes, received Tdap",No,No -No,26.63,Yes,No,No,0.0,0.0,No,Male,50-54,White,No,Yes,Fair,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,23.03,,,,,,,Female,Age 35 to 39,,,,,,,,,New Jersey,30.0,30.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,No,No,No,No,No,Yes,No,Yes,Yes,No,No,Yes,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.6,58.97,Yes,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",Yes,Yes -No,31.89,No,No,No,5.0,0.0,Yes,Female,70-74,White,No,Yes,Good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,25.1,,,,,,,Female,Age 55 to 59,,,,,,,,,Michigan,2.0,5.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,81.65,Yes,Yes,Yes,No,"Yes, received tetanus shot, but not Tdap",No,No -,30.0,,,,,,,Male,Age 70 to 74,,,,,,,,,Florida,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.91,108.86,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -No,24.89,Yes,No,No,0.0,0.0,Yes,Female,80 or older,White,No,Yes,Good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,29.95,No,No,No,0.0,0.0,Yes,Female,55-59,White,Yes,Yes,Good,8.0,Yes,Yes,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,32.98,No,No,No,0.0,0.0,No,Male,55-59,White,No,Yes,Excellent,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Yes,36.58,Yes,No,No,0.0,0.0,No,Female,70-74,Other,Yes,Yes,Good,8.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,25.39,,,,,,,Female,Age 35 to 39,,,,,,,,,New Hampshire,15.0,30.0,Within past 5 years (2 years but less than 5 years ago),Yes,4.0,None of them,No,Yes,Yes,Yes,No,No,Yes,No,Yes,No,No,No,Yes,No,No,Yes,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.52,58.97,Yes,Yes,No,Yes,"Yes, received tetanus shot but not sure what type",No,Yes -No,21.91,Yes,No,No,0.0,0.0,No,Male,35-39,White,No,Yes,Excellent,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,28.29,,,,,,,Female,Age 40 to 44,,,,,,,,,Georgia,0.0,30.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,77.11,Yes,No,No,No,"Yes, received Tdap",No,No -,21.77,,,,,,,Male,Age 50 to 54,,,,,,,,,Colorado,30.0,0.0,,Yes,8.0,All,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.85,74.84,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,23.03,,,,,,,Female,Age 80 or older,,,,,,,,,Kansas,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,,All,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.6,58.97,No,No,No,No,,No,No -,,,,,,,,Female,Age 35 to 39,,,,,,,,,Washington,3.0,20.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -,,,,,,,,Female,Age 70 to 74,,,,,,,,,New Jersey,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",Yes,Yes,No,No,Yes,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Tested positive using home test without a health professional -,27.1,,,,,,,Female,Age 50 to 54,,,,,,,,,Wisconsin,30.0,25.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,69.4,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -No,31.87,Yes,No,No,0.0,0.0,Yes,Male,65-69,White,No,No,Good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,18.75,No,No,No,0.0,0.0,No,Female,40-44,White,No,Yes,Excellent,8.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,25.1,Yes,No,No,0.0,0.0,No,Male,25-29,Other,No,Yes,Excellent,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,23.49,,,,,,,Male,Age 30 to 34,,,,,,,,,New York,3.0,20.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.7,68.04,Yes,Yes,No,Yes,"Yes, received Tdap",No,No -,29.84,,,,,,,Male,Age 40 to 44,,,,,,,,,Ohio,0.0,0.0,5 or more years ago,Yes,7.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Multiracial, Non-Hispanic",1.83,99.79,No,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -No,22.31,No,No,No,1.0,0.0,No,Female,60-64,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,39.44,,,,,,,Female,Age 65 to 69,,,,,,,,,Kansas,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,1 to 5,No,No,No,No,No,Yes,No,No,No,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,107.5,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,21.47,,,,,,,Female,Age 55 to 59,,,,,,,,,Florida,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,58.51,Yes,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No -,,,,,,,,Female,Age 80 or older,,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,Yes,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,,No,No,No,No,,No,No -,37.43,,,,,,,Male,Age 70 to 74,,,,,,,,,Hawaii,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"Other race only, Non-Hispanic",1.7,108.41,No,No,Yes,Yes,"Yes, received Tdap",No,No -,30.68,,,,,,,Male,Age 40 to 44,,,,,,,,,South Dakota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,Yes,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Current smoker - now smokes every day,Use them some days,No,"White only, Non-Hispanic",1.8,99.79,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -,34.33,,,,,,,Female,Age 65 to 69,,,,,,,,,South Carolina,20.0,10.0,Within past year (anytime less than 12 months ago),Yes,9.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.63,90.72,Yes,No,Yes,Yes,"Yes, received Tdap",No,No -No,23.17,No,No,No,0.0,0.0,No,Female,65-69,White,No,Yes,Good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +HeartDisease,BMI,Smoking,AlcoholDrinking,Stroke,PhysicalHealth,MentalHealth,DiffWalking,Sex,AgeCategory,Race,Diabetic,PhysicalActivity,SleepTime,Asthma,KidneyDisease,SkinCancer,State,PhysicalHealthDays,MentalHealthDays,LastCheckupTime,PhysicalActivities,SleepHours,RemovedTeeth,HadHeartAttack,HadAngina,HadStroke,HadAsthma,HadSkinCancer,HadCOPD,HadDepressiveDisorder,HadKidneyDisease,HadArthritis,HadDiabetes,DeafOrHardOfHearing,BlindOrVisionDifficulty,DifficultyConcentrating,DifficultyWalking,DifficultyDressingBathing,DifficultyErrands,SmokerStatus,ECigaretteUsage,ChestScan,RaceEthnicityCategory,HeightInMeters,WeightInKilograms,AlcoholDrinkers,HIVTesting,FluVaxLast12,PneumoVaxEver,TetanusLast10Tdap,HighRiskLastYear,CovidPos +,28.15,,,,,,,Female,Age 55 to 59,,,,,,,,Alabama,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,Yes,Yes,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.63,74.39,Yes,Yes,Yes,No,"Yes, received Tdap",No,No +,26.58,,,,,,,Male,Age 80 or older,,,,,,,,Missouri,30.0,30.0,,Yes,8.0,"6 or more, but not all",No,No,No,No,,No,No,No,Yes,No,No,No,Yes,Yes,No,Yes,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,81.65,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +No,38.02,Yes,No,No,5.0,20.0,No,Male,30-34,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,25.11,,,,,,,Male,Age 25 to 29,,,,,,,,Maryland,0.0,15.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.78,79.38,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No +,24.41,,,,,,,Male,Age 55 to 59,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,83.91,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,30.17,No,No,No,0.0,0.0,No,Male,60-64,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,28.06,,,,,,,Female,Age 75 to 79,,,,,,,,Utah,,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,Yes,Yes,Yes,Yes,No,No,No,No,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,86.18,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,23.3,,,,,,,Female,Age 80 or older,,,,,,,,Maine,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,63.5,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +,27.8,,,,,,,Male,Age 80 or older,,,,,,,,Alabama,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,Yes,Yes,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.83,92.99,No,No,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +No,49.92,Yes,No,No,28.0,0.0,Yes,Female,55-59,White,No,No,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,38.74,,,,,,,Female,Age 55 to 59,,,,,,,,Texas,17.0,1.0,Within past year (anytime less than 12 months ago),Yes,10.0,None of them,No,No,No,Yes,No,Yes,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,108.86,No,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No +No,23.3,No,No,No,0.0,0.0,No,Male,25-29,Black,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,30.67,,,,,,,Female,Age 25 to 29,,,,,,,,Kansas,0.0,10.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,86.18,No,No,No,,"Yes, received tetanus shot but not sure what type",Yes,Yes +,22.15,,,,,,,Male,Age 70 to 74,,,,,,,,Alaska,30.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,Yes,No,No,No,Yes,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,68.04,Yes,No,Yes,,"Yes, received tetanus shot but not sure what type",No,No +No,31.71,No,No,No,20.0,0.0,Yes,Female,80 or older,White,Yes,No,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,18.33,Yes,No,No,12.0,30.0,No,Female,80 or older,White,No,Yes,8.0,Yes,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,28.34,No,No,No,3.0,2.0,No,Female,50-54,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,27.71,Yes,No,No,30.0,30.0,No,Male,55-59,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Yes,50.79,Yes,No,No,0.0,2.0,Yes,Male,70-74,White,Yes,No,7.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,45.26,,,,,,,Female,Age 60 to 64,,,,,,,,Maine,30.0,30.0,Within past year (anytime less than 12 months ago),No,5.0,"6 or more, but not all",No,No,Yes,No,No,No,Yes,No,Yes,No,No,No,No,Yes,No,Yes,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,123.38,No,Yes,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,Yes +,34.67,,,,,,,Male,Age 30 to 34,,,,,,,,Florida,0.0,14.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,Yes,No,No,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.88,122.47,No,No,No,No,"Yes, received tetanus shot, but not Tdap",No,No +,23.35,,,,,,,Male,Age 45 to 49,,,,,,,,Idaho,1.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,Yes,No,Yes,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,80.29,No,No,No,No,"Yes, received Tdap",No,No +,32.26,,,,,,,Male,Age 65 to 69,,,,,,,,Oklahoma,0.0,5.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,Yes,No,No,No,No,Yes,No,No,Yes,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.93,120.2,Yes,No,No,Yes,"Yes, received Tdap",No,Yes +,30.67,,,,,,,Female,Age 45 to 49,,,,,,,,Idaho,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,,,,"White only, Non-Hispanic",1.68,86.18,,,,,,, +,30.9,,,,,,,Female,Age 50 to 54,,,,,,,,Maryland,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Multiracial, Non-Hispanic",1.63,81.65,No,Yes,No,Yes,"Yes, received Tdap",No,No +,32.64,,,,,,,Male,Age 60 to 64,,,,,,,,South Carolina,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.8,106.14,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,34.46,,,,,,,Male,Age 65 to 69,,,,,,,,New York,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,1 to 5,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,99.79,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +No,33.15,No,No,No,0.0,4.0,No,Female,50-54,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,27.2,,,,,,,Male,Age 70 to 74,,,,,,,,South Carolina,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,Yes,Yes,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,88.45,No,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,16.13,,,,,,,Female,Age 65 to 69,,,,,,,,Virginia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,No,Yes,No,Yes,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,42.64,Yes,No,Yes,Yes,"Yes, received Tdap",No,No +Yes,28.25,Yes,No,Yes,30.0,0.0,Yes,Male,75-79,White,No,Yes,8.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,31.12,No,No,No,14.0,0.0,No,Male,55-59,Hispanic,Yes,No,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,29.08,,,,,,,Female,Age 65 to 69,,,,,,,,Puerto Rico,3.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,Yes,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,Hispanic,1.57,72.12,No,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes +No,23.11,Yes,No,No,0.0,0.0,No,Male,30-34,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,32.49,,,,,,,Male,Age 50 to 54,,,,,,,,Maryland,14.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,9.0,None of them,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,99.79,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No +,34.96,,,,,,,Male,Age 60 to 64,,,,,,,,Wisconsin,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.85,120.2,No,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +No,29.29,No,No,No,0.0,0.0,No,Female,70-74,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,25.77,,,,,,,Male,Age 40 to 44,,,,,,,,Michigan,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,86.18,Yes,No,Yes,No,"Yes, received Tdap",No,No +No,26.64,No,No,No,0.0,2.0,No,Female,60-64,White,No,Yes,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,32.69,Yes,Yes,No,8.0,30.0,No,Female,40-44,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,27.12,No,No,No,5.0,2.0,No,Male,40-44,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,,,,,,,,Female,Age 60 to 64,,,,,,,,Florida,15.0,15.0,Within past year (anytime less than 12 months ago),No,3.0,All,No,No,No,No,No,Yes,Yes,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,,No,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No +,25.96,,,,,,,Female,Age 25 to 29,,,,,,,,Puerto Rico,6.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,70.76,Yes,No,No,No,"Yes, received Tdap",No,No +,35.43,,,,,,,Female,Age 30 to 34,,,,,,,,Nebraska,7.0,2.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,90.72,Yes,Yes,Yes,No,"Yes, received Tdap",No,Yes +No,29.05,No,No,No,0.0,0.0,No,Female,80 or older,White,Yes,No,6.0,No,Yes,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,28.19,,,,,,,Female,Age 30 to 34,,,,,,,,Idaho,7.0,4.0,Within past 5 years (2 years but less than 5 years ago),No,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,81.65,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,29.69,,,,,,,Female,Age 75 to 79,,,,,,,,Guam,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,Yes,No,No,Yes,No,No,No,Yes,Yes,"No, pre-diabetes or borderline diabetes",Yes,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Other race only, Non-Hispanic",1.52,68.95,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes +,28.8,,,,,,,Male,Age 55 to 59,,,,,,,,South Carolina,12.0,4.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,88.45,Yes,No,No,No,"Yes, received tetanus shot, but not Tdap",No,Yes +No,27.07,No,No,No,0.0,15.0,No,Female,18-24,American Indian/Alaskan Native,No,Yes,4.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,32.45,No,No,No,2.0,2.0,No,Female,45-49,Black,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,31.62,,,,,,,Male,Age 70 to 74,,,,,,,,Ohio,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,Yes,No,Yes,No,No,No,Yes,No,No,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,86.18,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +,26.85,,,,,,,Male,Age 65 to 69,,,,,,,,Pennsylvania,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,89.81,Yes,No,No,Yes,"Yes, received Tdap",No,No +No,32.89,No,No,No,0.0,0.0,No,Male,55-59,White,Yes,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,27.41,,,,,,,Female,Age 40 to 44,,,,,,,,North Dakota,6.0,2.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.7,79.38,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,Yes +,23.87,,,,,,,Male,Age 55 to 59,,,,,,,,Arizona,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,79.83,No,No,No,No,"Yes, received Tdap",No,No +,27.12,,,,,,,Male,Age 70 to 74,,,,,,,,Alaska,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,6.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,90.72,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,42.05,,,,,,,Female,Age 65 to 69,,,,,,,,Alaska,0.0,3.0,Within past year (anytime less than 12 months ago),No,8.0,All,No,No,No,No,No,No,Yes,No,Yes,Yes,No,No,Yes,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,111.13,No,No,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +No,27.75,No,No,No,0.0,0.0,No,Male,40-44,Hispanic,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,31.28,,,,,,,Female,Age 80 or older,,,,,,,,Kansas,30.0,5.0,Within past year (anytime less than 12 months ago),Yes,7.0,All,No,Yes,No,No,No,No,No,No,Yes,Yes,Yes,No,No,Yes,No,Yes,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.57,77.56,No,Yes,Yes,Yes,"Yes, received Tdap",No,No +,30.54,,,,,,,Male,Age 60 to 64,,,,,,,,Georgia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,88.45,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +,,,,,,,,Male,Age 80 or older,,,,,,,,Minnesota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,Yes,Yes,No,No,No,No,No,No,Yes,Yes,Yes,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,,Yes,,,,,, +No,27.37,No,No,No,0.0,12.0,No,Male,30-34,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,23.03,No,No,No,3.0,30.0,No,Female,65-69,White,No,Yes,3.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,29.53,,,,,,,Male,Age 65 to 69,,,,,,,,Idaho,14.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,90.72,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +,28.57,,,,,,,Female,Age 45 to 49,,,,,,,,Washington,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),No,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,80.29,Yes,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,24.11,,,,,,,Male,Age 80 or older,,,,,,,,Arizona,21.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,No,No,No,Yes,No,No,No,No,"No, pre-diabetes or borderline diabetes",No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,76.2,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes +No,26.78,Yes,No,No,0.0,0.0,No,Male,65-69,American Indian/Alaskan Native,No,No,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,22.31,No,No,No,30.0,0.0,Yes,Female,55-59,Black,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,33.67,,,,,,,Female,Age 40 to 44,,,,,,,,Maryland,5.0,15.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,97.52,Yes,Yes,Yes,No,"Yes, received Tdap",No,Yes +No,35.51,No,No,No,0.0,0.0,No,Female,18-24,White,No,Yes,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,22.83,,,,,,,Female,Age 60 to 64,,,,,,,,Indiana,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,Yes,Yes,Yes,Yes,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,60.33,No,Yes,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +,30.12,,,,,,,Female,Age 65 to 69,,,,,,,,Florida,3.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,All,No,No,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.65,82.1,Yes,Yes,Yes,Yes,,No,No +,,,,,,,,Female,Age 70 to 74,,,,,,,,Connecticut,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,Hispanic,,,,,,,,, +,26.63,,,,,,,Female,Age 40 to 44,,,,,,,,Vermont,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,74.84,Yes,,No,No,,, +,30.54,,,,,,,Male,Age 30 to 34,,,,,,,,Maryland,0.0,20.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,Yes,Yes,No,No,No,Current smoker - now smokes every day,Not at all (right now),No,Hispanic,1.7,88.45,No,Yes,No,No,"Yes, received tetanus shot, but not Tdap",No,No +,41.2,,,,,,,Female,Age 50 to 54,,,,,,,,Arkansas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,1.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,108.86,No,Yes,Yes,No,"Yes, received Tdap",No,No +,38.74,,,,,,,Male,Age 60 to 64,,,,,,,,Hawaii,2.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,Yes,Yes,Yes,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Multiracial, Non-Hispanic",1.78,122.47,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +No,24.39,Yes,No,No,0.0,3.0,No,Male,30-34,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,24.63,,,,,,,Female,Age 70 to 74,,,,,,,,Alabama,30.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.65,67.13,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,27.32,,,,,,,Male,Age 65 to 69,,,,,,,,Connecticut,30.0,7.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,83.91,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes +,,,,,,,,Female,Age 70 to 74,,,,,,,,Florida,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,33.23,,,,,,,Female,Age 80 or older,,,,,,,,Indiana,5.0,1.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,1 to 5,No,No,Yes,No,No,No,No,No,Yes,Yes,No,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,102.06,No,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +No,27.46,Yes,No,No,0.0,0.0,Yes,Female,80 or older,White,No,No,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,30.68,No,No,No,0.0,2.0,No,Male,45-49,White,No,No,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,24.68,No,No,No,0.0,0.0,No,Male,55-59,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,34.46,No,No,No,15.0,0.0,No,Male,50-54,White,No,Yes,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,29.05,,,,,,,Female,Age 80 or older,,,,,,,,Tennessee,7.0,2.0,Within past 2 years (1 year but less than 2 years ago),Yes,10.0,None of them,No,No,No,No,No,No,Yes,No,Yes,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,81.65,Yes,,Yes,Yes,"Yes, received Tdap",No,Yes +,,,,,,,,Male,Age 55 to 59,,,,,,,,Washington,8.0,3.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, +,29.84,,,,,,,Male,Age 65 to 69,,,,,,,,New York,0.0,2.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.83,99.79,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +No,29.47,No,No,No,13.0,0.0,No,Male,45-49,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,31.12,,,,,,,Female,Age 60 to 64,,,,,,,,West Virginia,15.0,30.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,Yes,No,No,Yes,No,Yes,Yes,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,84.82,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,21.52,,,,,,,Male,Age 80 or older,,,,,,,,New York,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,1 to 5,No,,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,68.04,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,23.4,,,,,,,Female,Age 30 to 34,,,,,,,,Louisiana,0.0,10.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.68,65.77,Yes,Yes,No,Yes,"No, did not receive any tetanus shot in the past 10 years",Yes,No +,24.24,,,,,,,Male,Age 50 to 54,,,,,,,,Rhode Island,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,Yes,,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,Hispanic,1.5,54.43,No,Yes,No,,,No,Yes +No,38.26,No,No,No,0.0,0.0,No,Male,35-39,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,51.49,Yes,No,No,0.0,0.0,Yes,Female,50-54,White,No,Yes,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,,,,,,,,Female,,,,,,,,,Arizona,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,9.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,,Yes,No,Yes,No,"Yes, received Tdap",No,No +,32.77,,,,,,,Female,Age 45 to 49,,,,,,,,New Mexico,3.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,Hispanic,1.6,83.91,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Tested positive using home test without a health professional +No,28.19,Yes,No,No,30.0,14.0,Yes,Female,30-34,White,No,No,5.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,25.1,Yes,No,No,0.0,4.0,No,Male,35-39,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,22.98,,,,,,,Female,Age 40 to 44,,,,,,,,New Jersey,,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,,,,Hispanic,1.5,51.71,,,,,,, +,32.69,,,,,,,Male,Age 70 to 74,,,,,,,,New Jersey,30.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,"6 or more, but not all",Yes,Yes,Yes,No,No,No,No,Yes,No,Yes,No,Yes,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,97.52,Yes,No,No,Yes,"Yes, received tetanus shot but not sure what type",No,Yes +No,26.3,No,No,No,10.0,0.0,Yes,Female,45-49,White,Yes,No,4.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,26.61,,,,,,,Male,Age 50 to 54,,,,,,,,Virginia,0.0,0.0,5 or more years ago,Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,79.38,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,26.89,No,No,No,3.0,0.0,No,Female,65-69,Black,Yes,Yes,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,31.47,,,,,,,Female,Age 65 to 69,,,,,,,,Georgia,0.0,15.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,88.45,Yes,No,Yes,Yes,"Yes, received Tdap",No,No +,25.84,,,,,,,Male,Age 70 to 74,,,,,,,,Missouri,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,74.84,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",, +,32.28,,,,,,,Male,Age 55 to 59,,,,,,,,Minnesota,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,1 to 5,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,90.72,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,29.99,,,,,,,Male,Age 50 to 54,,,,,,,,Idaho,0.0,0.0,5 or more years ago,Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.8,97.52,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +,,,,,,,,Female,Age 65 to 69,,,,,,,,Florida,30.0,30.0,Within past year (anytime less than 12 months ago),No,10.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,,,,,,,,"Black only, Non-Hispanic",,,,,,,,, +,28.89,,,,,,,Male,Age 70 to 74,,,,,,,,Tennessee,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,"No, pre-diabetes or borderline diabetes",No,No,No,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.73,86.18,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,39.06,,,,,,,Female,Age 75 to 79,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,"6 or more, but not all",No,No,Yes,Yes,No,No,No,Yes,Yes,Yes,Yes,No,No,Yes,Yes,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.52,90.72,No,No,Yes,Yes,"Yes, received Tdap",No,No +,,,,,,,,Male,Age 60 to 64,,,,,,,,Colorado,10.0,5.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,Yes,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.8,,No,,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes +No,24.41,No,No,No,0.0,0.0,No,Male,65-69,White,Yes,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,25.29,Yes,No,No,0.0,0.0,No,Female,70-74,White,No,Yes,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,19.49,,,,,,,Female,Age 65 to 69,,,,,,,,Washington,3.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,All,No,No,No,No,No,Yes,No,No,No,Yes,No,No,No,No,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,49.9,Yes,,No,Yes,"Yes, received Tdap",No,No +,26.54,,,,,,,Male,Age 75 to 79,,,,,,,,Texas,3.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,"6 or more, but not all",No,Yes,No,Yes,No,No,No,Yes,Yes,Yes,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,83.91,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,32.95,,,,,,,Female,Age 50 to 54,,,,,,,,South Dakota,0.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,Hispanic,1.65,89.81,No,Yes,No,No,"Yes, received tetanus shot, but not Tdap",No,No +,22.86,,,,,,,Female,Age 70 to 74,,,,,,,,Maryland,0.0,1.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,Yes,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.57,56.7,No,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No +,30.0,,,,,,,Male,Age 30 to 34,,,,,,,,Montana,15.0,22.0,Within past year (anytime less than 12 months ago),Yes,4.0,None of them,No,No,No,No,No,No,Yes,No,Yes,No,No,No,Yes,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.91,108.86,No,No,No,No,"Yes, received Tdap",No,No +No,34.87,No,No,No,1.0,3.0,No,Male,35-39,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,35.12,,,,,,,Female,Age 35 to 39,,,,,,,,South Carolina,7.0,30.0,Within past year (anytime less than 12 months ago),Yes,5.0,"6 or more, but not all",No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"Black only, Non-Hispanic",1.57,87.09,Yes,,,,,, +,27.89,,,,,,,Male,Age 45 to 49,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,None of them,Yes,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,Hispanic,1.8,90.72,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes +No,26.58,No,No,No,0.0,0.0,Yes,Female,50-54,White,No,No,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,34.46,,,,,,,Female,Age 45 to 49,,,,,,,,Louisiana,0.0,5.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,99.79,Yes,Yes,Yes,No,"Yes, received Tdap",No,No +,,,,,,,,Female,Age 75 to 79,,,,,,,,New Hampshire,2.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,All,No,Yes,No,No,No,Yes,No,No,Yes,No,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, +No,29.23,No,Yes,No,0.0,0.0,No,Female,75-79,White,No,Yes,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,23.57,Yes,No,No,0.0,0.0,No,Male,70-74,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,27.79,Yes,No,No,6.0,20.0,No,Female,30-34,White,No,No,3.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,24.33,,,,,,,Male,Age 35 to 39,,,,,,,,Massachusetts,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.73,72.57,Yes,No,Yes,No,"Yes, received Tdap",, +No,22.14,Yes,No,No,0.0,0.0,No,Female,70-74,Asian,No,Yes,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,29.05,,,,,,,Female,Age 70 to 74,,,,,,,,Minnesota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,81.65,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,23.91,,,,,,,Female,Age 60 to 64,,,,,,,,Colorado,0.0,28.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,Yes,No,No,Yes,"No, pre-diabetes or borderline diabetes",No,Yes,Yes,No,No,No,Former smoker,Use them every day,Yes,"White only, Non-Hispanic",1.6,61.23,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No +,32.98,,,,,,,Male,Age 50 to 54,,,,,,,,Kansas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.85,113.4,No,No,Yes,No,"Yes, received Tdap",No,Yes +,27.98,,,,,,,Male,Age 75 to 79,,,,,,,,Maine,30.0,30.0,Within past year (anytime less than 12 months ago),No,6.0,None of them,No,Yes,No,No,No,No,No,No,Yes,Yes,No,No,No,Yes,Yes,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,83.46,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,21.93,,,,,,,Male,Age 55 to 59,,,,,,,,Hawaii,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"Other race only, Non-Hispanic",1.7,63.5,No,No,Yes,No,,No,No +No,23.73,Yes,No,No,0.0,30.0,No,Female,70-74,White,Yes,No,8.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,38.27,Yes,No,No,0.0,0.0,No,Female,55-59,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,22.14,No,No,No,0.0,0.0,No,Female,30-34,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,24.81,,,,,,,Male,Age 40 to 44,,,,,,,,Tennessee,0.0,30.0,5 or more years ago,Yes,5.0,1 to 5,No,No,No,No,No,No,Yes,No,Yes,No,No,Yes,Yes,No,No,Yes,Current smoker - now smokes every day,Not at all (right now),No,"White only, Non-Hispanic",1.75,76.2,No,Yes,No,Yes,"Yes, received tetanus shot but not sure what type",Yes,Yes +,36.85,,,,,,,Female,Age 60 to 64,,,,,,,,Minnesota,0.0,30.0,Within past year (anytime less than 12 months ago),No,7.0,None of them,No,No,No,No,No,No,Yes,No,Yes,Yes,Yes,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,94.35,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +,27.98,,,,,,,Female,Age 55 to 59,,,,,,,,Colorado,3.0,10.0,5 or more years ago,Yes,6.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,73.94,Yes,Yes,No,No,"Yes, received Tdap",No,No +,25.82,,,,,,,Female,Age 25 to 29,,,,,,,,California,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.68,72.57,No,No,Yes,,"No, did not receive any tetanus shot in the past 10 years",No,No +,32.89,,,,,,,Male,Age 18 to 24,,,,,,,,Connecticut,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.7,95.25,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,22.07,,,,,,,Female,Age 60 to 64,,,,,,,,Puerto Rico,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,Yes,No,No,No,No,Yes,No,No,Yes,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.52,51.26,No,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +Yes,33.48,No,No,No,0.0,30.0,Yes,Female,70-74,White,No,Yes,8.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,23.91,,,,,,,Female,Age 55 to 59,,,,,,,,Maine,0.0,1.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,61.23,No,No,Yes,No,"Yes, received Tdap",No,No +,26.63,,,,,,,Male,Age 40 to 44,,,,,,,,Missouri,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,74.84,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No +,28.29,,,,,,,Female,Age 65 to 69,,,,,,,,New Mexico,2.0,2.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,None of them,No,No,No,No,Yes,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,77.11,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +,20.37,,,,,,,Female,Age 45 to 49,,,,,,,,Indiana,8.0,30.0,Within past year (anytime less than 12 months ago),No,9.0,1 to 5,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,No,Yes,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,64.41,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +,28.35,,,,,,,Female,Age 18 to 24,,,,,,,,California,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.57,70.31,Yes,No,Yes,,,No,Yes +No,29.12,No,No,No,0.0,0.0,No,Female,55-59,Black,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,30.54,No,No,No,0.0,0.0,No,Male,35-39,Hispanic,No,No,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,18.64,,,,,,,Female,Age 60 to 64,,,,,,,,New York,0.0,1.0,Within past year (anytime less than 12 months ago),Yes,9.0,None of them,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,50.8,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,27.44,,,,,,,Female,Age 65 to 69,,,,,,,,Florida,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,None of them,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,77.11,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,No +,29.12,,,,,,,Female,Age 40 to 44,,,,,,,,North Carolina,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.65,79.38,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +,25.68,,,,,,,Male,Age 65 to 69,,,,,,,,West Virginia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.88,90.72,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,21.95,,,,,,,Female,Age 70 to 74,,,,,,,,Florida,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,12.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,54.43,Yes,No,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,22.43,,,,,,,Male,Age 55 to 59,,,,,,,,Puerto Rico,4.0,5.0,Within past year (anytime less than 12 months ago),No,4.0,None of them,No,No,No,No,No,No,Yes,No,No,Yes,Yes,No,Yes,Yes,Yes,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,Hispanic,1.85,77.11,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,33.28,,,,,,,Male,Age 40 to 44,,,,,,,,South Carolina,5.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,Hispanic,1.65,90.72,No,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes +,32.93,,,,,,,Female,Age 60 to 64,,,,,,,,Washington,30.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,1 to 5,No,No,No,No,No,Yes,No,No,Yes,No,No,No,No,Yes,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.68,92.53,Yes,No,Yes,Yes,"Yes, received Tdap",No,Yes +,26.58,,,,,,,Female,Age 45 to 49,,,,,,,,Hawaii,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,81.65,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +No,25.8,No,No,No,0.0,0.0,No,Male,30-34,White,No,No,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,33.66,,,,,,,Female,Age 70 to 74,,,,,,,,Georgia,0.0,8.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,Yes,No,Yes,"No, pre-diabetes or borderline diabetes",Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,86.18,Yes,No,Yes,Yes,"Yes, received Tdap",No,Yes +,34.26,,,,,,,Male,Age 45 to 49,,,,,,,,Puerto Rico,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,Hispanic,1.75,105.23,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +,31.75,,,,,,,Female,Age 45 to 49,,,,,,,,Indiana,2.0,2.0,Within past year (anytime less than 12 months ago),Yes,7.0,,No,No,No,Yes,No,No,Yes,No,Yes,No,No,No,Yes,No,No,No,Current smoker - now smokes every day,Use them some days,No,"White only, Non-Hispanic",1.63,83.91,Yes,Yes,Yes,No,"Yes, received Tdap",No,Yes +,,,,,,,,Female,Age 35 to 39,,,,,,,,Vermont,0.0,10.0,Within past year (anytime less than 12 months ago),No,7.0,None of them,No,No,No,Yes,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.63,,Yes,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,,,,,,,,Male,Age 35 to 39,,,,,,,,Washington,5.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,,83.01,Yes,No,No,,"No, did not receive any tetanus shot in the past 10 years",No,No +No,28.29,No,No,No,0.0,0.0,No,Female,80 or older,Black,No,No,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,23.49,,,,,,,Male,Age 35 to 39,,,,,,,,Iowa,1.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.7,68.04,Yes,,No,No,"Yes, received Tdap",No,No +No,26.63,Yes,No,No,0.0,0.0,No,Male,50-54,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,23.03,,,,,,,Female,Age 35 to 39,,,,,,,,New Jersey,30.0,30.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,No,No,No,No,No,Yes,No,Yes,Yes,No,No,Yes,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.6,58.97,Yes,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",Yes,Yes +No,31.89,No,No,No,5.0,0.0,Yes,Female,70-74,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,25.1,,,,,,,Female,Age 55 to 59,,,,,,,,Michigan,2.0,5.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,81.65,Yes,Yes,Yes,No,"Yes, received tetanus shot, but not Tdap",No,No +,30.0,,,,,,,Male,Age 70 to 74,,,,,,,,Florida,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.91,108.86,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +No,24.89,Yes,No,No,0.0,0.0,Yes,Female,80 or older,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,29.95,No,No,No,0.0,0.0,Yes,Female,55-59,White,Yes,Yes,8.0,Yes,Yes,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,32.98,No,No,No,0.0,0.0,No,Male,55-59,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Yes,36.58,Yes,No,No,0.0,0.0,No,Female,70-74,Other,Yes,Yes,8.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,25.39,,,,,,,Female,Age 35 to 39,,,,,,,,New Hampshire,15.0,30.0,Within past 5 years (2 years but less than 5 years ago),Yes,4.0,None of them,No,Yes,Yes,Yes,No,No,Yes,No,Yes,No,No,No,Yes,No,No,Yes,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.52,58.97,Yes,Yes,No,Yes,"Yes, received tetanus shot but not sure what type",No,Yes +No,21.91,Yes,No,No,0.0,0.0,No,Male,35-39,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,28.29,,,,,,,Female,Age 40 to 44,,,,,,,,Georgia,0.0,30.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,77.11,Yes,No,No,No,"Yes, received Tdap",No,No +,21.77,,,,,,,Male,Age 50 to 54,,,,,,,,Colorado,30.0,0.0,,Yes,8.0,All,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.85,74.84,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,23.03,,,,,,,Female,Age 80 or older,,,,,,,,Kansas,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,,All,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.6,58.97,No,No,No,No,,No,No +,,,,,,,,Female,Age 35 to 39,,,,,,,,Washington,3.0,20.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +,,,,,,,,Female,Age 70 to 74,,,,,,,,New Jersey,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",Yes,Yes,No,No,Yes,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Tested positive using home test without a health professional +,27.1,,,,,,,Female,Age 50 to 54,,,,,,,,Wisconsin,30.0,25.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,69.4,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +No,31.87,Yes,No,No,0.0,0.0,Yes,Male,65-69,White,No,No,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,18.75,No,No,No,0.0,0.0,No,Female,40-44,White,No,Yes,8.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,25.1,Yes,No,No,0.0,0.0,No,Male,25-29,Other,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,23.49,,,,,,,Male,Age 30 to 34,,,,,,,,New York,3.0,20.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.7,68.04,Yes,Yes,No,Yes,"Yes, received Tdap",No,No +,29.84,,,,,,,Male,Age 40 to 44,,,,,,,,Ohio,0.0,0.0,5 or more years ago,Yes,7.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Multiracial, Non-Hispanic",1.83,99.79,No,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,22.31,No,No,No,1.0,0.0,No,Female,60-64,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,39.44,,,,,,,Female,Age 65 to 69,,,,,,,,Kansas,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,1 to 5,No,No,No,No,No,Yes,No,No,No,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,107.5,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,21.47,,,,,,,Female,Age 55 to 59,,,,,,,,Florida,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,58.51,Yes,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No +,,,,,,,,Female,Age 80 or older,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,Yes,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,,No,No,No,No,,No,No +,37.43,,,,,,,Male,Age 70 to 74,,,,,,,,Hawaii,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"Other race only, Non-Hispanic",1.7,108.41,No,No,Yes,Yes,"Yes, received Tdap",No,No +,30.68,,,,,,,Male,Age 40 to 44,,,,,,,,South Dakota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,Yes,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Current smoker - now smokes every day,Use them some days,No,"White only, Non-Hispanic",1.8,99.79,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +,34.33,,,,,,,Female,Age 65 to 69,,,,,,,,South Carolina,20.0,10.0,Within past year (anytime less than 12 months ago),Yes,9.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.63,90.72,Yes,No,Yes,Yes,"Yes, received Tdap",No,No +No,23.17,No,No,No,0.0,0.0,No,Female,65-69,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_003/history.csv b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_003/history.csv index 633149c240b74e40fd1347fa195dbe8d0b20a37a..16502782a1b650cfe52b12e0744a5949c1111777 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_003/history.csv +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_003/history.csv @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:2a73e4970eb07c1b0cdfb4e76517cd2562c9fc1b48b8242e30f14b1d727f6a33 -size 271616341 +oid sha256:93a5fa806551d9edca11cbec8aa5ba901f7535aff40bcc7f8c920ad42f5f81e2 +size 270594961 diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_003/info.json b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_003/info.json index 53272e3074451c673817a05ef4da5ee438d07863..c876b7e9aa18ec2175d72a1a6b59bf487c77e98c 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_003/info.json +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_003/info.json @@ -1,401 +1,393 @@ -{ - "name": "Indicators of Heart Disease (2022 UPDATE)", - "source": "https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease/data", - "data_intro": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", - "is_splited": false, - "overall_size": 1010949, - "train_size": 0, - "test_size": 0, - "c_classes": 46, - "n_classes": 9, - "cat_feature_intro": { - "HeartDisease": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", - "Smoking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", - "AlcoholDrinking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", - "Stroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", - "DiffWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", - "Sex": "Sex is a biological factor that can influence the risk of heart disease. Men and women may experience different symptoms and risk factors for heart disease.", - "AgeCategory": "Age is a significant risk factor for heart disease. As people age, their risk of developing heart disease increases. It is important to monitor heart health as individuals get older.", - "Race": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", - "Diabetic": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", - "PhysicalActivity": "Regular physical activity is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", - "GenHealth": "General health status can influence the risk of heart disease. It is important to maintain overall health and address any underlying health issues to reduce the risk of heart-related complications.", - "Asthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", - "KidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", - "SkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", - "State": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", - "GeneralHealth": "General health status can influence the risk of heart disease. It is important to maintain overall health and address any underlying health issues to reduce the risk of heart-related complications.", - "LastCheckupTime": "The time since the last medical checkup can be an important factor in heart health. Regular checkups can help identify and address risk factors for heart disease early.", - "PhysicalActivities": "Engaging in regular physical activities is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", - "RemovedTeeth": "The number of removed teeth can be an indicator of overall health, including heart health. Poor oral health has been linked to an increased risk of heart disease.", - "HadHeartAttack": "As described above, the original dataset of nearly 300 variables was reduced to 40 variables. In addition to classical EDA, this dataset can be used to apply a number of machine learning methods, especially classifier models (logistic regression, SVM, random forest, etc.). You should treat the variable \"HadHeartAttack\" as binary (\"Yes\" - respondent had heart disease; \"No\" - respondent did not have heart disease). Note, however, that the classes are unbalanced, so the classic approach of applying a model is not advisable. Fixing the weights/undersampling should yield much better results. Based on the data set, I built a logistic regression model and embedded it in an application that might inspire you: https://share.streamlit.io/kamilpytlak/heart-condition-checker/main/app.py. Can you indicate which variables have a significant effect on the likelihood of heart disease?", - "HadAngina": "Angina is a type of chest pain caused by reduced blood flow to the heart. It is a symptom of coronary artery disease and can indicate an increased risk of heart disease.", - "HadStroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", - "HadAsthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", - "HadSkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", - "HadCOPD": "Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease that causes obstructed airflow from the lungs. It can increase the risk of heart disease.", - "HadDepressiveDisorder": "Depressive disorders can affect overall health and increase the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", - "HadKidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", - "HadArthritis": "Arthritis is a condition that causes inflammation of the joints. It can affect overall health and increase the risk of heart disease.", - "HadDiabetes": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", - "DeafOrHardOfHearing": "Hearing loss can affect overall health and quality of life. It is important to address and manage hearing issues to maintain overall health and reduce the risk of heart disease.", - "BlindOrVisionDifficulty": "Vision difficulties can affect overall health and quality of life. It is important to address and manage vision issues to maintain overall health and reduce the risk of heart disease.", - "DifficultyConcentrating": "Difficulty concentrating can be a sign of underlying health issues, including heart disease. It is important to address cognitive issues to improve overall health and reduce the risk of heart-related complications.", - "DifficultyWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", - "DifficultyDressingBathing": "Difficulty with daily activities such as dressing and bathing can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", - "DifficultyErrands": "Difficulty running errands can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", - "SmokerStatus": "Smoking is a major risk factor for heart disease. It is important to address and manage smoking habits to reduce the risk of heart-related complications.", - "ECigaretteUsage": "E-cigarette usage can affect heart health. It is important to monitor and manage e-cigarette usage to reduce the risk of heart disease.", - "ChestScan": "Chest scans can help identify underlying health issues, including heart disease. It is important to monitor and address any abnormalities found in chest scans to maintain overall health.", - "RaceEthnicityCategory": "Race and ethnicity can influence the risk of heart disease. It is important to consider these factors when assessing and managing heart health.", - "AlcoholDrinkers": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", - "HIVTesting": "HIV testing is important for overall health. It is important to address and manage HIV status to reduce the risk of heart-related complications.", - "FluVaxLast12": "Receiving a flu vaccine in the last 12 months can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", - "PneumoVaxEver": "Receiving a pneumococcal vaccine can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", - "TetanusLast10Tdap": "Receiving a tetanus vaccine in the last 10 years can help reduce the risk of infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", - "HighRiskLastYear": "Being at high risk for health issues in the last year can affect overall health and increase the risk of heart disease. It is important to address and manage high-risk factors to reduce the risk of heart-related complications.", - "CovidPos": "Testing positive for COVID-19 can affect overall health and increase the risk of heart disease. It is important to monitor and manage COVID-19 symptoms to reduce the risk of heart-related complications." - }, - "num_feature_intro": { - "BMI": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", - "PhysicalHealth": "Physical health is an important factor in overall well-being and can influence the risk of heart disease. It is important to maintain good physical health to reduce the risk of heart-related complications.", - "MentalHealth": "Mental health is an important factor in overall well-being and can influence the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", - "SleepTime": "Sleep time is an important factor in overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", - "PhysicalHealthDays": "The number of days with good physical health can be an indicator of overall well-being. It is important to maintain good physical health to reduce the risk of heart-related complications.", - "MentalHealthDays": "The number of days with good mental health can be an indicator of overall well-being. It is important to address and manage mental health to reduce the risk of heart-related complications.", - "SleepHours": "The number of hours of sleep can affect overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", - "HeightInMeters": "Height can be an indicator of overall health. It is important to monitor and maintain a healthy height-to-weight ratio to reduce the risk of heart disease.", - "WeightInKilograms": "Weight can be an indicator of overall health. It is important to maintain a healthy weight to reduce the risk of heart disease." - }, - "evaluation_metric": "The evaluation metric for this dataset can include accuracy, precision, recall, F1-score, and AUC-ROC. These metrics can help assess the performance of machine learning models in predicting heart disease.", - "task_type": "classification", - "target": "GeneralHealth", - "num_feature_value": { - "BMI": [ - 12.02, - 99.64 - ], - "HeightInMeters": [ - 0.91, - 2.41 - ], - "MentalHealth": [ - 0.0, - 30.0 - ], - "MentalHealthDays": [ - 0.0, - 30.0 - ], - "PhysicalHealth": [ - 0.0, - 30.0 - ], - "PhysicalHealthDays": [ - 0.0, - 30.0 - ], - "SleepHours": [ - 1.0, - 24.0 - ], - "SleepTime": [ - 1.0, - 24.0 - ], - "WeightInKilograms": [ - 22.68, - 292.57 - ] - }, - "cat_feature_value": { - "AgeCategory": [ - "18-24", - "25-29", - "30-34", - "35-39", - "40-44", - "45-49", - "50-54", - "55-59", - "60-64", - "65-69", - "70-74", - "75-79", - "80 or older", - "Age 18 to 24", - "Age 25 to 29", - "Age 30 to 34", - "Age 35 to 39", - "Age 40 to 44", - "Age 45 to 49", - "Age 50 to 54", - "Age 55 to 59", - "Age 60 to 64", - "Age 65 to 69", - "Age 70 to 74", - "Age 75 to 79", - "Age 80 or older" - ], - "AlcoholDrinkers": [ - "No", - "Yes" - ], - "AlcoholDrinking": [ - "No", - "Yes" - ], - "Asthma": [ - "No", - "Yes" - ], - "BlindOrVisionDifficulty": [ - "No", - "Yes" - ], - "ChestScan": [ - "No", - "Yes" - ], - "CovidPos": [ - "No", - "Tested positive using home test without a health professional", - "Yes" - ], - "DeafOrHardOfHearing": [ - "No", - "Yes" - ], - "Diabetic": [ - "No", - "No, borderline diabetes", - "Yes", - "Yes (during pregnancy)" - ], - "DiffWalking": [ - "No", - "Yes" - ], - "DifficultyConcentrating": [ - "No", - "Yes" - ], - "DifficultyDressingBathing": [ - "No", - "Yes" - ], - "DifficultyErrands": [ - "No", - "Yes" - ], - "DifficultyWalking": [ - "No", - "Yes" - ], - "ECigaretteUsage": [ - "Never used e-cigarettes in my entire life", - "Not at all (right now)", - "Use them every day", - "Use them some days" - ], - "FluVaxLast12": [ - "No", - "Yes" - ], - "GenHealth": [ - "Excellent", - "Fair", - "Good", - "Poor", - "Very good" - ], - "GeneralHealth": [ - "Excellent", - "Fair", - "Good", - "Poor", - "Very good" - ], - "HIVTesting": [ - "No", - "Yes" - ], - "HadAngina": [ - "No", - "Yes" - ], - "HadArthritis": [ - "No", - "Yes" - ], - "HadAsthma": [ - "No", - "Yes" - ], - "HadCOPD": [ - "No", - "Yes" - ], - "HadDepressiveDisorder": [ - "No", - "Yes" - ], - "HadDiabetes": [ - "No", - "No, pre-diabetes or borderline diabetes", - "Yes", - "Yes, but only during pregnancy (female)" - ], - "HadHeartAttack": [ - "No", - "Yes" - ], - "HadKidneyDisease": [ - "No", - "Yes" - ], - "HadSkinCancer": [ - "No", - "Yes" - ], - "HadStroke": [ - "No", - "Yes" - ], - "HeartDisease": [ - "No", - "Yes" - ], - "HighRiskLastYear": [ - "No", - "Yes" - ], - "KidneyDisease": [ - "No", - "Yes" - ], - "LastCheckupTime": [ - "5 or more years ago", - "Within past 2 years (1 year but less than 2 years ago)", - "Within past 5 years (2 years but less than 5 years ago)", - "Within past year (anytime less than 12 months ago)" - ], - "PhysicalActivities": [ - "No", - "Yes" - ], - "PhysicalActivity": [ - "No", - "Yes" - ], - "PneumoVaxEver": [ - "No", - "Yes" - ], - "Race": [ - "American Indian/Alaskan Native", - "Asian", - "Black", - "Hispanic", - "Other", - "White" - ], - "RaceEthnicityCategory": [ - "Black only, Non-Hispanic", - "Hispanic", - "Multiracial, Non-Hispanic", - "Other race only, Non-Hispanic", - "White only, Non-Hispanic" - ], - "RemovedTeeth": [ - "1 to 5", - "6 or more, but not all", - "All", - "None of them" - ], - "Sex": [ - "Female", - "Male" - ], - "SkinCancer": [ - "No", - "Yes" - ], - "SmokerStatus": [ - "Current smoker - now smokes every day", - "Current smoker - now smokes some days", - "Former smoker", - "Never smoked" - ], - "Smoking": [ - "No", - "Yes" - ], - "State": [ - "Alabama", - "Alaska", - "Arizona", - "Arkansas", - "California", - "Colorado", - "Connecticut", - "Delaware", - "District of Columbia", - "Florida", - "Georgia", - "Guam", - "Hawaii", - "Idaho", - "Illinois", - "Indiana", - "Iowa", - "Kansas", - "Kentucky", - "Louisiana", - "Maine", - "Maryland", - "Massachusetts", - "Michigan", - "Minnesota", - "Mississippi", - "Missouri", - "Montana", - "Nebraska", - "Nevada", - "New Hampshire", - "New Jersey", - "New Mexico", - "New York", - "North Carolina", - "North Dakota", - "Ohio", - "Oklahoma", - "Oregon", - "Pennsylvania", - "Puerto Rico", - "Rhode Island", - "South Carolina", - "South Dakota", - "Tennessee", - "Texas", - "Utah", - "Vermont", - "Virgin Islands", - "Virginia", - "Washington", - "West Virginia", - "Wisconsin", - "Wyoming" - ], - "Stroke": [ - "No", - "Yes" - ], - "TetanusLast10Tdap": [ - "No, did not receive any tetanus shot in the past 10 years", - "Yes, received Tdap", - "Yes, received tetanus shot but not sure what type", - "Yes, received tetanus shot, but not Tdap" - ] - } -} \ No newline at end of file +{ + "name": "Indicators of Heart Disease (2022 UPDATE)", + "source": "https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease/data", + "data_intro": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", + "is_splited": false, + "overall_size": 1010949, + "train_size": 0, + "test_size": 0, + "c_classes": 45, + "n_classes": 9, + "cat_feature_intro": { + "HeartDisease": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", + "Smoking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", + "AlcoholDrinking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", + "Stroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", + "DiffWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", + "Sex": "Sex is a biological factor that can influence the risk of heart disease. Men and women may experience different symptoms and risk factors for heart disease.", + "AgeCategory": "Age is a significant risk factor for heart disease. As people age, their risk of developing heart disease increases. It is important to monitor heart health as individuals get older.", + "Race": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", + "Diabetic": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", + "PhysicalActivity": "Regular physical activity is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", + "Asthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", + "KidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", + "SkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", + "State": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", + "GeneralHealth": "Self-reported general health from BRFSS GENHLTH: Excellent, Very good, Good, Fair, or Poor.", + "LastCheckupTime": "The time since the last medical checkup can be an important factor in heart health. Regular checkups can help identify and address risk factors for heart disease early.", + "PhysicalActivities": "Engaging in regular physical activities is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", + "RemovedTeeth": "The number of removed teeth can be an indicator of overall health, including heart health. Poor oral health has been linked to an increased risk of heart disease.", + "HadHeartAttack": "As described above, the original dataset of nearly 300 variables was reduced to 40 variables. In addition to classical EDA, this dataset can be used to apply a number of machine learning methods, especially classifier models (logistic regression, SVM, random forest, etc.). You should treat the variable \"HadHeartAttack\" as binary (\"Yes\" - respondent had heart disease; \"No\" - respondent did not have heart disease). Note, however, that the classes are unbalanced, so the classic approach of applying a model is not advisable. Fixing the weights/undersampling should yield much better results. Based on the data set, I built a logistic regression model and embedded it in an application that might inspire you: https://share.streamlit.io/kamilpytlak/heart-condition-checker/main/app.py. Can you indicate which variables have a significant effect on the likelihood of heart disease?", + "HadAngina": "Angina is a type of chest pain caused by reduced blood flow to the heart. It is a symptom of coronary artery disease and can indicate an increased risk of heart disease.", + "HadStroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", + "HadAsthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", + "HadSkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", + "HadCOPD": "Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease that causes obstructed airflow from the lungs. It can increase the risk of heart disease.", + "HadDepressiveDisorder": "Depressive disorders can affect overall health and increase the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", + "HadKidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", + "HadArthritis": "Arthritis is a condition that causes inflammation of the joints. It can affect overall health and increase the risk of heart disease.", + "HadDiabetes": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", + "DeafOrHardOfHearing": "Hearing loss can affect overall health and quality of life. It is important to address and manage hearing issues to maintain overall health and reduce the risk of heart disease.", + "BlindOrVisionDifficulty": "Vision difficulties can affect overall health and quality of life. It is important to address and manage vision issues to maintain overall health and reduce the risk of heart disease.", + "DifficultyConcentrating": "Difficulty concentrating can be a sign of underlying health issues, including heart disease. It is important to address cognitive issues to improve overall health and reduce the risk of heart-related complications.", + "DifficultyWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", + "DifficultyDressingBathing": "Difficulty with daily activities such as dressing and bathing can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", + "DifficultyErrands": "Difficulty running errands can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", + "SmokerStatus": "Smoking is a major risk factor for heart disease. It is important to address and manage smoking habits to reduce the risk of heart-related complications.", + "ECigaretteUsage": "E-cigarette usage can affect heart health. It is important to monitor and manage e-cigarette usage to reduce the risk of heart disease.", + "ChestScan": "Chest scans can help identify underlying health issues, including heart disease. It is important to monitor and address any abnormalities found in chest scans to maintain overall health.", + "RaceEthnicityCategory": "Race and ethnicity can influence the risk of heart disease. It is important to consider these factors when assessing and managing heart health.", + "AlcoholDrinkers": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", + "HIVTesting": "Whether the respondent has ever been tested for HIV (Yes/No), not the result of that test.", + "FluVaxLast12": "Receiving a flu vaccine in the last 12 months can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", + "PneumoVaxEver": "Receiving a pneumococcal vaccine can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", + "TetanusLast10Tdap": "Receiving a tetanus vaccine in the last 10 years can help reduce the risk of infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", + "HighRiskLastYear": "Being at high risk for health issues in the last year can affect overall health and increase the risk of heart disease. It is important to address and manage high-risk factors to reduce the risk of heart-related complications.", + "CovidPos": "Testing positive for COVID-19 can affect overall health and increase the risk of heart disease. It is important to monitor and manage COVID-19 symptoms to reduce the risk of heart-related complications." + }, + "num_feature_intro": { + "BMI": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", + "PhysicalHealth": "Physical health is an important factor in overall well-being and can influence the risk of heart disease. It is important to maintain good physical health to reduce the risk of heart-related complications.", + "MentalHealth": "Mental health is an important factor in overall well-being and can influence the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", + "SleepTime": "Sleep time is an important factor in overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", + "PhysicalHealthDays": "The number of days with good physical health can be an indicator of overall well-being. It is important to maintain good physical health to reduce the risk of heart-related complications.", + "MentalHealthDays": "The number of days with good mental health can be an indicator of overall well-being. It is important to address and manage mental health to reduce the risk of heart-related complications.", + "SleepHours": "The number of hours of sleep can affect overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", + "HeightInMeters": "Height can be an indicator of overall health. It is important to monitor and maintain a healthy height-to-weight ratio to reduce the risk of heart disease.", + "WeightInKilograms": "Weight can be an indicator of overall health. It is important to maintain a healthy weight to reduce the risk of heart disease." + }, + "evaluation_metric": "The evaluation metric for this dataset can include accuracy, precision, recall, F1-score, and AUC-ROC. These metrics can help assess the performance of machine learning models in predicting heart disease.", + "task_type": "classification", + "target": "GeneralHealth", + "num_feature_value": { + "BMI": [ + 12.02, + 99.64 + ], + "HeightInMeters": [ + 0.91, + 2.41 + ], + "MentalHealth": [ + 0.0, + 30.0 + ], + "MentalHealthDays": [ + 0.0, + 30.0 + ], + "PhysicalHealth": [ + 0.0, + 30.0 + ], + "PhysicalHealthDays": [ + 0.0, + 30.0 + ], + "SleepHours": [ + 1.0, + 24.0 + ], + "SleepTime": [ + 1.0, + 24.0 + ], + "WeightInKilograms": [ + 22.68, + 292.57 + ] + }, + "cat_feature_value": { + "AgeCategory": [ + "18-24", + "25-29", + "30-34", + "35-39", + "40-44", + "45-49", + "50-54", + "55-59", + "60-64", + "65-69", + "70-74", + "75-79", + "80 or older", + "Age 18 to 24", + "Age 25 to 29", + "Age 30 to 34", + "Age 35 to 39", + "Age 40 to 44", + "Age 45 to 49", + "Age 50 to 54", + "Age 55 to 59", + "Age 60 to 64", + "Age 65 to 69", + "Age 70 to 74", + "Age 75 to 79", + "Age 80 or older" + ], + "AlcoholDrinkers": [ + "No", + "Yes" + ], + "AlcoholDrinking": [ + "No", + "Yes" + ], + "Asthma": [ + "No", + "Yes" + ], + "BlindOrVisionDifficulty": [ + "No", + "Yes" + ], + "ChestScan": [ + "No", + "Yes" + ], + "CovidPos": [ + "No", + "Tested positive using home test without a health professional", + "Yes" + ], + "DeafOrHardOfHearing": [ + "No", + "Yes" + ], + "Diabetic": [ + "No", + "No, borderline diabetes", + "Yes", + "Yes (during pregnancy)" + ], + "DiffWalking": [ + "No", + "Yes" + ], + "DifficultyConcentrating": [ + "No", + "Yes" + ], + "DifficultyDressingBathing": [ + "No", + "Yes" + ], + "DifficultyErrands": [ + "No", + "Yes" + ], + "DifficultyWalking": [ + "No", + "Yes" + ], + "ECigaretteUsage": [ + "Never used e-cigarettes in my entire life", + "Not at all (right now)", + "Use them every day", + "Use them some days" + ], + "FluVaxLast12": [ + "No", + "Yes" + ], + "GeneralHealth": [ + "Excellent", + "Very good", + "Good", + "Fair", + "Poor" + ], + "HIVTesting": [ + "No", + "Yes" + ], + "HadAngina": [ + "No", + "Yes" + ], + "HadArthritis": [ + "No", + "Yes" + ], + "HadAsthma": [ + "No", + "Yes" + ], + "HadCOPD": [ + "No", + "Yes" + ], + "HadDepressiveDisorder": [ + "No", + "Yes" + ], + "HadDiabetes": [ + "No", + "No, pre-diabetes or borderline diabetes", + "Yes", + "Yes, but only during pregnancy (female)" + ], + "HadHeartAttack": [ + "No", + "Yes" + ], + "HadKidneyDisease": [ + "No", + "Yes" + ], + "HadSkinCancer": [ + "No", + "Yes" + ], + "HadStroke": [ + "No", + "Yes" + ], + "HeartDisease": [ + "No", + "Yes" + ], + "HighRiskLastYear": [ + "No", + "Yes" + ], + "KidneyDisease": [ + "No", + "Yes" + ], + "LastCheckupTime": [ + "5 or more years ago", + "Within past 2 years (1 year but less than 2 years ago)", + "Within past 5 years (2 years but less than 5 years ago)", + "Within past year (anytime less than 12 months ago)" + ], + "PhysicalActivities": [ + "No", + "Yes" + ], + "PhysicalActivity": [ + "No", + "Yes" + ], + "PneumoVaxEver": [ + "No", + "Yes" + ], + "Race": [ + "American Indian/Alaskan Native", + "Asian", + "Black", + "Hispanic", + "Other", + "White" + ], + "RaceEthnicityCategory": [ + "Black only, Non-Hispanic", + "Hispanic", + "Multiracial, Non-Hispanic", + "Other race only, Non-Hispanic", + "White only, Non-Hispanic" + ], + "RemovedTeeth": [ + "1 to 5", + "6 or more, but not all", + "All", + "None of them" + ], + "Sex": [ + "Female", + "Male" + ], + "SkinCancer": [ + "No", + "Yes" + ], + "SmokerStatus": [ + "Current smoker - now smokes every day", + "Current smoker - now smokes some days", + "Former smoker", + "Never smoked" + ], + "Smoking": [ + "No", + "Yes" + ], + "State": [ + "Alabama", + "Alaska", + "Arizona", + "Arkansas", + "California", + "Colorado", + "Connecticut", + "Delaware", + "District of Columbia", + "Florida", + "Georgia", + "Guam", + "Hawaii", + "Idaho", + "Illinois", + "Indiana", + "Iowa", + "Kansas", + "Kentucky", + "Louisiana", + "Maine", + "Maryland", + "Massachusetts", + "Michigan", + "Minnesota", + "Mississippi", + "Missouri", + "Montana", + "Nebraska", + "Nevada", + "New Hampshire", + "New Jersey", + "New Mexico", + "New York", + "North Carolina", + "North Dakota", + "Ohio", + "Oklahoma", + "Oregon", + "Pennsylvania", + "Puerto Rico", + "Rhode Island", + "South Carolina", + "South Dakota", + "Tennessee", + "Texas", + "Utah", + "Vermont", + "Virgin Islands", + "Virginia", + "Washington", + "West Virginia", + "Wisconsin", + "Wyoming" + ], + "Stroke": [ + "No", + "Yes" + ], + "TetanusLast10Tdap": [ + "No, did not receive any tetanus shot in the past 10 years", + "Yes, received Tdap", + "Yes, received tetanus shot but not sure what type", + "Yes, received tetanus shot, but not Tdap" + ] + } +} diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_003/test_current.csv b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_003/test_current.csv index b47045c506e1d008b4662806273426ef3b15bc2a..4e759ade2ad0c7ca8937f6211b4ef41887f5c45a 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_003/test_current.csv +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_003/test_current.csv @@ -1,16 +1,25 @@ -HeartDisease,BMI,Smoking,AlcoholDrinking,Stroke,PhysicalHealth,MentalHealth,DiffWalking,Sex,AgeCategory,Race,Diabetic,PhysicalActivity,GenHealth,SleepTime,Asthma,KidneyDisease,SkinCancer,State,GeneralHealth,PhysicalHealthDays,MentalHealthDays,LastCheckupTime,PhysicalActivities,SleepHours,RemovedTeeth,HadHeartAttack,HadAngina,HadStroke,HadAsthma,HadSkinCancer,HadCOPD,HadDepressiveDisorder,HadKidneyDisease,HadArthritis,HadDiabetes,DeafOrHardOfHearing,BlindOrVisionDifficulty,DifficultyConcentrating,DifficultyWalking,DifficultyDressingBathing,DifficultyErrands,SmokerStatus,ECigaretteUsage,ChestScan,RaceEthnicityCategory,HeightInMeters,WeightInKilograms,AlcoholDrinkers,HIVTesting,FluVaxLast12,PneumoVaxEver,TetanusLast10Tdap,HighRiskLastYear,CovidPos -,28.06,,,,,,,Female,Age 75 to 79,,,,,,,,,Utah,Fair,,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,Yes,Yes,Yes,Yes,No,No,No,No,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,86.18,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,45.26,,,,,,,Female,Age 60 to 64,,,,,,,,,Maine,Fair,30.0,30.0,Within past year (anytime less than 12 months ago),No,5.0,"6 or more, but not all",No,No,Yes,No,No,No,Yes,No,Yes,No,No,No,No,Yes,No,Yes,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,123.38,No,Yes,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,Yes -,29.08,,,,,,,Female,Age 65 to 69,,,,,,,,,Puerto Rico,Fair,3.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,Yes,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,Hispanic,1.57,72.12,No,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,,,,,,,,Female,Age 60 to 64,,,,,,,,,Florida,Fair,15.0,15.0,Within past year (anytime less than 12 months ago),No,3.0,All,No,No,No,No,No,Yes,Yes,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,,No,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No -,24.63,,,,,,,Female,Age 70 to 74,,,,,,,,,Alabama,Fair,30.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.65,67.13,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,31.12,,,,,,,Female,Age 60 to 64,,,,,,,,,West Virginia,Fair,15.0,30.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,Yes,No,No,Yes,No,Yes,Yes,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,84.82,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,32.69,,,,,,,Male,Age 70 to 74,,,,,,,,,New Jersey,Fair,30.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,"6 or more, but not all",Yes,Yes,Yes,No,No,No,No,Yes,No,Yes,No,Yes,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,97.52,Yes,No,No,Yes,"Yes, received tetanus shot but not sure what type",No,Yes -,,,,,,,,Male,Age 60 to 64,,,,,,,,,Colorado,Fair,10.0,5.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,Yes,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.8,,No,,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes -,19.49,,,,,,,Female,Age 65 to 69,,,,,,,,,Washington,Fair,3.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,All,No,No,No,No,No,Yes,No,No,No,Yes,No,No,No,No,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,49.9,Yes,,No,Yes,"Yes, received Tdap",No,No -,29.05,,,,,,,Female,Age 70 to 74,,,,,,,,,Minnesota,Fair,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,81.65,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,23.91,,,,,,,Female,Age 60 to 64,,,,,,,,,Colorado,Fair,0.0,28.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,Yes,No,No,Yes,"No, pre-diabetes or borderline diabetes",No,Yes,Yes,No,No,No,Former smoker,Use them every day,Yes,"White only, Non-Hispanic",1.6,61.23,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No -,20.37,,,,,,,Female,Age 45 to 49,,,,,,,,,Indiana,Fair,8.0,30.0,Within past year (anytime less than 12 months ago),No,9.0,1 to 5,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,No,Yes,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,64.41,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -,32.93,,,,,,,Female,Age 60 to 64,,,,,,,,,Washington,Fair,30.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,1 to 5,No,No,No,No,No,Yes,No,No,Yes,No,No,No,No,Yes,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.68,92.53,Yes,No,Yes,Yes,"Yes, received Tdap",No,Yes -,,,,,,,,Female,Age 35 to 39,,,,,,,,,Washington,Fair,3.0,20.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -,37.43,,,,,,,Male,Age 70 to 74,,,,,,,,,Hawaii,Fair,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"Other race only, Non-Hispanic",1.7,108.41,No,No,Yes,Yes,"Yes, received Tdap",No,No +HeartDisease,BMI,Smoking,AlcoholDrinking,Stroke,PhysicalHealth,MentalHealth,DiffWalking,Sex,AgeCategory,Race,Diabetic,PhysicalActivity,SleepTime,Asthma,KidneyDisease,SkinCancer,State,GeneralHealth,PhysicalHealthDays,MentalHealthDays,LastCheckupTime,PhysicalActivities,SleepHours,RemovedTeeth,HadHeartAttack,HadAngina,HadStroke,HadAsthma,HadSkinCancer,HadCOPD,HadDepressiveDisorder,HadKidneyDisease,HadArthritis,HadDiabetes,DeafOrHardOfHearing,BlindOrVisionDifficulty,DifficultyConcentrating,DifficultyWalking,DifficultyDressingBathing,DifficultyErrands,SmokerStatus,ECigaretteUsage,ChestScan,RaceEthnicityCategory,HeightInMeters,WeightInKilograms,AlcoholDrinkers,HIVTesting,FluVaxLast12,PneumoVaxEver,TetanusLast10Tdap,HighRiskLastYear,CovidPos +,28.06,,,,,,,Female,Age 75 to 79,,,,,,,,Utah,Fair,,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,Yes,Yes,Yes,Yes,No,No,No,No,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,86.18,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +No,49.92,Yes,No,No,28.0,0.0,Yes,Female,55-59,White,No,No,9.0,No,No,No,,Fair,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,45.26,,,,,,,Female,Age 60 to 64,,,,,,,,Maine,Fair,30.0,30.0,Within past year (anytime less than 12 months ago),No,5.0,"6 or more, but not all",No,No,Yes,No,No,No,Yes,No,Yes,No,No,No,No,Yes,No,Yes,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,123.38,No,Yes,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,Yes +Yes,28.25,Yes,No,Yes,30.0,0.0,Yes,Male,75-79,White,No,Yes,8.0,No,No,Yes,,Fair,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,29.08,,,,,,,Female,Age 65 to 69,,,,,,,,Puerto Rico,Fair,3.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,Yes,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,Hispanic,1.57,72.12,No,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes +No,27.12,No,No,No,5.0,2.0,No,Male,40-44,White,No,Yes,6.0,No,No,No,,Fair,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,,,,,,,,Female,Age 60 to 64,,,,,,,,Florida,Fair,15.0,15.0,Within past year (anytime less than 12 months ago),No,3.0,All,No,No,No,No,No,Yes,Yes,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,,No,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No +,24.63,,,,,,,Female,Age 70 to 74,,,,,,,,Alabama,Fair,30.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.65,67.13,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +No,34.46,No,No,No,15.0,0.0,No,Male,50-54,White,No,Yes,9.0,No,No,No,,Fair,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,31.12,,,,,,,Female,Age 60 to 64,,,,,,,,West Virginia,Fair,15.0,30.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,Yes,No,No,Yes,No,Yes,Yes,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,84.82,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,28.19,Yes,No,No,30.0,14.0,Yes,Female,30-34,White,No,No,5.0,Yes,No,No,,Fair,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,32.69,,,,,,,Male,Age 70 to 74,,,,,,,,New Jersey,Fair,30.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,"6 or more, but not all",Yes,Yes,Yes,No,No,No,No,Yes,No,Yes,No,Yes,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,97.52,Yes,No,No,Yes,"Yes, received tetanus shot but not sure what type",No,Yes +No,26.3,No,No,No,10.0,0.0,Yes,Female,45-49,White,Yes,No,4.0,No,No,No,,Fair,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,26.89,No,No,No,3.0,0.0,No,Female,65-69,Black,Yes,Yes,9.0,No,No,No,,Fair,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,,,,,,,,Male,Age 60 to 64,,,,,,,,Colorado,Fair,10.0,5.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,Yes,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.8,,No,,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes +,19.49,,,,,,,Female,Age 65 to 69,,,,,,,,Washington,Fair,3.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,All,No,No,No,No,No,Yes,No,No,No,Yes,No,No,No,No,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,49.9,Yes,,No,Yes,"Yes, received Tdap",No,No +No,27.79,Yes,No,No,6.0,20.0,No,Female,30-34,White,No,No,3.0,No,No,No,,Fair,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,29.05,,,,,,,Female,Age 70 to 74,,,,,,,,Minnesota,Fair,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,81.65,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,23.91,,,,,,,Female,Age 60 to 64,,,,,,,,Colorado,Fair,0.0,28.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,Yes,No,No,Yes,"No, pre-diabetes or borderline diabetes",No,Yes,Yes,No,No,No,Former smoker,Use them every day,Yes,"White only, Non-Hispanic",1.6,61.23,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No +,20.37,,,,,,,Female,Age 45 to 49,,,,,,,,Indiana,Fair,8.0,30.0,Within past year (anytime less than 12 months ago),No,9.0,1 to 5,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,No,Yes,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,64.41,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +,32.93,,,,,,,Female,Age 60 to 64,,,,,,,,Washington,Fair,30.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,1 to 5,No,No,No,No,No,Yes,No,No,Yes,No,No,No,No,Yes,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.68,92.53,Yes,No,Yes,Yes,"Yes, received Tdap",No,Yes +No,26.63,Yes,No,No,0.0,0.0,No,Male,50-54,White,No,Yes,7.0,No,No,No,,Fair,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,,,,,,,,Female,Age 35 to 39,,,,,,,,Washington,Fair,3.0,20.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +,37.43,,,,,,,Male,Age 70 to 74,,,,,,,,Hawaii,Fair,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"Other race only, Non-Hispanic",1.7,108.41,No,No,Yes,Yes,"Yes, received Tdap",No,No diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_004/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_004_current.json b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_004/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_004_current.json index 5b4e558940f381a78511b85a8e28a521a34beb5a..6e15ccab5f1fab06af6098ade9a33270c2497245 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_004/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_004_current.json +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_004/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_004_current.json @@ -1,12043 +1,11868 @@ { - "id": "004", - "task_type": "B4", - "subtask_type": "list", - "perspective": "data_holder", - "dataset_name": "Indicators of Heart Disease (2022 UPDATE)", - "table_path": "output/medical/classification/Indicators of Heart Disease (2022 UPDATE)_B4/Indicators of Heart Disease (2022 UPDATE)_B4_004/test.csv", - "query": "We've just compiled new responses from our state-wide health initiative. Using the past case history I've uploaded, can you list all new participants whose health indicators and lifestyle factors are consistent with individuals who have historically described their health as \"Good\"?", - "meta_info": { - "domain": "medical" + "id": "004", + "task_type": "B4", + "subtask_type": "list", + "perspective": "data_holder", + "dataset_name": "Indicators of Heart Disease (2022 UPDATE)", + "table_path": "output/medical/classification/Indicators of Heart Disease (2022 UPDATE)_B4/Indicators of Heart Disease (2022 UPDATE)_B4_004/test.csv", + "query": "We've just compiled new responses from our state-wide health initiative. Using the past case history I've uploaded, can you list all new participants whose health indicators and lifestyle factors are consistent with individuals who have historically described their health as \"Good\"?", + "meta_info": { + "domain": "medical" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "HeartDisease": null, + "BMI": 24.41, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 80 or older", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "New Mexico", + "GeneralHealth": "Very good", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "No", + "SleepHours": 10.0, + "RemovedTeeth": "1 to 5", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "Yes", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "Yes", + "HadDiabetes": "No, pre-diabetes or borderline diabetes", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Former smoker", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "Yes", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.83, + "WeightInKilograms": 81.65, + "AlcoholDrinkers": "Yes", + "HIVTesting": "No", + "FluVaxLast12": "Yes", + "PneumoVaxEver": "Yes", + "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", + "HighRiskLastYear": "No", + "CovidPos": "Yes" + } + }, + { + "scenario_id": "002", + "features": { + "HeartDisease": null, + "BMI": 22.65, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 65 to 69", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Massachusetts", + "GeneralHealth": "Excellent", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 3.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 10.0, + "RemovedTeeth": "1 to 5", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "Yes", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "Yes", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Former smoker", + "ECigaretteUsage": "Use them every day", + "ChestScan": "No", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.52, + "WeightInKilograms": 52.62, + "AlcoholDrinkers": "No", + "HIVTesting": "No", + "FluVaxLast12": "Yes", + "PneumoVaxEver": "No", + "TetanusLast10Tdap": "Yes, received tetanus shot but not sure what type", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "004", + "features": { + "HeartDisease": "Yes", + "BMI": 26.33, + "Smoking": "Yes", + "AlcoholDrinking": "No", + "Stroke": "No", + "PhysicalHealth": 0.0, + "MentalHealth": 0.0, + "DiffWalking": "Yes", + "Sex": "Female", + "AgeCategory": "80 or older", + "Race": "Hispanic", + "Diabetic": "No", + "PhysicalActivity": "No", + "SleepTime": 8.0, + "Asthma": "No", + "KidneyDisease": "No", + "SkinCancer": "No", + "State": null, + "GeneralHealth": "Very good", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + "LastCheckupTime": null, + "PhysicalActivities": null, + "SleepHours": null, + "RemovedTeeth": null, + "HadHeartAttack": null, + "HadAngina": null, + "HadStroke": null, + "HadAsthma": null, + "HadSkinCancer": null, + "HadCOPD": null, + "HadDepressiveDisorder": null, + "HadKidneyDisease": null, + "HadArthritis": null, + "HadDiabetes": null, + "DeafOrHardOfHearing": null, + "BlindOrVisionDifficulty": null, + "DifficultyConcentrating": null, + "DifficultyWalking": null, + "DifficultyDressingBathing": null, + "DifficultyErrands": null, + "SmokerStatus": null, + "ECigaretteUsage": null, + "ChestScan": null, + "RaceEthnicityCategory": null, + "HeightInMeters": null, + "WeightInKilograms": null, + "AlcoholDrinkers": null, + "HIVTesting": null, + "FluVaxLast12": null, + "PneumoVaxEver": null, + "TetanusLast10Tdap": null, + "HighRiskLastYear": null, + "CovidPos": null + } + }, + { + "scenario_id": "005", + "features": { + "HeartDisease": null, + "BMI": 33.99, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 75 to 79", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Nebraska", + "GeneralHealth": "Very good", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 6.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 7.0, + "RemovedTeeth": "6 or more, but not all", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "Yes", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "Yes", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.63, + "WeightInKilograms": 89.81, + "AlcoholDrinkers": "No", + "HIVTesting": "No", + "FluVaxLast12": "No", + "PneumoVaxEver": "No", + "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", + "HighRiskLastYear": "No", + "CovidPos": "Yes" + } + }, + { + "scenario_id": "006", + "features": { + "HeartDisease": null, + "BMI": 38.74, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 65 to 69", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Virginia", + "GeneralHealth": "Poor", + "PhysicalHealthDays": 30.0, + "MentalHealthDays": 5.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "No", + "SleepHours": 6.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "Yes", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "Yes", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.68, + "WeightInKilograms": 108.86, + "AlcoholDrinkers": "No", + "HIVTesting": "No", + "FluVaxLast12": "Yes", + "PneumoVaxEver": "Yes", + "TetanusLast10Tdap": "Yes, received tetanus shot, but not Tdap", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "007", + "features": { + "HeartDisease": "No", + "BMI": 22.24, + "Smoking": "No", + "AlcoholDrinking": "No", + "Stroke": "No", + "PhysicalHealth": 0.0, + "MentalHealth": 0.0, + "DiffWalking": "No", + "Sex": "Male", + "AgeCategory": "45-49", + "Race": "Black", + "Diabetic": "Yes", + "PhysicalActivity": "Yes", + "SleepTime": 7.0, + "Asthma": "No", + "KidneyDisease": "No", + "SkinCancer": "No", + "State": null, + "GeneralHealth": "Very good", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + "LastCheckupTime": null, + "PhysicalActivities": null, + "SleepHours": null, + "RemovedTeeth": null, + "HadHeartAttack": null, + "HadAngina": null, + "HadStroke": null, + "HadAsthma": null, + "HadSkinCancer": null, + "HadCOPD": null, + "HadDepressiveDisorder": null, + "HadKidneyDisease": null, + "HadArthritis": null, + "HadDiabetes": null, + "DeafOrHardOfHearing": null, + "BlindOrVisionDifficulty": null, + "DifficultyConcentrating": null, + "DifficultyWalking": null, + "DifficultyDressingBathing": null, + "DifficultyErrands": null, + "SmokerStatus": null, + "ECigaretteUsage": null, + "ChestScan": null, + "RaceEthnicityCategory": null, + "HeightInMeters": null, + "WeightInKilograms": null, + "AlcoholDrinkers": null, + "HIVTesting": null, + "FluVaxLast12": null, + "PneumoVaxEver": null, + "TetanusLast10Tdap": null, + "HighRiskLastYear": null, + "CovidPos": null + } + }, + { + "scenario_id": "008", + "features": { + "HeartDisease": "No", + "BMI": 28.19, + "Smoking": "Yes", + "AlcoholDrinking": "No", + "Stroke": "No", + "PhysicalHealth": 14.0, + "MentalHealth": 0.0, + "DiffWalking": "Yes", + "Sex": "Male", + "AgeCategory": "60-64", + "Race": "White", + "Diabetic": "Yes", + "PhysicalActivity": "No", + "SleepTime": 7.0, + "Asthma": "No", + "KidneyDisease": "No", + "SkinCancer": "No", + "State": null, + "GeneralHealth": "Good", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + "LastCheckupTime": null, + "PhysicalActivities": null, + "SleepHours": null, + "RemovedTeeth": null, + "HadHeartAttack": null, + "HadAngina": null, + "HadStroke": null, + "HadAsthma": null, + "HadSkinCancer": null, + "HadCOPD": null, + "HadDepressiveDisorder": null, + "HadKidneyDisease": null, + "HadArthritis": null, + "HadDiabetes": null, + "DeafOrHardOfHearing": null, + "BlindOrVisionDifficulty": null, + "DifficultyConcentrating": null, + "DifficultyWalking": null, + "DifficultyDressingBathing": null, + "DifficultyErrands": null, + "SmokerStatus": null, + "ECigaretteUsage": null, + "ChestScan": null, + "RaceEthnicityCategory": null, + "HeightInMeters": null, + "WeightInKilograms": null, + "AlcoholDrinkers": null, + "HIVTesting": null, + "FluVaxLast12": null, + "PneumoVaxEver": null, + "TetanusLast10Tdap": null, + "HighRiskLastYear": null, + "CovidPos": null + } + }, + { + "scenario_id": "013", + "features": { + "HeartDisease": "No", + "BMI": 25.1, + "Smoking": "No", + "AlcoholDrinking": "No", + "Stroke": "No", + "PhysicalHealth": 0.0, + "MentalHealth": 0.0, + "DiffWalking": "No", + "Sex": "Male", + "AgeCategory": "70-74", + "Race": "White", + "Diabetic": "Yes", + "PhysicalActivity": "Yes", + "SleepTime": 8.0, + "Asthma": "No", + "KidneyDisease": "No", + "SkinCancer": "Yes", + "State": null, + "GeneralHealth": "Excellent", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + "LastCheckupTime": null, + "PhysicalActivities": null, + "SleepHours": null, + "RemovedTeeth": null, + "HadHeartAttack": null, + "HadAngina": null, + "HadStroke": null, + "HadAsthma": null, + "HadSkinCancer": null, + "HadCOPD": null, + "HadDepressiveDisorder": null, + "HadKidneyDisease": null, + "HadArthritis": null, + "HadDiabetes": null, + "DeafOrHardOfHearing": null, + "BlindOrVisionDifficulty": null, + "DifficultyConcentrating": null, + "DifficultyWalking": null, + "DifficultyDressingBathing": null, + "DifficultyErrands": null, + "SmokerStatus": null, + "ECigaretteUsage": null, + "ChestScan": null, + "RaceEthnicityCategory": null, + "HeightInMeters": null, + "WeightInKilograms": null, + "AlcoholDrinkers": null, + "HIVTesting": null, + "FluVaxLast12": null, + "PneumoVaxEver": null, + "TetanusLast10Tdap": null, + "HighRiskLastYear": null, + "CovidPos": null + } + }, + { + "scenario_id": "017", + "features": { + "HeartDisease": "No", + "BMI": 31.0, + "Smoking": "No", + "AlcoholDrinking": "No", + "Stroke": "No", + "PhysicalHealth": 30.0, + "MentalHealth": 30.0, + "DiffWalking": "Yes", + "Sex": "Female", + "AgeCategory": "50-54", + "Race": "White", + "Diabetic": "No", + "PhysicalActivity": "Yes", + "SleepTime": 7.0, + "Asthma": "Yes", + "KidneyDisease": "No", + "SkinCancer": "No", + "State": null, + "GeneralHealth": "Poor", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + "LastCheckupTime": null, + "PhysicalActivities": null, + "SleepHours": null, + "RemovedTeeth": null, + "HadHeartAttack": null, + "HadAngina": null, + "HadStroke": null, + "HadAsthma": null, + "HadSkinCancer": null, + "HadCOPD": null, + "HadDepressiveDisorder": null, + "HadKidneyDisease": null, + "HadArthritis": null, + "HadDiabetes": null, + "DeafOrHardOfHearing": null, + "BlindOrVisionDifficulty": null, + "DifficultyConcentrating": null, + "DifficultyWalking": null, + "DifficultyDressingBathing": null, + "DifficultyErrands": null, + "SmokerStatus": null, + "ECigaretteUsage": null, + "ChestScan": null, + "RaceEthnicityCategory": null, + "HeightInMeters": null, + "WeightInKilograms": null, + "AlcoholDrinkers": null, + "HIVTesting": null, + "FluVaxLast12": null, + "PneumoVaxEver": null, + "TetanusLast10Tdap": null, + "HighRiskLastYear": null, + "CovidPos": null + } + }, + { + "scenario_id": "020", + "features": { + "HeartDisease": "No", + "BMI": 32.28, + "Smoking": "Yes", + "AlcoholDrinking": "No", + "Stroke": "No", + "PhysicalHealth": 0.0, + "MentalHealth": 0.0, + "DiffWalking": "No", + "Sex": "Male", + "AgeCategory": "35-39", + "Race": "White", + "Diabetic": "Yes", + "PhysicalActivity": "Yes", + "SleepTime": 6.0, + "Asthma": "No", + "KidneyDisease": "No", + "SkinCancer": "No", + "State": null, + "GeneralHealth": "Good", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + "LastCheckupTime": null, + "PhysicalActivities": null, + "SleepHours": null, + "RemovedTeeth": null, + "HadHeartAttack": null, + "HadAngina": null, + "HadStroke": null, + "HadAsthma": null, + "HadSkinCancer": null, + "HadCOPD": null, + "HadDepressiveDisorder": null, + "HadKidneyDisease": null, + "HadArthritis": null, + "HadDiabetes": null, + "DeafOrHardOfHearing": null, + "BlindOrVisionDifficulty": null, + "DifficultyConcentrating": null, + "DifficultyWalking": null, + "DifficultyDressingBathing": null, + "DifficultyErrands": null, + "SmokerStatus": null, + "ECigaretteUsage": null, + "ChestScan": null, + "RaceEthnicityCategory": null, + "HeightInMeters": null, + "WeightInKilograms": null, + "AlcoholDrinkers": null, + "HIVTesting": null, + "FluVaxLast12": null, + "PneumoVaxEver": null, + "TetanusLast10Tdap": null, + "HighRiskLastYear": null, + "CovidPos": null + } + }, + { + "scenario_id": "021", + "features": { + "HeartDisease": null, + "BMI": 27.87, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 65 to 69", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Nebraska", + "GeneralHealth": "Good", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "5 or more years ago", + "PhysicalActivities": "Yes", + "SleepHours": 6.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "No", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.91, + "WeightInKilograms": 101.15, + "AlcoholDrinkers": "No", + "HIVTesting": "No", + "FluVaxLast12": "No", + "PneumoVaxEver": "No", + "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "022", + "features": { + "HeartDisease": "No", + "BMI": 18.6, + "Smoking": "Yes", + "AlcoholDrinking": "No", + "Stroke": "No", + "PhysicalHealth": 0.0, + "MentalHealth": 0.0, + "DiffWalking": "No", + "Sex": "Female", + "AgeCategory": "60-64", + "Race": "White", + "Diabetic": "No", + "PhysicalActivity": "Yes", + "SleepTime": 6.0, + "Asthma": "Yes", + "KidneyDisease": "No", + "SkinCancer": "No", + "State": null, + "GeneralHealth": "Excellent", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + "LastCheckupTime": null, + "PhysicalActivities": null, + "SleepHours": null, + "RemovedTeeth": null, + "HadHeartAttack": null, + "HadAngina": null, + "HadStroke": null, + "HadAsthma": null, + "HadSkinCancer": null, + "HadCOPD": null, + "HadDepressiveDisorder": null, + "HadKidneyDisease": null, + "HadArthritis": null, + "HadDiabetes": null, + "DeafOrHardOfHearing": null, + "BlindOrVisionDifficulty": null, + "DifficultyConcentrating": null, + "DifficultyWalking": null, + "DifficultyDressingBathing": null, + "DifficultyErrands": null, + "SmokerStatus": null, + "ECigaretteUsage": null, + "ChestScan": null, + "RaceEthnicityCategory": null, + "HeightInMeters": null, + "WeightInKilograms": null, + "AlcoholDrinkers": null, + "HIVTesting": null, + "FluVaxLast12": null, + "PneumoVaxEver": null, + "TetanusLast10Tdap": null, + "HighRiskLastYear": null, + "CovidPos": null + } + }, + { + "scenario_id": "023", + "features": { + "HeartDisease": "No", + "BMI": 44.76, + "Smoking": "Yes", + "AlcoholDrinking": "No", + "Stroke": "No", + "PhysicalHealth": 2.0, + "MentalHealth": 0.0, + "DiffWalking": "No", + "Sex": "Male", + "AgeCategory": "35-39", + "Race": "White", + "Diabetic": "Yes", + "PhysicalActivity": "Yes", + "SleepTime": 6.0, + "Asthma": "No", + "KidneyDisease": "No", + "SkinCancer": "No", + "State": null, + "GeneralHealth": "Good", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + "LastCheckupTime": null, + "PhysicalActivities": null, + "SleepHours": null, + "RemovedTeeth": null, + "HadHeartAttack": null, + "HadAngina": null, + "HadStroke": null, + "HadAsthma": null, + 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"scenario_id": "085", + "features": { + "HeartDisease": null, + "BMI": 22.4, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 60 to 64", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "New Mexico", + "GeneralHealth": "Very good", + "PhysicalHealthDays": 7.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past 5 years (2 years but less than 5 years ago)", + "PhysicalActivities": "Yes", + "SleepHours": 6.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "Yes", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.7, + "WeightInKilograms": 64.86, + "AlcoholDrinkers": "Yes", + "HIVTesting": "Yes", + "FluVaxLast12": "Yes", + "PneumoVaxEver": "Yes", + "TetanusLast10Tdap": "Yes, received Tdap", + "HighRiskLastYear": "No", + "CovidPos": "Yes" + } + }, + { + "scenario_id": "089", + "features": { + "HeartDisease": null, + "BMI": 27.26, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 65 to 69", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": 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"TetanusLast10Tdap": "Yes, received Tdap", + "HighRiskLastYear": "No", + "CovidPos": "Yes" + } + }, + { + "scenario_id": "404", + "features": { + "HeartDisease": null, + "BMI": 29.03, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 25 to 29", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "South Carolina", + "GeneralHealth": "Excellent", + "PhysicalHealthDays": 4.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 7.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "Yes", + "HadKidneyDisease": "No", + 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+ "CovidPos": "No" + } + }, + { + "scenario_id": "459", + "features": { + "HeartDisease": null, + "BMI": 28.13, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 60 to 64", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Connecticut", + "GeneralHealth": "Very good", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 5.0, + "RemovedTeeth": "1 to 5", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "Yes", + "DeafOrHardOfHearing": "No", + 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"HadArthritis": null, - "HadDiabetes": null, - "DeafOrHardOfHearing": null, - "BlindOrVisionDifficulty": null, - "DifficultyConcentrating": null, - "DifficultyWalking": null, - "DifficultyDressingBathing": null, - "DifficultyErrands": null, - "SmokerStatus": null, - "ECigaretteUsage": null, - "ChestScan": null, - "RaceEthnicityCategory": null, - "HeightInMeters": null, - "WeightInKilograms": null, - "AlcoholDrinkers": null, - "HIVTesting": null, - "FluVaxLast12": null, - "PneumoVaxEver": null, - "TetanusLast10Tdap": null, - "HighRiskLastYear": null, - "CovidPos": null - } - }, - { - "scenario_id": "492", - "features": { - "HeartDisease": "No", - "BMI": 22.15, - "Smoking": "Yes", - "AlcoholDrinking": "No", - "Stroke": "No", - "PhysicalHealth": 0.0, - "MentalHealth": 0.0, - "DiffWalking": "No", - "Sex": "Male", - "AgeCategory": "30-34", - "Race": "White", - "Diabetic": "No", - "PhysicalActivity": "Yes", - "GenHealth": "Good", - "SleepTime": 6.0, - "Asthma": "No", - "KidneyDisease": "No", - "SkinCancer": "No", - "State": null, - "GeneralHealth": null, - "PhysicalHealthDays": null, - "MentalHealthDays": null, - "LastCheckupTime": null, - "PhysicalActivities": null, - "SleepHours": null, - "RemovedTeeth": null, - "HadHeartAttack": null, - "HadAngina": null, - "HadStroke": null, - "HadAsthma": null, - "HadSkinCancer": null, - "HadCOPD": null, - "HadDepressiveDisorder": null, - "HadKidneyDisease": null, - "HadArthritis": null, - "HadDiabetes": null, - "DeafOrHardOfHearing": null, - "BlindOrVisionDifficulty": null, - "DifficultyConcentrating": null, - "DifficultyWalking": null, - "DifficultyDressingBathing": null, - "DifficultyErrands": null, - "SmokerStatus": null, - "ECigaretteUsage": null, - "ChestScan": null, - "RaceEthnicityCategory": null, - "HeightInMeters": null, - "WeightInKilograms": null, - "AlcoholDrinkers": null, - "HIVTesting": null, - "FluVaxLast12": null, - "PneumoVaxEver": null, - "TetanusLast10Tdap": null, - "HighRiskLastYear": null, - "CovidPos": null - } - }, - { - "scenario_id": "493", - "features": { - "HeartDisease": "No", - "BMI": 25.1, - "Smoking": "No", - "AlcoholDrinking": "No", - "Stroke": "No", - "PhysicalHealth": 0.0, - "MentalHealth": 0.0, - "DiffWalking": "No", - "Sex": "Male", - "AgeCategory": "60-64", - "Race": "White", - "Diabetic": "No", - "PhysicalActivity": "Yes", - "GenHealth": "Good", - "SleepTime": 7.0, - "Asthma": "Yes", - "KidneyDisease": "No", - "SkinCancer": "No", - "State": null, - "GeneralHealth": null, - "PhysicalHealthDays": null, - "MentalHealthDays": null, - "LastCheckupTime": null, - "PhysicalActivities": null, - "SleepHours": null, - "RemovedTeeth": null, - "HadHeartAttack": null, - "HadAngina": null, - "HadStroke": null, - "HadAsthma": null, - "HadSkinCancer": null, - "HadCOPD": null, - "HadDepressiveDisorder": null, - "HadKidneyDisease": null, - "HadArthritis": null, - "HadDiabetes": null, - "DeafOrHardOfHearing": null, - "BlindOrVisionDifficulty": null, - "DifficultyConcentrating": null, - "DifficultyWalking": null, - "DifficultyDressingBathing": null, - "DifficultyErrands": null, - "SmokerStatus": null, - "ECigaretteUsage": null, - "ChestScan": null, - "RaceEthnicityCategory": null, - "HeightInMeters": null, - "WeightInKilograms": null, - "AlcoholDrinkers": null, - "HIVTesting": null, - "FluVaxLast12": null, - "PneumoVaxEver": null, - "TetanusLast10Tdap": null, - "HighRiskLastYear": null, - "CovidPos": null - } - }, - { - "scenario_id": "495", - "features": { - "HeartDisease": "No", - "BMI": 22.67, - "Smoking": "No", - "AlcoholDrinking": "No", - "Stroke": "No", - "PhysicalHealth": 0.0, - "MentalHealth": 0.0, - "DiffWalking": "No", - "Sex": "Female", - "AgeCategory": "30-34", - "Race": "Asian", - "Diabetic": "No", - "PhysicalActivity": "Yes", - "GenHealth": "Good", - "SleepTime": 8.0, - "Asthma": "No", - "KidneyDisease": "No", - "SkinCancer": "No", - "State": null, - "GeneralHealth": null, - "PhysicalHealthDays": null, - "MentalHealthDays": null, - "LastCheckupTime": null, - "PhysicalActivities": null, - "SleepHours": null, - "RemovedTeeth": null, - "HadHeartAttack": null, - "HadAngina": null, - "HadStroke": null, - "HadAsthma": null, - "HadSkinCancer": null, - "HadCOPD": null, - "HadDepressiveDisorder": null, - "HadKidneyDisease": null, - "HadArthritis": null, - "HadDiabetes": null, - "DeafOrHardOfHearing": null, - "BlindOrVisionDifficulty": null, - "DifficultyConcentrating": null, - "DifficultyWalking": null, - "DifficultyDressingBathing": null, - "DifficultyErrands": null, - "SmokerStatus": null, - "ECigaretteUsage": null, - "ChestScan": null, - "RaceEthnicityCategory": null, - "HeightInMeters": null, - "WeightInKilograms": null, - "AlcoholDrinkers": null, - "HIVTesting": null, - "FluVaxLast12": null, - "PneumoVaxEver": null, - "TetanusLast10Tdap": null, - "HighRiskLastYear": null, - "CovidPos": null - } - }, - { - "scenario_id": "497", - "features": { - "HeartDisease": null, - "BMI": 21.63, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Female", - "AgeCategory": "Age 70 to 74", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "Michigan", - "GeneralHealth": "Very good", - "PhysicalHealthDays": 0.0, - "MentalHealthDays": 0.0, - "LastCheckupTime": "Within past year (anytime less than 12 months ago)", - "PhysicalActivities": "Yes", - "SleepHours": 6.0, - "RemovedTeeth": "None of them", - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "No", - "HadSkinCancer": "No", - "HadCOPD": "No", - "HadDepressiveDisorder": "No", - "HadKidneyDisease": "No", - "HadArthritis": "No", - "HadDiabetes": "No", - "DeafOrHardOfHearing": "No", - "BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "No", - "DifficultyWalking": "No", - "DifficultyDressingBathing": "No", - "DifficultyErrands": "No", - "SmokerStatus": "Former smoker", - "ECigaretteUsage": "Never used e-cigarettes in my entire life", - "ChestScan": "No", - "RaceEthnicityCategory": "White only, Non-Hispanic", - "HeightInMeters": 1.65, - "WeightInKilograms": 58.97, - "AlcoholDrinkers": "Yes", - "HIVTesting": "No", - "FluVaxLast12": "No", - "PneumoVaxEver": "Yes", - "TetanusLast10Tdap": "Yes, received tetanus shot but not sure what type", - "HighRiskLastYear": "No", - "CovidPos": "No" - } - }, - { - "scenario_id": "498", - "features": { - "HeartDisease": "No", - "BMI": 25.62, - "Smoking": "Yes", - "AlcoholDrinking": "No", - "Stroke": "No", - "PhysicalHealth": 0.0, - "MentalHealth": 2.0, - "DiffWalking": "No", - "Sex": "Male", - "AgeCategory": "25-29", - "Race": "White", - "Diabetic": "No", - "PhysicalActivity": "Yes", - "GenHealth": "Very good", - "SleepTime": 8.0, - "Asthma": "No", - "KidneyDisease": "No", - "SkinCancer": "No", - "State": null, - "GeneralHealth": null, - "PhysicalHealthDays": null, - "MentalHealthDays": null, - "LastCheckupTime": null, - "PhysicalActivities": null, - "SleepHours": null, - "RemovedTeeth": null, - "HadHeartAttack": null, - "HadAngina": null, - "HadStroke": null, - "HadAsthma": null, - "HadSkinCancer": null, - "HadCOPD": null, - "HadDepressiveDisorder": null, - "HadKidneyDisease": null, - "HadArthritis": null, - "HadDiabetes": null, - "DeafOrHardOfHearing": null, - "BlindOrVisionDifficulty": null, - "DifficultyConcentrating": null, - "DifficultyWalking": null, - "DifficultyDressingBathing": null, - "DifficultyErrands": null, - "SmokerStatus": null, - "ECigaretteUsage": null, - "ChestScan": null, - "RaceEthnicityCategory": null, - "HeightInMeters": null, - "WeightInKilograms": null, - "AlcoholDrinkers": null, - "HIVTesting": null, - "FluVaxLast12": null, - "PneumoVaxEver": null, - "TetanusLast10Tdap": null, - "HighRiskLastYear": null, - "CovidPos": null - } - }, - { - "scenario_id": "499", - "features": { - "HeartDisease": null, - "BMI": 27.06, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Female", - "AgeCategory": "Age 60 to 64", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "Florida", - "GeneralHealth": "Very good", - "PhysicalHealthDays": 0.0, - "MentalHealthDays": 0.0, - "LastCheckupTime": "Within past year (anytime less than 12 months ago)", - "PhysicalActivities": "Yes", - "SleepHours": 7.0, - "RemovedTeeth": "None of them", - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "No", - "HadSkinCancer": "No", - "HadCOPD": "No", - "HadDepressiveDisorder": "No", - "HadKidneyDisease": "No", - "HadArthritis": "No", - "HadDiabetes": "No", - "DeafOrHardOfHearing": "Yes", - "BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "No", - "DifficultyWalking": "No", - "DifficultyDressingBathing": "No", - "DifficultyErrands": "No", - "SmokerStatus": "Current smoker - now smokes some days", - "ECigaretteUsage": "Never used e-cigarettes in my entire life", - "ChestScan": "No", - "RaceEthnicityCategory": "White only, Non-Hispanic", - "HeightInMeters": 1.73, - "WeightInKilograms": 80.74, - "AlcoholDrinkers": "Yes", - "HIVTesting": "Yes", - "FluVaxLast12": "Yes", - "PneumoVaxEver": "No", - "TetanusLast10Tdap": "Yes, received tetanus shot but not sure what type", - "HighRiskLastYear": "No", - "CovidPos": "Yes" - } - }, - { - "scenario_id": "500", - "features": { - "HeartDisease": null, - "BMI": 32.28, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Female", - "AgeCategory": "Age 60 to 64", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "Colorado", - "GeneralHealth": "Very good", - "PhysicalHealthDays": 15.0, - "MentalHealthDays": 8.0, - "LastCheckupTime": "Within past year (anytime less than 12 months ago)", - "PhysicalActivities": "No", - "SleepHours": 9.0, - "RemovedTeeth": "None of them", - "HadHeartAttack": "Yes", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "Yes", - "HadSkinCancer": "No", - "HadCOPD": "No", - "HadDepressiveDisorder": "Yes", - "HadKidneyDisease": "No", - "HadArthritis": "No", - "HadDiabetes": "No", - "DeafOrHardOfHearing": "No", - "BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "No", - "DifficultyWalking": "No", - "DifficultyDressingBathing": "No", - "DifficultyErrands": "No", - "SmokerStatus": "Never smoked", - "ECigaretteUsage": "Never used e-cigarettes in my entire life", - "ChestScan": "No", - "RaceEthnicityCategory": "White only, Non-Hispanic", - "HeightInMeters": 1.68, - "WeightInKilograms": 90.72, - "AlcoholDrinkers": "No", - "HIVTesting": "No", - "FluVaxLast12": "No", - "PneumoVaxEver": "No", - "TetanusLast10Tdap": "Yes, received tetanus shot but not sure what type", - "HighRiskLastYear": "No", - "CovidPos": "No" - } - } - ], - "target_column": "GeneralHealth", - "task_sub_type": "classification", - "ranking_ground_truth": { - "top_k_ids": [ - "021", - "034", - "043", - "096", - "108", - "140", - "143", - "160", - "197", - "208", - "252", - "351", - "353", - "435", - "444" - ] - }, - "active_filters": [], - "target_class_value": "good" - }, - "response": "", - "evaluation_score": {} -} \ No newline at end of file + "active_filters": [], + "target_class_value": "good" + }, + "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_004/current.csv b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_004/current.csv index 1eeb83c0299a9d56dda7442b73e08103afb51331..3a8fa31ac8284a3bfec2128f659ead1ff3aa4086 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_004/current.csv +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_004/current.csv @@ -1,201 +1,201 @@ -HeartDisease,BMI,Smoking,AlcoholDrinking,Stroke,PhysicalHealth,MentalHealth,DiffWalking,Sex,AgeCategory,Race,Diabetic,PhysicalActivity,GenHealth,SleepTime,Asthma,KidneyDisease,SkinCancer,State,PhysicalHealthDays,MentalHealthDays,LastCheckupTime,PhysicalActivities,SleepHours,RemovedTeeth,HadHeartAttack,HadAngina,HadStroke,HadAsthma,HadSkinCancer,HadCOPD,HadDepressiveDisorder,HadKidneyDisease,HadArthritis,HadDiabetes,DeafOrHardOfHearing,BlindOrVisionDifficulty,DifficultyConcentrating,DifficultyWalking,DifficultyDressingBathing,DifficultyErrands,SmokerStatus,ECigaretteUsage,ChestScan,RaceEthnicityCategory,HeightInMeters,WeightInKilograms,AlcoholDrinkers,HIVTesting,FluVaxLast12,PneumoVaxEver,TetanusLast10Tdap,HighRiskLastYear,CovidPos -,24.41,,,,,,,Male,Age 80 or older,,,,,,,,,New Mexico,0.0,0.0,Within past year (anytime less than 12 months ago),No,10.0,1 to 5,No,No,No,No,Yes,No,No,No,Yes,"No, pre-diabetes or borderline diabetes",No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.83,81.65,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,22.65,,,,,,,Female,Age 65 to 69,,,,,,,,,Massachusetts,0.0,3.0,Within past year (anytime less than 12 months ago),Yes,10.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,Former smoker,Use them every day,No,"White only, Non-Hispanic",1.52,52.62,No,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -Yes,26.33,Yes,No,No,0.0,0.0,Yes,Female,80 or older,Hispanic,No,No,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,33.99,,,,,,,Female,Age 75 to 79,,,,,,,,,Nebraska,0.0,6.0,Within past year (anytime less than 12 months ago),Yes,7.0,"6 or more, but not all",No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,89.81,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,38.74,,,,,,,Female,Age 65 to 69,,,,,,,,,Virginia,30.0,5.0,Within past year (anytime less than 12 months ago),No,6.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,108.86,No,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No -No,22.24,No,No,No,0.0,0.0,No,Male,45-49,Black,Yes,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,28.19,Yes,No,No,14.0,0.0,Yes,Male,60-64,White,Yes,No,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,25.1,No,No,No,0.0,0.0,No,Male,70-74,White,Yes,Yes,Excellent,8.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,31.0,No,No,No,30.0,30.0,Yes,Female,50-54,White,No,Yes,Poor,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,32.28,Yes,No,No,0.0,0.0,No,Male,35-39,White,Yes,Yes,Good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,27.87,,,,,,,Male,Age 65 to 69,,,,,,,,,Nebraska,0.0,0.0,5 or more years ago,Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.91,101.15,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -No,18.6,Yes,No,No,0.0,0.0,No,Female,60-64,White,No,Yes,Excellent,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,44.76,Yes,No,No,2.0,0.0,No,Male,35-39,White,Yes,Yes,Good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,28.19,No,No,No,0.0,30.0,No,Male,60-64,Black,No,No,Excellent,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,27.4,No,No,No,0.0,0.0,No,Female,65-69,White,No,Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,30.41,,,,,,,Male,Age 65 to 69,,,,,,,,,Maryland,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,90.72,Yes,Yes,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -,39.16,,,,,,,Male,Age 35 to 39,,,,,,,,,Wisconsin,4.0,5.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Not at all (right now),No,"White only, Non-Hispanic",1.88,138.35,Yes,No,No,No,"Yes, received Tdap",No,Yes -No,24.62,Yes,No,No,0.0,0.0,No,Female,60-64,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,23.71,Yes,No,No,15.0,6.0,Yes,Male,55-59,White,No,Yes,Fair,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,26.54,,,,,,,Male,Age 30 to 34,,,,,,,,,Iowa,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Not at all (right now),Yes,"White only, Non-Hispanic",1.78,83.91,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Tested positive using home test without a health professional -,31.93,,,,,,,Female,Age 65 to 69,,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,84.37,Yes,No,Yes,No,"Yes, received Tdap",No,No -,19.69,,,,,,,Female,Age 80 or older,,,,,,,,,Iowa,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,55.34,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes -No,25.61,No,No,No,6.0,20.0,No,Female,18-24,White,No,Yes,Very good,8.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,40.72,No,No,No,1.0,1.0,No,Female,25-29,Black,No,Yes,Good,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,38.73,No,No,No,0.0,2.0,No,Female,25-29,American Indian/Alaskan Native,No,Yes,Good,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,19.46,No,No,No,3.0,10.0,No,Female,18-24,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,23.49,,,,,,,Male,Age 35 to 39,,,,,,,,,Rhode Island,3.0,2.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",Yes,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.65,63.96,No,Yes,Yes,Yes,"Yes, received Tdap",No,Yes -No,20.92,Yes,No,No,0.0,0.0,No,Male,40-44,White,No,Yes,Good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,25.68,,,,,,,Male,Age 65 to 69,,,,,,,,,Connecticut,5.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.88,90.72,No,No,Yes,Yes,"Yes, received Tdap",No,No -No,28.84,No,No,No,0.0,0.0,No,Female,70-74,Black,No,Yes,Excellent,10.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,29.18,,,,,,,Female,Age 60 to 64,,,,,,,,,Iowa,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,77.11,No,No,Yes,No,"Yes, received Tdap",No,Yes -No,29.95,No,No,No,0.0,8.0,No,Female,55-59,Hispanic,No,Yes,Excellent,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,33.2,No,No,No,0.0,15.0,No,Male,40-44,Other,No,Yes,Fair,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,35.51,No,No,No,0.0,0.0,No,Female,70-74,White,No,No,Very good,8.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,22.71,,,,,,,Female,Age 18 to 24,,,,,,,,,Virginia,0.0,2.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,65.77,Yes,Yes,No,No,"Yes, received Tdap",No,Yes -No,32.61,Yes,No,Yes,5.0,0.0,No,Female,80 or older,White,No,Yes,Very good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,28.94,,,,,,,Female,Age 60 to 64,,,,,,,,,Florida,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,88.9,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -,29.16,,,,,,,Male,Age 75 to 79,,,,,,,,,Colorado,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,97.52,Yes,No,No,Yes,"Yes, received tetanus shot, but not Tdap",No,No -,22.4,,,,,,,Male,Age 60 to 64,,,,,,,,,New Mexico,7.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,64.86,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,Yes -,27.26,,,,,,,Male,Age 65 to 69,,,,,,,,,Vermont,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,86.18,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,24.03,,,,,,,Female,Age 45 to 49,,,,,,,,,Texas,0.0,15.0,Within past year (anytime less than 12 months ago),No,10.0,"6 or more, but not all",Yes,Yes,No,Yes,No,Yes,Yes,No,Yes,No,No,No,Yes,Yes,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.63,63.5,No,Yes,Yes,Yes,"Yes, received Tdap",No,No -Yes,29.18,Yes,No,No,0.0,0.0,Yes,Male,75-79,Hispanic,Yes,No,Fair,10.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,16.64,No,No,No,0.0,0.0,No,Female,80 or older,White,No,Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,20.12,,,,,,,Female,Age 75 to 79,,,,,,,,,Michigan,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,49.9,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,41.88,,,,,,,Female,Age 55 to 59,,,,,,,,,Nebraska,3.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,Yes,No,Yes,No,No,Yes,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,110.68,Yes,No,Yes,No,"Yes, received Tdap",No,Yes -,36.32,,,,,,,Male,Age 25 to 29,,,,,,,,,Guam,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Use them every day,No,Hispanic,1.68,102.06,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",Yes,Yes -No,29.05,No,No,No,0.0,0.0,No,Female,45-49,White,No,Yes,Very good,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,32.55,,,,,,,Male,Age 65 to 69,,,,,,,,,Indiana,4.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,"6 or more, but not all",No,No,No,No,No,Yes,No,No,Yes,Yes,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.83,108.86,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,19.9,,,,,,,Male,Age 45 to 49,,,,,,,,,Utah,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.88,70.31,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -No,30.23,No,No,No,0.0,0.0,No,Female,40-44,Hispanic,No,Yes,Good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,,,,,,,,Male,Age 45 to 49,,,,,,,,,North Carolina,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,"Other race only, Non-Hispanic",,,,,,,,, -,33.66,,,,,,,Female,Age 30 to 34,,,,,,,,,Colorado,0.0,15.0,Within past 2 years (1 year but less than 2 years ago),Yes,6.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,Yes,Former smoker,Use them every day,No,"White only, Non-Hispanic",1.6,86.18,Yes,No,No,Yes,"Yes, received Tdap",No,Yes -,22.31,,,,,,,Female,Age 55 to 59,,,,,,,,,New Mexico,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,,1.63,58.97,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -,24.33,,,,,,,Male,Age 75 to 79,,,,,,,,,Iowa,1.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,72.57,Yes,No,Yes,Yes,"Yes, received Tdap",No,Yes -,22.46,,,,,,,Female,Age 40 to 44,,,,,,,,,Washington,0.0,1.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,61.23,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,No -,21.63,,,,,,,Female,Age 55 to 59,,,,,,,,,Utah,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,58.97,Yes,Yes,No,No,"Yes, received Tdap",No,No -,26.63,,,,,,,Female,Age 25 to 29,,,,,,,,,Maryland,0.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),Yes,"Black only, Non-Hispanic",1.68,74.84,No,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -No,25.84,No,No,No,1.0,5.0,No,Female,75-79,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Yes,36.18,No,No,No,5.0,0.0,No,Male,50-54,American Indian/Alaskan Native,Yes,Yes,Good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,26.09,,,,,,,Female,Age 75 to 79,,,,,,,,,Florida,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,Yes,No,Yes,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.63,68.95,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,27.46,,,,,,,Female,Age 50 to 54,,,,,,,,,Missouri,30.0,30.0,Within past year (anytime less than 12 months ago),No,5.0,None of them,Yes,No,No,Yes,No,Yes,Yes,No,Yes,Yes,No,No,Yes,Yes,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.63,72.57,No,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -No,20.25,Yes,No,No,16.0,30.0,No,Female,25-29,Hispanic,No,Yes,Very good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,27.37,,,,,,,Male,Age 50 to 54,,,,,,,,,Georgia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,81.65,No,Yes,No,No,"Yes, received Tdap",No,Yes -,31.0,,,,,,,Female,Age 75 to 79,,,,,,,,,Florida,2.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,Yes,Yes,No,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,79.38,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,36.05,,,,,,,Female,Age 50 to 54,,,,,,,,,California,0.0,15.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.63,95.25,Yes,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,25.23,,,,,,,Male,Age 60 to 64,,,,,,,,,Maryland,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.83,84.37,No,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,31.89,,,,,,,Female,Age 80 or older,,,,,,,,,Kansas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.6,81.65,No,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No -,33.28,,,,,,,Female,Age 60 to 64,,,,,,,,,South Dakota,0.0,4.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,Yes,No,Yes,No,Yes,"No, pre-diabetes or borderline diabetes",No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,90.72,Yes,No,Yes,Yes,,No,Yes -No,29.26,Yes,No,No,0.0,0.0,No,Male,60-64,Asian,No,Yes,Fair,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,29.26,Yes,No,No,0.0,30.0,No,Female,60-64,White,No,Yes,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,18.25,No,No,No,0.0,0.0,No,Female,45-49,White,No,Yes,Excellent,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,27.07,No,No,No,0.0,14.0,No,Female,30-34,Hispanic,No,No,Good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,39.11,No,No,No,2.0,10.0,Yes,Female,65-69,White,Yes,No,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,35.78,,,,,,,Female,Age 18 to 24,,,,,,,,,Rhode Island,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,91.63,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,24.75,,,,,,,Female,Age 45 to 49,,,,,,,,,Utah,25.0,30.0,Within past year (anytime less than 12 months ago),No,5.0,"6 or more, but not all",No,No,No,No,No,No,Yes,No,Yes,No,No,No,Yes,No,No,Yes,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,71.67,No,Yes,No,Yes,"Yes, received Tdap",No,Yes -No,37.86,No,No,No,0.0,0.0,Yes,Female,60-64,Hispanic,Yes,Yes,Fair,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,22.14,,,,,,,Female,Age 75 to 79,,,,,,,,,Washington,30.0,5.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,Yes,No,No,No,Yes,Yes,No,Yes,No,Yes,No,Yes,No,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,56.7,Yes,Yes,No,Yes,"Yes, received Tdap",No,No -,22.62,,,,,,,Female,Age 50 to 54,,,,,,,,,Puerto Rico,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.5,50.8,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -No,19.89,No,Yes,No,2.0,2.0,No,Female,45-49,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,21.95,,,,,,,Female,Age 35 to 39,,,,,,,,,Maryland,8.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.57,54.43,No,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,Yes -,41.01,,,,,,,Male,Age 65 to 69,,,,,,,,,New Hampshire,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.52,95.25,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,Yes -No,21.46,No,No,No,1.0,2.0,No,Female,40-44,White,No,Yes,Good,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,18.47,,,,,,,Male,Age 18 to 24,,,,,,,,,Colorado,0.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Use them some days,No,Hispanic,1.85,63.5,Yes,No,Yes,No,"Yes, received Tdap",No,No -,25.83,,,,,,,Male,Age 65 to 69,,,,,,,,,Iowa,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,Yes,Yes,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,81.65,No,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,Yes -No,39.48,Yes,No,No,0.0,0.0,Yes,Female,70-74,White,Yes,No,Good,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,,,,,,,,Female,Age 70 to 74,,,,,,,,,New Hampshire,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,Yes,Yes,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, -,25.33,,,,,,,Female,Age 60 to 64,,,,,,,,,South Carolina,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.6,64.86,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -No,22.14,No,No,No,0.0,0.0,No,Female,60-64,Asian,No,Yes,Very good,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,34.87,,,,,,,Male,Age 45 to 49,,,,,,,,,Idaho,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.8,113.4,No,No,Yes,No,"Yes, received Tdap",No,No -,33.23,,,,,,,Male,Age 80 or older,,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,Yes,No,No,No,No,No,Yes,No,Yes,No,No,Yes,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,102.06,No,No,No,Yes,"Yes, received tetanus shot, but not Tdap",No,No -,32.98,,,,,,,Male,Age 40 to 44,,,,,,,,,Arizona,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,Hispanic,1.85,113.4,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -No,29.05,No,No,No,1.0,0.0,No,Female,50-54,White,No,Yes,Very good,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,40.69,Yes,No,No,0.0,0.0,No,Male,50-54,White,No,Yes,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,23.87,Yes,No,No,0.0,0.0,No,Female,40-44,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,34.01,Yes,No,No,1.0,0.0,No,Female,65-69,White,No,No,Good,6.0,Yes,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,39.06,No,No,No,0.0,15.0,No,Female,40-44,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,25.53,Yes,No,No,0.0,0.0,No,Female,70-74,White,No,Yes,Poor,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,18.29,Yes,No,No,0.0,30.0,No,Female,70-74,White,No,Yes,Good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,28.25,No,No,No,0.0,0.0,No,Male,70-74,White,No,Yes,Excellent,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,31.47,,,,,,,Male,Age 75 to 79,,,,,,,,,Ohio,30.0,30.0,Within past year (anytime less than 12 months ago),No,10.0,"6 or more, but not all",No,No,Yes,No,No,No,No,No,Yes,No,Yes,Yes,No,Yes,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.68,88.45,No,No,No,No,"Yes, received tetanus shot, but not Tdap",No,Yes -,26.13,,,,,,,Female,Age 55 to 59,,,,,,,,,Ohio,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.65,71.21,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -No,24.41,Yes,No,No,7.0,1.0,Yes,Male,60-64,White,No,Yes,Very good,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,22.25,No,No,No,0.0,0.0,No,Male,75-79,White,No,Yes,Excellent,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,31.32,Yes,Yes,No,0.0,0.0,No,Female,75-79,White,No,Yes,Very good,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,22.31,,,,,,,Female,Age 75 to 79,,,,,,,,,Virginia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,,,,,,"White only, Non-Hispanic",1.63,58.97,,,,,,, -,33.29,,,,,,,Female,Age 70 to 74,,,,,,,,,Puerto Rico,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,Yes,No,No,No,No,No,Yes,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,Hispanic,1.57,82.55,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,29.26,,,,,,,Female,Age 55 to 59,,,,,,,,,Kansas,3.0,3.0,Within past year (anytime less than 12 months ago),Yes,9.0,None of them,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,,,,"White only, Non-Hispanic",1.57,72.57,,,,,,, -No,21.31,Yes,No,No,0.0,0.0,No,Female,60-64,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,23.49,No,No,No,2.0,7.0,No,Female,30-34,Black,No,Yes,Excellent,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,26.65,Yes,No,No,0.0,0.0,No,Male,35-39,White,No,Yes,Very good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,24.96,,,,,,,Female,Age 75 to 79,,,,,,,,,Connecticut,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,68.04,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -Yes,25.75,No,No,No,0.0,0.0,No,Female,75-79,American Indian/Alaskan Native,No,No,Very good,7.0,No,Yes,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,26.39,,,,,,,Male,Age 65 to 69,,,,,,,,,South Carolina,30.0,30.0,Within past year (anytime less than 12 months ago),Yes,5.0,"6 or more, but not all",No,No,No,No,No,Yes,Yes,No,No,Yes,No,No,Yes,Yes,Yes,Yes,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,Yes,"Multiracial, Non-Hispanic",1.85,90.72,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,31.14,,,,,,,Male,Age 65 to 69,,,,,,,,,South Carolina,2.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,All,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"Other race only, Non-Hispanic",1.85,107.05,No,No,No,Yes,"Yes, received Tdap",No,No -,26.63,,,,,,,Male,Age 40 to 44,,,,,,,,,Missouri,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,74.84,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No -No,19.9,No,No,No,1.0,5.0,No,Male,18-24,White,No,Yes,Good,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,30.43,Yes,No,No,0.0,0.0,No,Male,25-29,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,31.09,Yes,No,No,30.0,30.0,Yes,Female,50-54,White,No,Yes,Fair,5.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,22.31,,,,,,,Female,Age 35 to 39,,,,,,,,,Washington,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.63,58.97,,,,,,, -,35.87,,,,,,,Male,Age 60 to 64,,,,,,,,,Nevada,,30.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,No,Yes,Yes,No,Yes,No,No,No,No,No,No,No,No,No,No,,,,"White only, Non-Hispanic",1.78,113.4,,,,,,, -No,24.41,No,No,No,2.0,0.0,No,Male,60-64,White,No,Yes,Excellent,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,21.79,,,,,,,Female,Age 40 to 44,,,,,,,,,Vermont,0.0,2.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Multiracial, Non-Hispanic",1.68,61.23,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,No -,36.9,,,,,,,Female,Age 40 to 44,,,,,,,,,Missouri,1.0,1.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,"Yes, but only during pregnancy (female)",No,Yes,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,97.52,Yes,No,Yes,No,"Yes, received Tdap",No,No -No,27.46,No,No,No,0.0,0.0,No,Female,30-34,White,No,Yes,Good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,,,,,,,,Male,,,,,,,,,,Minnesota,4.0,23.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,Yes,No,No,No,No,Yes,No,,,,,,,,,,,,,,,,,,, -No,27.22,Yes,No,No,0.0,2.0,No,Male,55-59,Other,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,22.24,,,,,,,Male,Age 70 to 74,,,,,,,,,South Dakota,12.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,"6 or more, but not all",No,No,No,No,No,Yes,No,No,No,No,No,Yes,No,No,No,Yes,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,70.31,No,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,20.36,,,,,,,Female,Age 60 to 64,,,,,,,,,Georgia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,58.97,Yes,No,Yes,No,,No,No -,,,,,,,,Female,Age 65 to 69,,,,,,,,,New Mexico,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,10.0,None of them,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, -No,23.96,No,No,No,0.0,0.0,No,Female,75-79,White,"No, borderline diabetes",Yes,Excellent,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,30.08,Yes,No,No,0.0,0.0,No,Female,80 or older,White,No,Yes,Good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,27.44,Yes,No,No,0.0,0.0,Yes,Female,65-69,White,Yes,No,Good,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,,,,,,,,Male,Age 35 to 39,,,,,,,,,Massachusetts,2.0,15.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, -No,32.69,No,No,No,0.0,0.0,No,Male,70-74,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,21.97,No,No,No,0.0,0.0,No,Female,45-49,White,No,Yes,Excellent,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,24.21,,,,,,,Female,Age 80 or older,,,,,,,,,Michigan,0.0,1.0,Within past year (anytime less than 12 months ago),No,,,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,Yes,Yes,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.68,68.04,No,No,Yes,No,,No,No -No,43.58,No,No,No,0.0,0.0,No,Female,45-49,Black,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,23.17,Yes,Yes,No,0.0,0.0,No,Female,75-79,Hispanic,No,Yes,Excellent,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,24.33,,,,,,,Male,Age 70 to 74,,,,,,,,,Maryland,0.0,10.0,Within past year (anytime less than 12 months ago),Yes,7.0,All,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Not at all (right now),Yes,"White only, Non-Hispanic",1.73,72.57,Yes,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,39.16,,,,,,,Female,Age 65 to 69,,,,,,,,,Oklahoma,0.0,0.0,Within past year (anytime less than 12 months ago),No,4.0,1 to 5,No,Yes,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,113.4,No,No,No,No,"Yes, received Tdap",No,Yes -No,23.73,No,No,No,2.0,0.0,No,Male,65-69,White,Yes,Yes,Fair,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,27.8,,,,,,,Male,Age 40 to 44,,,,,,,,,Idaho,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.83,92.99,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -,36.32,,,,,,,Female,Age 65 to 69,,,,,,,,,Ohio,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,102.06,No,No,Yes,Yes,"Yes, received Tdap",No,Tested positive using home test without a health professional -No,26.31,Yes,Yes,No,0.0,0.0,No,Female,40-44,White,No,Yes,Good,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,21.77,,,,,,,Male,Age 60 to 64,,,,,,,,,Wisconsin,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.85,74.84,Yes,No,Yes,Yes,"Yes, received Tdap",No,No -No,27.41,No,No,No,0.0,5.0,No,Male,55-59,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,27.12,,,,,,,Female,Age 35 to 39,,,,,,,,,Colorado,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,"Yes, but only during pregnancy (female)",No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,90.72,Yes,Yes,Yes,No,"Yes, received Tdap",No,No -,25.75,,,,,,,Female,Age 70 to 74,,,,,,,,,Virginia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,68.04,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,21.62,,,,,,,Male,Age 40 to 44,,,,,,,,,Nebraska,0.0,2.0,Within past 5 years (2 years but less than 5 years ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.8,70.31,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No -,32.01,,,,,,,Female,Age 75 to 79,,,,,,,,,California,0.0,3.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,Yes,No,No,Yes,No,Yes,No,Yes,No,Yes,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.57,79.38,Yes,No,No,Yes,,No,Yes -,31.28,,,,,,,Male,Age 50 to 54,,,,,,,,,Texas,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),No,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),Yes,Hispanic,1.78,98.88,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -No,24.89,No,No,No,0.0,0.0,No,Female,75-79,Hispanic,No,Yes,Very good,6.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,20.67,,,,,,,Female,Age 50 to 54,,,,,,,,,Texas,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,4.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,63.5,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -,22.92,,,,,,,Female,Age 75 to 79,,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,64.41,Yes,No,Yes,No,"Yes, received Tdap",No,No -No,23.17,No,No,No,30.0,0.0,Yes,Female,70-74,White,No,No,Good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,26.85,Yes,No,No,0.0,7.0,No,Male,40-44,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,23.89,Yes,No,No,0.0,3.0,No,Female,40-44,White,No,Yes,Very good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,28.8,,,,,,,Male,Age 65 to 69,,,,,,,,,West Virginia,10.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.75,88.45,No,No,Yes,Yes,"Yes, received Tdap",No,Yes -,29.03,,,,,,,Male,Age 25 to 29,,,,,,,,,South Carolina,4.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,99.79,No,Yes,No,No,"Yes, received Tdap",No,Yes -,25.8,,,,,,,Male,Age 50 to 54,,,,,,,,,Utah,14.0,30.0,Within past year (anytime less than 12 months ago),Yes,3.0,None of them,No,No,No,No,No,No,Yes,No,Yes,No,No,No,Yes,Yes,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,83.91,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,No -,31.32,,,,,,,Male,Age 18 to 24,,,,,,,,,Iowa,0.0,3.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.7,90.72,Yes,No,No,Yes,"Yes, received Tdap",No,No -,31.24,,,,,,,Female,Age 60 to 64,,,,,,,,,Washington,28.0,30.0,,No,15.0,1 to 5,No,No,No,Yes,No,Yes,Yes,Yes,No,No,No,No,No,Yes,Yes,Yes,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,82.55,No,Yes,Yes,,"Yes, received tetanus shot but not sure what type",No,No -,,,,,,,,Female,Age 45 to 49,,,,,,,,,Massachusetts,0.0,2.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,,Yes,Yes,Yes,,"Yes, received tetanus shot but not sure what type",No,Yes -,22.31,,,,,,,Female,Age 25 to 29,,,,,,,,,New York,0.0,5.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.63,58.97,Yes,No,No,No,"Yes, received Tdap",No,Yes -,21.26,,,,,,,Female,Age 18 to 24,,,,,,,,,Florida,2.0,20.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.6,54.43,Yes,No,No,,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,28.89,,,,,,,Female,Age 40 to 44,,,,,,,,,Georgia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,86.18,No,No,No,Yes,"Yes, received Tdap",No,Yes -,24.33,,,,,,,Male,Age 45 to 49,,,,,,,,,New Hampshire,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,72.57,Yes,No,Yes,No,"Yes, received Tdap",No,No -,28.89,,,,,,,Male,Age 80 or older,,,,,,,,,Florida,5.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,Yes,No,No,No,No,Yes,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,86.18,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes -No,21.79,No,No,No,0.0,4.0,No,Female,65-69,White,No,Yes,Very good,10.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,28.41,Yes,No,Yes,30.0,0.0,Yes,Male,75-79,White,Yes,No,Fair,4.0,No,Yes,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,30.13,Yes,Yes,No,0.0,0.0,No,Male,45-49,White,No,No,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,28.19,,,,,,,Female,Age 70 to 74,,,,,,,,,Mississippi,5.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,No,Yes,No,Yes,Yes,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,81.65,No,No,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -No,19.77,No,No,No,1.0,30.0,No,Female,18-24,White,No,Yes,Very good,10.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,25.09,No,No,No,0.0,0.0,No,Male,30-34,White,No,Yes,Very good,8.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,29.54,,,,,,,Female,Age 18 to 24,,,,,,,,,Alaska,30.0,0.0,Within past year (anytime less than 12 months ago),No,10.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,83.01,No,No,Yes,No,"Yes, received Tdap",No,No -No,19.97,No,No,No,0.0,0.0,No,Male,60-64,White,No,No,Excellent,4.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,24.65,,,,,,,Male,Age 55 to 59,,,,,,,,,Utah,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.88,87.09,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,25.0,,,,,,,Male,Age 60 to 64,,,,,,,,,Kansas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.91,90.72,Yes,No,No,No,"Yes, received Tdap",No,Yes -,28.75,,,,,,,Male,Age 65 to 69,,,,,,,,,Maine,15.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,"6 or more, but not all",Yes,Yes,No,Yes,No,Yes,No,No,Yes,No,No,No,No,Yes,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.91,104.33,No,Yes,No,Yes,"Yes, received Tdap",No,No -,32.01,,,,,,,Female,Age 65 to 69,,,,,,,,,South Carolina,0.0,0.0,Within past year (anytime less than 12 months ago),No,5.0,1 to 5,No,Yes,Yes,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.57,79.38,No,,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,28.13,,,,,,,Female,Age 60 to 64,,,,,,,,,Connecticut,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,1 to 5,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,83.91,Yes,No,No,Yes,,No,Yes -,22.67,,,,,,,Female,Age 30 to 34,,,,,,,,,Michigan,0.0,10.0,5 or more years ago,Yes,4.0,None of them,No,No,No,Yes,No,No,Yes,No,No,"No, pre-diabetes or borderline diabetes",No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.55,54.43,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,Tested positive using home test without a health professional -,,,,,,,,Female,Age 65 to 69,,,,,,,,,Connecticut,30.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,6.0,1 to 5,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,,No,No,No,Yes,"Yes, received Tdap",No,No -,28.25,,,,,,,Male,Age 70 to 74,,,,,,,,,Arizona,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,79.38,No,,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,34.17,,,,,,,Male,Age 45 to 49,,,,,,,,,South Dakota,0.0,5.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.8,111.13,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,24.37,,,,,,,Male,Age 35 to 39,,,,,,,,,Florida,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,10.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.75,74.84,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No -,24.41,,,,,,,Male,Age 75 to 79,,,,,,,,,Missouri,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,"6 or more, but not all",No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.8,79.38,No,No,Yes,No,"Yes, received Tdap",No,No -,27.12,,,,,,,Male,Age 60 to 64,,,,,,,,,Kentucky,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Use them some days,Yes,"White only, Non-Hispanic",1.83,90.72,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,21.12,,,,,,,Male,Age 65 to 69,,,,,,,,,Nebraska,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,64.86,Yes,No,Yes,Yes,"Yes, received Tdap",No,No -,19.77,,,,,,,Male,Age 25 to 29,,,,,,,,,Virginia,0.0,30.0,Within past 5 years (2 years but less than 5 years ago),Yes,10.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Use them every day,No,"White only, Non-Hispanic",1.73,58.97,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -No,40.76,Yes,No,Yes,1.0,0.0,No,Male,80 or older,White,Yes,No,Very good,2.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,24.97,,,,,,,Male,Age 80 or older,,,,,,,,,Alaska,0.0,0.0,Within past year (anytime less than 12 months ago),No,9.0,1 to 5,No,Yes,No,No,No,No,No,No,No,No,Yes,No,Yes,Yes,No,Yes,Never smoked,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.8,81.19,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes -Yes,28.7,Yes,No,No,0.0,0.0,No,Male,65-69,Other,Yes,Yes,Fair,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,22.15,Yes,No,No,0.0,0.0,No,Male,30-34,White,No,Yes,Good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,25.1,No,No,No,0.0,0.0,No,Male,60-64,White,No,Yes,Good,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,22.67,No,No,No,0.0,0.0,No,Female,30-34,Asian,No,Yes,Good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,21.63,,,,,,,Female,Age 70 to 74,,,,,,,,,Michigan,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,58.97,Yes,No,No,Yes,"Yes, received tetanus shot but not sure what type",No,No -No,25.62,Yes,No,No,0.0,2.0,No,Male,25-29,White,No,Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,27.06,,,,,,,Female,Age 60 to 64,,,,,,,,,Florida,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,80.74,Yes,Yes,Yes,No,"Yes, received tetanus shot but not sure what type",No,Yes -,32.28,,,,,,,Female,Age 60 to 64,,,,,,,,,Colorado,15.0,8.0,Within past year (anytime less than 12 months ago),No,9.0,None of them,Yes,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,90.72,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No +HeartDisease,BMI,Smoking,AlcoholDrinking,Stroke,PhysicalHealth,MentalHealth,DiffWalking,Sex,AgeCategory,Race,Diabetic,PhysicalActivity,SleepTime,Asthma,KidneyDisease,SkinCancer,State,PhysicalHealthDays,MentalHealthDays,LastCheckupTime,PhysicalActivities,SleepHours,RemovedTeeth,HadHeartAttack,HadAngina,HadStroke,HadAsthma,HadSkinCancer,HadCOPD,HadDepressiveDisorder,HadKidneyDisease,HadArthritis,HadDiabetes,DeafOrHardOfHearing,BlindOrVisionDifficulty,DifficultyConcentrating,DifficultyWalking,DifficultyDressingBathing,DifficultyErrands,SmokerStatus,ECigaretteUsage,ChestScan,RaceEthnicityCategory,HeightInMeters,WeightInKilograms,AlcoholDrinkers,HIVTesting,FluVaxLast12,PneumoVaxEver,TetanusLast10Tdap,HighRiskLastYear,CovidPos +,24.41,,,,,,,Male,Age 80 or older,,,,,,,,New Mexico,0.0,0.0,Within past year (anytime less than 12 months ago),No,10.0,1 to 5,No,No,No,No,Yes,No,No,No,Yes,"No, pre-diabetes or borderline diabetes",No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.83,81.65,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,22.65,,,,,,,Female,Age 65 to 69,,,,,,,,Massachusetts,0.0,3.0,Within past year (anytime less than 12 months ago),Yes,10.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,Former smoker,Use them every day,No,"White only, Non-Hispanic",1.52,52.62,No,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +Yes,26.33,Yes,No,No,0.0,0.0,Yes,Female,80 or older,Hispanic,No,No,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,33.99,,,,,,,Female,Age 75 to 79,,,,,,,,Nebraska,0.0,6.0,Within past year (anytime less than 12 months ago),Yes,7.0,"6 or more, but not all",No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,89.81,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,38.74,,,,,,,Female,Age 65 to 69,,,,,,,,Virginia,30.0,5.0,Within past year (anytime less than 12 months ago),No,6.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,108.86,No,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No +No,22.24,No,No,No,0.0,0.0,No,Male,45-49,Black,Yes,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,28.19,Yes,No,No,14.0,0.0,Yes,Male,60-64,White,Yes,No,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,25.1,No,No,No,0.0,0.0,No,Male,70-74,White,Yes,Yes,8.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,31.0,No,No,No,30.0,30.0,Yes,Female,50-54,White,No,Yes,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,32.28,Yes,No,No,0.0,0.0,No,Male,35-39,White,Yes,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,27.87,,,,,,,Male,Age 65 to 69,,,,,,,,Nebraska,0.0,0.0,5 or more years ago,Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.91,101.15,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,18.6,Yes,No,No,0.0,0.0,No,Female,60-64,White,No,Yes,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,44.76,Yes,No,No,2.0,0.0,No,Male,35-39,White,Yes,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,28.19,No,No,No,0.0,30.0,No,Male,60-64,Black,No,No,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,27.4,No,No,No,0.0,0.0,No,Female,65-69,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,30.41,,,,,,,Male,Age 65 to 69,,,,,,,,Maryland,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,90.72,Yes,Yes,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +,39.16,,,,,,,Male,Age 35 to 39,,,,,,,,Wisconsin,4.0,5.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Not at all (right now),No,"White only, Non-Hispanic",1.88,138.35,Yes,No,No,No,"Yes, received Tdap",No,Yes +No,24.62,Yes,No,No,0.0,0.0,No,Female,60-64,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,23.71,Yes,No,No,15.0,6.0,Yes,Male,55-59,White,No,Yes,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,26.54,,,,,,,Male,Age 30 to 34,,,,,,,,Iowa,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Not at all (right now),Yes,"White only, Non-Hispanic",1.78,83.91,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Tested positive using home test without a health professional +,31.93,,,,,,,Female,Age 65 to 69,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,84.37,Yes,No,Yes,No,"Yes, received Tdap",No,No +,19.69,,,,,,,Female,Age 80 or older,,,,,,,,Iowa,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,55.34,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes +No,25.61,No,No,No,6.0,20.0,No,Female,18-24,White,No,Yes,8.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,40.72,No,No,No,1.0,1.0,No,Female,25-29,Black,No,Yes,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,38.73,No,No,No,0.0,2.0,No,Female,25-29,American Indian/Alaskan Native,No,Yes,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,19.46,No,No,No,3.0,10.0,No,Female,18-24,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,23.49,,,,,,,Male,Age 35 to 39,,,,,,,,Rhode Island,3.0,2.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",Yes,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.65,63.96,No,Yes,Yes,Yes,"Yes, received Tdap",No,Yes +No,20.92,Yes,No,No,0.0,0.0,No,Male,40-44,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,25.68,,,,,,,Male,Age 65 to 69,,,,,,,,Connecticut,5.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.88,90.72,No,No,Yes,Yes,"Yes, received Tdap",No,No +No,28.84,No,No,No,0.0,0.0,No,Female,70-74,Black,No,Yes,10.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,29.18,,,,,,,Female,Age 60 to 64,,,,,,,,Iowa,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,77.11,No,No,Yes,No,"Yes, received Tdap",No,Yes +No,29.95,No,No,No,0.0,8.0,No,Female,55-59,Hispanic,No,Yes,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,33.2,No,No,No,0.0,15.0,No,Male,40-44,Other,No,Yes,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,35.51,No,No,No,0.0,0.0,No,Female,70-74,White,No,No,8.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,22.71,,,,,,,Female,Age 18 to 24,,,,,,,,Virginia,0.0,2.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,65.77,Yes,Yes,No,No,"Yes, received Tdap",No,Yes +No,32.61,Yes,No,Yes,5.0,0.0,No,Female,80 or older,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,28.94,,,,,,,Female,Age 60 to 64,,,,,,,,Florida,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,88.9,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +,29.16,,,,,,,Male,Age 75 to 79,,,,,,,,Colorado,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,97.52,Yes,No,No,Yes,"Yes, received tetanus shot, but not Tdap",No,No +,22.4,,,,,,,Male,Age 60 to 64,,,,,,,,New Mexico,7.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,64.86,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,Yes +,27.26,,,,,,,Male,Age 65 to 69,,,,,,,,Vermont,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,86.18,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,24.03,,,,,,,Female,Age 45 to 49,,,,,,,,Texas,0.0,15.0,Within past year (anytime less than 12 months ago),No,10.0,"6 or more, but not all",Yes,Yes,No,Yes,No,Yes,Yes,No,Yes,No,No,No,Yes,Yes,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.63,63.5,No,Yes,Yes,Yes,"Yes, received Tdap",No,No +Yes,29.18,Yes,No,No,0.0,0.0,Yes,Male,75-79,Hispanic,Yes,No,10.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,16.64,No,No,No,0.0,0.0,No,Female,80 or older,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,20.12,,,,,,,Female,Age 75 to 79,,,,,,,,Michigan,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,49.9,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,41.88,,,,,,,Female,Age 55 to 59,,,,,,,,Nebraska,3.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,Yes,No,Yes,No,No,Yes,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,110.68,Yes,No,Yes,No,"Yes, received Tdap",No,Yes +,36.32,,,,,,,Male,Age 25 to 29,,,,,,,,Guam,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Use them every day,No,Hispanic,1.68,102.06,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",Yes,Yes +No,29.05,No,No,No,0.0,0.0,No,Female,45-49,White,No,Yes,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,32.55,,,,,,,Male,Age 65 to 69,,,,,,,,Indiana,4.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,"6 or more, but not all",No,No,No,No,No,Yes,No,No,Yes,Yes,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.83,108.86,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,19.9,,,,,,,Male,Age 45 to 49,,,,,,,,Utah,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.88,70.31,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,30.23,No,No,No,0.0,0.0,No,Female,40-44,Hispanic,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,,,,,,,,Male,Age 45 to 49,,,,,,,,North Carolina,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,"Other race only, Non-Hispanic",,,,,,,,, +,33.66,,,,,,,Female,Age 30 to 34,,,,,,,,Colorado,0.0,15.0,Within past 2 years (1 year but less than 2 years ago),Yes,6.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,Yes,Former smoker,Use them every day,No,"White only, Non-Hispanic",1.6,86.18,Yes,No,No,Yes,"Yes, received Tdap",No,Yes +,22.31,,,,,,,Female,Age 55 to 59,,,,,,,,New Mexico,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,,1.63,58.97,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +,24.33,,,,,,,Male,Age 75 to 79,,,,,,,,Iowa,1.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,72.57,Yes,No,Yes,Yes,"Yes, received Tdap",No,Yes +,22.46,,,,,,,Female,Age 40 to 44,,,,,,,,Washington,0.0,1.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,61.23,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,No +,21.63,,,,,,,Female,Age 55 to 59,,,,,,,,Utah,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,58.97,Yes,Yes,No,No,"Yes, received Tdap",No,No +,26.63,,,,,,,Female,Age 25 to 29,,,,,,,,Maryland,0.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),Yes,"Black only, Non-Hispanic",1.68,74.84,No,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +No,25.84,No,No,No,1.0,5.0,No,Female,75-79,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Yes,36.18,No,No,No,5.0,0.0,No,Male,50-54,American Indian/Alaskan Native,Yes,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,26.09,,,,,,,Female,Age 75 to 79,,,,,,,,Florida,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,Yes,No,Yes,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.63,68.95,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,27.46,,,,,,,Female,Age 50 to 54,,,,,,,,Missouri,30.0,30.0,Within past year (anytime less than 12 months ago),No,5.0,None of them,Yes,No,No,Yes,No,Yes,Yes,No,Yes,Yes,No,No,Yes,Yes,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.63,72.57,No,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +No,20.25,Yes,No,No,16.0,30.0,No,Female,25-29,Hispanic,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,27.37,,,,,,,Male,Age 50 to 54,,,,,,,,Georgia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,81.65,No,Yes,No,No,"Yes, received Tdap",No,Yes +,31.0,,,,,,,Female,Age 75 to 79,,,,,,,,Florida,2.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,Yes,Yes,No,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,79.38,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,36.05,,,,,,,Female,Age 50 to 54,,,,,,,,California,0.0,15.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.63,95.25,Yes,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,25.23,,,,,,,Male,Age 60 to 64,,,,,,,,Maryland,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.83,84.37,No,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,31.89,,,,,,,Female,Age 80 or older,,,,,,,,Kansas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.6,81.65,No,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No +,33.28,,,,,,,Female,Age 60 to 64,,,,,,,,South Dakota,0.0,4.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,Yes,No,Yes,No,Yes,"No, pre-diabetes or borderline diabetes",No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,90.72,Yes,No,Yes,Yes,,No,Yes +No,29.26,Yes,No,No,0.0,0.0,No,Male,60-64,Asian,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,29.26,Yes,No,No,0.0,30.0,No,Female,60-64,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,18.25,No,No,No,0.0,0.0,No,Female,45-49,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,27.07,No,No,No,0.0,14.0,No,Female,30-34,Hispanic,No,No,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,39.11,No,No,No,2.0,10.0,Yes,Female,65-69,White,Yes,No,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,35.78,,,,,,,Female,Age 18 to 24,,,,,,,,Rhode Island,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,91.63,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,24.75,,,,,,,Female,Age 45 to 49,,,,,,,,Utah,25.0,30.0,Within past year (anytime less than 12 months ago),No,5.0,"6 or more, but not all",No,No,No,No,No,No,Yes,No,Yes,No,No,No,Yes,No,No,Yes,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,71.67,No,Yes,No,Yes,"Yes, received Tdap",No,Yes +No,37.86,No,No,No,0.0,0.0,Yes,Female,60-64,Hispanic,Yes,Yes,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,22.14,,,,,,,Female,Age 75 to 79,,,,,,,,Washington,30.0,5.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,Yes,No,No,No,Yes,Yes,No,Yes,No,Yes,No,Yes,No,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,56.7,Yes,Yes,No,Yes,"Yes, received Tdap",No,No +,22.62,,,,,,,Female,Age 50 to 54,,,,,,,,Puerto Rico,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.5,50.8,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,19.89,No,Yes,No,2.0,2.0,No,Female,45-49,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,21.95,,,,,,,Female,Age 35 to 39,,,,,,,,Maryland,8.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.57,54.43,No,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,Yes +,41.01,,,,,,,Male,Age 65 to 69,,,,,,,,New Hampshire,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.52,95.25,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,Yes +No,21.46,No,No,No,1.0,2.0,No,Female,40-44,White,No,Yes,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,18.47,,,,,,,Male,Age 18 to 24,,,,,,,,Colorado,0.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Use them some days,No,Hispanic,1.85,63.5,Yes,No,Yes,No,"Yes, received Tdap",No,No +,25.83,,,,,,,Male,Age 65 to 69,,,,,,,,Iowa,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,Yes,Yes,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,81.65,No,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,Yes +No,39.48,Yes,No,No,0.0,0.0,Yes,Female,70-74,White,Yes,No,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,,,,,,,,Female,Age 70 to 74,,,,,,,,New Hampshire,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,Yes,Yes,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, +,25.33,,,,,,,Female,Age 60 to 64,,,,,,,,South Carolina,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.6,64.86,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +No,22.14,No,No,No,0.0,0.0,No,Female,60-64,Asian,No,Yes,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,34.87,,,,,,,Male,Age 45 to 49,,,,,,,,Idaho,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.8,113.4,No,No,Yes,No,"Yes, received Tdap",No,No +,33.23,,,,,,,Male,Age 80 or older,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,Yes,No,No,No,No,No,Yes,No,Yes,No,No,Yes,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,102.06,No,No,No,Yes,"Yes, received tetanus shot, but not Tdap",No,No +,32.98,,,,,,,Male,Age 40 to 44,,,,,,,,Arizona,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,Hispanic,1.85,113.4,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,29.05,No,No,No,1.0,0.0,No,Female,50-54,White,No,Yes,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,40.69,Yes,No,No,0.0,0.0,No,Male,50-54,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,23.87,Yes,No,No,0.0,0.0,No,Female,40-44,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,34.01,Yes,No,No,1.0,0.0,No,Female,65-69,White,No,No,6.0,Yes,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,39.06,No,No,No,0.0,15.0,No,Female,40-44,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,25.53,Yes,No,No,0.0,0.0,No,Female,70-74,White,No,Yes,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,18.29,Yes,No,No,0.0,30.0,No,Female,70-74,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,28.25,No,No,No,0.0,0.0,No,Male,70-74,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,31.47,,,,,,,Male,Age 75 to 79,,,,,,,,Ohio,30.0,30.0,Within past year (anytime less than 12 months ago),No,10.0,"6 or more, but not all",No,No,Yes,No,No,No,No,No,Yes,No,Yes,Yes,No,Yes,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.68,88.45,No,No,No,No,"Yes, received tetanus shot, but not Tdap",No,Yes +,26.13,,,,,,,Female,Age 55 to 59,,,,,,,,Ohio,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.65,71.21,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +No,24.41,Yes,No,No,7.0,1.0,Yes,Male,60-64,White,No,Yes,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,22.25,No,No,No,0.0,0.0,No,Male,75-79,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,31.32,Yes,Yes,No,0.0,0.0,No,Female,75-79,White,No,Yes,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,22.31,,,,,,,Female,Age 75 to 79,,,,,,,,Virginia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,,,,,,"White only, Non-Hispanic",1.63,58.97,,,,,,, +,33.29,,,,,,,Female,Age 70 to 74,,,,,,,,Puerto Rico,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,Yes,No,No,No,No,No,Yes,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,Hispanic,1.57,82.55,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,29.26,,,,,,,Female,Age 55 to 59,,,,,,,,Kansas,3.0,3.0,Within past year (anytime less than 12 months ago),Yes,9.0,None of them,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,,,,"White only, Non-Hispanic",1.57,72.57,,,,,,, +No,21.31,Yes,No,No,0.0,0.0,No,Female,60-64,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,23.49,No,No,No,2.0,7.0,No,Female,30-34,Black,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,26.65,Yes,No,No,0.0,0.0,No,Male,35-39,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,24.96,,,,,,,Female,Age 75 to 79,,,,,,,,Connecticut,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,68.04,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +Yes,25.75,No,No,No,0.0,0.0,No,Female,75-79,American Indian/Alaskan Native,No,No,7.0,No,Yes,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,26.39,,,,,,,Male,Age 65 to 69,,,,,,,,South Carolina,30.0,30.0,Within past year (anytime less than 12 months ago),Yes,5.0,"6 or more, but not all",No,No,No,No,No,Yes,Yes,No,No,Yes,No,No,Yes,Yes,Yes,Yes,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,Yes,"Multiracial, Non-Hispanic",1.85,90.72,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,31.14,,,,,,,Male,Age 65 to 69,,,,,,,,South Carolina,2.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,All,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"Other race only, Non-Hispanic",1.85,107.05,No,No,No,Yes,"Yes, received Tdap",No,No +,26.63,,,,,,,Male,Age 40 to 44,,,,,,,,Missouri,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,74.84,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No +No,19.9,No,No,No,1.0,5.0,No,Male,18-24,White,No,Yes,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,30.43,Yes,No,No,0.0,0.0,No,Male,25-29,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,31.09,Yes,No,No,30.0,30.0,Yes,Female,50-54,White,No,Yes,5.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,22.31,,,,,,,Female,Age 35 to 39,,,,,,,,Washington,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.63,58.97,,,,,,, +,35.87,,,,,,,Male,Age 60 to 64,,,,,,,,Nevada,,30.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,No,Yes,Yes,No,Yes,No,No,No,No,No,No,No,No,No,No,,,,"White only, Non-Hispanic",1.78,113.4,,,,,,, +No,24.41,No,No,No,2.0,0.0,No,Male,60-64,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,21.79,,,,,,,Female,Age 40 to 44,,,,,,,,Vermont,0.0,2.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Multiracial, Non-Hispanic",1.68,61.23,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,No +,36.9,,,,,,,Female,Age 40 to 44,,,,,,,,Missouri,1.0,1.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,"Yes, but only during pregnancy (female)",No,Yes,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,97.52,Yes,No,Yes,No,"Yes, received Tdap",No,No +No,27.46,No,No,No,0.0,0.0,No,Female,30-34,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,,,,,,,,Male,,,,,,,,,Minnesota,4.0,23.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,Yes,No,No,No,No,Yes,No,,,,,,,,,,,,,,,,,,, +No,27.22,Yes,No,No,0.0,2.0,No,Male,55-59,Other,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,22.24,,,,,,,Male,Age 70 to 74,,,,,,,,South Dakota,12.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,"6 or more, but not all",No,No,No,No,No,Yes,No,No,No,No,No,Yes,No,No,No,Yes,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,70.31,No,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,20.36,,,,,,,Female,Age 60 to 64,,,,,,,,Georgia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,58.97,Yes,No,Yes,No,,No,No +,,,,,,,,Female,Age 65 to 69,,,,,,,,New Mexico,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,10.0,None of them,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, +No,23.96,No,No,No,0.0,0.0,No,Female,75-79,White,"No, borderline diabetes",Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,30.08,Yes,No,No,0.0,0.0,No,Female,80 or older,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,27.44,Yes,No,No,0.0,0.0,Yes,Female,65-69,White,Yes,No,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,,,,,,,,Male,Age 35 to 39,,,,,,,,Massachusetts,2.0,15.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, +No,32.69,No,No,No,0.0,0.0,No,Male,70-74,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,21.97,No,No,No,0.0,0.0,No,Female,45-49,White,No,Yes,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,24.21,,,,,,,Female,Age 80 or older,,,,,,,,Michigan,0.0,1.0,Within past year (anytime less than 12 months ago),No,,,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,Yes,Yes,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.68,68.04,No,No,Yes,No,,No,No +No,43.58,No,No,No,0.0,0.0,No,Female,45-49,Black,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,23.17,Yes,Yes,No,0.0,0.0,No,Female,75-79,Hispanic,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,24.33,,,,,,,Male,Age 70 to 74,,,,,,,,Maryland,0.0,10.0,Within past year (anytime less than 12 months ago),Yes,7.0,All,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Not at all (right now),Yes,"White only, Non-Hispanic",1.73,72.57,Yes,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,39.16,,,,,,,Female,Age 65 to 69,,,,,,,,Oklahoma,0.0,0.0,Within past year (anytime less than 12 months ago),No,4.0,1 to 5,No,Yes,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,113.4,No,No,No,No,"Yes, received Tdap",No,Yes +No,23.73,No,No,No,2.0,0.0,No,Male,65-69,White,Yes,Yes,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,27.8,,,,,,,Male,Age 40 to 44,,,,,,,,Idaho,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.83,92.99,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +,36.32,,,,,,,Female,Age 65 to 69,,,,,,,,Ohio,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,102.06,No,No,Yes,Yes,"Yes, received Tdap",No,Tested positive using home test without a health professional +No,26.31,Yes,Yes,No,0.0,0.0,No,Female,40-44,White,No,Yes,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,21.77,,,,,,,Male,Age 60 to 64,,,,,,,,Wisconsin,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.85,74.84,Yes,No,Yes,Yes,"Yes, received Tdap",No,No +No,27.41,No,No,No,0.0,5.0,No,Male,55-59,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,27.12,,,,,,,Female,Age 35 to 39,,,,,,,,Colorado,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,"Yes, but only during pregnancy (female)",No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,90.72,Yes,Yes,Yes,No,"Yes, received Tdap",No,No +,25.75,,,,,,,Female,Age 70 to 74,,,,,,,,Virginia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,68.04,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,21.62,,,,,,,Male,Age 40 to 44,,,,,,,,Nebraska,0.0,2.0,Within past 5 years (2 years but less than 5 years ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.8,70.31,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No +,32.01,,,,,,,Female,Age 75 to 79,,,,,,,,California,0.0,3.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,Yes,No,No,Yes,No,Yes,No,Yes,No,Yes,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.57,79.38,Yes,No,No,Yes,,No,Yes +,31.28,,,,,,,Male,Age 50 to 54,,,,,,,,Texas,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),No,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),Yes,Hispanic,1.78,98.88,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +No,24.89,No,No,No,0.0,0.0,No,Female,75-79,Hispanic,No,Yes,6.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,20.67,,,,,,,Female,Age 50 to 54,,,,,,,,Texas,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,4.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,63.5,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +,22.92,,,,,,,Female,Age 75 to 79,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,64.41,Yes,No,Yes,No,"Yes, received Tdap",No,No +No,23.17,No,No,No,30.0,0.0,Yes,Female,70-74,White,No,No,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,26.85,Yes,No,No,0.0,7.0,No,Male,40-44,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,23.89,Yes,No,No,0.0,3.0,No,Female,40-44,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,28.8,,,,,,,Male,Age 65 to 69,,,,,,,,West Virginia,10.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.75,88.45,No,No,Yes,Yes,"Yes, received Tdap",No,Yes +,29.03,,,,,,,Male,Age 25 to 29,,,,,,,,South Carolina,4.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,99.79,No,Yes,No,No,"Yes, received Tdap",No,Yes +,25.8,,,,,,,Male,Age 50 to 54,,,,,,,,Utah,14.0,30.0,Within past year (anytime less than 12 months ago),Yes,3.0,None of them,No,No,No,No,No,No,Yes,No,Yes,No,No,No,Yes,Yes,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,83.91,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,No +,31.32,,,,,,,Male,Age 18 to 24,,,,,,,,Iowa,0.0,3.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.7,90.72,Yes,No,No,Yes,"Yes, received Tdap",No,No +,31.24,,,,,,,Female,Age 60 to 64,,,,,,,,Washington,28.0,30.0,,No,15.0,1 to 5,No,No,No,Yes,No,Yes,Yes,Yes,No,No,No,No,No,Yes,Yes,Yes,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,82.55,No,Yes,Yes,,"Yes, received tetanus shot but not sure what type",No,No +,,,,,,,,Female,Age 45 to 49,,,,,,,,Massachusetts,0.0,2.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,,Yes,Yes,Yes,,"Yes, received tetanus shot but not sure what type",No,Yes +,22.31,,,,,,,Female,Age 25 to 29,,,,,,,,New York,0.0,5.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.63,58.97,Yes,No,No,No,"Yes, received Tdap",No,Yes +,21.26,,,,,,,Female,Age 18 to 24,,,,,,,,Florida,2.0,20.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.6,54.43,Yes,No,No,,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,28.89,,,,,,,Female,Age 40 to 44,,,,,,,,Georgia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,86.18,No,No,No,Yes,"Yes, received Tdap",No,Yes +,24.33,,,,,,,Male,Age 45 to 49,,,,,,,,New Hampshire,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,72.57,Yes,No,Yes,No,"Yes, received Tdap",No,No +,28.89,,,,,,,Male,Age 80 or older,,,,,,,,Florida,5.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,Yes,No,No,No,No,Yes,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,86.18,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes +No,21.79,No,No,No,0.0,4.0,No,Female,65-69,White,No,Yes,10.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,28.41,Yes,No,Yes,30.0,0.0,Yes,Male,75-79,White,Yes,No,4.0,No,Yes,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,30.13,Yes,Yes,No,0.0,0.0,No,Male,45-49,White,No,No,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,28.19,,,,,,,Female,Age 70 to 74,,,,,,,,Mississippi,5.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,No,Yes,No,Yes,Yes,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,81.65,No,No,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +No,19.77,No,No,No,1.0,30.0,No,Female,18-24,White,No,Yes,10.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,25.09,No,No,No,0.0,0.0,No,Male,30-34,White,No,Yes,8.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,29.54,,,,,,,Female,Age 18 to 24,,,,,,,,Alaska,30.0,0.0,Within past year (anytime less than 12 months ago),No,10.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,83.01,No,No,Yes,No,"Yes, received Tdap",No,No +No,19.97,No,No,No,0.0,0.0,No,Male,60-64,White,No,No,4.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,24.65,,,,,,,Male,Age 55 to 59,,,,,,,,Utah,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.88,87.09,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,25.0,,,,,,,Male,Age 60 to 64,,,,,,,,Kansas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.91,90.72,Yes,No,No,No,"Yes, received Tdap",No,Yes +,28.75,,,,,,,Male,Age 65 to 69,,,,,,,,Maine,15.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,"6 or more, but not all",Yes,Yes,No,Yes,No,Yes,No,No,Yes,No,No,No,No,Yes,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.91,104.33,No,Yes,No,Yes,"Yes, received Tdap",No,No +,32.01,,,,,,,Female,Age 65 to 69,,,,,,,,South Carolina,0.0,0.0,Within past year (anytime less than 12 months ago),No,5.0,1 to 5,No,Yes,Yes,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.57,79.38,No,,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,28.13,,,,,,,Female,Age 60 to 64,,,,,,,,Connecticut,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,1 to 5,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,83.91,Yes,No,No,Yes,,No,Yes +,22.67,,,,,,,Female,Age 30 to 34,,,,,,,,Michigan,0.0,10.0,5 or more years ago,Yes,4.0,None of them,No,No,No,Yes,No,No,Yes,No,No,"No, pre-diabetes or borderline diabetes",No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.55,54.43,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,Tested positive using home test without a health professional +,,,,,,,,Female,Age 65 to 69,,,,,,,,Connecticut,30.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,6.0,1 to 5,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,,No,No,No,Yes,"Yes, received Tdap",No,No +,28.25,,,,,,,Male,Age 70 to 74,,,,,,,,Arizona,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,79.38,No,,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,34.17,,,,,,,Male,Age 45 to 49,,,,,,,,South Dakota,0.0,5.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.8,111.13,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,24.37,,,,,,,Male,Age 35 to 39,,,,,,,,Florida,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,10.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.75,74.84,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No +,24.41,,,,,,,Male,Age 75 to 79,,,,,,,,Missouri,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,"6 or more, but not all",No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.8,79.38,No,No,Yes,No,"Yes, received Tdap",No,No +,27.12,,,,,,,Male,Age 60 to 64,,,,,,,,Kentucky,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Use them some days,Yes,"White only, Non-Hispanic",1.83,90.72,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,21.12,,,,,,,Male,Age 65 to 69,,,,,,,,Nebraska,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,64.86,Yes,No,Yes,Yes,"Yes, received Tdap",No,No +,19.77,,,,,,,Male,Age 25 to 29,,,,,,,,Virginia,0.0,30.0,Within past 5 years (2 years but less than 5 years ago),Yes,10.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Use them every day,No,"White only, Non-Hispanic",1.73,58.97,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,40.76,Yes,No,Yes,1.0,0.0,No,Male,80 or older,White,Yes,No,2.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,24.97,,,,,,,Male,Age 80 or older,,,,,,,,Alaska,0.0,0.0,Within past year (anytime less than 12 months ago),No,9.0,1 to 5,No,Yes,No,No,No,No,No,No,No,No,Yes,No,Yes,Yes,No,Yes,Never smoked,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.8,81.19,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes +Yes,28.7,Yes,No,No,0.0,0.0,No,Male,65-69,Other,Yes,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,22.15,Yes,No,No,0.0,0.0,No,Male,30-34,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,25.1,No,No,No,0.0,0.0,No,Male,60-64,White,No,Yes,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,22.67,No,No,No,0.0,0.0,No,Female,30-34,Asian,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,21.63,,,,,,,Female,Age 70 to 74,,,,,,,,Michigan,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,58.97,Yes,No,No,Yes,"Yes, received tetanus shot but not sure what type",No,No +No,25.62,Yes,No,No,0.0,2.0,No,Male,25-29,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,27.06,,,,,,,Female,Age 60 to 64,,,,,,,,Florida,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,80.74,Yes,Yes,Yes,No,"Yes, received tetanus shot but not sure what type",No,Yes +,32.28,,,,,,,Female,Age 60 to 64,,,,,,,,Colorado,15.0,8.0,Within past year (anytime less than 12 months ago),No,9.0,None of them,Yes,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,90.72,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_004/history.csv b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_004/history.csv index 9093f869652d17a30aaf20b99606a6f1cc2e52c5..bc93ad8a6dd9aed8e10d3f41428f2567b21f801e 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_004/history.csv +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_004/history.csv @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:b4d564b31a93092969a7afc7c48fe0771a51b32a6d9219175fd4ff276bb9b5e3 -size 271621218 +oid sha256:b53f367dbe4a7a9ba4955ba31efd2afc5b76f4668fa5e8406512a2971c28807e +size 270600562 diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_004/info.json b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_004/info.json index 53272e3074451c673817a05ef4da5ee438d07863..c876b7e9aa18ec2175d72a1a6b59bf487c77e98c 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_004/info.json +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_004/info.json @@ -1,401 +1,393 @@ -{ - "name": "Indicators of Heart Disease (2022 UPDATE)", - "source": "https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease/data", - "data_intro": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", - "is_splited": false, - "overall_size": 1010949, - "train_size": 0, - "test_size": 0, - "c_classes": 46, - "n_classes": 9, - "cat_feature_intro": { - "HeartDisease": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", - "Smoking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", - "AlcoholDrinking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", - "Stroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", - "DiffWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", - "Sex": "Sex is a biological factor that can influence the risk of heart disease. Men and women may experience different symptoms and risk factors for heart disease.", - "AgeCategory": "Age is a significant risk factor for heart disease. As people age, their risk of developing heart disease increases. It is important to monitor heart health as individuals get older.", - "Race": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", - "Diabetic": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", - "PhysicalActivity": "Regular physical activity is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", - "GenHealth": "General health status can influence the risk of heart disease. It is important to maintain overall health and address any underlying health issues to reduce the risk of heart-related complications.", - "Asthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", - "KidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", - "SkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", - "State": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", - "GeneralHealth": "General health status can influence the risk of heart disease. It is important to maintain overall health and address any underlying health issues to reduce the risk of heart-related complications.", - "LastCheckupTime": "The time since the last medical checkup can be an important factor in heart health. Regular checkups can help identify and address risk factors for heart disease early.", - "PhysicalActivities": "Engaging in regular physical activities is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", - "RemovedTeeth": "The number of removed teeth can be an indicator of overall health, including heart health. Poor oral health has been linked to an increased risk of heart disease.", - "HadHeartAttack": "As described above, the original dataset of nearly 300 variables was reduced to 40 variables. In addition to classical EDA, this dataset can be used to apply a number of machine learning methods, especially classifier models (logistic regression, SVM, random forest, etc.). You should treat the variable \"HadHeartAttack\" as binary (\"Yes\" - respondent had heart disease; \"No\" - respondent did not have heart disease). Note, however, that the classes are unbalanced, so the classic approach of applying a model is not advisable. Fixing the weights/undersampling should yield much better results. Based on the data set, I built a logistic regression model and embedded it in an application that might inspire you: https://share.streamlit.io/kamilpytlak/heart-condition-checker/main/app.py. Can you indicate which variables have a significant effect on the likelihood of heart disease?", - "HadAngina": "Angina is a type of chest pain caused by reduced blood flow to the heart. It is a symptom of coronary artery disease and can indicate an increased risk of heart disease.", - "HadStroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", - "HadAsthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", - "HadSkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", - "HadCOPD": "Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease that causes obstructed airflow from the lungs. It can increase the risk of heart disease.", - "HadDepressiveDisorder": "Depressive disorders can affect overall health and increase the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", - "HadKidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", - "HadArthritis": "Arthritis is a condition that causes inflammation of the joints. It can affect overall health and increase the risk of heart disease.", - "HadDiabetes": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", - "DeafOrHardOfHearing": "Hearing loss can affect overall health and quality of life. It is important to address and manage hearing issues to maintain overall health and reduce the risk of heart disease.", - "BlindOrVisionDifficulty": "Vision difficulties can affect overall health and quality of life. It is important to address and manage vision issues to maintain overall health and reduce the risk of heart disease.", - "DifficultyConcentrating": "Difficulty concentrating can be a sign of underlying health issues, including heart disease. It is important to address cognitive issues to improve overall health and reduce the risk of heart-related complications.", - "DifficultyWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", - "DifficultyDressingBathing": "Difficulty with daily activities such as dressing and bathing can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", - "DifficultyErrands": "Difficulty running errands can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", - "SmokerStatus": "Smoking is a major risk factor for heart disease. It is important to address and manage smoking habits to reduce the risk of heart-related complications.", - "ECigaretteUsage": "E-cigarette usage can affect heart health. It is important to monitor and manage e-cigarette usage to reduce the risk of heart disease.", - "ChestScan": "Chest scans can help identify underlying health issues, including heart disease. It is important to monitor and address any abnormalities found in chest scans to maintain overall health.", - "RaceEthnicityCategory": "Race and ethnicity can influence the risk of heart disease. It is important to consider these factors when assessing and managing heart health.", - "AlcoholDrinkers": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", - "HIVTesting": "HIV testing is important for overall health. It is important to address and manage HIV status to reduce the risk of heart-related complications.", - "FluVaxLast12": "Receiving a flu vaccine in the last 12 months can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", - "PneumoVaxEver": "Receiving a pneumococcal vaccine can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", - "TetanusLast10Tdap": "Receiving a tetanus vaccine in the last 10 years can help reduce the risk of infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", - "HighRiskLastYear": "Being at high risk for health issues in the last year can affect overall health and increase the risk of heart disease. It is important to address and manage high-risk factors to reduce the risk of heart-related complications.", - "CovidPos": "Testing positive for COVID-19 can affect overall health and increase the risk of heart disease. It is important to monitor and manage COVID-19 symptoms to reduce the risk of heart-related complications." - }, - "num_feature_intro": { - "BMI": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", - "PhysicalHealth": "Physical health is an important factor in overall well-being and can influence the risk of heart disease. It is important to maintain good physical health to reduce the risk of heart-related complications.", - "MentalHealth": "Mental health is an important factor in overall well-being and can influence the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", - "SleepTime": "Sleep time is an important factor in overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", - "PhysicalHealthDays": "The number of days with good physical health can be an indicator of overall well-being. It is important to maintain good physical health to reduce the risk of heart-related complications.", - "MentalHealthDays": "The number of days with good mental health can be an indicator of overall well-being. It is important to address and manage mental health to reduce the risk of heart-related complications.", - "SleepHours": "The number of hours of sleep can affect overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", - "HeightInMeters": "Height can be an indicator of overall health. It is important to monitor and maintain a healthy height-to-weight ratio to reduce the risk of heart disease.", - "WeightInKilograms": "Weight can be an indicator of overall health. It is important to maintain a healthy weight to reduce the risk of heart disease." - }, - "evaluation_metric": "The evaluation metric for this dataset can include accuracy, precision, recall, F1-score, and AUC-ROC. These metrics can help assess the performance of machine learning models in predicting heart disease.", - "task_type": "classification", - "target": "GeneralHealth", - "num_feature_value": { - "BMI": [ - 12.02, - 99.64 - ], - "HeightInMeters": [ - 0.91, - 2.41 - ], - "MentalHealth": [ - 0.0, - 30.0 - ], - "MentalHealthDays": [ - 0.0, - 30.0 - ], - "PhysicalHealth": [ - 0.0, - 30.0 - ], - "PhysicalHealthDays": [ - 0.0, - 30.0 - ], - "SleepHours": [ - 1.0, - 24.0 - ], - "SleepTime": [ - 1.0, - 24.0 - ], - "WeightInKilograms": [ - 22.68, - 292.57 - ] - }, - "cat_feature_value": { - "AgeCategory": [ - "18-24", - "25-29", - "30-34", - "35-39", - "40-44", - "45-49", - "50-54", - "55-59", - "60-64", - "65-69", - "70-74", - "75-79", - "80 or older", - "Age 18 to 24", - "Age 25 to 29", - "Age 30 to 34", - "Age 35 to 39", - "Age 40 to 44", - "Age 45 to 49", - "Age 50 to 54", - "Age 55 to 59", - "Age 60 to 64", - "Age 65 to 69", - "Age 70 to 74", - "Age 75 to 79", - "Age 80 or older" - ], - "AlcoholDrinkers": [ - "No", - "Yes" - ], - "AlcoholDrinking": [ - "No", - "Yes" - ], - "Asthma": [ - "No", - "Yes" - ], - "BlindOrVisionDifficulty": [ - "No", - "Yes" - ], - "ChestScan": [ - "No", - "Yes" - ], - "CovidPos": [ - "No", - "Tested positive using home test without a health professional", - "Yes" - ], - "DeafOrHardOfHearing": [ - "No", - "Yes" - ], - "Diabetic": [ - "No", - "No, borderline diabetes", - "Yes", - "Yes (during pregnancy)" - ], - "DiffWalking": [ - "No", - "Yes" - ], - "DifficultyConcentrating": [ - "No", - "Yes" - ], - "DifficultyDressingBathing": [ - "No", - "Yes" - ], - "DifficultyErrands": [ - "No", - "Yes" - ], - "DifficultyWalking": [ - "No", - "Yes" - ], - "ECigaretteUsage": [ - "Never used e-cigarettes in my entire life", - "Not at all (right now)", - "Use them every day", - "Use them some days" - ], - "FluVaxLast12": [ - "No", - "Yes" - ], - "GenHealth": [ - "Excellent", - "Fair", - "Good", - "Poor", - "Very good" - ], - "GeneralHealth": [ - "Excellent", - "Fair", - "Good", - "Poor", - "Very good" - ], - "HIVTesting": [ - "No", - "Yes" - ], - "HadAngina": [ - "No", - "Yes" - ], - "HadArthritis": [ - "No", - "Yes" - ], - "HadAsthma": [ - "No", - "Yes" - ], - "HadCOPD": [ - "No", - "Yes" - ], - "HadDepressiveDisorder": [ - "No", - "Yes" - ], - "HadDiabetes": [ - "No", - "No, pre-diabetes or borderline diabetes", - "Yes", - "Yes, but only during pregnancy (female)" - ], - "HadHeartAttack": [ - "No", - "Yes" - ], - "HadKidneyDisease": [ - "No", - "Yes" - ], - "HadSkinCancer": [ - "No", - "Yes" - ], - "HadStroke": [ - "No", - "Yes" - ], - "HeartDisease": [ - "No", - "Yes" - ], - "HighRiskLastYear": [ - "No", - "Yes" - ], - "KidneyDisease": [ - "No", - "Yes" - ], - "LastCheckupTime": [ - "5 or more years ago", - "Within past 2 years (1 year but less than 2 years ago)", - "Within past 5 years (2 years but less than 5 years ago)", - "Within past year (anytime less than 12 months ago)" - ], - "PhysicalActivities": [ - "No", - "Yes" - ], - "PhysicalActivity": [ - "No", - "Yes" - ], - "PneumoVaxEver": [ - "No", - "Yes" - ], - "Race": [ - "American Indian/Alaskan Native", - "Asian", - "Black", - "Hispanic", - "Other", - "White" - ], - "RaceEthnicityCategory": [ - "Black only, Non-Hispanic", - "Hispanic", - "Multiracial, Non-Hispanic", - "Other race only, Non-Hispanic", - "White only, Non-Hispanic" - ], - "RemovedTeeth": [ - "1 to 5", - "6 or more, but not all", - "All", - "None of them" - ], - "Sex": [ - "Female", - "Male" - ], - "SkinCancer": [ - "No", - "Yes" - ], - "SmokerStatus": [ - "Current smoker - now smokes every day", - "Current smoker - now smokes some days", - "Former smoker", - "Never smoked" - ], - "Smoking": [ - "No", - "Yes" - ], - "State": [ - "Alabama", - "Alaska", - "Arizona", - "Arkansas", - "California", - "Colorado", - "Connecticut", - "Delaware", - "District of Columbia", - "Florida", - "Georgia", - "Guam", - "Hawaii", - "Idaho", - "Illinois", - "Indiana", - "Iowa", - "Kansas", - "Kentucky", - "Louisiana", - "Maine", - "Maryland", - "Massachusetts", - "Michigan", - "Minnesota", - "Mississippi", - "Missouri", - "Montana", - "Nebraska", - "Nevada", - "New Hampshire", - "New Jersey", - "New Mexico", - "New York", - "North Carolina", - "North Dakota", - "Ohio", - "Oklahoma", - "Oregon", - "Pennsylvania", - "Puerto Rico", - "Rhode Island", - "South Carolina", - "South Dakota", - "Tennessee", - "Texas", - "Utah", - "Vermont", - "Virgin Islands", - "Virginia", - "Washington", - "West Virginia", - "Wisconsin", - "Wyoming" - ], - "Stroke": [ - "No", - "Yes" - ], - "TetanusLast10Tdap": [ - "No, did not receive any tetanus shot in the past 10 years", - "Yes, received Tdap", - "Yes, received tetanus shot but not sure what type", - "Yes, received tetanus shot, but not Tdap" - ] - } -} \ No newline at end of file +{ + "name": "Indicators of Heart Disease (2022 UPDATE)", + "source": "https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease/data", + "data_intro": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", + "is_splited": false, + "overall_size": 1010949, + "train_size": 0, + "test_size": 0, + "c_classes": 45, + "n_classes": 9, + "cat_feature_intro": { + "HeartDisease": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", + "Smoking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", + "AlcoholDrinking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", + "Stroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", + "DiffWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", + "Sex": "Sex is a biological factor that can influence the risk of heart disease. Men and women may experience different symptoms and risk factors for heart disease.", + "AgeCategory": "Age is a significant risk factor for heart disease. As people age, their risk of developing heart disease increases. It is important to monitor heart health as individuals get older.", + "Race": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", + "Diabetic": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", + "PhysicalActivity": "Regular physical activity is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", + "Asthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", + "KidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", + "SkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", + "State": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", + "GeneralHealth": "Self-reported general health from BRFSS GENHLTH: Excellent, Very good, Good, Fair, or Poor.", + "LastCheckupTime": "The time since the last medical checkup can be an important factor in heart health. Regular checkups can help identify and address risk factors for heart disease early.", + "PhysicalActivities": "Engaging in regular physical activities is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", + "RemovedTeeth": "The number of removed teeth can be an indicator of overall health, including heart health. Poor oral health has been linked to an increased risk of heart disease.", + "HadHeartAttack": "As described above, the original dataset of nearly 300 variables was reduced to 40 variables. In addition to classical EDA, this dataset can be used to apply a number of machine learning methods, especially classifier models (logistic regression, SVM, random forest, etc.). You should treat the variable \"HadHeartAttack\" as binary (\"Yes\" - respondent had heart disease; \"No\" - respondent did not have heart disease). Note, however, that the classes are unbalanced, so the classic approach of applying a model is not advisable. Fixing the weights/undersampling should yield much better results. Based on the data set, I built a logistic regression model and embedded it in an application that might inspire you: https://share.streamlit.io/kamilpytlak/heart-condition-checker/main/app.py. Can you indicate which variables have a significant effect on the likelihood of heart disease?", + "HadAngina": "Angina is a type of chest pain caused by reduced blood flow to the heart. It is a symptom of coronary artery disease and can indicate an increased risk of heart disease.", + "HadStroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", + "HadAsthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", + "HadSkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", + "HadCOPD": "Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease that causes obstructed airflow from the lungs. It can increase the risk of heart disease.", + "HadDepressiveDisorder": "Depressive disorders can affect overall health and increase the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", + "HadKidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", + "HadArthritis": "Arthritis is a condition that causes inflammation of the joints. It can affect overall health and increase the risk of heart disease.", + "HadDiabetes": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", + "DeafOrHardOfHearing": "Hearing loss can affect overall health and quality of life. It is important to address and manage hearing issues to maintain overall health and reduce the risk of heart disease.", + "BlindOrVisionDifficulty": "Vision difficulties can affect overall health and quality of life. It is important to address and manage vision issues to maintain overall health and reduce the risk of heart disease.", + "DifficultyConcentrating": "Difficulty concentrating can be a sign of underlying health issues, including heart disease. It is important to address cognitive issues to improve overall health and reduce the risk of heart-related complications.", + "DifficultyWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", + "DifficultyDressingBathing": "Difficulty with daily activities such as dressing and bathing can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", + "DifficultyErrands": "Difficulty running errands can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", + "SmokerStatus": "Smoking is a major risk factor for heart disease. It is important to address and manage smoking habits to reduce the risk of heart-related complications.", + "ECigaretteUsage": "E-cigarette usage can affect heart health. It is important to monitor and manage e-cigarette usage to reduce the risk of heart disease.", + "ChestScan": "Chest scans can help identify underlying health issues, including heart disease. It is important to monitor and address any abnormalities found in chest scans to maintain overall health.", + "RaceEthnicityCategory": "Race and ethnicity can influence the risk of heart disease. It is important to consider these factors when assessing and managing heart health.", + "AlcoholDrinkers": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", + "HIVTesting": "Whether the respondent has ever been tested for HIV (Yes/No), not the result of that test.", + "FluVaxLast12": "Receiving a flu vaccine in the last 12 months can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", + "PneumoVaxEver": "Receiving a pneumococcal vaccine can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", + "TetanusLast10Tdap": "Receiving a tetanus vaccine in the last 10 years can help reduce the risk of infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", + "HighRiskLastYear": "Being at high risk for health issues in the last year can affect overall health and increase the risk of heart disease. It is important to address and manage high-risk factors to reduce the risk of heart-related complications.", + "CovidPos": "Testing positive for COVID-19 can affect overall health and increase the risk of heart disease. It is important to monitor and manage COVID-19 symptoms to reduce the risk of heart-related complications." + }, + "num_feature_intro": { + "BMI": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", + "PhysicalHealth": "Physical health is an important factor in overall well-being and can influence the risk of heart disease. It is important to maintain good physical health to reduce the risk of heart-related complications.", + "MentalHealth": "Mental health is an important factor in overall well-being and can influence the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", + "SleepTime": "Sleep time is an important factor in overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", + "PhysicalHealthDays": "The number of days with good physical health can be an indicator of overall well-being. It is important to maintain good physical health to reduce the risk of heart-related complications.", + "MentalHealthDays": "The number of days with good mental health can be an indicator of overall well-being. It is important to address and manage mental health to reduce the risk of heart-related complications.", + "SleepHours": "The number of hours of sleep can affect overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", + "HeightInMeters": "Height can be an indicator of overall health. It is important to monitor and maintain a healthy height-to-weight ratio to reduce the risk of heart disease.", + "WeightInKilograms": "Weight can be an indicator of overall health. It is important to maintain a healthy weight to reduce the risk of heart disease." + }, + "evaluation_metric": "The evaluation metric for this dataset can include accuracy, precision, recall, F1-score, and AUC-ROC. These metrics can help assess the performance of machine learning models in predicting heart disease.", + "task_type": "classification", + "target": "GeneralHealth", + "num_feature_value": { + "BMI": [ + 12.02, + 99.64 + ], + "HeightInMeters": [ + 0.91, + 2.41 + ], + "MentalHealth": [ + 0.0, + 30.0 + ], + "MentalHealthDays": [ + 0.0, + 30.0 + ], + "PhysicalHealth": [ + 0.0, + 30.0 + ], + "PhysicalHealthDays": [ + 0.0, + 30.0 + ], + "SleepHours": [ + 1.0, + 24.0 + ], + "SleepTime": [ + 1.0, + 24.0 + ], + "WeightInKilograms": [ + 22.68, + 292.57 + ] + }, + "cat_feature_value": { + "AgeCategory": [ + "18-24", + "25-29", + "30-34", + "35-39", + "40-44", + "45-49", + "50-54", + "55-59", + "60-64", + "65-69", + "70-74", + "75-79", + "80 or older", + "Age 18 to 24", + "Age 25 to 29", + "Age 30 to 34", + "Age 35 to 39", + "Age 40 to 44", + "Age 45 to 49", + "Age 50 to 54", + "Age 55 to 59", + "Age 60 to 64", + "Age 65 to 69", + "Age 70 to 74", + "Age 75 to 79", + "Age 80 or older" + ], + "AlcoholDrinkers": [ + "No", + "Yes" + ], + "AlcoholDrinking": [ + "No", + "Yes" + ], + "Asthma": [ + "No", + "Yes" + ], + "BlindOrVisionDifficulty": [ + "No", + "Yes" + ], + "ChestScan": [ + "No", + "Yes" + ], + "CovidPos": [ + "No", + "Tested positive using home test without a health professional", + "Yes" + ], + "DeafOrHardOfHearing": [ + "No", + "Yes" + ], + "Diabetic": [ + "No", + "No, borderline diabetes", + "Yes", + "Yes (during pregnancy)" + ], + "DiffWalking": [ + "No", + "Yes" + ], + "DifficultyConcentrating": [ + "No", + "Yes" + ], + "DifficultyDressingBathing": [ + "No", + "Yes" + ], + "DifficultyErrands": [ + "No", + "Yes" + ], + "DifficultyWalking": [ + "No", + "Yes" + ], + "ECigaretteUsage": [ + "Never used e-cigarettes in my entire life", + "Not at all (right now)", + "Use them every day", + "Use them some days" + ], + "FluVaxLast12": [ + "No", + "Yes" + ], + "GeneralHealth": [ + "Excellent", + "Very good", + "Good", + "Fair", + "Poor" + ], + "HIVTesting": [ + "No", + "Yes" + ], + "HadAngina": [ + "No", + "Yes" + ], + "HadArthritis": [ + "No", + "Yes" + ], + "HadAsthma": [ + "No", + "Yes" + ], + "HadCOPD": [ + "No", + "Yes" + ], + "HadDepressiveDisorder": [ + "No", + "Yes" + ], + "HadDiabetes": [ + "No", + "No, pre-diabetes or borderline diabetes", + "Yes", + "Yes, but only during pregnancy (female)" + ], + "HadHeartAttack": [ + "No", + "Yes" + ], + "HadKidneyDisease": [ + "No", + "Yes" + ], + "HadSkinCancer": [ + "No", + "Yes" + ], + "HadStroke": [ + "No", + "Yes" + ], + "HeartDisease": [ + "No", + "Yes" + ], + "HighRiskLastYear": [ + "No", + "Yes" + ], + "KidneyDisease": [ + "No", + "Yes" + ], + "LastCheckupTime": [ + "5 or more years ago", + "Within past 2 years (1 year but less than 2 years ago)", + "Within past 5 years (2 years but less than 5 years ago)", + "Within past year (anytime less than 12 months ago)" + ], + "PhysicalActivities": [ + "No", + "Yes" + ], + "PhysicalActivity": [ + "No", + "Yes" + ], + "PneumoVaxEver": [ + "No", + "Yes" + ], + "Race": [ + "American Indian/Alaskan Native", + "Asian", + "Black", + "Hispanic", + "Other", + "White" + ], + "RaceEthnicityCategory": [ + "Black only, Non-Hispanic", + "Hispanic", + "Multiracial, Non-Hispanic", + "Other race only, Non-Hispanic", + "White only, Non-Hispanic" + ], + "RemovedTeeth": [ + "1 to 5", + "6 or more, but not all", + "All", + "None of them" + ], + "Sex": [ + "Female", + "Male" + ], + "SkinCancer": [ + "No", + "Yes" + ], + "SmokerStatus": [ + "Current smoker - now smokes every day", + "Current smoker - now smokes some days", + "Former smoker", + "Never smoked" + ], + "Smoking": [ + "No", + "Yes" + ], + "State": [ + "Alabama", + "Alaska", + "Arizona", + "Arkansas", + "California", + "Colorado", + "Connecticut", + "Delaware", + "District of Columbia", + "Florida", + "Georgia", + "Guam", + "Hawaii", + "Idaho", + "Illinois", + "Indiana", + "Iowa", + "Kansas", + "Kentucky", + "Louisiana", + "Maine", + "Maryland", + "Massachusetts", + "Michigan", + "Minnesota", + "Mississippi", + "Missouri", + "Montana", + "Nebraska", + "Nevada", + "New Hampshire", + "New Jersey", + "New Mexico", + "New York", + "North Carolina", + "North Dakota", + "Ohio", + "Oklahoma", + "Oregon", + "Pennsylvania", + "Puerto Rico", + "Rhode Island", + "South Carolina", + "South Dakota", + "Tennessee", + "Texas", + "Utah", + "Vermont", + "Virgin Islands", + "Virginia", + "Washington", + "West Virginia", + "Wisconsin", + "Wyoming" + ], + "Stroke": [ + "No", + "Yes" + ], + "TetanusLast10Tdap": [ + "No, did not receive any tetanus shot in the past 10 years", + "Yes, received Tdap", + "Yes, received tetanus shot but not sure what type", + "Yes, received tetanus shot, but not Tdap" + ] + } +} diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_004/test_current.csv b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_004/test_current.csv index 9ff9814e11b957a1740720bd1ae5f3a23892b4bc..8abecd3e0257056860f5a3d28e14802f1c098f3f 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_004/test_current.csv +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_004/test_current.csv @@ -1,16 +1,41 @@ -HeartDisease,BMI,Smoking,AlcoholDrinking,Stroke,PhysicalHealth,MentalHealth,DiffWalking,Sex,AgeCategory,Race,Diabetic,PhysicalActivity,GenHealth,SleepTime,Asthma,KidneyDisease,SkinCancer,State,GeneralHealth,PhysicalHealthDays,MentalHealthDays,LastCheckupTime,PhysicalActivities,SleepHours,RemovedTeeth,HadHeartAttack,HadAngina,HadStroke,HadAsthma,HadSkinCancer,HadCOPD,HadDepressiveDisorder,HadKidneyDisease,HadArthritis,HadDiabetes,DeafOrHardOfHearing,BlindOrVisionDifficulty,DifficultyConcentrating,DifficultyWalking,DifficultyDressingBathing,DifficultyErrands,SmokerStatus,ECigaretteUsage,ChestScan,RaceEthnicityCategory,HeightInMeters,WeightInKilograms,AlcoholDrinkers,HIVTesting,FluVaxLast12,PneumoVaxEver,TetanusLast10Tdap,HighRiskLastYear,CovidPos -,27.87,,,,,,,Male,Age 65 to 69,,,,,,,,,Nebraska,Good,0.0,0.0,5 or more years ago,Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.91,101.15,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,39.16,,,,,,,Male,Age 35 to 39,,,,,,,,,Wisconsin,Good,4.0,5.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Not at all (right now),No,"White only, Non-Hispanic",1.88,138.35,Yes,No,No,No,"Yes, received Tdap",No,Yes -,19.69,,,,,,,Female,Age 80 or older,,,,,,,,,Iowa,Good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,55.34,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,41.88,,,,,,,Female,Age 55 to 59,,,,,,,,,Nebraska,Good,3.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,Yes,No,Yes,No,No,Yes,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,110.68,Yes,No,Yes,No,"Yes, received Tdap",No,Yes -,22.31,,,,,,,Female,Age 55 to 59,,,,,,,,,New Mexico,Good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,,1.63,58.97,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -,36.05,,,,,,,Female,Age 50 to 54,,,,,,,,,California,Good,0.0,15.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.63,95.25,Yes,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,31.89,,,,,,,Female,Age 80 or older,,,,,,,,,Kansas,Good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.6,81.65,No,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No -,35.78,,,,,,,Female,Age 18 to 24,,,,,,,,,Rhode Island,Good,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,91.63,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,25.33,,,,,,,Female,Age 60 to 64,,,,,,,,,South Carolina,Good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.6,64.86,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,32.98,,,,,,,Male,Age 40 to 44,,,,,,,,,Arizona,Good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,Hispanic,1.85,113.4,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,33.29,,,,,,,Female,Age 70 to 74,,,,,,,,,Puerto Rico,Good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,Yes,No,No,No,No,No,Yes,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,Hispanic,1.57,82.55,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,24.33,,,,,,,Male,Age 70 to 74,,,,,,,,,Maryland,Good,0.0,10.0,Within past year (anytime less than 12 months ago),Yes,7.0,All,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Not at all (right now),Yes,"White only, Non-Hispanic",1.73,72.57,Yes,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,39.16,,,,,,,Female,Age 65 to 69,,,,,,,,,Oklahoma,Good,0.0,0.0,Within past year (anytime less than 12 months ago),No,4.0,1 to 5,No,Yes,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,113.4,No,No,No,No,"Yes, received Tdap",No,Yes -,28.19,,,,,,,Female,Age 70 to 74,,,,,,,,,Mississippi,Good,5.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,No,Yes,No,Yes,Yes,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,81.65,No,No,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,29.54,,,,,,,Female,Age 18 to 24,,,,,,,,,Alaska,Good,30.0,0.0,Within past year (anytime less than 12 months ago),No,10.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,83.01,No,No,Yes,No,"Yes, received Tdap",No,No +HeartDisease,BMI,Smoking,AlcoholDrinking,Stroke,PhysicalHealth,MentalHealth,DiffWalking,Sex,AgeCategory,Race,Diabetic,PhysicalActivity,SleepTime,Asthma,KidneyDisease,SkinCancer,State,GeneralHealth,PhysicalHealthDays,MentalHealthDays,LastCheckupTime,PhysicalActivities,SleepHours,RemovedTeeth,HadHeartAttack,HadAngina,HadStroke,HadAsthma,HadSkinCancer,HadCOPD,HadDepressiveDisorder,HadKidneyDisease,HadArthritis,HadDiabetes,DeafOrHardOfHearing,BlindOrVisionDifficulty,DifficultyConcentrating,DifficultyWalking,DifficultyDressingBathing,DifficultyErrands,SmokerStatus,ECigaretteUsage,ChestScan,RaceEthnicityCategory,HeightInMeters,WeightInKilograms,AlcoholDrinkers,HIVTesting,FluVaxLast12,PneumoVaxEver,TetanusLast10Tdap,HighRiskLastYear,CovidPos +No,28.19,Yes,No,No,14.0,0.0,Yes,Male,60-64,White,Yes,No,7.0,No,No,No,,Good,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,32.28,Yes,No,No,0.0,0.0,No,Male,35-39,White,Yes,Yes,6.0,No,No,No,,Good,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,27.87,,,,,,,Male,Age 65 to 69,,,,,,,,Nebraska,Good,0.0,0.0,5 or more years ago,Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.91,101.15,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,44.76,Yes,No,No,2.0,0.0,No,Male,35-39,White,Yes,Yes,6.0,No,No,No,,Good,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,39.16,,,,,,,Male,Age 35 to 39,,,,,,,,Wisconsin,Good,4.0,5.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Not at all (right now),No,"White only, Non-Hispanic",1.88,138.35,Yes,No,No,No,"Yes, received Tdap",No,Yes +,19.69,,,,,,,Female,Age 80 or older,,,,,,,,Iowa,Good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,55.34,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes +No,40.72,No,No,No,1.0,1.0,No,Female,25-29,Black,No,Yes,5.0,No,No,No,,Good,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,38.73,No,No,No,0.0,2.0,No,Female,25-29,American Indian/Alaskan Native,No,Yes,7.0,Yes,No,No,,Good,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,20.92,Yes,No,No,0.0,0.0,No,Male,40-44,White,No,Yes,6.0,No,No,No,,Good,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,41.88,,,,,,,Female,Age 55 to 59,,,,,,,,Nebraska,Good,3.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,Yes,No,Yes,No,No,Yes,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,110.68,Yes,No,Yes,No,"Yes, received Tdap",No,Yes +No,30.23,No,No,No,0.0,0.0,No,Female,40-44,Hispanic,No,Yes,8.0,No,No,No,,Good,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,22.31,,,,,,,Female,Age 55 to 59,,,,,,,,New Mexico,Good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,,1.63,58.97,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +Yes,36.18,No,No,No,5.0,0.0,No,Male,50-54,American Indian/Alaskan Native,Yes,Yes,6.0,No,No,No,,Good,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,36.05,,,,,,,Female,Age 50 to 54,,,,,,,,California,Good,0.0,15.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.63,95.25,Yes,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,31.89,,,,,,,Female,Age 80 or older,,,,,,,,Kansas,Good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.6,81.65,No,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No +No,29.26,Yes,No,No,0.0,30.0,No,Female,60-64,White,No,Yes,7.0,No,No,No,,Good,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,27.07,No,No,No,0.0,14.0,No,Female,30-34,Hispanic,No,No,6.0,No,No,No,,Good,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,39.11,No,No,No,2.0,10.0,Yes,Female,65-69,White,Yes,No,7.0,No,No,No,,Good,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,35.78,,,,,,,Female,Age 18 to 24,,,,,,,,Rhode Island,Good,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,91.63,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,21.46,No,No,No,1.0,2.0,No,Female,40-44,White,No,Yes,7.0,Yes,No,No,,Good,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,39.48,Yes,No,No,0.0,0.0,Yes,Female,70-74,White,Yes,No,7.0,Yes,No,No,,Good,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,25.33,,,,,,,Female,Age 60 to 64,,,,,,,,South Carolina,Good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.6,64.86,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,32.98,,,,,,,Male,Age 40 to 44,,,,,,,,Arizona,Good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,Hispanic,1.85,113.4,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,40.69,Yes,No,No,0.0,0.0,No,Male,50-54,White,No,Yes,7.0,No,No,No,,Good,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,34.01,Yes,No,No,1.0,0.0,No,Female,65-69,White,No,No,6.0,Yes,No,Yes,,Good,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,18.29,Yes,No,No,0.0,30.0,No,Female,70-74,White,No,Yes,8.0,No,No,No,,Good,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,33.29,,,,,,,Female,Age 70 to 74,,,,,,,,Puerto Rico,Good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,Yes,No,No,No,No,No,Yes,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,Hispanic,1.57,82.55,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +No,19.9,No,No,No,1.0,5.0,No,Male,18-24,White,No,Yes,5.0,No,No,No,,Good,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,27.46,No,No,No,0.0,0.0,No,Female,30-34,White,No,Yes,8.0,No,No,No,,Good,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,30.08,Yes,No,No,0.0,0.0,No,Female,80 or older,White,No,Yes,8.0,No,No,No,,Good,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,27.44,Yes,No,No,0.0,0.0,Yes,Female,65-69,White,Yes,No,9.0,No,No,No,,Good,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,24.33,,,,,,,Male,Age 70 to 74,,,,,,,,Maryland,Good,0.0,10.0,Within past year (anytime less than 12 months ago),Yes,7.0,All,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Not at all (right now),Yes,"White only, Non-Hispanic",1.73,72.57,Yes,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,39.16,,,,,,,Female,Age 65 to 69,,,,,,,,Oklahoma,Good,0.0,0.0,Within past year (anytime less than 12 months ago),No,4.0,1 to 5,No,Yes,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,113.4,No,No,No,No,"Yes, received Tdap",No,Yes +No,26.31,Yes,Yes,No,0.0,0.0,No,Female,40-44,White,No,Yes,5.0,No,No,No,,Good,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,23.17,No,No,No,30.0,0.0,Yes,Female,70-74,White,No,No,8.0,No,No,No,,Good,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,28.19,,,,,,,Female,Age 70 to 74,,,,,,,,Mississippi,Good,5.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,No,Yes,No,Yes,Yes,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,81.65,No,No,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,29.54,,,,,,,Female,Age 18 to 24,,,,,,,,Alaska,Good,30.0,0.0,Within past year (anytime less than 12 months ago),No,10.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,83.01,No,No,Yes,No,"Yes, received Tdap",No,No +No,22.15,Yes,No,No,0.0,0.0,No,Male,30-34,White,No,Yes,6.0,No,No,No,,Good,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,25.1,No,No,No,0.0,0.0,No,Male,60-64,White,No,Yes,7.0,Yes,No,No,,Good,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,22.67,No,No,No,0.0,0.0,No,Female,30-34,Asian,No,Yes,8.0,No,No,No,,Good,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_005/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_005_current.json b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_005/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_005_current.json index 3ecb17eec6c8a6c956c827696a2aa73561ffafd0..780d5c49229e7e06ac1f794ab0888269509e96fd 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_005/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_005_current.json +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_005/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_005_current.json @@ -1,12049 +1,11851 @@ { - "id": "005", - "task_type": "B4", - "subtask_type": "filter", - "perspective": "data_holder", - "dataset_name": "Indicators of Heart Disease (2022 UPDATE)", - "table_path": "output/medical/classification/Indicators of Heart Disease (2022 UPDATE)_B4/Indicators of Heart Disease (2022 UPDATE)_B4_005/test.csv", - "query": "Based on the CDC dataset I provided, I want to identify female respondents who are now in the poor general health category in the new data. This will help our care coordination team prioritize follow-up care and support services for this vulnerable population.", - "meta_info": { - "domain": "medical" + "id": "005", + "task_type": "B4", + "subtask_type": "filter", + "perspective": "data_holder", + "dataset_name": "Indicators of Heart Disease (2022 UPDATE)", + "table_path": "output/medical/classification/Indicators of Heart Disease (2022 UPDATE)_B4/Indicators of Heart Disease (2022 UPDATE)_B4_005/test.csv", + "query": "Based on the CDC dataset I provided, I want to identify female respondents who are now in the poor general health category in the new data. This will help our care coordination team prioritize follow-up care and support services for this vulnerable population.", + "meta_info": { + "domain": "medical" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "002", + "features": { + "HeartDisease": "No", + "BMI": 36.32, + "Smoking": "No", + "AlcoholDrinking": "No", + "Stroke": "No", + "PhysicalHealth": 0.0, + "MentalHealth": 0.0, + "DiffWalking": "Yes", + "Sex": "Female", + "AgeCategory": "60-64", + "Race": "White", + "Diabetic": "Yes", + "PhysicalActivity": "Yes", + "SleepTime": 8.0, + "Asthma": "No", + "KidneyDisease": "No", + "SkinCancer": "Yes", + "State": null, + "GeneralHealth": "Good", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + "LastCheckupTime": null, + "PhysicalActivities": null, + "SleepHours": null, + "RemovedTeeth": null, + "HadHeartAttack": null, + "HadAngina": null, + "HadStroke": null, + "HadAsthma": null, + "HadSkinCancer": null, + "HadCOPD": null, + "HadDepressiveDisorder": null, + "HadKidneyDisease": null, + "HadArthritis": null, + "HadDiabetes": null, + "DeafOrHardOfHearing": null, + "BlindOrVisionDifficulty": null, + "DifficultyConcentrating": null, + "DifficultyWalking": null, + "DifficultyDressingBathing": null, + "DifficultyErrands": null, + "SmokerStatus": null, + "ECigaretteUsage": null, + "ChestScan": null, + "RaceEthnicityCategory": null, + "HeightInMeters": null, + "WeightInKilograms": null, + "AlcoholDrinkers": null, + "HIVTesting": null, + "FluVaxLast12": null, + "PneumoVaxEver": null, + "TetanusLast10Tdap": null, + "HighRiskLastYear": null, + "CovidPos": null + } + }, + { + "scenario_id": "004", + "features": { + "HeartDisease": "No", + "BMI": 32.45, + "Smoking": "Yes", + "AlcoholDrinking": "Yes", + "Stroke": "No", + "PhysicalHealth": 0.0, + "MentalHealth": 2.0, + "DiffWalking": "No", + "Sex": "Female", + "AgeCategory": "30-34", + "Race": "Black", + "Diabetic": "No", + "PhysicalActivity": "Yes", + "SleepTime": 6.0, + "Asthma": "No", + "KidneyDisease": "No", + "SkinCancer": "No", + "State": null, + "GeneralHealth": "Good", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + "LastCheckupTime": null, + "PhysicalActivities": null, + "SleepHours": null, + "RemovedTeeth": null, + "HadHeartAttack": null, + "HadAngina": null, + "HadStroke": null, + "HadAsthma": null, + "HadSkinCancer": null, + "HadCOPD": null, + "HadDepressiveDisorder": null, + "HadKidneyDisease": null, + "HadArthritis": null, + "HadDiabetes": null, + "DeafOrHardOfHearing": null, + "BlindOrVisionDifficulty": null, + "DifficultyConcentrating": null, + "DifficultyWalking": null, + "DifficultyDressingBathing": null, + "DifficultyErrands": null, + "SmokerStatus": null, + "ECigaretteUsage": null, + "ChestScan": null, + "RaceEthnicityCategory": null, + "HeightInMeters": null, + "WeightInKilograms": null, + "AlcoholDrinkers": null, + "HIVTesting": null, + "FluVaxLast12": null, + "PneumoVaxEver": null, + "TetanusLast10Tdap": null, + "HighRiskLastYear": null, + "CovidPos": null + } + }, + { + "scenario_id": "005", + "features": { + "HeartDisease": null, + "BMI": 29.26, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 75 to 79", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Tennessee", + "GeneralHealth": "Very good", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 5.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 9.0, + "RemovedTeeth": "1 to 5", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "Yes", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "Yes", + "HadDiabetes": "Yes", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Not at all (right now)", + "ChestScan": "Yes", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.57, + "WeightInKilograms": 72.57, + "AlcoholDrinkers": "No", + "HIVTesting": "No", + "FluVaxLast12": "Yes", + "PneumoVaxEver": "Yes", + "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "009", + "features": { + "HeartDisease": null, + "BMI": 25.42, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 80 or older", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Maryland", + "GeneralHealth": "Poor", + "PhysicalHealthDays": 30.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "No", + "SleepHours": 8.0, + "RemovedTeeth": "6 or more, but not all", + "HadHeartAttack": "Yes", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "Yes", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "Yes", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Former smoker", + "ECigaretteUsage": "Not at all (right now)", + "ChestScan": "Yes", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.57, + "WeightInKilograms": 63.05, + "AlcoholDrinkers": "No", + "HIVTesting": "No", + "FluVaxLast12": "No", + "PneumoVaxEver": "Yes", + "TetanusLast10Tdap": "Yes, received Tdap", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "010", + "features": { + "HeartDisease": null, + "BMI": 35.24, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 60 to 64", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Idaho", + "GeneralHealth": "Very good", + "PhysicalHealthDays": 5.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 6.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "Yes", + "HadSkinCancer": "Yes", + "HadCOPD": "Yes", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "Yes", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "Yes", + "DifficultyWalking": "Yes", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Current smoker - now smokes some days", + "ECigaretteUsage": "Use them some days", + "ChestScan": "No", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.7, + "WeightInKilograms": 102.06, + "AlcoholDrinkers": "Yes", + "HIVTesting": null, + "FluVaxLast12": "No", + "PneumoVaxEver": "Yes", + "TetanusLast10Tdap": "Yes, received tetanus shot but not sure what type", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "012", + "features": { + "HeartDisease": null, + "BMI": 20.8, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 80 or older", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Georgia", + "GeneralHealth": "Poor", + "PhysicalHealthDays": 30.0, + "MentalHealthDays": 30.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 8.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "No", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.65, + "WeightInKilograms": 56.7, + "AlcoholDrinkers": "No", + "HIVTesting": null, + "FluVaxLast12": null, + "PneumoVaxEver": null, + "TetanusLast10Tdap": null, + "HighRiskLastYear": null, + "CovidPos": null + } + }, + { + "scenario_id": "014", + "features": { + "HeartDisease": null, + "BMI": 30.04, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 60 to 64", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Wisconsin", + "GeneralHealth": "Fair", + "PhysicalHealthDays": 3.0, + "MentalHealthDays": 30.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 10.0, + "RemovedTeeth": "1 to 5", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "Yes", + "HadKidneyDisease": "No", + "HadArthritis": "Yes", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "Yes", + "DifficultyConcentrating": "Yes", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "Yes", + "SmokerStatus": "Current smoker - now smokes every day", + "ECigaretteUsage": "Use them some days", + "ChestScan": "Yes", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.63, + "WeightInKilograms": 79.38, + "AlcoholDrinkers": "Yes", + "HIVTesting": "Yes", + "FluVaxLast12": "Yes", + "PneumoVaxEver": "Yes", + "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "015", + "features": { + "HeartDisease": null, + "BMI": 37.12, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 40 to 44", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Vermont", + "GeneralHealth": "Fair", + "PhysicalHealthDays": 18.0, + "MentalHealthDays": 30.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 4.0, + "RemovedTeeth": "All", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "Yes", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "Yes", + "HadKidneyDisease": "No", + "HadArthritis": "Yes", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "Yes", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "Yes", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Current smoker - now smokes every day", + "ECigaretteUsage": "Use them some days", + "ChestScan": "Yes", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.68, + "WeightInKilograms": 104.33, + "AlcoholDrinkers": "Yes", + "HIVTesting": "Yes", + "FluVaxLast12": "Yes", + "PneumoVaxEver": "Yes", + "TetanusLast10Tdap": "Yes, received tetanus shot but not sure what type", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "016", + "features": { + "HeartDisease": "No", + "BMI": 34.46, + "Smoking": "No", + "AlcoholDrinking": "No", + "Stroke": "No", + "PhysicalHealth": 0.0, + "MentalHealth": 0.0, + "DiffWalking": "No", + "Sex": "Male", + "AgeCategory": "65-69", + "Race": "White", + "Diabetic": "No, borderline diabetes", + "PhysicalActivity": "Yes", + "SleepTime": 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"scenario_id": "222", + "features": { + "HeartDisease": "No", + "BMI": 28.45, + "Smoking": "Yes", + "AlcoholDrinking": "No", + "Stroke": "No", + "PhysicalHealth": 0.0, + "MentalHealth": 0.0, + "DiffWalking": "No", + "Sex": "Male", + "AgeCategory": "80 or older", + "Race": "White", + "Diabetic": "No", + "PhysicalActivity": "No", + "SleepTime": 8.0, + "Asthma": "No", + "KidneyDisease": "No", + "SkinCancer": "No", + "State": null, + "GeneralHealth": "Excellent", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + "LastCheckupTime": null, + "PhysicalActivities": null, + "SleepHours": null, + "RemovedTeeth": null, + "HadHeartAttack": null, + "HadAngina": null, + "HadStroke": null, + "HadAsthma": null, + "HadSkinCancer": null, + "HadCOPD": null, + "HadDepressiveDisorder": null, + "HadKidneyDisease": null, + "HadArthritis": null, + "HadDiabetes": null, + "DeafOrHardOfHearing": null, + "BlindOrVisionDifficulty": null, + "DifficultyConcentrating": null, + "DifficultyWalking": null, + 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"scenario_id": "227", + "features": { + "HeartDisease": null, + "BMI": 27.46, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 60 to 64", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Louisiana", + "GeneralHealth": "Very good", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 9.0, + "RemovedTeeth": "1 to 5", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + 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"TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", + "HighRiskLastYear": "No", + "CovidPos": "Yes" + } + }, + { + "scenario_id": "231", + "features": { + "HeartDisease": null, + "BMI": 41.01, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 25 to 29", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Florida", + "GeneralHealth": "Good", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 8.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "Yes", + 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"DifficultyWalking": "Yes", + "DifficultyDressingBathing": "Yes", + "DifficultyErrands": "Yes", + "SmokerStatus": "Former smoker", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "Yes", + "RaceEthnicityCategory": "Other race only, Non-Hispanic", + "HeightInMeters": 1.78, + "WeightInKilograms": 83.91, + "AlcoholDrinkers": "No", + "HIVTesting": "Yes", + "FluVaxLast12": "No", + "PneumoVaxEver": "No", + "TetanusLast10Tdap": "Yes, received Tdap", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "248", + "features": { + "HeartDisease": null, + "BMI": 27.34, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 75 to 79", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Washington", + 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received Tdap", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "253", + "features": { + "HeartDisease": "No", + "BMI": 32.08, + "Smoking": "No", + "AlcoholDrinking": "No", + "Stroke": "No", + "PhysicalHealth": 0.0, + "MentalHealth": 0.0, + "DiffWalking": "No", + "Sex": "Male", + "AgeCategory": "65-69", + "Race": "White", + "Diabetic": "No", + "PhysicalActivity": "Yes", + "SleepTime": 7.0, + "Asthma": "Yes", + "KidneyDisease": "No", + "SkinCancer": "Yes", + "State": null, + "GeneralHealth": "Excellent", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + "LastCheckupTime": null, + "PhysicalActivities": null, + "SleepHours": null, + "RemovedTeeth": null, + "HadHeartAttack": null, + "HadAngina": null, + "HadStroke": null, + "HadAsthma": null, + "HadSkinCancer": null, + "HadCOPD": null, + "HadDepressiveDisorder": null, + "HadKidneyDisease": null, + "HadArthritis": null, + "HadDiabetes": null, + "DeafOrHardOfHearing": null, + "BlindOrVisionDifficulty": 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"HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "259", + "features": { + "HeartDisease": null, + "BMI": 20.63, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 70 to 74", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Puerto Rico", + "GeneralHealth": "Fair", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "No", + "SleepHours": 7.0, + "RemovedTeeth": "6 or more, but not all", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "Yes", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "Yes", + "RaceEthnicityCategory": "Hispanic", + "HeightInMeters": 1.65, + "WeightInKilograms": 56.25, + "AlcoholDrinkers": "No", + "HIVTesting": "Yes", + "FluVaxLast12": "No", + "PneumoVaxEver": "No", + "TetanusLast10Tdap": "Yes, received Tdap", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "261", + "features": { + "HeartDisease": null, + "BMI": 37.76, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 45 to 49", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + 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"Tested positive using home test without a health professional" + } + }, + { + "scenario_id": "345", + "features": { + "HeartDisease": null, + "BMI": 21.93, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 50 to 54", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "South Dakota", + "GeneralHealth": "Excellent", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 7.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + 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"CovidPos": "No" + } + }, + { + "scenario_id": "480", + "features": { + "HeartDisease": null, + "BMI": 25.61, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 35 to 39", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "South Dakota", + "GeneralHealth": "Good", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 8.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + 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"PneumoVaxEver": "No", + "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "492", + "features": { + "HeartDisease": "No", + "BMI": 30.27, + "Smoking": "Yes", + "AlcoholDrinking": "No", + "Stroke": "No", + "PhysicalHealth": 0.0, + "MentalHealth": 0.0, + "DiffWalking": "No", + "Sex": "Male", + "AgeCategory": "40-44", + "Race": "White", + "Diabetic": "No", + "PhysicalActivity": "Yes", + "SleepTime": 7.0, + "Asthma": "No", + "KidneyDisease": "No", + "SkinCancer": "No", + "State": null, + "GeneralHealth": "Good", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + "LastCheckupTime": null, + "PhysicalActivities": null, + "SleepHours": null, + "RemovedTeeth": null, + "HadHeartAttack": null, + "HadAngina": null, + "HadStroke": null, + "HadAsthma": null, + "HadSkinCancer": null, + "HadCOPD": null, + "HadDepressiveDisorder": null, + "HadKidneyDisease": null, + "HadArthritis": 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"No", + "PneumoVaxEver": "No", + "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "499", + "features": { + "HeartDisease": null, + "BMI": 24.75, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 45 to 49", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Maine", + "GeneralHealth": "Very good", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 7.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + 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+ "444" + ] }, - "final_decision": {}, - "what_if": {}, - "ground_truth": { - "extracted_features": [ - { - "scenario_id": "002", - "features": { - "HeartDisease": "No", - "BMI": 36.32, - "Smoking": "No", - "AlcoholDrinking": "No", - "Stroke": "No", - "PhysicalHealth": 0.0, - "MentalHealth": 0.0, - "DiffWalking": "Yes", - "Sex": "Female", - "AgeCategory": "60-64", - "Race": "White", - "Diabetic": "Yes", - "PhysicalActivity": "Yes", - "GenHealth": "Good", - "SleepTime": 8.0, - "Asthma": "No", - "KidneyDisease": "No", - "SkinCancer": "Yes", - "State": null, - "GeneralHealth": null, - "PhysicalHealthDays": null, - "MentalHealthDays": null, - "LastCheckupTime": null, - "PhysicalActivities": null, - "SleepHours": null, - "RemovedTeeth": null, - "HadHeartAttack": null, - "HadAngina": null, - "HadStroke": null, - "HadAsthma": null, - "HadSkinCancer": null, - "HadCOPD": null, - "HadDepressiveDisorder": null, - "HadKidneyDisease": null, - "HadArthritis": null, - "HadDiabetes": null, - 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positive using home test without a health professional" - } - }, - { - "scenario_id": "145", - "features": { - "HeartDisease": "No", - "BMI": 36.56, - "Smoking": "Yes", - "AlcoholDrinking": "No", - "Stroke": "No", - "PhysicalHealth": 2.0, - "MentalHealth": 0.0, - "DiffWalking": "No", - "Sex": "Female", - "AgeCategory": "40-44", - "Race": "Hispanic", - "Diabetic": "No", - "PhysicalActivity": "Yes", - "GenHealth": "Very good", - "SleepTime": 6.0, - "Asthma": "No", - "KidneyDisease": "No", - "SkinCancer": "No", - "State": null, - "GeneralHealth": null, - "PhysicalHealthDays": null, - "MentalHealthDays": null, - "LastCheckupTime": null, - "PhysicalActivities": null, - "SleepHours": null, - "RemovedTeeth": null, - "HadHeartAttack": null, - "HadAngina": null, - "HadStroke": null, - "HadAsthma": null, - "HadSkinCancer": null, - "HadCOPD": null, - "HadDepressiveDisorder": null, - "HadKidneyDisease": null, - "HadArthritis": null, - "HadDiabetes": null, - "DeafOrHardOfHearing": null, - 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"BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "No", - "DifficultyWalking": "No", - "DifficultyDressingBathing": "No", - "DifficultyErrands": "No", - "SmokerStatus": "Former smoker", - "ECigaretteUsage": "Never used e-cigarettes in my entire life", - "ChestScan": "No", - "RaceEthnicityCategory": "White only, Non-Hispanic", - "HeightInMeters": 1.57, - "WeightInKilograms": 63.5, - "AlcoholDrinkers": "Yes", - "HIVTesting": "Yes", - "FluVaxLast12": "No", - "PneumoVaxEver": null, - "TetanusLast10Tdap": null, - "HighRiskLastYear": "No", - "CovidPos": "No" - } - }, - { - "scenario_id": "490", - "features": { - "HeartDisease": null, - "BMI": 26.63, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Female", - "AgeCategory": "Age 45 to 49", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "Texas", - "GeneralHealth": "Good", - "PhysicalHealthDays": 0.0, - "MentalHealthDays": 0.0, - "LastCheckupTime": "Within past year (anytime less than 12 months ago)", - "PhysicalActivities": "No", - "SleepHours": 8.0, - "RemovedTeeth": "None of them", - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "No", - "HadSkinCancer": "No", - "HadCOPD": "No", - "HadDepressiveDisorder": "No", - "HadKidneyDisease": "No", - "HadArthritis": "No", - "HadDiabetes": "No", - "DeafOrHardOfHearing": "No", - "BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "No", - "DifficultyWalking": "Yes", - "DifficultyDressingBathing": "Yes", - "DifficultyErrands": "Yes", - "SmokerStatus": "Never smoked", - "ECigaretteUsage": "Not at all (right now)", - "ChestScan": "Yes", - "RaceEthnicityCategory": "Black only, Non-Hispanic", - "HeightInMeters": 1.65, - "WeightInKilograms": 72.57, - "AlcoholDrinkers": "No", - "HIVTesting": "Yes", - "FluVaxLast12": "No", - "PneumoVaxEver": "No", - "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", - "HighRiskLastYear": "No", - "CovidPos": "No" - } - }, - { - "scenario_id": "492", - "features": { - "HeartDisease": "No", - "BMI": 30.27, - "Smoking": "Yes", - "AlcoholDrinking": "No", - "Stroke": "No", - "PhysicalHealth": 0.0, - "MentalHealth": 0.0, - "DiffWalking": "No", - "Sex": "Male", - "AgeCategory": "40-44", - "Race": "White", - "Diabetic": "No", - "PhysicalActivity": "Yes", - "GenHealth": "Good", - "SleepTime": 7.0, - "Asthma": "No", - "KidneyDisease": "No", - "SkinCancer": "No", - "State": null, - "GeneralHealth": null, - "PhysicalHealthDays": null, - "MentalHealthDays": null, - "LastCheckupTime": null, - "PhysicalActivities": null, - "SleepHours": null, - "RemovedTeeth": null, - "HadHeartAttack": null, - "HadAngina": null, - "HadStroke": null, - "HadAsthma": null, - "HadSkinCancer": null, - "HadCOPD": null, - "HadDepressiveDisorder": null, - "HadKidneyDisease": null, - "HadArthritis": null, - "HadDiabetes": null, - "DeafOrHardOfHearing": null, - "BlindOrVisionDifficulty": null, - "DifficultyConcentrating": null, - "DifficultyWalking": null, - "DifficultyDressingBathing": null, - "DifficultyErrands": null, - "SmokerStatus": null, - "ECigaretteUsage": null, - "ChestScan": null, - "RaceEthnicityCategory": null, - "HeightInMeters": null, - "WeightInKilograms": null, - "AlcoholDrinkers": null, - "HIVTesting": null, - "FluVaxLast12": null, - "PneumoVaxEver": null, - "TetanusLast10Tdap": null, - "HighRiskLastYear": null, - "CovidPos": null - } - }, - { - "scenario_id": "496", - "features": { - "HeartDisease": null, - "BMI": 20.25, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Male", - "AgeCategory": "Age 65 to 69", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "Maine", - "GeneralHealth": "Very good", - "PhysicalHealthDays": 0.0, - "MentalHealthDays": 0.0, - "LastCheckupTime": "Within past year (anytime less than 12 months ago)", - "PhysicalActivities": "Yes", - "SleepHours": 8.0, - "RemovedTeeth": "None of them", - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "No", - "HadSkinCancer": "No", - "HadCOPD": "No", - "HadDepressiveDisorder": "No", - "HadKidneyDisease": "No", - "HadArthritis": "No", - "HadDiabetes": "No", - "DeafOrHardOfHearing": "No", - "BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "No", - "DifficultyWalking": "No", - "DifficultyDressingBathing": "No", - "DifficultyErrands": "No", - "SmokerStatus": "Current smoker - now smokes every day", - "ECigaretteUsage": "Not at all (right now)", - "ChestScan": "No", - "RaceEthnicityCategory": "White only, Non-Hispanic", - "HeightInMeters": 1.91, - "WeightInKilograms": 73.48, - "AlcoholDrinkers": null, - "HIVTesting": "No", - "FluVaxLast12": "No", - "PneumoVaxEver": "No", - "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", - "HighRiskLastYear": "No", - "CovidPos": "No" - } - }, - { - "scenario_id": "499", - "features": { - "HeartDisease": null, - "BMI": 24.75, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Female", - "AgeCategory": "Age 45 to 49", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "Maine", - "GeneralHealth": "Very good", - "PhysicalHealthDays": 0.0, - "MentalHealthDays": 0.0, - "LastCheckupTime": "Within past year (anytime less than 12 months ago)", - "PhysicalActivities": "Yes", - "SleepHours": 7.0, - "RemovedTeeth": "None of them", - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "No", - "HadSkinCancer": "No", - "HadCOPD": "No", - "HadDepressiveDisorder": "No", - "HadKidneyDisease": "No", - "HadArthritis": "No", - "HadDiabetes": "No", - "DeafOrHardOfHearing": "No", - "BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "No", - "DifficultyWalking": "No", - "DifficultyDressingBathing": "No", - "DifficultyErrands": "No", - "SmokerStatus": "Never smoked", - "ECigaretteUsage": "Never used e-cigarettes in my entire life", - "ChestScan": "No", - "RaceEthnicityCategory": "White only, Non-Hispanic", - "HeightInMeters": 1.7, - "WeightInKilograms": 71.67, - "AlcoholDrinkers": "Yes", - "HIVTesting": null, - "FluVaxLast12": "Yes", - "PneumoVaxEver": "No", - "TetanusLast10Tdap": "Yes, received Tdap", - "HighRiskLastYear": "No", - "CovidPos": "No" - } - } - ], - "target_column": "GeneralHealth", - "task_sub_type": "classification", - "ranking_ground_truth": { - "top_k_ids": [ - "009", - "012", - "040", - "098", - "104", - "111", - "129", - "142", - "179", - "213", - "218", - "238", - "301", - "361", - "444" - ] - }, - "active_filters": [ - { - "col": "Sex", - "op": "==", - "val": "Female" - } - ], - "target_class_value": "poor" - }, - "response": "", - "evaluation_score": {} -} \ No newline at end of file + "active_filters": [ + { + "col": "Sex", + "op": "==", + "val": "Female" + } + ], + "target_class_value": "poor" + }, + "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_005/current.csv b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_005/current.csv index 4d1aa15cad51ea613d3aed935b70ea9577fb72ba..90434a55142c2133999f02e1b7d0f1f901bfa75e 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_005/current.csv +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_005/current.csv @@ -1,201 +1,201 @@ -HeartDisease,BMI,Smoking,AlcoholDrinking,Stroke,PhysicalHealth,MentalHealth,DiffWalking,Sex,AgeCategory,Race,Diabetic,PhysicalActivity,GenHealth,SleepTime,Asthma,KidneyDisease,SkinCancer,State,PhysicalHealthDays,MentalHealthDays,LastCheckupTime,PhysicalActivities,SleepHours,RemovedTeeth,HadHeartAttack,HadAngina,HadStroke,HadAsthma,HadSkinCancer,HadCOPD,HadDepressiveDisorder,HadKidneyDisease,HadArthritis,HadDiabetes,DeafOrHardOfHearing,BlindOrVisionDifficulty,DifficultyConcentrating,DifficultyWalking,DifficultyDressingBathing,DifficultyErrands,SmokerStatus,ECigaretteUsage,ChestScan,RaceEthnicityCategory,HeightInMeters,WeightInKilograms,AlcoholDrinkers,HIVTesting,FluVaxLast12,PneumoVaxEver,TetanusLast10Tdap,HighRiskLastYear,CovidPos -No,36.32,No,No,No,0.0,0.0,Yes,Female,60-64,White,Yes,Yes,Good,8.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,32.45,Yes,Yes,No,0.0,2.0,No,Female,30-34,Black,No,Yes,Good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,29.26,,,,,,,Female,Age 75 to 79,,,,,,,,,Tennessee,0.0,5.0,Within past year (anytime less than 12 months ago),Yes,9.0,1 to 5,No,No,No,No,No,Yes,No,No,Yes,Yes,No,No,No,No,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.57,72.57,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,25.42,,,,,,,Female,Age 80 or older,,,,,,,,,Maryland,30.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,"6 or more, but not all",Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Yes,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.57,63.05,No,No,No,Yes,"Yes, received Tdap",No,No -,35.24,,,,,,,Female,Age 60 to 64,,,,,,,,,Idaho,5.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,Yes,Yes,Yes,No,No,Yes,No,No,No,Yes,Yes,No,No,Current smoker - now smokes some days,Use them some days,No,"White only, Non-Hispanic",1.7,102.06,Yes,,No,Yes,"Yes, received tetanus shot but not sure what type",No,No -,20.8,,,,,,,Female,Age 80 or older,,,,,,,,,Georgia,30.0,30.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,56.7,No,,,,,, -,30.04,,,,,,,Female,Age 60 to 64,,,,,,,,,Wisconsin,3.0,30.0,Within past year (anytime less than 12 months ago),Yes,10.0,1 to 5,No,No,No,No,No,No,Yes,No,Yes,No,No,Yes,Yes,No,No,Yes,Current smoker - now smokes every day,Use them some days,Yes,"White only, Non-Hispanic",1.63,79.38,Yes,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,37.12,,,,,,,Female,Age 40 to 44,,,,,,,,,Vermont,18.0,30.0,Within past year (anytime less than 12 months ago),Yes,4.0,All,No,No,No,Yes,No,No,Yes,No,Yes,No,Yes,No,Yes,No,No,No,Current smoker - now smokes every day,Use them some days,Yes,"White only, Non-Hispanic",1.68,104.33,Yes,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -No,34.46,No,No,No,0.0,0.0,No,Male,65-69,White,"No, borderline diabetes",Yes,Very good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,31.09,,,,,,,Female,Age 30 to 34,,,,,,,,,Illinois,0.0,0.0,5 or more years ago,Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.57,77.11,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -,25.07,,,,,,,Male,Age 80 or older,,,,,,,,,Rhode Island,6.0,2.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,Yes,Yes,Yes,Yes,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,86.18,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes -No,29.53,No,No,No,0.0,2.0,No,Male,55-59,White,No,Yes,Very good,7.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,26.45,,,,,,,Female,Age 30 to 34,,,,,,,,,Texas,1.0,1.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.55,63.5,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,34.28,,,,,,,Female,Age 18 to 24,,,,,,,,,New Jersey,3.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,12.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.65,93.44,No,No,Yes,No,"Yes, received Tdap",No,No -,22.31,,,,,,,Female,Age 35 to 39,,,,,,,,,Utah,3.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,58.97,No,No,No,No,"Yes, received Tdap",No,No -No,31.32,No,No,No,30.0,30.0,No,Female,70-74,White,No,No,Poor,8.0,No,Yes,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,20.36,No,No,No,0.0,0.0,No,Female,75-79,White,No,Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,27.46,,,,,,,Female,Age 60 to 64,,,,,,,,,Nebraska,30.0,0.0,Within past year (anytime less than 12 months ago),No,5.0,1 to 5,No,No,No,No,No,No,No,No,No,Yes,No,Yes,No,Yes,Yes,Yes,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,Yes,Hispanic,1.63,72.57,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -Yes,46.64,No,No,No,30.0,10.0,Yes,Female,80 or older,White,Yes,No,Fair,10.0,No,Yes,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,34.48,,,,,,,Female,Age 80 or older,,,,,,,,,Kansas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.47,74.84,No,No,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,24.63,,,,,,,Male,Age 45 to 49,,,,,,,,,Wisconsin,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,73.48,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No -Yes,37.55,No,No,No,20.0,0.0,Yes,Female,65-69,White,No,No,Poor,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,26.63,,,,,,,Male,Age 60 to 64,,,,,,,,,Minnesota,,,Within past year (anytime less than 12 months ago),Yes,,"6 or more, but not all",No,No,No,,No,Yes,No,No,Yes,No,No,No,No,Yes,Yes,Yes,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.68,74.84,Yes,No,No,Yes,,No,No -No,33.25,Yes,No,No,2.0,10.0,No,Male,65-69,White,Yes,No,Fair,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,25.11,,,,,,,Male,Age 55 to 59,,,,,,,,,Nevada,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,79.38,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No -,30.83,,,,,,,Female,Age 65 to 69,,,,,,,,,Maryland,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,None of them,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.68,86.64,Yes,Yes,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -,27.37,,,,,,,Male,Age 80 or older,,,,,,,,,North Dakota,30.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,None of them,No,No,No,No,Yes,No,No,No,No,No,No,Yes,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,81.65,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,30.52,,,,,,,Male,Age 65 to 69,,,,,,,,,Oregon,5.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,Yes,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.83,102.06,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Tested positive using home test without a health professional -,27.06,,,,,,,Female,Age 18 to 24,,,,,,,,,Louisiana,0.0,1.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Use them every day,No,"White only, Non-Hispanic",1.5,60.78,No,No,No,Yes,"Yes, received tetanus shot, but not Tdap",, -,27.71,,,,,,,Male,Age 30 to 34,,,,,,,,,Connecticut,0.0,4.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.85,95.25,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,No -No,32.61,No,No,No,0.0,3.0,Yes,Female,70-74,White,Yes,No,Fair,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,23.71,No,No,No,30.0,2.0,No,Male,25-29,White,No,Yes,Very good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,22.6,,,,,,,Female,Age 50 to 54,,,,,,,,,Wisconsin,7.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,63.5,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,Tested positive using home test without a health professional -No,28.06,No,No,No,0.0,0.0,No,Female,50-54,White,No,Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Yes,41.57,Yes,No,No,0.0,0.0,No,Female,75-79,White,No,Yes,Good,4.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,,,,,,,,Female,Age 40 to 44,,,,,,,,,Oklahoma,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",,,Yes,No,Yes,,,No,Yes -Yes,18.87,Yes,No,No,0.0,25.0,No,Male,25-29,White,No,Yes,Good,6.0,No,Yes,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,32.69,No,No,No,0.0,0.0,No,Male,50-54,White,No,Yes,Excellent,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,32.86,No,No,No,0.0,0.0,No,Female,75-79,White,No,No,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,44.29,,,,,,,Female,Age 75 to 79,,,,,,,,,Arizona,0.0,0.0,Within past year (anytime less than 12 months ago),No,9.0,1 to 5,No,Yes,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,113.4,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -No,24.37,No,No,No,0.0,0.0,No,Female,65-69,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,24.8,,,,,,,Female,Age 55 to 59,,,,,,,,,Oklahoma,2.0,3.0,Within past year (anytime less than 12 months ago),No,6.0,"6 or more, but not all",No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.6,63.5,No,Yes,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes -No,32.23,Yes,Yes,No,0.0,30.0,No,Female,45-49,White,No,Yes,Very good,8.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,29.23,Yes,No,No,0.0,3.0,No,Female,60-64,Black,"No, borderline diabetes",No,Fair,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,26.63,,,,,,,Female,Age 25 to 29,,,,,,,,,Mississippi,0.0,4.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,74.84,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,38.67,,,,,,,Female,Age 65 to 69,,,,,,,,,Michigan,20.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,Yes,No,No,No,No,Yes,No,No,Yes,No,No,No,No,Yes,Yes,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.52,89.81,No,No,Yes,Yes,"Yes, received Tdap",No,No -,,,,,,,,Female,Age 18 to 24,,,,,,,,,Georgia,20.0,30.0,Within past 2 years (1 year but less than 2 years ago),No,5.0,None of them,No,No,No,Yes,,No,Yes,No,No,No,,No,Yes,,No,Yes,Never smoked,Use them every day,No,"White only, Non-Hispanic",,61.23,Yes,Yes,No,Yes,"Yes, received tetanus shot, but not Tdap",No,Yes -,17.34,,,,,,,Female,Age 80 or older,,,,,,,,,Missouri,30.0,0.0,Within past year (anytime less than 12 months ago),No,4.0,All,Yes,,No,No,No,,No,,Yes,No,Yes,Yes,No,Yes,No,Yes,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,45.81,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,23.01,,,,,,,Male,Age 65 to 69,,,,,,,,,Massachusetts,0.0,,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,,No,No,No,No,,No,,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.8,74.84,No,,Yes,Yes,,No,No -,27.06,,,,,,,Male,Age 70 to 74,,,,,,,,,Vermont,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,80.74,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -No,23.91,No,No,No,0.0,4.0,No,Female,25-29,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,14.88,,,,,,,Female,Age 70 to 74,,,,,,,,,Tennessee,30.0,25.0,Within past year (anytime less than 12 months ago),No,1.0,"6 or more, but not all",No,No,No,No,No,Yes,Yes,Yes,Yes,Yes,No,No,Yes,Yes,Yes,Yes,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,43.09,No,No,No,Yes,"Yes, received Tdap",No,Yes -,24.45,,,,,,,Female,Age 30 to 34,,,,,,,,,Texas,3.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.6,62.6,Yes,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -No,44.94,Yes,No,No,0.0,4.0,No,Male,18-24,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,,,,,,,,Male,Age 40 to 44,,,,,,,,,New Hampshire,0.0,10.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, -,27.4,,,,,,,Male,Age 45 to 49,,,,,,,,,Wyoming,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Use them some days,No,"White only, Non-Hispanic",1.83,91.63,Yes,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,30.67,,,,,,,Male,Age 40 to 44,,,,,,,,,Utah,20.0,30.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,Hispanic,1.68,86.18,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,27.46,,,,,,,Female,Age 70 to 74,,,,,,,,,Montana,20.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,All,No,Yes,No,No,Yes,No,Yes,Yes,Yes,Yes,No,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,70.31,No,No,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Tested positive using home test without a health professional -No,36.56,Yes,No,No,2.0,0.0,No,Female,40-44,Hispanic,No,Yes,Very good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,19.11,,,,,,,Female,Age 40 to 44,,,,,,,,,New York,30.0,,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,"Yes, but only during pregnancy (female)",No,No,No,No,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.7,55.34,No,Yes,Yes,,"Yes, received Tdap",No,Yes -,36.94,,,,,,,Male,Age 50 to 54,,,,,,,,,Kentucky,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,5.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.85,127.01,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,No -No,23.02,No,No,No,0.0,5.0,No,Male,60-64,Asian,Yes,Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,20.38,Yes,No,No,0.0,2.0,No,Male,70-74,White,No,Yes,Very good,5.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,29.05,,,,,,,Female,Age 45 to 49,,,,,,,,,Washington,5.0,5.0,Within past 2 years (1 year but less than 2 years ago),Yes,6.0,1 to 5,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),No,"Multiracial, Non-Hispanic",1.68,81.65,Yes,No,No,No,,No,No -,23.3,,,,,,,Female,Age 45 to 49,,,,,,,,,New York,1.0,5.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,63.5,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No -,24.89,,,,,,,Female,Age 60 to 64,,,,,,,,,Missouri,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,11.0,1 to 5,No,No,No,No,No,No,No,No,No,"No, pre-diabetes or borderline diabetes",No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.63,65.77,No,No,Yes,No,"Yes, received Tdap",No,No -No,25.69,Yes,No,No,0.0,0.0,No,Female,60-64,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,28.06,,,,,,,Male,Age 30 to 34,,,,,,,,,Indiana,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.75,86.18,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No -,23.75,,,,,,,Male,Age 50 to 54,,,,,,,,,Puerto Rico,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.85,81.65,No,Yes,No,No,,No,Yes -,,,,,,,,Female,Age 60 to 64,,,,,,,,,Utah,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,,,,"White only, Non-Hispanic",1.65,,,,,,,, -,23.03,,,,,,,Female,Age 70 to 74,,,,,,,,,Michigan,30.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,58.97,Yes,No,Yes,Yes,"Yes, received Tdap",No,No -,25.34,,,,,,,Male,Age 80 or older,,,,,,,,,Indiana,3.0,0.0,Within past year (anytime less than 12 months ago),No,5.0,"6 or more, but not all",No,No,No,No,No,Yes,No,No,Yes,No,Yes,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,71.21,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,38.96,,,,,,,Female,Age 30 to 34,,,,,,,,,South Carolina,2.0,3.0,5 or more years ago,Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Use them some days,No,"White only, Non-Hispanic",1.63,102.97,No,No,No,,"No, did not receive any tetanus shot in the past 10 years",No,No -,23.06,,,,,,,Male,Age 25 to 29,,,,,,,,,Alaska,0.0,0.0,Within past year (anytime less than 12 months ago),No,12.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.83,77.11,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,23.11,,,,,,,Male,Age 70 to 74,,,,,,,,,Montana,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.88,81.65,Yes,Yes,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -,37.11,,,,,,,Female,Age 60 to 64,,,,,,,,,Michigan,1.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,None of them,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.52,86.18,Yes,No,No,Yes,"Yes, received Tdap",No,No -,25.68,,,,,,,Male,Age 60 to 64,,,,,,,,,Washington,10.0,1.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.88,90.72,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,Tested positive using home test without a health professional -,32.92,,,,,,,Female,Age 50 to 54,,,,,,,,,Washington,2.0,15.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.57,81.65,Yes,,Yes,,"Yes, received Tdap",No,No -No,30.34,No,No,No,0.0,0.0,No,Male,55-59,White,No,Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,19.2,,,,,,,Female,Age 18 to 24,,,,,,,,,Texas,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,Hispanic,1.57,47.63,Yes,Yes,No,,"Yes, received tetanus shot but not sure what type",Yes,No -No,23.3,Yes,No,No,0.0,2.0,No,Female,40-44,White,No,Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,37.59,Yes,No,No,0.0,15.0,Yes,Female,35-39,White,No,No,Good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,23.03,,,,,,,Female,Age 50 to 54,,,,,,,,,Massachusetts,30.0,5.0,Within past year (anytime less than 12 months ago),No,10.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,58.97,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No -,,,,,,,,Male,Age 30 to 34,,,,,,,,,Indiana,30.0,0.0,Within past year (anytime less than 12 months ago),No,12.0,None of them,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, -,21.46,,,,,,,Female,Age 70 to 74,,,,,,,,,Florida,8.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,"6 or more, but not all",Yes,Yes,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,56.7,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,51.49,,,,,,,Female,Age 55 to 59,,,,,,,,,Pennsylvania,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,136.08,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -No,28.45,Yes,No,No,0.0,0.0,No,Male,80 or older,White,No,No,Excellent,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,38.02,,,,,,,Male,Age 35 to 39,,,,,,,,,Rhode Island,1.0,30.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,1 to 5,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.78,120.2,Yes,Yes,No,Yes,"Yes, received tetanus shot, but not Tdap",No,No -,27.46,,,,,,,Female,Age 60 to 64,,,,,,,,,Louisiana,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,70.31,No,Yes,No,No,"Yes, received Tdap",No,No -,22.05,,,,,,,Male,Age 18 to 24,,,,,,,,,Wyoming,0.0,,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,,No,No,No,No,No,Yes,No,No,Yes,Former smoker,Use them every day,No,Hispanic,1.73,65.77,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,41.01,,,,,,,Female,Age 25 to 29,,,,,,,,,Florida,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.52,95.25,No,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,25.61,,,,,,,Female,Age 75 to 79,,,,,,,,,Arizona,0.0,0.0,5 or more years ago,Yes,9.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,63.5,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,36.61,,,,,,,Female,Age 55 to 59,,,,,,,,,West Virginia,30.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,11.0,"6 or more, but not all",No,No,No,No,No,No,Yes,No,Yes,Yes,No,Yes,No,Yes,No,Yes,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,99.79,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,26.54,,,,,,,Male,Age 70 to 74,,,,,,,,,Arizona,30.0,0.0,Within past year (anytime less than 12 months ago),Yes,10.0,"6 or more, but not all",No,No,Yes,No,No,No,No,Yes,Yes,No,No,No,No,Yes,Yes,Yes,Former smoker,Never used e-cigarettes in my entire life,Yes,"Other race only, Non-Hispanic",1.78,83.91,No,Yes,No,No,"Yes, received Tdap",No,No -,27.34,,,,,,,Female,Age 75 to 79,,,,,,,,,Washington,30.0,25.0,,No,7.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,Yes,Yes,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.52,63.5,Yes,No,Yes,Yes,"Yes, received Tdap",No,No -No,32.08,No,No,No,0.0,0.0,No,Male,65-69,White,No,Yes,Excellent,7.0,Yes,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,21.97,,,,,,,Male,Age 75 to 79,,,,,,,,,Wyoming,4.0,0.0,Within past year (anytime less than 12 months ago),Yes,3.0,None of them,Yes,No,No,No,No,No,No,No,No,No,Yes,Yes,No,Yes,No,No,Current smoker - now smokes every day,,Yes,,1.83,73.48,No,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,20.63,,,,,,,Female,Age 70 to 74,,,,,,,,,Puerto Rico,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,Hispanic,1.65,56.25,No,Yes,No,No,"Yes, received Tdap",No,No -,37.76,,,,,,,Female,Age 45 to 49,,,,,,,,,Maryland,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.63,99.79,Yes,Yes,Yes,No,"Yes, received tetanus shot but not sure what type",No,Yes -No,28.7,No,No,No,0.0,10.0,No,Male,18-24,Hispanic,No,Yes,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,28.89,,,,,,,Male,Age 65 to 69,,,,,,,,,Ohio,5.0,,Within past 2 years (1 year but less than 2 years ago),No,8.0,1 to 5,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"Black only, Non-Hispanic",1.73,86.18,Yes,Yes,No,Yes,"Yes, received Tdap",No,No -No,23.06,Yes,No,No,0.0,0.0,No,Male,55-59,White,No,Yes,Excellent,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,26.37,,,,,,,Female,Age 55 to 59,,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,1.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.52,61.23,No,No,No,Yes,"Yes, received Tdap",No,No -No,22.43,No,No,No,0.0,7.0,No,Male,35-39,Hispanic,No,Yes,Excellent,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,22.15,,,,,,,Male,Age 45 to 49,,,,,,,,,New York,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),No,9.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,68.04,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -No,26.32,No,No,No,0.0,0.0,No,Male,30-34,White,No,Yes,Very good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,27.89,No,No,No,0.0,2.0,No,Male,50-54,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,22.5,Yes,No,No,0.0,0.0,No,Male,45-49,Hispanic,No,No,Excellent,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,23.03,,,,,,,Female,Age 65 to 69,,,,,,,,,Minnesota,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,58.97,Yes,No,Yes,No,"Yes, received Tdap",No,No -No,21.3,Yes,No,No,0.0,30.0,No,Female,35-39,Asian,No,Yes,Good,4.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,27.37,Yes,No,No,0.0,0.0,No,Male,60-64,Asian,No,Yes,Good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,25.75,,,,,,,Female,Age 80 or older,,,,,,,,,Missouri,2.0,0.0,Within past year (anytime less than 12 months ago),Yes,4.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,68.04,Yes,No,Yes,Yes,,No,Yes -No,33.23,Yes,No,No,0.0,0.0,No,Male,45-49,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,38.65,,,,,,,Male,Age 30 to 34,,,,,,,,,Ohio,15.0,15.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,129.27,No,No,Yes,,"Yes, received Tdap",No,No -,36.26,,,,,,,Male,Age 40 to 44,,,,,,,,,Tennessee,14.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,All,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Use them every day,Yes,"White only, Non-Hispanic",1.8,117.93,Yes,Yes,No,No,"Yes, received Tdap",Yes,No -,21.48,,,,,,,Female,Age 80 or older,,,,,,,,,Massachusetts,1.0,2.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,Yes,No,No,No,Yes,No,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.52,49.9,No,No,Yes,Yes,"Yes, received Tdap",No,No -No,30.42,No,No,No,0.0,0.0,No,Male,55-59,White,Yes,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,37.23,,,,,,,Male,Age 70 to 74,,,,,,,,,Virginia,20.0,10.0,Within past year (anytime less than 12 months ago),No,4.0,"6 or more, but not all",No,No,No,No,No,No,Yes,Yes,Yes,Yes,No,Yes,Yes,Yes,No,No,Former smoker,Not at all (right now),Yes,"Black only, Non-Hispanic",1.88,131.54,No,No,No,Yes,"Yes, received tetanus shot but not sure what type",No,No -,27.99,,,,,,,Female,Age 55 to 59,,,,,,,,,Kentucky,30.0,15.0,Within past year (anytime less than 12 months ago),Yes,12.0,"6 or more, but not all",No,No,No,Yes,No,Yes,Yes,No,Yes,No,No,No,Yes,Yes,No,Yes,Former smoker,Use them some days,No,"White only, Non-Hispanic",1.6,71.67,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No -,30.41,,,,,,,Female,Age 60 to 64,,,,,,,,,Georgia,10.0,0.0,Within past year (anytime less than 12 months ago),Yes,3.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.73,90.72,No,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -No,30.26,No,No,No,0.0,0.0,No,Male,65-69,White,No,Yes,Very good,7.0,No,Yes,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,25.06,,,,,,,Male,Age 40 to 44,,,,,,,,,Nebraska,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,72.57,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,No -No,24.41,No,No,No,0.0,1.0,No,Male,70-74,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,24.21,,,,,,,Female,Age 55 to 59,,,,,,,,,Virginia,0.0,10.0,5 or more years ago,Yes,7.0,All,No,No,No,No,Yes,No,Yes,No,Yes,"No, pre-diabetes or borderline diabetes",No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,68.04,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -No,28.37,Yes,No,No,0.0,29.0,No,Male,55-59,White,No,Yes,Excellent,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,41.09,,,,,,,Male,Age 30 to 34,,,,,,,,,Louisiana,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.88,145.15,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,29.53,,,,,,,Male,Age 45 to 49,,,,,,,,,Ohio,0.0,1.0,5 or more years ago,Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),,"White only, Non-Hispanic",1.75,90.72,,,,,,, -,27.76,,,,,,,Male,Age 65 to 69,,,,,,,,,South Carolina,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,78.02,No,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,23.3,,,,,,,Female,Age 50 to 54,,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Multiracial, Non-Hispanic",1.65,63.5,Yes,No,Yes,No,"Yes, received tetanus shot, but not Tdap",No,No -,25.84,,,,,,,Female,Age 75 to 79,,,,,,,,,Utah,30.0,,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,Yes,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,74.84,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -No,37.49,No,No,No,3.0,7.0,No,Female,35-39,White,No,Yes,Excellent,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,21.52,,,,,,,Male,Age 35 to 39,,,,,,,,,New York,2.0,10.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,68.04,Yes,Yes,No,No,,Yes,No -,33.3,,,,,,,Female,Age 45 to 49,,,,,,,,,Wyoming,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,1 to 5,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Use them every day,No,"White only, Non-Hispanic",1.6,85.28,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,18.32,,,,,,,Female,Age 65 to 69,,,,,,,,,Arkansas,0.0,30.0,Within past year (anytime less than 12 months ago),Yes,9.0,All,Yes,Yes,No,No,No,Yes,Yes,No,Yes,No,No,No,Yes,No,No,No,Current smoker - now smokes every day,Not at all (right now),No,"White only, Non-Hispanic",1.7,53.07,No,No,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,29.76,,,,,,,Male,Age 65 to 69,,,,,,,,,South Dakota,3.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,86.18,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,Tested positive using home test without a health professional -,21.93,,,,,,,Female,Age 50 to 54,,,,,,,,,South Dakota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,63.5,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -,40.24,,,,,,,Male,Age 35 to 39,,,,,,,,,Arizona,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,Hispanic,1.85,138.35,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,No -No,30.79,No,No,No,0.0,0.0,No,Female,70-74,White,No,Yes,Excellent,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,37.59,,,,,,,Female,Age 65 to 69,,,,,,,,,Mississippi,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,None of them,No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,,"Black only, Non-Hispanic",1.7,108.86,No,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,24.89,,,,,,,Female,Age 60 to 64,,,,,,,,,New Jersey,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,65.77,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,25.75,,,,,,,Female,Age 30 to 34,,,,,,,,,South Dakota,5.0,30.0,Within past 2 years (1 year but less than 2 years ago),Yes,6.0,None of them,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,68.04,No,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,25.33,,,,,,,Female,Age 60 to 64,,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,64.86,Yes,No,Yes,Yes,"Yes, received Tdap",No,No -,34.01,,,,,,,Female,Age 45 to 49,,,,,,,,,Vermont,14.0,2.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,Yes,No,No,No,No,No,No,,Yes,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.55,81.65,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -No,34.87,Yes,No,No,0.0,0.0,No,Male,50-54,Other,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,27.44,,,,,,,Female,Age 25 to 29,,,,,,,,,Kansas,2.0,20.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,Yes,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,77.11,Yes,No,Yes,Yes,"Yes, received Tdap",No,No -,21.29,,,,,,,Male,Age 25 to 29,,,,,,,,,Colorado,0.0,15.0,5 or more years ago,Yes,6.0,1 to 5,No,No,No,Yes,No,Yes,No,No,No,No,No,No,Yes,No,No,No,Former smoker,Not at all (right now),No,Hispanic,1.73,63.5,No,No,No,No,"Yes, received Tdap",No,Yes -,,,,,,,,Male,Age 60 to 64,,,,,,,,,New Jersey,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,"White only, Non-Hispanic",,79.38,,,,,,, -,28.25,,,,,,,Female,Age 35 to 39,,,,,,,,,Florida,1.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"Black only, Non-Hispanic",1.68,79.38,No,Yes,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,21.97,,,,,,,Female,Age 60 to 64,,,,,,,,,Indiana,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Use them some days,No,"White only, Non-Hispanic",1.6,56.25,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -No,28.29,No,No,No,0.0,7.0,No,Female,25-29,White,No,Yes,Very good,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Yes,38.74,Yes,No,No,30.0,15.0,No,Male,70-74,White,Yes,No,Fair,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,26.63,,,,,,,Female,Age 65 to 69,,,,,,,,,Minnesota,3.0,1.0,Within past year (anytime less than 12 months ago),Yes,9.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,74.84,,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,Tested positive using home test without a health professional -,28.19,,,,,,,Female,Age 60 to 64,,,,,,,,,Wisconsin,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,81.65,Yes,No,Yes,No,"Yes, received tetanus shot, but not Tdap",No,No -,23.75,,,,,,,Male,Age 40 to 44,,,,,,,,,Maine,0.0,30.0,5 or more years ago,Yes,9.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,,1.85,81.65,No,No,No,,"No, did not receive any tetanus shot in the past 10 years",No,No -,32.77,,,,,,,Female,Age 80 or older,,,,,,,,,Wisconsin,3.0,2.0,Within past year (anytime less than 12 months ago),No,6.0,,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.6,83.91,No,No,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes -No,26.57,No,No,No,0.0,0.0,No,Female,45-49,White,No,Yes,Very good,8.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,24.03,,,,,,,Female,Age 45 to 49,,,,,,,,,Maryland,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,63.5,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,Yes -,35.0,,,,,,,Male,Age 25 to 29,,,,,,,,,Virginia,15.0,15.0,5 or more years ago,Yes,5.0,None of them,No,No,No,Yes,No,No,Yes,No,No,No,No,No,Yes,No,No,Yes,Never smoked,Use them some days,No,"Black only, Non-Hispanic",1.91,127.01,Yes,Yes,No,No,"Yes, received Tdap",No,Yes -No,30.85,Yes,No,No,0.0,0.0,No,Male,55-59,White,No,Yes,Excellent,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,,,,,,,,Female,Age 35 to 39,,,,,,,,,Oklahoma,,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,,,,Hispanic,1.55,,,,,,,, -Yes,30.85,No,No,No,0.0,0.0,No,Male,80 or older,White,No,Yes,Good,8.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,23.49,,,,,,,Male,Age 18 to 24,,,,,,,,,Colorado,2.0,20.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,Yes,No,No,Yes,Never smoked,Not at all (right now),No,Hispanic,1.7,68.04,Yes,No,No,Yes,"Yes, received tetanus shot, but not Tdap",Yes,Yes -No,24.41,No,No,No,0.0,0.0,No,Female,60-64,White,No,Yes,Very good,10.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,34.46,,,,,,,Female,Age 40 to 44,,,,,,,,,Nebraska,28.0,30.0,Within past 2 years (1 year but less than 2 years ago),No,6.0,1 to 5,Yes,No,No,No,No,No,Yes,No,Yes,No,No,No,Yes,Yes,Yes,Yes,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,99.79,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No -No,25.7,Yes,No,No,0.0,0.0,No,Female,80 or older,White,No,Yes,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,24.69,No,No,No,0.0,0.0,No,Male,18-24,Asian,No,No,Excellent,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,21.95,No,No,No,2.0,1.0,Yes,Female,50-54,White,No,Yes,Fair,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,26.26,,,,,,,Male,Age 25 to 29,,,,,,,,,Massachusetts,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,83.01,Yes,No,No,Yes,"Yes, received tetanus shot but not sure what type",No,No -,25.1,,,,,,,Male,Age 55 to 59,,,,,,,,,Minnesota,0.0,0.0,5 or more years ago,Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.8,81.65,Yes,No,No,No,"Yes, received tetanus shot, but not Tdap",No,No -,20.6,,,,,,,Female,Age 55 to 59,,,,,,,,,Montana,3.0,3.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,54.43,Yes,No,Yes,No,"Yes, received tetanus shot, but not Tdap",No,Yes -,30.23,,,,,,,Female,Age 70 to 74,,,,,,,,,North Dakota,5.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.55,72.57,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,33.23,,,,,,,Male,Age 18 to 24,,,,,,,,,Maryland,7.0,7.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.83,111.13,Yes,Yes,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -,31.62,,,,,,,Female,Age 40 to 44,,,,,,,,,Texas,7.0,12.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,86.18,Yes,No,No,No,"Yes, received Tdap",No,No -,28.35,,,,,,,Female,Age 80 or older,,,,,,,,,Nebraska,5.0,0.0,Within past year (anytime less than 12 months ago),Yes,3.0,1 to 5,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.57,70.31,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,29.05,,,,,,,Female,Age 60 to 64,,,,,,,,,Guam,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,None of them,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,Hispanic,1.68,81.65,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",Yes,Tested positive using home test without a health professional -No,30.18,No,No,No,0.0,0.0,No,Female,60-64,White,No,Yes,Very good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,22.32,Yes,Yes,No,1.0,0.0,No,Female,60-64,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,27.37,,,,,,,Male,Age 75 to 79,,,,,,,,,South Dakota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,3.0,None of them,No,Yes,No,No,Yes,No,No,,No,Yes,No,Yes,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,81.65,No,No,Yes,Yes,"Yes, received Tdap",No,No -,19.53,,,,,,,Female,Age 55 to 59,,,,,,,,,Minnesota,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.52,45.36,,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -No,25.06,No,No,No,0.0,10.0,No,Female,25-29,White,No,Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,28.89,,,,,,,Male,Age 75 to 79,,,,,,,,,Virginia,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,86.18,No,Yes,No,Yes,"Yes, received tetanus shot but not sure what type",, -,28.29,,,,,,,Female,Age 18 to 24,,,,,,,,,Puerto Rico,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),No,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,Hispanic,1.65,77.11,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -,,,,,,,,Female,Age 35 to 39,,,,,,,,,New Jersey,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,Hispanic,,,,,,,,, -,28.37,,,,,,,Male,Age 75 to 79,,,,,,,,,Maryland,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,97.52,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -No,53.81,Yes,No,No,0.0,25.0,No,Male,35-39,Hispanic,No,No,Fair,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,30.62,,,,,,,Female,Age 75 to 79,,,,,,,,,New Hampshire,30.0,0.0,Within past 5 years (2 years but less than 5 years ago),No,4.0,None of them,No,No,No,Yes,No,Yes,Yes,Yes,Yes,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,83.46,No,No,Yes,Yes,"Yes, received Tdap",No,No -No,26.93,Yes,Yes,No,4.0,2.0,No,Female,55-59,White,No,Yes,Good,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,27.12,Yes,No,No,0.0,0.0,Yes,Male,60-64,White,No,Yes,Fair,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,29.18,,,,,,,Female,Age 25 to 29,,,,,,,,,New Jersey,0.0,2.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.63,77.11,Yes,No,Yes,No,"Yes, received tetanus shot, but not Tdap",No,No -,30.27,,,,,,,Male,Age 50 to 54,,,,,,,,,Puerto Rico,5.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,Hispanic,1.75,92.99,No,No,No,No,"Yes, received Tdap",No,Yes -,36.13,,,,,,,Female,Age 30 to 34,,,,,,,,,Ohio,14.0,5.0,Within past year (anytime less than 12 months ago),No,6.0,1 to 5,No,No,No,No,No,Yes,Yes,No,Yes,No,No,Yes,Yes,Yes,No,Yes,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"Multiracial, Non-Hispanic",1.52,83.91,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -,22.71,,,,,,,Female,Age 18 to 24,,,,,,,,,Utah,0.0,5.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,65.77,No,No,Yes,Yes,"Yes, received Tdap",No,No -No,22.92,No,No,No,0.0,2.0,No,Female,60-64,White,No,Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,27.89,,,,,,,Male,,,,,,,,,,Georgia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,,,No,"Black only, Non-Hispanic",1.8,90.72,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -,29.23,,,,,,,Female,Age 70 to 74,,,,,,,,,Texas,0.0,2.0,Within past year (anytime less than 12 months ago),Yes,8.0,All,No,No,No,No,No,No,No,No,Yes,"No, pre-diabetes or borderline diabetes",No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.6,74.84,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,No -,25.61,,,,,,,Female,Age 35 to 39,,,,,,,,,South Dakota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,63.5,Yes,Yes,No,,,No,No -,26.63,,,,,,,Female,Age 45 to 49,,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,Yes,Yes,Yes,Never smoked,Not at all (right now),Yes,"Black only, Non-Hispanic",1.65,72.57,No,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -No,30.27,Yes,No,No,0.0,0.0,No,Male,40-44,White,No,Yes,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,20.25,,,,,,,Male,Age 65 to 69,,,,,,,,,Maine,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),No,"White only, Non-Hispanic",1.91,73.48,,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,24.75,,,,,,,Female,Age 45 to 49,,,,,,,,,Maine,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,71.67,Yes,,Yes,No,"Yes, received Tdap",No,No +HeartDisease,BMI,Smoking,AlcoholDrinking,Stroke,PhysicalHealth,MentalHealth,DiffWalking,Sex,AgeCategory,Race,Diabetic,PhysicalActivity,SleepTime,Asthma,KidneyDisease,SkinCancer,State,PhysicalHealthDays,MentalHealthDays,LastCheckupTime,PhysicalActivities,SleepHours,RemovedTeeth,HadHeartAttack,HadAngina,HadStroke,HadAsthma,HadSkinCancer,HadCOPD,HadDepressiveDisorder,HadKidneyDisease,HadArthritis,HadDiabetes,DeafOrHardOfHearing,BlindOrVisionDifficulty,DifficultyConcentrating,DifficultyWalking,DifficultyDressingBathing,DifficultyErrands,SmokerStatus,ECigaretteUsage,ChestScan,RaceEthnicityCategory,HeightInMeters,WeightInKilograms,AlcoholDrinkers,HIVTesting,FluVaxLast12,PneumoVaxEver,TetanusLast10Tdap,HighRiskLastYear,CovidPos +No,36.32,No,No,No,0.0,0.0,Yes,Female,60-64,White,Yes,Yes,8.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,32.45,Yes,Yes,No,0.0,2.0,No,Female,30-34,Black,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,29.26,,,,,,,Female,Age 75 to 79,,,,,,,,Tennessee,0.0,5.0,Within past year (anytime less than 12 months ago),Yes,9.0,1 to 5,No,No,No,No,No,Yes,No,No,Yes,Yes,No,No,No,No,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.57,72.57,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,25.42,,,,,,,Female,Age 80 or older,,,,,,,,Maryland,30.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,"6 or more, but not all",Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Yes,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.57,63.05,No,No,No,Yes,"Yes, received Tdap",No,No +,35.24,,,,,,,Female,Age 60 to 64,,,,,,,,Idaho,5.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,Yes,Yes,Yes,No,No,Yes,No,No,No,Yes,Yes,No,No,Current smoker - now smokes some days,Use them some days,No,"White only, Non-Hispanic",1.7,102.06,Yes,,No,Yes,"Yes, received tetanus shot but not sure what type",No,No +,20.8,,,,,,,Female,Age 80 or older,,,,,,,,Georgia,30.0,30.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,56.7,No,,,,,, +,30.04,,,,,,,Female,Age 60 to 64,,,,,,,,Wisconsin,3.0,30.0,Within past year (anytime less than 12 months ago),Yes,10.0,1 to 5,No,No,No,No,No,No,Yes,No,Yes,No,No,Yes,Yes,No,No,Yes,Current smoker - now smokes every day,Use them some days,Yes,"White only, Non-Hispanic",1.63,79.38,Yes,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,37.12,,,,,,,Female,Age 40 to 44,,,,,,,,Vermont,18.0,30.0,Within past year (anytime less than 12 months ago),Yes,4.0,All,No,No,No,Yes,No,No,Yes,No,Yes,No,Yes,No,Yes,No,No,No,Current smoker - now smokes every day,Use them some days,Yes,"White only, Non-Hispanic",1.68,104.33,Yes,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +No,34.46,No,No,No,0.0,0.0,No,Male,65-69,White,"No, borderline diabetes",Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,31.09,,,,,,,Female,Age 30 to 34,,,,,,,,Illinois,0.0,0.0,5 or more years ago,Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.57,77.11,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +,25.07,,,,,,,Male,Age 80 or older,,,,,,,,Rhode Island,6.0,2.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,Yes,Yes,Yes,Yes,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,86.18,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes +No,29.53,No,No,No,0.0,2.0,No,Male,55-59,White,No,Yes,7.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,26.45,,,,,,,Female,Age 30 to 34,,,,,,,,Texas,1.0,1.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.55,63.5,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,34.28,,,,,,,Female,Age 18 to 24,,,,,,,,New Jersey,3.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,12.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.65,93.44,No,No,Yes,No,"Yes, received Tdap",No,No +,22.31,,,,,,,Female,Age 35 to 39,,,,,,,,Utah,3.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,58.97,No,No,No,No,"Yes, received Tdap",No,No +No,31.32,No,No,No,30.0,30.0,No,Female,70-74,White,No,No,8.0,No,Yes,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,20.36,No,No,No,0.0,0.0,No,Female,75-79,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,27.46,,,,,,,Female,Age 60 to 64,,,,,,,,Nebraska,30.0,0.0,Within past year (anytime less than 12 months ago),No,5.0,1 to 5,No,No,No,No,No,No,No,No,No,Yes,No,Yes,No,Yes,Yes,Yes,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,Yes,Hispanic,1.63,72.57,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +Yes,46.64,No,No,No,30.0,10.0,Yes,Female,80 or older,White,Yes,No,10.0,No,Yes,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,34.48,,,,,,,Female,Age 80 or older,,,,,,,,Kansas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.47,74.84,No,No,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,24.63,,,,,,,Male,Age 45 to 49,,,,,,,,Wisconsin,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,73.48,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No +Yes,37.55,No,No,No,20.0,0.0,Yes,Female,65-69,White,No,No,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,26.63,,,,,,,Male,Age 60 to 64,,,,,,,,Minnesota,,,Within past year (anytime less than 12 months ago),Yes,,"6 or more, but not all",No,No,No,,No,Yes,No,No,Yes,No,No,No,No,Yes,Yes,Yes,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.68,74.84,Yes,No,No,Yes,,No,No +No,33.25,Yes,No,No,2.0,10.0,No,Male,65-69,White,Yes,No,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,25.11,,,,,,,Male,Age 55 to 59,,,,,,,,Nevada,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,79.38,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No +,30.83,,,,,,,Female,Age 65 to 69,,,,,,,,Maryland,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,None of them,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.68,86.64,Yes,Yes,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +,27.37,,,,,,,Male,Age 80 or older,,,,,,,,North Dakota,30.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,None of them,No,No,No,No,Yes,No,No,No,No,No,No,Yes,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,81.65,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,30.52,,,,,,,Male,Age 65 to 69,,,,,,,,Oregon,5.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,Yes,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.83,102.06,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Tested positive using home test without a health professional +,27.06,,,,,,,Female,Age 18 to 24,,,,,,,,Louisiana,0.0,1.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Use them every day,No,"White only, Non-Hispanic",1.5,60.78,No,No,No,Yes,"Yes, received tetanus shot, but not Tdap",, +,27.71,,,,,,,Male,Age 30 to 34,,,,,,,,Connecticut,0.0,4.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.85,95.25,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,No +No,32.61,No,No,No,0.0,3.0,Yes,Female,70-74,White,Yes,No,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,23.71,No,No,No,30.0,2.0,No,Male,25-29,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,22.6,,,,,,,Female,Age 50 to 54,,,,,,,,Wisconsin,7.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,63.5,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,Tested positive using home test without a health professional +No,28.06,No,No,No,0.0,0.0,No,Female,50-54,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Yes,41.57,Yes,No,No,0.0,0.0,No,Female,75-79,White,No,Yes,4.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,,,,,,,,Female,Age 40 to 44,,,,,,,,Oklahoma,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",,,Yes,No,Yes,,,No,Yes +Yes,18.87,Yes,No,No,0.0,25.0,No,Male,25-29,White,No,Yes,6.0,No,Yes,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,32.69,No,No,No,0.0,0.0,No,Male,50-54,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,32.86,No,No,No,0.0,0.0,No,Female,75-79,White,No,No,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,44.29,,,,,,,Female,Age 75 to 79,,,,,,,,Arizona,0.0,0.0,Within past year (anytime less than 12 months ago),No,9.0,1 to 5,No,Yes,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,113.4,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +No,24.37,No,No,No,0.0,0.0,No,Female,65-69,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,24.8,,,,,,,Female,Age 55 to 59,,,,,,,,Oklahoma,2.0,3.0,Within past year (anytime less than 12 months ago),No,6.0,"6 or more, but not all",No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.6,63.5,No,Yes,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes +No,32.23,Yes,Yes,No,0.0,30.0,No,Female,45-49,White,No,Yes,8.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,29.23,Yes,No,No,0.0,3.0,No,Female,60-64,Black,"No, borderline diabetes",No,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,26.63,,,,,,,Female,Age 25 to 29,,,,,,,,Mississippi,0.0,4.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,74.84,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,38.67,,,,,,,Female,Age 65 to 69,,,,,,,,Michigan,20.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,Yes,No,No,No,No,Yes,No,No,Yes,No,No,No,No,Yes,Yes,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.52,89.81,No,No,Yes,Yes,"Yes, received Tdap",No,No +,,,,,,,,Female,Age 18 to 24,,,,,,,,Georgia,20.0,30.0,Within past 2 years (1 year but less than 2 years ago),No,5.0,None of them,No,No,No,Yes,,No,Yes,No,No,No,,No,Yes,,No,Yes,Never smoked,Use them every day,No,"White only, Non-Hispanic",,61.23,Yes,Yes,No,Yes,"Yes, received tetanus shot, but not Tdap",No,Yes +,17.34,,,,,,,Female,Age 80 or older,,,,,,,,Missouri,30.0,0.0,Within past year (anytime less than 12 months ago),No,4.0,All,Yes,,No,No,No,,No,,Yes,No,Yes,Yes,No,Yes,No,Yes,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,45.81,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,23.01,,,,,,,Male,Age 65 to 69,,,,,,,,Massachusetts,0.0,,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,,No,No,No,No,,No,,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.8,74.84,No,,Yes,Yes,,No,No +,27.06,,,,,,,Male,Age 70 to 74,,,,,,,,Vermont,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,80.74,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +No,23.91,No,No,No,0.0,4.0,No,Female,25-29,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,14.88,,,,,,,Female,Age 70 to 74,,,,,,,,Tennessee,30.0,25.0,Within past year (anytime less than 12 months ago),No,1.0,"6 or more, but not all",No,No,No,No,No,Yes,Yes,Yes,Yes,Yes,No,No,Yes,Yes,Yes,Yes,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,43.09,No,No,No,Yes,"Yes, received Tdap",No,Yes +,24.45,,,,,,,Female,Age 30 to 34,,,,,,,,Texas,3.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.6,62.6,Yes,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +No,44.94,Yes,No,No,0.0,4.0,No,Male,18-24,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,,,,,,,,Male,Age 40 to 44,,,,,,,,New Hampshire,0.0,10.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, +,27.4,,,,,,,Male,Age 45 to 49,,,,,,,,Wyoming,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Use them some days,No,"White only, Non-Hispanic",1.83,91.63,Yes,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,30.67,,,,,,,Male,Age 40 to 44,,,,,,,,Utah,20.0,30.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,Hispanic,1.68,86.18,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,27.46,,,,,,,Female,Age 70 to 74,,,,,,,,Montana,20.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,All,No,Yes,No,No,Yes,No,Yes,Yes,Yes,Yes,No,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,70.31,No,No,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Tested positive using home test without a health professional +No,36.56,Yes,No,No,2.0,0.0,No,Female,40-44,Hispanic,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,19.11,,,,,,,Female,Age 40 to 44,,,,,,,,New York,30.0,,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,"Yes, but only during pregnancy (female)",No,No,No,No,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.7,55.34,No,Yes,Yes,,"Yes, received Tdap",No,Yes +,36.94,,,,,,,Male,Age 50 to 54,,,,,,,,Kentucky,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,5.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.85,127.01,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,No +No,23.02,No,No,No,0.0,5.0,No,Male,60-64,Asian,Yes,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,20.38,Yes,No,No,0.0,2.0,No,Male,70-74,White,No,Yes,5.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,29.05,,,,,,,Female,Age 45 to 49,,,,,,,,Washington,5.0,5.0,Within past 2 years (1 year but less than 2 years ago),Yes,6.0,1 to 5,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),No,"Multiracial, Non-Hispanic",1.68,81.65,Yes,No,No,No,,No,No +,23.3,,,,,,,Female,Age 45 to 49,,,,,,,,New York,1.0,5.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,63.5,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No +,24.89,,,,,,,Female,Age 60 to 64,,,,,,,,Missouri,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,11.0,1 to 5,No,No,No,No,No,No,No,No,No,"No, pre-diabetes or borderline diabetes",No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.63,65.77,No,No,Yes,No,"Yes, received Tdap",No,No +No,25.69,Yes,No,No,0.0,0.0,No,Female,60-64,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,28.06,,,,,,,Male,Age 30 to 34,,,,,,,,Indiana,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.75,86.18,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No +,23.75,,,,,,,Male,Age 50 to 54,,,,,,,,Puerto Rico,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.85,81.65,No,Yes,No,No,,No,Yes +,,,,,,,,Female,Age 60 to 64,,,,,,,,Utah,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,,,,"White only, Non-Hispanic",1.65,,,,,,,, +,23.03,,,,,,,Female,Age 70 to 74,,,,,,,,Michigan,30.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,58.97,Yes,No,Yes,Yes,"Yes, received Tdap",No,No +,25.34,,,,,,,Male,Age 80 or older,,,,,,,,Indiana,3.0,0.0,Within past year (anytime less than 12 months ago),No,5.0,"6 or more, but not all",No,No,No,No,No,Yes,No,No,Yes,No,Yes,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,71.21,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,38.96,,,,,,,Female,Age 30 to 34,,,,,,,,South Carolina,2.0,3.0,5 or more years ago,Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Use them some days,No,"White only, Non-Hispanic",1.63,102.97,No,No,No,,"No, did not receive any tetanus shot in the past 10 years",No,No +,23.06,,,,,,,Male,Age 25 to 29,,,,,,,,Alaska,0.0,0.0,Within past year (anytime less than 12 months ago),No,12.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.83,77.11,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,23.11,,,,,,,Male,Age 70 to 74,,,,,,,,Montana,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.88,81.65,Yes,Yes,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +,37.11,,,,,,,Female,Age 60 to 64,,,,,,,,Michigan,1.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,None of them,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.52,86.18,Yes,No,No,Yes,"Yes, received Tdap",No,No +,25.68,,,,,,,Male,Age 60 to 64,,,,,,,,Washington,10.0,1.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.88,90.72,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,Tested positive using home test without a health professional +,32.92,,,,,,,Female,Age 50 to 54,,,,,,,,Washington,2.0,15.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.57,81.65,Yes,,Yes,,"Yes, received Tdap",No,No +No,30.34,No,No,No,0.0,0.0,No,Male,55-59,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,19.2,,,,,,,Female,Age 18 to 24,,,,,,,,Texas,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,Hispanic,1.57,47.63,Yes,Yes,No,,"Yes, received tetanus shot but not sure what type",Yes,No +No,23.3,Yes,No,No,0.0,2.0,No,Female,40-44,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,37.59,Yes,No,No,0.0,15.0,Yes,Female,35-39,White,No,No,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,23.03,,,,,,,Female,Age 50 to 54,,,,,,,,Massachusetts,30.0,5.0,Within past year (anytime less than 12 months ago),No,10.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,58.97,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No +,,,,,,,,Male,Age 30 to 34,,,,,,,,Indiana,30.0,0.0,Within past year (anytime less than 12 months ago),No,12.0,None of them,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, +,21.46,,,,,,,Female,Age 70 to 74,,,,,,,,Florida,8.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,"6 or more, but not all",Yes,Yes,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,56.7,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,51.49,,,,,,,Female,Age 55 to 59,,,,,,,,Pennsylvania,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,136.08,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,28.45,Yes,No,No,0.0,0.0,No,Male,80 or older,White,No,No,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,38.02,,,,,,,Male,Age 35 to 39,,,,,,,,Rhode Island,1.0,30.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,1 to 5,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.78,120.2,Yes,Yes,No,Yes,"Yes, received tetanus shot, but not Tdap",No,No +,27.46,,,,,,,Female,Age 60 to 64,,,,,,,,Louisiana,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,70.31,No,Yes,No,No,"Yes, received Tdap",No,No +,22.05,,,,,,,Male,Age 18 to 24,,,,,,,,Wyoming,0.0,,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,,No,No,No,No,No,Yes,No,No,Yes,Former smoker,Use them every day,No,Hispanic,1.73,65.77,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,41.01,,,,,,,Female,Age 25 to 29,,,,,,,,Florida,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.52,95.25,No,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,25.61,,,,,,,Female,Age 75 to 79,,,,,,,,Arizona,0.0,0.0,5 or more years ago,Yes,9.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,63.5,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,36.61,,,,,,,Female,Age 55 to 59,,,,,,,,West Virginia,30.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,11.0,"6 or more, but not all",No,No,No,No,No,No,Yes,No,Yes,Yes,No,Yes,No,Yes,No,Yes,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,99.79,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,26.54,,,,,,,Male,Age 70 to 74,,,,,,,,Arizona,30.0,0.0,Within past year (anytime less than 12 months ago),Yes,10.0,"6 or more, but not all",No,No,Yes,No,No,No,No,Yes,Yes,No,No,No,No,Yes,Yes,Yes,Former smoker,Never used e-cigarettes in my entire life,Yes,"Other race only, Non-Hispanic",1.78,83.91,No,Yes,No,No,"Yes, received Tdap",No,No +,27.34,,,,,,,Female,Age 75 to 79,,,,,,,,Washington,30.0,25.0,,No,7.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,Yes,Yes,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.52,63.5,Yes,No,Yes,Yes,"Yes, received Tdap",No,No +No,32.08,No,No,No,0.0,0.0,No,Male,65-69,White,No,Yes,7.0,Yes,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,21.97,,,,,,,Male,Age 75 to 79,,,,,,,,Wyoming,4.0,0.0,Within past year (anytime less than 12 months ago),Yes,3.0,None of them,Yes,No,No,No,No,No,No,No,No,No,Yes,Yes,No,Yes,No,No,Current smoker - now smokes every day,,Yes,,1.83,73.48,No,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,20.63,,,,,,,Female,Age 70 to 74,,,,,,,,Puerto Rico,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,Hispanic,1.65,56.25,No,Yes,No,No,"Yes, received Tdap",No,No +,37.76,,,,,,,Female,Age 45 to 49,,,,,,,,Maryland,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.63,99.79,Yes,Yes,Yes,No,"Yes, received tetanus shot but not sure what type",No,Yes +No,28.7,No,No,No,0.0,10.0,No,Male,18-24,Hispanic,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,28.89,,,,,,,Male,Age 65 to 69,,,,,,,,Ohio,5.0,,Within past 2 years (1 year but less than 2 years ago),No,8.0,1 to 5,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"Black only, Non-Hispanic",1.73,86.18,Yes,Yes,No,Yes,"Yes, received Tdap",No,No +No,23.06,Yes,No,No,0.0,0.0,No,Male,55-59,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,26.37,,,,,,,Female,Age 55 to 59,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,1.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.52,61.23,No,No,No,Yes,"Yes, received Tdap",No,No +No,22.43,No,No,No,0.0,7.0,No,Male,35-39,Hispanic,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,22.15,,,,,,,Male,Age 45 to 49,,,,,,,,New York,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),No,9.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,68.04,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,26.32,No,No,No,0.0,0.0,No,Male,30-34,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,27.89,No,No,No,0.0,2.0,No,Male,50-54,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,22.5,Yes,No,No,0.0,0.0,No,Male,45-49,Hispanic,No,No,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,23.03,,,,,,,Female,Age 65 to 69,,,,,,,,Minnesota,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,58.97,Yes,No,Yes,No,"Yes, received Tdap",No,No +No,21.3,Yes,No,No,0.0,30.0,No,Female,35-39,Asian,No,Yes,4.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,27.37,Yes,No,No,0.0,0.0,No,Male,60-64,Asian,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,25.75,,,,,,,Female,Age 80 or older,,,,,,,,Missouri,2.0,0.0,Within past year (anytime less than 12 months ago),Yes,4.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,68.04,Yes,No,Yes,Yes,,No,Yes +No,33.23,Yes,No,No,0.0,0.0,No,Male,45-49,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,38.65,,,,,,,Male,Age 30 to 34,,,,,,,,Ohio,15.0,15.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,129.27,No,No,Yes,,"Yes, received Tdap",No,No +,36.26,,,,,,,Male,Age 40 to 44,,,,,,,,Tennessee,14.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,All,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Use them every day,Yes,"White only, Non-Hispanic",1.8,117.93,Yes,Yes,No,No,"Yes, received Tdap",Yes,No +,21.48,,,,,,,Female,Age 80 or older,,,,,,,,Massachusetts,1.0,2.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,Yes,No,No,No,Yes,No,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.52,49.9,No,No,Yes,Yes,"Yes, received Tdap",No,No +No,30.42,No,No,No,0.0,0.0,No,Male,55-59,White,Yes,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,37.23,,,,,,,Male,Age 70 to 74,,,,,,,,Virginia,20.0,10.0,Within past year (anytime less than 12 months ago),No,4.0,"6 or more, but not all",No,No,No,No,No,No,Yes,Yes,Yes,Yes,No,Yes,Yes,Yes,No,No,Former smoker,Not at all (right now),Yes,"Black only, Non-Hispanic",1.88,131.54,No,No,No,Yes,"Yes, received tetanus shot but not sure what type",No,No +,27.99,,,,,,,Female,Age 55 to 59,,,,,,,,Kentucky,30.0,15.0,Within past year (anytime less than 12 months ago),Yes,12.0,"6 or more, but not all",No,No,No,Yes,No,Yes,Yes,No,Yes,No,No,No,Yes,Yes,No,Yes,Former smoker,Use them some days,No,"White only, Non-Hispanic",1.6,71.67,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No +,30.41,,,,,,,Female,Age 60 to 64,,,,,,,,Georgia,10.0,0.0,Within past year (anytime less than 12 months ago),Yes,3.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.73,90.72,No,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +No,30.26,No,No,No,0.0,0.0,No,Male,65-69,White,No,Yes,7.0,No,Yes,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,25.06,,,,,,,Male,Age 40 to 44,,,,,,,,Nebraska,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,72.57,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,No +No,24.41,No,No,No,0.0,1.0,No,Male,70-74,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,24.21,,,,,,,Female,Age 55 to 59,,,,,,,,Virginia,0.0,10.0,5 or more years ago,Yes,7.0,All,No,No,No,No,Yes,No,Yes,No,Yes,"No, pre-diabetes or borderline diabetes",No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,68.04,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,28.37,Yes,No,No,0.0,29.0,No,Male,55-59,White,No,Yes,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,41.09,,,,,,,Male,Age 30 to 34,,,,,,,,Louisiana,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.88,145.15,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,29.53,,,,,,,Male,Age 45 to 49,,,,,,,,Ohio,0.0,1.0,5 or more years ago,Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),,"White only, Non-Hispanic",1.75,90.72,,,,,,, +,27.76,,,,,,,Male,Age 65 to 69,,,,,,,,South Carolina,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,78.02,No,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,23.3,,,,,,,Female,Age 50 to 54,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Multiracial, Non-Hispanic",1.65,63.5,Yes,No,Yes,No,"Yes, received tetanus shot, but not Tdap",No,No +,25.84,,,,,,,Female,Age 75 to 79,,,,,,,,Utah,30.0,,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,Yes,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,74.84,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +No,37.49,No,No,No,3.0,7.0,No,Female,35-39,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,21.52,,,,,,,Male,Age 35 to 39,,,,,,,,New York,2.0,10.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,68.04,Yes,Yes,No,No,,Yes,No +,33.3,,,,,,,Female,Age 45 to 49,,,,,,,,Wyoming,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,1 to 5,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Use them every day,No,"White only, Non-Hispanic",1.6,85.28,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,18.32,,,,,,,Female,Age 65 to 69,,,,,,,,Arkansas,0.0,30.0,Within past year (anytime less than 12 months ago),Yes,9.0,All,Yes,Yes,No,No,No,Yes,Yes,No,Yes,No,No,No,Yes,No,No,No,Current smoker - now smokes every day,Not at all (right now),No,"White only, Non-Hispanic",1.7,53.07,No,No,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,29.76,,,,,,,Male,Age 65 to 69,,,,,,,,South Dakota,3.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,86.18,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,Tested positive using home test without a health professional +,21.93,,,,,,,Female,Age 50 to 54,,,,,,,,South Dakota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,63.5,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +,40.24,,,,,,,Male,Age 35 to 39,,,,,,,,Arizona,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,Hispanic,1.85,138.35,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,No +No,30.79,No,No,No,0.0,0.0,No,Female,70-74,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,37.59,,,,,,,Female,Age 65 to 69,,,,,,,,Mississippi,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,None of them,No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,,"Black only, Non-Hispanic",1.7,108.86,No,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,24.89,,,,,,,Female,Age 60 to 64,,,,,,,,New Jersey,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,65.77,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,25.75,,,,,,,Female,Age 30 to 34,,,,,,,,South Dakota,5.0,30.0,Within past 2 years (1 year but less than 2 years ago),Yes,6.0,None of them,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,68.04,No,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,25.33,,,,,,,Female,Age 60 to 64,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,64.86,Yes,No,Yes,Yes,"Yes, received Tdap",No,No +,34.01,,,,,,,Female,Age 45 to 49,,,,,,,,Vermont,14.0,2.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,Yes,No,No,No,No,No,No,,Yes,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.55,81.65,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +No,34.87,Yes,No,No,0.0,0.0,No,Male,50-54,Other,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,27.44,,,,,,,Female,Age 25 to 29,,,,,,,,Kansas,2.0,20.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,Yes,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,77.11,Yes,No,Yes,Yes,"Yes, received Tdap",No,No +,21.29,,,,,,,Male,Age 25 to 29,,,,,,,,Colorado,0.0,15.0,5 or more years ago,Yes,6.0,1 to 5,No,No,No,Yes,No,Yes,No,No,No,No,No,No,Yes,No,No,No,Former smoker,Not at all (right now),No,Hispanic,1.73,63.5,No,No,No,No,"Yes, received Tdap",No,Yes +,,,,,,,,Male,Age 60 to 64,,,,,,,,New Jersey,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,"White only, Non-Hispanic",,79.38,,,,,,, +,28.25,,,,,,,Female,Age 35 to 39,,,,,,,,Florida,1.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"Black only, Non-Hispanic",1.68,79.38,No,Yes,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,21.97,,,,,,,Female,Age 60 to 64,,,,,,,,Indiana,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Use them some days,No,"White only, Non-Hispanic",1.6,56.25,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +No,28.29,No,No,No,0.0,7.0,No,Female,25-29,White,No,Yes,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Yes,38.74,Yes,No,No,30.0,15.0,No,Male,70-74,White,Yes,No,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,26.63,,,,,,,Female,Age 65 to 69,,,,,,,,Minnesota,3.0,1.0,Within past year (anytime less than 12 months ago),Yes,9.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,74.84,,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,Tested positive using home test without a health professional +,28.19,,,,,,,Female,Age 60 to 64,,,,,,,,Wisconsin,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,81.65,Yes,No,Yes,No,"Yes, received tetanus shot, but not Tdap",No,No +,23.75,,,,,,,Male,Age 40 to 44,,,,,,,,Maine,0.0,30.0,5 or more years ago,Yes,9.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,,1.85,81.65,No,No,No,,"No, did not receive any tetanus shot in the past 10 years",No,No +,32.77,,,,,,,Female,Age 80 or older,,,,,,,,Wisconsin,3.0,2.0,Within past year (anytime less than 12 months ago),No,6.0,,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.6,83.91,No,No,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes +No,26.57,No,No,No,0.0,0.0,No,Female,45-49,White,No,Yes,8.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,24.03,,,,,,,Female,Age 45 to 49,,,,,,,,Maryland,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,63.5,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,Yes +,35.0,,,,,,,Male,Age 25 to 29,,,,,,,,Virginia,15.0,15.0,5 or more years ago,Yes,5.0,None of them,No,No,No,Yes,No,No,Yes,No,No,No,No,No,Yes,No,No,Yes,Never smoked,Use them some days,No,"Black only, Non-Hispanic",1.91,127.01,Yes,Yes,No,No,"Yes, received Tdap",No,Yes +No,30.85,Yes,No,No,0.0,0.0,No,Male,55-59,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,,,,,,,,Female,Age 35 to 39,,,,,,,,Oklahoma,,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,,,,Hispanic,1.55,,,,,,,, +Yes,30.85,No,No,No,0.0,0.0,No,Male,80 or older,White,No,Yes,8.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,23.49,,,,,,,Male,Age 18 to 24,,,,,,,,Colorado,2.0,20.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,Yes,No,No,Yes,Never smoked,Not at all (right now),No,Hispanic,1.7,68.04,Yes,No,No,Yes,"Yes, received tetanus shot, but not Tdap",Yes,Yes +No,24.41,No,No,No,0.0,0.0,No,Female,60-64,White,No,Yes,10.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,34.46,,,,,,,Female,Age 40 to 44,,,,,,,,Nebraska,28.0,30.0,Within past 2 years (1 year but less than 2 years ago),No,6.0,1 to 5,Yes,No,No,No,No,No,Yes,No,Yes,No,No,No,Yes,Yes,Yes,Yes,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,99.79,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No +No,25.7,Yes,No,No,0.0,0.0,No,Female,80 or older,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,24.69,No,No,No,0.0,0.0,No,Male,18-24,Asian,No,No,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,21.95,No,No,No,2.0,1.0,Yes,Female,50-54,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,26.26,,,,,,,Male,Age 25 to 29,,,,,,,,Massachusetts,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,83.01,Yes,No,No,Yes,"Yes, received tetanus shot but not sure what type",No,No +,25.1,,,,,,,Male,Age 55 to 59,,,,,,,,Minnesota,0.0,0.0,5 or more years ago,Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.8,81.65,Yes,No,No,No,"Yes, received tetanus shot, but not Tdap",No,No +,20.6,,,,,,,Female,Age 55 to 59,,,,,,,,Montana,3.0,3.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,54.43,Yes,No,Yes,No,"Yes, received tetanus shot, but not Tdap",No,Yes +,30.23,,,,,,,Female,Age 70 to 74,,,,,,,,North Dakota,5.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.55,72.57,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,33.23,,,,,,,Male,Age 18 to 24,,,,,,,,Maryland,7.0,7.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.83,111.13,Yes,Yes,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +,31.62,,,,,,,Female,Age 40 to 44,,,,,,,,Texas,7.0,12.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,86.18,Yes,No,No,No,"Yes, received Tdap",No,No +,28.35,,,,,,,Female,Age 80 or older,,,,,,,,Nebraska,5.0,0.0,Within past year (anytime less than 12 months ago),Yes,3.0,1 to 5,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.57,70.31,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,29.05,,,,,,,Female,Age 60 to 64,,,,,,,,Guam,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,None of them,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,Hispanic,1.68,81.65,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",Yes,Tested positive using home test without a health professional +No,30.18,No,No,No,0.0,0.0,No,Female,60-64,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,22.32,Yes,Yes,No,1.0,0.0,No,Female,60-64,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,27.37,,,,,,,Male,Age 75 to 79,,,,,,,,South Dakota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,3.0,None of them,No,Yes,No,No,Yes,No,No,,No,Yes,No,Yes,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,81.65,No,No,Yes,Yes,"Yes, received Tdap",No,No +,19.53,,,,,,,Female,Age 55 to 59,,,,,,,,Minnesota,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.52,45.36,,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +No,25.06,No,No,No,0.0,10.0,No,Female,25-29,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,28.89,,,,,,,Male,Age 75 to 79,,,,,,,,Virginia,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,86.18,No,Yes,No,Yes,"Yes, received tetanus shot but not sure what type",, +,28.29,,,,,,,Female,Age 18 to 24,,,,,,,,Puerto Rico,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),No,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,Hispanic,1.65,77.11,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +,,,,,,,,Female,Age 35 to 39,,,,,,,,New Jersey,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,Hispanic,,,,,,,,, +,28.37,,,,,,,Male,Age 75 to 79,,,,,,,,Maryland,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,97.52,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +No,53.81,Yes,No,No,0.0,25.0,No,Male,35-39,Hispanic,No,No,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,30.62,,,,,,,Female,Age 75 to 79,,,,,,,,New Hampshire,30.0,0.0,Within past 5 years (2 years but less than 5 years ago),No,4.0,None of them,No,No,No,Yes,No,Yes,Yes,Yes,Yes,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,83.46,No,No,Yes,Yes,"Yes, received Tdap",No,No +No,26.93,Yes,Yes,No,4.0,2.0,No,Female,55-59,White,No,Yes,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,27.12,Yes,No,No,0.0,0.0,Yes,Male,60-64,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,29.18,,,,,,,Female,Age 25 to 29,,,,,,,,New Jersey,0.0,2.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.63,77.11,Yes,No,Yes,No,"Yes, received tetanus shot, but not Tdap",No,No +,30.27,,,,,,,Male,Age 50 to 54,,,,,,,,Puerto Rico,5.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,Hispanic,1.75,92.99,No,No,No,No,"Yes, received Tdap",No,Yes +,36.13,,,,,,,Female,Age 30 to 34,,,,,,,,Ohio,14.0,5.0,Within past year (anytime less than 12 months ago),No,6.0,1 to 5,No,No,No,No,No,Yes,Yes,No,Yes,No,No,Yes,Yes,Yes,No,Yes,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"Multiracial, Non-Hispanic",1.52,83.91,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +,22.71,,,,,,,Female,Age 18 to 24,,,,,,,,Utah,0.0,5.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,65.77,No,No,Yes,Yes,"Yes, received Tdap",No,No +No,22.92,No,No,No,0.0,2.0,No,Female,60-64,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,27.89,,,,,,,Male,,,,,,,,,Georgia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,,,No,"Black only, Non-Hispanic",1.8,90.72,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +,29.23,,,,,,,Female,Age 70 to 74,,,,,,,,Texas,0.0,2.0,Within past year (anytime less than 12 months ago),Yes,8.0,All,No,No,No,No,No,No,No,No,Yes,"No, pre-diabetes or borderline diabetes",No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.6,74.84,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,No +,25.61,,,,,,,Female,Age 35 to 39,,,,,,,,South Dakota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,63.5,Yes,Yes,No,,,No,No +,26.63,,,,,,,Female,Age 45 to 49,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,Yes,Yes,Yes,Never smoked,Not at all (right now),Yes,"Black only, Non-Hispanic",1.65,72.57,No,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,30.27,Yes,No,No,0.0,0.0,No,Male,40-44,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,20.25,,,,,,,Male,Age 65 to 69,,,,,,,,Maine,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),No,"White only, Non-Hispanic",1.91,73.48,,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,24.75,,,,,,,Female,Age 45 to 49,,,,,,,,Maine,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,71.67,Yes,,Yes,No,"Yes, received Tdap",No,No diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_005/history.csv b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_005/history.csv index 3979453fb9ec8e79a49380e0d266ac67173b77a8..de95b7473de04beebc8180154645691d955fe4eb 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_005/history.csv +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_005/history.csv @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:32f0b62dce3fcf8de262581beb10ec8f7547c89138f684b86ccef48c69cc6ed7 -size 271613617 +oid sha256:4239cc1e06862b79d5f7c765cc88f34859e8f5cae19517395d11178745b47592 +size 270592976 diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_005/info.json b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_005/info.json index 53272e3074451c673817a05ef4da5ee438d07863..c876b7e9aa18ec2175d72a1a6b59bf487c77e98c 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_005/info.json +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_005/info.json @@ -1,401 +1,393 @@ -{ - "name": "Indicators of Heart Disease (2022 UPDATE)", - "source": "https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease/data", - "data_intro": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", - "is_splited": false, - "overall_size": 1010949, - "train_size": 0, - "test_size": 0, - "c_classes": 46, - "n_classes": 9, - "cat_feature_intro": { - "HeartDisease": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", - "Smoking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", - "AlcoholDrinking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", - "Stroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", - "DiffWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", - "Sex": "Sex is a biological factor that can influence the risk of heart disease. Men and women may experience different symptoms and risk factors for heart disease.", - "AgeCategory": "Age is a significant risk factor for heart disease. As people age, their risk of developing heart disease increases. It is important to monitor heart health as individuals get older.", - "Race": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", - "Diabetic": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", - "PhysicalActivity": "Regular physical activity is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", - "GenHealth": "General health status can influence the risk of heart disease. It is important to maintain overall health and address any underlying health issues to reduce the risk of heart-related complications.", - "Asthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", - "KidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", - "SkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", - "State": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", - "GeneralHealth": "General health status can influence the risk of heart disease. It is important to maintain overall health and address any underlying health issues to reduce the risk of heart-related complications.", - "LastCheckupTime": "The time since the last medical checkup can be an important factor in heart health. Regular checkups can help identify and address risk factors for heart disease early.", - "PhysicalActivities": "Engaging in regular physical activities is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", - "RemovedTeeth": "The number of removed teeth can be an indicator of overall health, including heart health. Poor oral health has been linked to an increased risk of heart disease.", - "HadHeartAttack": "As described above, the original dataset of nearly 300 variables was reduced to 40 variables. In addition to classical EDA, this dataset can be used to apply a number of machine learning methods, especially classifier models (logistic regression, SVM, random forest, etc.). You should treat the variable \"HadHeartAttack\" as binary (\"Yes\" - respondent had heart disease; \"No\" - respondent did not have heart disease). Note, however, that the classes are unbalanced, so the classic approach of applying a model is not advisable. Fixing the weights/undersampling should yield much better results. Based on the data set, I built a logistic regression model and embedded it in an application that might inspire you: https://share.streamlit.io/kamilpytlak/heart-condition-checker/main/app.py. Can you indicate which variables have a significant effect on the likelihood of heart disease?", - "HadAngina": "Angina is a type of chest pain caused by reduced blood flow to the heart. It is a symptom of coronary artery disease and can indicate an increased risk of heart disease.", - "HadStroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", - "HadAsthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", - "HadSkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", - "HadCOPD": "Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease that causes obstructed airflow from the lungs. It can increase the risk of heart disease.", - "HadDepressiveDisorder": "Depressive disorders can affect overall health and increase the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", - "HadKidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", - "HadArthritis": "Arthritis is a condition that causes inflammation of the joints. It can affect overall health and increase the risk of heart disease.", - "HadDiabetes": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", - "DeafOrHardOfHearing": "Hearing loss can affect overall health and quality of life. It is important to address and manage hearing issues to maintain overall health and reduce the risk of heart disease.", - "BlindOrVisionDifficulty": "Vision difficulties can affect overall health and quality of life. It is important to address and manage vision issues to maintain overall health and reduce the risk of heart disease.", - "DifficultyConcentrating": "Difficulty concentrating can be a sign of underlying health issues, including heart disease. It is important to address cognitive issues to improve overall health and reduce the risk of heart-related complications.", - "DifficultyWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", - "DifficultyDressingBathing": "Difficulty with daily activities such as dressing and bathing can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", - "DifficultyErrands": "Difficulty running errands can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", - "SmokerStatus": "Smoking is a major risk factor for heart disease. It is important to address and manage smoking habits to reduce the risk of heart-related complications.", - "ECigaretteUsage": "E-cigarette usage can affect heart health. It is important to monitor and manage e-cigarette usage to reduce the risk of heart disease.", - "ChestScan": "Chest scans can help identify underlying health issues, including heart disease. It is important to monitor and address any abnormalities found in chest scans to maintain overall health.", - "RaceEthnicityCategory": "Race and ethnicity can influence the risk of heart disease. It is important to consider these factors when assessing and managing heart health.", - "AlcoholDrinkers": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", - "HIVTesting": "HIV testing is important for overall health. It is important to address and manage HIV status to reduce the risk of heart-related complications.", - "FluVaxLast12": "Receiving a flu vaccine in the last 12 months can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", - "PneumoVaxEver": "Receiving a pneumococcal vaccine can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", - "TetanusLast10Tdap": "Receiving a tetanus vaccine in the last 10 years can help reduce the risk of infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", - "HighRiskLastYear": "Being at high risk for health issues in the last year can affect overall health and increase the risk of heart disease. It is important to address and manage high-risk factors to reduce the risk of heart-related complications.", - "CovidPos": "Testing positive for COVID-19 can affect overall health and increase the risk of heart disease. It is important to monitor and manage COVID-19 symptoms to reduce the risk of heart-related complications." - }, - "num_feature_intro": { - "BMI": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", - "PhysicalHealth": "Physical health is an important factor in overall well-being and can influence the risk of heart disease. It is important to maintain good physical health to reduce the risk of heart-related complications.", - "MentalHealth": "Mental health is an important factor in overall well-being and can influence the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", - "SleepTime": "Sleep time is an important factor in overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", - "PhysicalHealthDays": "The number of days with good physical health can be an indicator of overall well-being. It is important to maintain good physical health to reduce the risk of heart-related complications.", - "MentalHealthDays": "The number of days with good mental health can be an indicator of overall well-being. It is important to address and manage mental health to reduce the risk of heart-related complications.", - "SleepHours": "The number of hours of sleep can affect overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", - "HeightInMeters": "Height can be an indicator of overall health. It is important to monitor and maintain a healthy height-to-weight ratio to reduce the risk of heart disease.", - "WeightInKilograms": "Weight can be an indicator of overall health. It is important to maintain a healthy weight to reduce the risk of heart disease." - }, - "evaluation_metric": "The evaluation metric for this dataset can include accuracy, precision, recall, F1-score, and AUC-ROC. These metrics can help assess the performance of machine learning models in predicting heart disease.", - "task_type": "classification", - "target": "GeneralHealth", - "num_feature_value": { - "BMI": [ - 12.02, - 99.64 - ], - "HeightInMeters": [ - 0.91, - 2.41 - ], - "MentalHealth": [ - 0.0, - 30.0 - ], - "MentalHealthDays": [ - 0.0, - 30.0 - ], - "PhysicalHealth": [ - 0.0, - 30.0 - ], - "PhysicalHealthDays": [ - 0.0, - 30.0 - ], - "SleepHours": [ - 1.0, - 24.0 - ], - "SleepTime": [ - 1.0, - 24.0 - ], - "WeightInKilograms": [ - 22.68, - 292.57 - ] - }, - "cat_feature_value": { - "AgeCategory": [ - "18-24", - "25-29", - "30-34", - "35-39", - "40-44", - "45-49", - "50-54", - "55-59", - "60-64", - "65-69", - "70-74", - "75-79", - "80 or older", - "Age 18 to 24", - "Age 25 to 29", - "Age 30 to 34", - "Age 35 to 39", - "Age 40 to 44", - "Age 45 to 49", - "Age 50 to 54", - "Age 55 to 59", - "Age 60 to 64", - "Age 65 to 69", - "Age 70 to 74", - "Age 75 to 79", - "Age 80 or older" - ], - "AlcoholDrinkers": [ - "No", - "Yes" - ], - "AlcoholDrinking": [ - "No", - "Yes" - ], - "Asthma": [ - "No", - "Yes" - ], - "BlindOrVisionDifficulty": [ - "No", - "Yes" - ], - "ChestScan": [ - "No", - "Yes" - ], - "CovidPos": [ - "No", - "Tested positive using home test without a health professional", - "Yes" - ], - "DeafOrHardOfHearing": [ - "No", - "Yes" - ], - "Diabetic": [ - "No", - "No, borderline diabetes", - "Yes", - "Yes (during pregnancy)" - ], - "DiffWalking": [ - "No", - "Yes" - ], - "DifficultyConcentrating": [ - "No", - "Yes" - ], - "DifficultyDressingBathing": [ - "No", - "Yes" - ], - "DifficultyErrands": [ - "No", - "Yes" - ], - "DifficultyWalking": [ - "No", - "Yes" - ], - "ECigaretteUsage": [ - "Never used e-cigarettes in my entire life", - "Not at all (right now)", - "Use them every day", - "Use them some days" - ], - "FluVaxLast12": [ - "No", - "Yes" - ], - "GenHealth": [ - "Excellent", - "Fair", - "Good", - "Poor", - "Very good" - ], - "GeneralHealth": [ - "Excellent", - "Fair", - "Good", - "Poor", - "Very good" - ], - "HIVTesting": [ - "No", - "Yes" - ], - "HadAngina": [ - "No", - "Yes" - ], - "HadArthritis": [ - "No", - "Yes" - ], - "HadAsthma": [ - "No", - "Yes" - ], - "HadCOPD": [ - "No", - "Yes" - ], - "HadDepressiveDisorder": [ - "No", - "Yes" - ], - "HadDiabetes": [ - "No", - "No, pre-diabetes or borderline diabetes", - "Yes", - "Yes, but only during pregnancy (female)" - ], - "HadHeartAttack": [ - "No", - "Yes" - ], - "HadKidneyDisease": [ - "No", - "Yes" - ], - "HadSkinCancer": [ - "No", - "Yes" - ], - "HadStroke": [ - "No", - "Yes" - ], - "HeartDisease": [ - "No", - "Yes" - ], - "HighRiskLastYear": [ - "No", - "Yes" - ], - "KidneyDisease": [ - "No", - "Yes" - ], - "LastCheckupTime": [ - "5 or more years ago", - "Within past 2 years (1 year but less than 2 years ago)", - "Within past 5 years (2 years but less than 5 years ago)", - "Within past year (anytime less than 12 months ago)" - ], - "PhysicalActivities": [ - "No", - "Yes" - ], - "PhysicalActivity": [ - "No", - "Yes" - ], - "PneumoVaxEver": [ - "No", - "Yes" - ], - "Race": [ - "American Indian/Alaskan Native", - "Asian", - "Black", - "Hispanic", - "Other", - "White" - ], - "RaceEthnicityCategory": [ - "Black only, Non-Hispanic", - "Hispanic", - "Multiracial, Non-Hispanic", - "Other race only, Non-Hispanic", - "White only, Non-Hispanic" - ], - "RemovedTeeth": [ - "1 to 5", - "6 or more, but not all", - "All", - "None of them" - ], - "Sex": [ - "Female", - "Male" - ], - "SkinCancer": [ - "No", - "Yes" - ], - "SmokerStatus": [ - "Current smoker - now smokes every day", - "Current smoker - now smokes some days", - "Former smoker", - "Never smoked" - ], - "Smoking": [ - "No", - "Yes" - ], - "State": [ - "Alabama", - "Alaska", - "Arizona", - "Arkansas", - "California", - "Colorado", - "Connecticut", - "Delaware", - "District of Columbia", - "Florida", - "Georgia", - "Guam", - "Hawaii", - "Idaho", - "Illinois", - "Indiana", - "Iowa", - "Kansas", - "Kentucky", - "Louisiana", - "Maine", - "Maryland", - "Massachusetts", - "Michigan", - "Minnesota", - "Mississippi", - "Missouri", - "Montana", - "Nebraska", - "Nevada", - "New Hampshire", - "New Jersey", - "New Mexico", - "New York", - "North Carolina", - "North Dakota", - "Ohio", - "Oklahoma", - "Oregon", - "Pennsylvania", - "Puerto Rico", - "Rhode Island", - "South Carolina", - "South Dakota", - "Tennessee", - "Texas", - "Utah", - "Vermont", - "Virgin Islands", - "Virginia", - "Washington", - "West Virginia", - "Wisconsin", - "Wyoming" - ], - "Stroke": [ - "No", - "Yes" - ], - "TetanusLast10Tdap": [ - "No, did not receive any tetanus shot in the past 10 years", - "Yes, received Tdap", - "Yes, received tetanus shot but not sure what type", - "Yes, received tetanus shot, but not Tdap" - ] - } -} \ No newline at end of file +{ + "name": "Indicators of Heart Disease (2022 UPDATE)", + "source": "https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease/data", + "data_intro": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", + "is_splited": false, + "overall_size": 1010949, + "train_size": 0, + "test_size": 0, + "c_classes": 45, + "n_classes": 9, + "cat_feature_intro": { + "HeartDisease": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", + "Smoking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", + "AlcoholDrinking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", + "Stroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", + "DiffWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", + "Sex": "Sex is a biological factor that can influence the risk of heart disease. Men and women may experience different symptoms and risk factors for heart disease.", + "AgeCategory": "Age is a significant risk factor for heart disease. As people age, their risk of developing heart disease increases. It is important to monitor heart health as individuals get older.", + "Race": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", + "Diabetic": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", + "PhysicalActivity": "Regular physical activity is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", + "Asthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", + "KidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", + "SkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", + "State": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", + "GeneralHealth": "Self-reported general health from BRFSS GENHLTH: Excellent, Very good, Good, Fair, or Poor.", + "LastCheckupTime": "The time since the last medical checkup can be an important factor in heart health. Regular checkups can help identify and address risk factors for heart disease early.", + "PhysicalActivities": "Engaging in regular physical activities is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", + "RemovedTeeth": "The number of removed teeth can be an indicator of overall health, including heart health. Poor oral health has been linked to an increased risk of heart disease.", + "HadHeartAttack": "As described above, the original dataset of nearly 300 variables was reduced to 40 variables. In addition to classical EDA, this dataset can be used to apply a number of machine learning methods, especially classifier models (logistic regression, SVM, random forest, etc.). You should treat the variable \"HadHeartAttack\" as binary (\"Yes\" - respondent had heart disease; \"No\" - respondent did not have heart disease). Note, however, that the classes are unbalanced, so the classic approach of applying a model is not advisable. Fixing the weights/undersampling should yield much better results. Based on the data set, I built a logistic regression model and embedded it in an application that might inspire you: https://share.streamlit.io/kamilpytlak/heart-condition-checker/main/app.py. Can you indicate which variables have a significant effect on the likelihood of heart disease?", + "HadAngina": "Angina is a type of chest pain caused by reduced blood flow to the heart. It is a symptom of coronary artery disease and can indicate an increased risk of heart disease.", + "HadStroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", + "HadAsthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", + "HadSkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", + "HadCOPD": "Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease that causes obstructed airflow from the lungs. It can increase the risk of heart disease.", + "HadDepressiveDisorder": "Depressive disorders can affect overall health and increase the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", + "HadKidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", + "HadArthritis": "Arthritis is a condition that causes inflammation of the joints. It can affect overall health and increase the risk of heart disease.", + "HadDiabetes": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", + "DeafOrHardOfHearing": "Hearing loss can affect overall health and quality of life. It is important to address and manage hearing issues to maintain overall health and reduce the risk of heart disease.", + "BlindOrVisionDifficulty": "Vision difficulties can affect overall health and quality of life. It is important to address and manage vision issues to maintain overall health and reduce the risk of heart disease.", + "DifficultyConcentrating": "Difficulty concentrating can be a sign of underlying health issues, including heart disease. It is important to address cognitive issues to improve overall health and reduce the risk of heart-related complications.", + "DifficultyWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", + "DifficultyDressingBathing": "Difficulty with daily activities such as dressing and bathing can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", + "DifficultyErrands": "Difficulty running errands can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", + "SmokerStatus": "Smoking is a major risk factor for heart disease. It is important to address and manage smoking habits to reduce the risk of heart-related complications.", + "ECigaretteUsage": "E-cigarette usage can affect heart health. It is important to monitor and manage e-cigarette usage to reduce the risk of heart disease.", + "ChestScan": "Chest scans can help identify underlying health issues, including heart disease. It is important to monitor and address any abnormalities found in chest scans to maintain overall health.", + "RaceEthnicityCategory": "Race and ethnicity can influence the risk of heart disease. It is important to consider these factors when assessing and managing heart health.", + "AlcoholDrinkers": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", + "HIVTesting": "Whether the respondent has ever been tested for HIV (Yes/No), not the result of that test.", + "FluVaxLast12": "Receiving a flu vaccine in the last 12 months can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", + "PneumoVaxEver": "Receiving a pneumococcal vaccine can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", + "TetanusLast10Tdap": "Receiving a tetanus vaccine in the last 10 years can help reduce the risk of infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", + "HighRiskLastYear": "Being at high risk for health issues in the last year can affect overall health and increase the risk of heart disease. It is important to address and manage high-risk factors to reduce the risk of heart-related complications.", + "CovidPos": "Testing positive for COVID-19 can affect overall health and increase the risk of heart disease. It is important to monitor and manage COVID-19 symptoms to reduce the risk of heart-related complications." + }, + "num_feature_intro": { + "BMI": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", + "PhysicalHealth": "Physical health is an important factor in overall well-being and can influence the risk of heart disease. It is important to maintain good physical health to reduce the risk of heart-related complications.", + "MentalHealth": "Mental health is an important factor in overall well-being and can influence the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", + "SleepTime": "Sleep time is an important factor in overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", + "PhysicalHealthDays": "The number of days with good physical health can be an indicator of overall well-being. It is important to maintain good physical health to reduce the risk of heart-related complications.", + "MentalHealthDays": "The number of days with good mental health can be an indicator of overall well-being. It is important to address and manage mental health to reduce the risk of heart-related complications.", + "SleepHours": "The number of hours of sleep can affect overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", + "HeightInMeters": "Height can be an indicator of overall health. It is important to monitor and maintain a healthy height-to-weight ratio to reduce the risk of heart disease.", + "WeightInKilograms": "Weight can be an indicator of overall health. It is important to maintain a healthy weight to reduce the risk of heart disease." + }, + "evaluation_metric": "The evaluation metric for this dataset can include accuracy, precision, recall, F1-score, and AUC-ROC. These metrics can help assess the performance of machine learning models in predicting heart disease.", + "task_type": "classification", + "target": "GeneralHealth", + "num_feature_value": { + "BMI": [ + 12.02, + 99.64 + ], + "HeightInMeters": [ + 0.91, + 2.41 + ], + "MentalHealth": [ + 0.0, + 30.0 + ], + "MentalHealthDays": [ + 0.0, + 30.0 + ], + "PhysicalHealth": [ + 0.0, + 30.0 + ], + "PhysicalHealthDays": [ + 0.0, + 30.0 + ], + "SleepHours": [ + 1.0, + 24.0 + ], + "SleepTime": [ + 1.0, + 24.0 + ], + "WeightInKilograms": [ + 22.68, + 292.57 + ] + }, + "cat_feature_value": { + "AgeCategory": [ + "18-24", + "25-29", + "30-34", + "35-39", + "40-44", + "45-49", + "50-54", + "55-59", + "60-64", + "65-69", + "70-74", + "75-79", + "80 or older", + "Age 18 to 24", + "Age 25 to 29", + "Age 30 to 34", + "Age 35 to 39", + "Age 40 to 44", + "Age 45 to 49", + "Age 50 to 54", + "Age 55 to 59", + "Age 60 to 64", + "Age 65 to 69", + "Age 70 to 74", + "Age 75 to 79", + "Age 80 or older" + ], + "AlcoholDrinkers": [ + "No", + "Yes" + ], + "AlcoholDrinking": [ + "No", + "Yes" + ], + "Asthma": [ + "No", + "Yes" + ], + "BlindOrVisionDifficulty": [ + "No", + "Yes" + ], + "ChestScan": [ + "No", + "Yes" + ], + "CovidPos": [ + "No", + "Tested positive using home test without a health professional", + "Yes" + ], + "DeafOrHardOfHearing": [ + "No", + "Yes" + ], + "Diabetic": [ + "No", + "No, borderline diabetes", + "Yes", + "Yes (during pregnancy)" + ], + "DiffWalking": [ + "No", + "Yes" + ], + "DifficultyConcentrating": [ + "No", + "Yes" + ], + "DifficultyDressingBathing": [ + "No", + "Yes" + ], + "DifficultyErrands": [ + "No", + "Yes" + ], + "DifficultyWalking": [ + "No", + "Yes" + ], + "ECigaretteUsage": [ + "Never used e-cigarettes in my entire life", + "Not at all (right now)", + "Use them every day", + "Use them some days" + ], + "FluVaxLast12": [ + "No", + "Yes" + ], + "GeneralHealth": [ + "Excellent", + "Very good", + "Good", + "Fair", + "Poor" + ], + "HIVTesting": [ + "No", + "Yes" + ], + "HadAngina": [ + "No", + "Yes" + ], + "HadArthritis": [ + "No", + "Yes" + ], + "HadAsthma": [ + "No", + "Yes" + ], + "HadCOPD": [ + "No", + "Yes" + ], + "HadDepressiveDisorder": [ + "No", + "Yes" + ], + "HadDiabetes": [ + "No", + "No, pre-diabetes or borderline diabetes", + "Yes", + "Yes, but only during pregnancy (female)" + ], + "HadHeartAttack": [ + "No", + "Yes" + ], + "HadKidneyDisease": [ + "No", + "Yes" + ], + "HadSkinCancer": [ + "No", + "Yes" + ], + "HadStroke": [ + "No", + "Yes" + ], + "HeartDisease": [ + "No", + "Yes" + ], + "HighRiskLastYear": [ + "No", + "Yes" + ], + "KidneyDisease": [ + "No", + "Yes" + ], + "LastCheckupTime": [ + "5 or more years ago", + "Within past 2 years (1 year but less than 2 years ago)", + "Within past 5 years (2 years but less than 5 years ago)", + "Within past year (anytime less than 12 months ago)" + ], + "PhysicalActivities": [ + "No", + "Yes" + ], + "PhysicalActivity": [ + "No", + "Yes" + ], + "PneumoVaxEver": [ + "No", + "Yes" + ], + "Race": [ + "American Indian/Alaskan Native", + "Asian", + "Black", + "Hispanic", + "Other", + "White" + ], + "RaceEthnicityCategory": [ + "Black only, Non-Hispanic", + "Hispanic", + "Multiracial, Non-Hispanic", + "Other race only, Non-Hispanic", + "White only, Non-Hispanic" + ], + "RemovedTeeth": [ + "1 to 5", + "6 or more, but not all", + "All", + "None of them" + ], + "Sex": [ + "Female", + "Male" + ], + "SkinCancer": [ + "No", + "Yes" + ], + "SmokerStatus": [ + "Current smoker - now smokes every day", + "Current smoker - now smokes some days", + "Former smoker", + "Never smoked" + ], + "Smoking": [ + "No", + "Yes" + ], + "State": [ + "Alabama", + "Alaska", + "Arizona", + "Arkansas", + "California", + "Colorado", + "Connecticut", + "Delaware", + "District of Columbia", + "Florida", + "Georgia", + "Guam", + "Hawaii", + "Idaho", + "Illinois", + "Indiana", + "Iowa", + "Kansas", + "Kentucky", + "Louisiana", + "Maine", + "Maryland", + "Massachusetts", + "Michigan", + "Minnesota", + "Mississippi", + "Missouri", + "Montana", + "Nebraska", + "Nevada", + "New Hampshire", + "New Jersey", + "New Mexico", + "New York", + "North Carolina", + "North Dakota", + "Ohio", + "Oklahoma", + "Oregon", + "Pennsylvania", + "Puerto Rico", + "Rhode Island", + "South Carolina", + "South Dakota", + "Tennessee", + "Texas", + "Utah", + "Vermont", + "Virgin Islands", + "Virginia", + "Washington", + "West Virginia", + "Wisconsin", + "Wyoming" + ], + "Stroke": [ + "No", + "Yes" + ], + "TetanusLast10Tdap": [ + "No, did not receive any tetanus shot in the past 10 years", + "Yes, received Tdap", + "Yes, received tetanus shot but not sure what type", + "Yes, received tetanus shot, but not Tdap" + ] + } +} diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_005/test_current.csv b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_005/test_current.csv index 91f29c9b87c802dcde4590fd6275202c300409d2..b882b142a2688db2e7d8083990f225bfa7e3a3c9 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_005/test_current.csv +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_005/test_current.csv @@ -1,16 +1,18 @@ -HeartDisease,BMI,Smoking,AlcoholDrinking,Stroke,PhysicalHealth,MentalHealth,DiffWalking,Sex,AgeCategory,Race,Diabetic,PhysicalActivity,GenHealth,SleepTime,Asthma,KidneyDisease,SkinCancer,State,GeneralHealth,PhysicalHealthDays,MentalHealthDays,LastCheckupTime,PhysicalActivities,SleepHours,RemovedTeeth,HadHeartAttack,HadAngina,HadStroke,HadAsthma,HadSkinCancer,HadCOPD,HadDepressiveDisorder,HadKidneyDisease,HadArthritis,HadDiabetes,DeafOrHardOfHearing,BlindOrVisionDifficulty,DifficultyConcentrating,DifficultyWalking,DifficultyDressingBathing,DifficultyErrands,SmokerStatus,ECigaretteUsage,ChestScan,RaceEthnicityCategory,HeightInMeters,WeightInKilograms,AlcoholDrinkers,HIVTesting,FluVaxLast12,PneumoVaxEver,TetanusLast10Tdap,HighRiskLastYear,CovidPos -,25.42,,,,,,,Female,Age 80 or older,,,,,,,,,Maryland,Poor,30.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,"6 or more, but not all",Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Yes,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.57,63.05,No,No,No,Yes,"Yes, received Tdap",No,No -,20.8,,,,,,,Female,Age 80 or older,,,,,,,,,Georgia,Poor,30.0,30.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,56.7,No,,,,,, -,27.46,,,,,,,Female,Age 60 to 64,,,,,,,,,Nebraska,Poor,30.0,0.0,Within past year (anytime less than 12 months ago),No,5.0,1 to 5,No,No,No,No,No,No,No,No,No,Yes,No,Yes,No,Yes,Yes,Yes,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,Yes,Hispanic,1.63,72.57,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,24.8,,,,,,,Female,Age 55 to 59,,,,,,,,,Oklahoma,Poor,2.0,3.0,Within past year (anytime less than 12 months ago),No,6.0,"6 or more, but not all",No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.6,63.5,No,Yes,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,38.67,,,,,,,Female,Age 65 to 69,,,,,,,,,Michigan,Poor,20.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,Yes,No,No,No,No,Yes,No,No,Yes,No,No,No,No,Yes,Yes,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.52,89.81,No,No,Yes,Yes,"Yes, received Tdap",No,No -,,,,,,,,Female,Age 18 to 24,,,,,,,,,Georgia,Poor,20.0,30.0,Within past 2 years (1 year but less than 2 years ago),No,5.0,None of them,No,No,No,Yes,,No,Yes,No,No,No,,No,Yes,,No,Yes,Never smoked,Use them every day,No,"White only, Non-Hispanic",,61.23,Yes,Yes,No,Yes,"Yes, received tetanus shot, but not Tdap",No,Yes -,14.88,,,,,,,Female,Age 70 to 74,,,,,,,,,Tennessee,Poor,30.0,25.0,Within past year (anytime less than 12 months ago),No,1.0,"6 or more, but not all",No,No,No,No,No,Yes,Yes,Yes,Yes,Yes,No,No,Yes,Yes,Yes,Yes,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,43.09,No,No,No,Yes,"Yes, received Tdap",No,Yes -,27.46,,,,,,,Female,Age 70 to 74,,,,,,,,,Montana,Poor,20.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,All,No,Yes,No,No,Yes,No,Yes,Yes,Yes,Yes,No,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,70.31,No,No,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Tested positive using home test without a health professional -,23.03,,,,,,,Female,Age 70 to 74,,,,,,,,,Michigan,Poor,30.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,58.97,Yes,No,Yes,Yes,"Yes, received Tdap",No,No -,23.03,,,,,,,Female,Age 50 to 54,,,,,,,,,Massachusetts,Poor,30.0,5.0,Within past year (anytime less than 12 months ago),No,10.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,58.97,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No -,21.46,,,,,,,Female,Age 70 to 74,,,,,,,,,Florida,Poor,8.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,"6 or more, but not all",Yes,Yes,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,56.7,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,36.61,,,,,,,Female,Age 55 to 59,,,,,,,,,West Virginia,Poor,30.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,11.0,"6 or more, but not all",No,No,No,No,No,No,Yes,No,Yes,Yes,No,Yes,No,Yes,No,Yes,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,99.79,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,27.99,,,,,,,Female,Age 55 to 59,,,,,,,,,Kentucky,Poor,30.0,15.0,Within past year (anytime less than 12 months ago),Yes,12.0,"6 or more, but not all",No,No,No,Yes,No,Yes,Yes,No,Yes,No,No,No,Yes,Yes,No,Yes,Former smoker,Use them some days,No,"White only, Non-Hispanic",1.6,71.67,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No -,34.01,,,,,,,Female,Age 45 to 49,,,,,,,,,Vermont,Poor,14.0,2.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,Yes,No,No,No,No,No,No,,Yes,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.55,81.65,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,30.62,,,,,,,Female,Age 75 to 79,,,,,,,,,New Hampshire,Poor,30.0,0.0,Within past 5 years (2 years but less than 5 years ago),No,4.0,None of them,No,No,No,Yes,No,Yes,Yes,Yes,Yes,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,83.46,No,No,Yes,Yes,"Yes, received Tdap",No,No +HeartDisease,BMI,Smoking,AlcoholDrinking,Stroke,PhysicalHealth,MentalHealth,DiffWalking,Sex,AgeCategory,Race,Diabetic,PhysicalActivity,SleepTime,Asthma,KidneyDisease,SkinCancer,State,GeneralHealth,PhysicalHealthDays,MentalHealthDays,LastCheckupTime,PhysicalActivities,SleepHours,RemovedTeeth,HadHeartAttack,HadAngina,HadStroke,HadAsthma,HadSkinCancer,HadCOPD,HadDepressiveDisorder,HadKidneyDisease,HadArthritis,HadDiabetes,DeafOrHardOfHearing,BlindOrVisionDifficulty,DifficultyConcentrating,DifficultyWalking,DifficultyDressingBathing,DifficultyErrands,SmokerStatus,ECigaretteUsage,ChestScan,RaceEthnicityCategory,HeightInMeters,WeightInKilograms,AlcoholDrinkers,HIVTesting,FluVaxLast12,PneumoVaxEver,TetanusLast10Tdap,HighRiskLastYear,CovidPos +,25.42,,,,,,,Female,Age 80 or older,,,,,,,,Maryland,Poor,30.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,"6 or more, but not all",Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Yes,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.57,63.05,No,No,No,Yes,"Yes, received Tdap",No,No +,20.8,,,,,,,Female,Age 80 or older,,,,,,,,Georgia,Poor,30.0,30.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,56.7,No,,,,,, +No,31.32,No,No,No,30.0,30.0,No,Female,70-74,White,No,No,8.0,No,Yes,No,,Poor,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,27.46,,,,,,,Female,Age 60 to 64,,,,,,,,Nebraska,Poor,30.0,0.0,Within past year (anytime less than 12 months ago),No,5.0,1 to 5,No,No,No,No,No,No,No,No,No,Yes,No,Yes,No,Yes,Yes,Yes,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,Yes,Hispanic,1.63,72.57,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +Yes,37.55,No,No,No,20.0,0.0,Yes,Female,65-69,White,No,No,6.0,Yes,No,No,,Poor,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,24.8,,,,,,,Female,Age 55 to 59,,,,,,,,Oklahoma,Poor,2.0,3.0,Within past year (anytime less than 12 months ago),No,6.0,"6 or more, but not all",No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.6,63.5,No,Yes,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,38.67,,,,,,,Female,Age 65 to 69,,,,,,,,Michigan,Poor,20.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,Yes,No,No,No,No,Yes,No,No,Yes,No,No,No,No,Yes,Yes,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.52,89.81,No,No,Yes,Yes,"Yes, received Tdap",No,No +,,,,,,,,Female,Age 18 to 24,,,,,,,,Georgia,Poor,20.0,30.0,Within past 2 years (1 year but less than 2 years ago),No,5.0,None of them,No,No,No,Yes,,No,Yes,No,No,No,,No,Yes,,No,Yes,Never smoked,Use them every day,No,"White only, Non-Hispanic",,61.23,Yes,Yes,No,Yes,"Yes, received tetanus shot, but not Tdap",No,Yes +,14.88,,,,,,,Female,Age 70 to 74,,,,,,,,Tennessee,Poor,30.0,25.0,Within past year (anytime less than 12 months ago),No,1.0,"6 or more, but not all",No,No,No,No,No,Yes,Yes,Yes,Yes,Yes,No,No,Yes,Yes,Yes,Yes,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,43.09,No,No,No,Yes,"Yes, received Tdap",No,Yes +,27.46,,,,,,,Female,Age 70 to 74,,,,,,,,Montana,Poor,20.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,All,No,Yes,No,No,Yes,No,Yes,Yes,Yes,Yes,No,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,70.31,No,No,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Tested positive using home test without a health professional +,23.03,,,,,,,Female,Age 70 to 74,,,,,,,,Michigan,Poor,30.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,58.97,Yes,No,Yes,Yes,"Yes, received Tdap",No,No +,23.03,,,,,,,Female,Age 50 to 54,,,,,,,,Massachusetts,Poor,30.0,5.0,Within past year (anytime less than 12 months ago),No,10.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,58.97,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No +,21.46,,,,,,,Female,Age 70 to 74,,,,,,,,Florida,Poor,8.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,"6 or more, but not all",Yes,Yes,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,56.7,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,36.61,,,,,,,Female,Age 55 to 59,,,,,,,,West Virginia,Poor,30.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,11.0,"6 or more, but not all",No,No,No,No,No,No,Yes,No,Yes,Yes,No,Yes,No,Yes,No,Yes,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,99.79,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,27.99,,,,,,,Female,Age 55 to 59,,,,,,,,Kentucky,Poor,30.0,15.0,Within past year (anytime less than 12 months ago),Yes,12.0,"6 or more, but not all",No,No,No,Yes,No,Yes,Yes,No,Yes,No,No,No,Yes,Yes,No,Yes,Former smoker,Use them some days,No,"White only, Non-Hispanic",1.6,71.67,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No +,34.01,,,,,,,Female,Age 45 to 49,,,,,,,,Vermont,Poor,14.0,2.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,Yes,No,No,No,No,No,No,,Yes,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.55,81.65,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,30.62,,,,,,,Female,Age 75 to 79,,,,,,,,New Hampshire,Poor,30.0,0.0,Within past 5 years (2 years but less than 5 years ago),No,4.0,None of them,No,No,No,Yes,No,Yes,Yes,Yes,Yes,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,83.46,No,No,Yes,Yes,"Yes, received Tdap",No,No diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_006/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_006_current.json b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_006/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_006_current.json index 00e425fc67b1d72e0bd9013257e0c852d731af21..66fda0777ef381dbd03f275b4a8bf1b8dc2e4d01 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_006/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_006_current.json +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_006/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_006_current.json @@ -1,12049 +1,11849 @@ { - "id": "006", - "task_type": "B4", - "subtask_type": "filter", - "perspective": "data_holder", - "dataset_name": "Indicators of Heart Disease (2022 UPDATE)", - "table_path": "output/medical/classification/Indicators of Heart Disease (2022 UPDATE)_B4/Indicators of Heart Disease (2022 UPDATE)_B4_006/test.csv", - "query": "I've uploaded our past BRFSS survey results. From respondents in the new batch who report being daily smokers, I want to shortlist those who still manage to maintain good general health for further behavioral research.", - "meta_info": { - "domain": "medical" + "id": "006", + "task_type": "B4", + "subtask_type": "filter", + "perspective": "data_holder", + "dataset_name": "Indicators of Heart Disease (2022 UPDATE)", + "table_path": "output/medical/classification/Indicators of Heart Disease (2022 UPDATE)_B4/Indicators of Heart Disease (2022 UPDATE)_B4_006/test.csv", + "query": "I've uploaded our past BRFSS survey results. From respondents in the new batch who report being daily smokers, I want to shortlist those who still manage to maintain good general health for further behavioral research.", + "meta_info": { + "domain": "medical" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "HeartDisease": "No", + "BMI": 35.44, + "Smoking": "Yes", + "AlcoholDrinking": "No", + "Stroke": "No", + "PhysicalHealth": 30.0, + "MentalHealth": 10.0, + "DiffWalking": "No", + "Sex": "Male", + "AgeCategory": "55-59", + "Race": "White", + "Diabetic": "No", + "PhysicalActivity": "Yes", + "SleepTime": 7.0, + "Asthma": "No", + "KidneyDisease": "No", + "SkinCancer": "No", + "State": null, + "GeneralHealth": "Poor", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + "LastCheckupTime": null, + "PhysicalActivities": null, + "SleepHours": null, + "RemovedTeeth": null, + "HadHeartAttack": null, + "HadAngina": null, + "HadStroke": null, + "HadAsthma": null, + "HadSkinCancer": null, + "HadCOPD": null, + "HadDepressiveDisorder": null, + "HadKidneyDisease": null, + "HadArthritis": null, + "HadDiabetes": null, + "DeafOrHardOfHearing": null, + "BlindOrVisionDifficulty": null, + "DifficultyConcentrating": null, + "DifficultyWalking": null, + "DifficultyDressingBathing": null, + "DifficultyErrands": null, + "SmokerStatus": null, + "ECigaretteUsage": null, + "ChestScan": null, + "RaceEthnicityCategory": null, + "HeightInMeters": null, + "WeightInKilograms": null, + "AlcoholDrinkers": null, + "HIVTesting": null, + "FluVaxLast12": null, + "PneumoVaxEver": null, + "TetanusLast10Tdap": null, + "HighRiskLastYear": null, + "CovidPos": null + } + }, + { + "scenario_id": "002", + "features": { + "HeartDisease": null, + "BMI": 44.63, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 55 to 59", + "Race": null, + 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"HighRiskLastYear": null, + "CovidPos": null + } + }, + { + "scenario_id": "006", + "features": { + "HeartDisease": "No", + "BMI": 30.34, + "Smoking": "No", + "AlcoholDrinking": "No", + "Stroke": "No", + "PhysicalHealth": 0.0, + "MentalHealth": 0.0, + "DiffWalking": "No", + "Sex": "Male", + "AgeCategory": "35-39", + "Race": "White", + "Diabetic": "No", + "PhysicalActivity": "Yes", + "SleepTime": 7.0, + "Asthma": "No", + "KidneyDisease": "No", + "SkinCancer": "No", + "State": null, + "GeneralHealth": "Very good", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + "LastCheckupTime": null, + "PhysicalActivities": null, + "SleepHours": null, + "RemovedTeeth": null, + "HadHeartAttack": null, + "HadAngina": null, + "HadStroke": null, + "HadAsthma": null, + "HadSkinCancer": null, + "HadCOPD": null, + "HadDepressiveDisorder": null, + "HadKidneyDisease": null, + "HadArthritis": null, + "HadDiabetes": null, + "DeafOrHardOfHearing": null, + "BlindOrVisionDifficulty": null, + "DifficultyConcentrating": null, + "DifficultyWalking": null, + "DifficultyDressingBathing": null, + "DifficultyErrands": null, + "SmokerStatus": null, + "ECigaretteUsage": null, + "ChestScan": null, + "RaceEthnicityCategory": null, + "HeightInMeters": null, + "WeightInKilograms": null, + "AlcoholDrinkers": null, + "HIVTesting": null, + "FluVaxLast12": null, + "PneumoVaxEver": null, + "TetanusLast10Tdap": null, + "HighRiskLastYear": null, + "CovidPos": null + } + }, + { + "scenario_id": "007", + "features": { + "HeartDisease": "No", + "BMI": 25.09, + "Smoking": "Yes", + "AlcoholDrinking": "No", + "Stroke": "No", + "PhysicalHealth": 0.0, + "MentalHealth": 0.0, + "DiffWalking": "No", + "Sex": "Male", + "AgeCategory": "40-44", + "Race": "Other", + "Diabetic": "No", + "PhysicalActivity": "Yes", + "SleepTime": 7.0, + "Asthma": "No", + "KidneyDisease": "No", + "SkinCancer": "No", + "State": null, + "GeneralHealth": "Very good", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + 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"AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 30 to 34", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Missouri", + "GeneralHealth": "Good", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 30.0, + "LastCheckupTime": "Within past 2 years (1 year but less than 2 years ago)", + "PhysicalActivities": "No", + "SleepHours": 4.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "Yes", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "Yes", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "Yes", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "Yes", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "No", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.52, + "WeightInKilograms": null, + "AlcoholDrinkers": "No", + "HIVTesting": "No", + "FluVaxLast12": "No", + "PneumoVaxEver": "Yes", + "TetanusLast10Tdap": "Yes, received Tdap", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "010", + "features": { + "HeartDisease": null, + "BMI": 44.93, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 65 to 69", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Florida", + "GeneralHealth": "Fair", + "PhysicalHealthDays": 5.0, + "MentalHealthDays": 10.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 10.0, + "RemovedTeeth": "1 to 5", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "Yes", + "HadCOPD": "No", + "HadDepressiveDisorder": "Yes", + "HadKidneyDisease": "No", + "HadArthritis": "Yes", + "HadDiabetes": "Yes", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "Yes", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "Yes", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.65, + "WeightInKilograms": 122.47, + "AlcoholDrinkers": "No", + "HIVTesting": "Yes", + "FluVaxLast12": "Yes", + "PneumoVaxEver": "No", + "TetanusLast10Tdap": "Yes, received Tdap", + "HighRiskLastYear": "No", + "CovidPos": "Tested positive using home test without a health professional" + } + }, + { + "scenario_id": "011", + "features": { + "HeartDisease": null, + "BMI": 29.12, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 70 to 74", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "New York", + "GeneralHealth": "Excellent", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 8.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Former smoker", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "No", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.65, + "WeightInKilograms": 79.38, + "AlcoholDrinkers": "Yes", + "HIVTesting": "No", + "FluVaxLast12": "Yes", + "PneumoVaxEver": "Yes", + "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "017", + "features": { + "HeartDisease": null, + "BMI": 21.14, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 75 to 79", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Washington", + "GeneralHealth": "Very good", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 15.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 5.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "Yes", + "HadCOPD": "No", + "HadDepressiveDisorder": "Yes", + "HadKidneyDisease": "Yes", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "Yes", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "No", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.7, + "WeightInKilograms": 61.23, + "AlcoholDrinkers": "Yes", + "HIVTesting": "No", + "FluVaxLast12": "Yes", + "PneumoVaxEver": "Yes", + "TetanusLast10Tdap": "Yes, received tetanus shot but not sure what type", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "024", + "features": { + "HeartDisease": null, + "BMI": 24.75, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 70 to 74", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Hawaii", + "GeneralHealth": "Fair", + "PhysicalHealthDays": 10.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 10.0, + "RemovedTeeth": "1 to 5", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "Yes", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Former smoker", + "ECigaretteUsage": "Not at all (right now)", + "ChestScan": "No", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.7, + "WeightInKilograms": 71.67, + "AlcoholDrinkers": "No", + "HIVTesting": "No", + "FluVaxLast12": "Yes", + "PneumoVaxEver": "No", + "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "027", + "features": { + "HeartDisease": "No", + "BMI": 28.13, + "Smoking": "No", + "AlcoholDrinking": "No", + "Stroke": "No", + "PhysicalHealth": 5.0, + "MentalHealth": 5.0, + "DiffWalking": "Yes", + "Sex": "Female", + "AgeCategory": "65-69", + "Race": "Black", + "Diabetic": "No", + "PhysicalActivity": "Yes", + "SleepTime": 6.0, + "Asthma": "Yes", + "KidneyDisease": "No", + "SkinCancer": "No", + "State": null, + "GeneralHealth": "Fair", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + "LastCheckupTime": null, + "PhysicalActivities": null, + "SleepHours": null, + "RemovedTeeth": null, + "HadHeartAttack": null, + "HadAngina": null, + "HadStroke": null, + "HadAsthma": null, + "HadSkinCancer": null, + "HadCOPD": null, + "HadDepressiveDisorder": null, + "HadKidneyDisease": null, + "HadArthritis": null, + "HadDiabetes": null, + "DeafOrHardOfHearing": null, + "BlindOrVisionDifficulty": null, + "DifficultyConcentrating": null, + "DifficultyWalking": null, + "DifficultyDressingBathing": null, + "DifficultyErrands": null, + "SmokerStatus": null, + "ECigaretteUsage": null, + "ChestScan": null, + "RaceEthnicityCategory": null, + "HeightInMeters": null, + "WeightInKilograms": null, + "AlcoholDrinkers": null, + "HIVTesting": null, + "FluVaxLast12": null, + "PneumoVaxEver": null, + "TetanusLast10Tdap": null, + "HighRiskLastYear": null, + "CovidPos": null + } + }, + { + "scenario_id": "028", + "features": { + "HeartDisease": null, + "BMI": 24.28, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 75 to 79", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Vermont", + "GeneralHealth": "Good", + "PhysicalHealthDays": 3.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "No", + "SleepHours": 7.0, + "RemovedTeeth": "1 to 5", + "HadHeartAttack": "Yes", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "Yes", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "Yes", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "Yes", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.7, + "WeightInKilograms": 70.31, + "AlcoholDrinkers": "Yes", + "HIVTesting": "No", + "FluVaxLast12": "Yes", + "PneumoVaxEver": "No", + "TetanusLast10Tdap": null, + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "030", + "features": { + "HeartDisease": null, + "BMI": 32.5, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + 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30.13, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 70 to 74", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Maine", + "GeneralHealth": "Very good", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 7.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "Yes", + "HadAngina": "Yes", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + 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"CovidPos": "No" + } + }, + { + "scenario_id": "318", + "features": { + "HeartDisease": null, + "BMI": 29.23, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 55 to 59", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Indiana", + "GeneralHealth": "Excellent", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 6.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + 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"RaceEthnicityCategory": "Hispanic", + "HeightInMeters": 1.63, + "WeightInKilograms": 72.57, + "AlcoholDrinkers": "No", + "HIVTesting": "Yes", + "FluVaxLast12": "No", + "PneumoVaxEver": "No", + "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", + "HighRiskLastYear": "No", + "CovidPos": "Yes" + } + }, + { + "scenario_id": "483", + "features": { + "HeartDisease": null, + "BMI": 27.26, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 18 to 24", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Rhode Island", + "GeneralHealth": "Fair", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 4.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 7.0, + 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"features": { + "HeartDisease": "No", + "BMI": 38.01, + "Smoking": "Yes", + "AlcoholDrinking": "No", + "Stroke": "No", + "PhysicalHealth": 0.0, + "MentalHealth": 0.0, + "DiffWalking": "No", + "Sex": "Male", + "AgeCategory": "35-39", + "Race": "White", + "Diabetic": "No", + "PhysicalActivity": "Yes", + "SleepTime": 7.0, + "Asthma": "No", + "KidneyDisease": "No", + "SkinCancer": "No", + "State": null, + "GeneralHealth": "Very good", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + "LastCheckupTime": null, + "PhysicalActivities": null, + "SleepHours": null, + "RemovedTeeth": null, + "HadHeartAttack": null, + "HadAngina": null, + "HadStroke": null, + "HadAsthma": null, + "HadSkinCancer": null, + "HadCOPD": null, + "HadDepressiveDisorder": null, + "HadKidneyDisease": null, + "HadArthritis": null, + "HadDiabetes": null, + "DeafOrHardOfHearing": null, + "BlindOrVisionDifficulty": null, + "DifficultyConcentrating": null, + "DifficultyWalking": null, + "DifficultyDressingBathing": 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- "CovidPos": null - } - }, - { - "scenario_id": "109", - "features": { - "HeartDisease": "Yes", - "BMI": 28.63, - "Smoking": "No", - "AlcoholDrinking": "No", - "Stroke": "No", - "PhysicalHealth": 10.0, - "MentalHealth": 0.0, - "DiffWalking": "Yes", - "Sex": "Male", - "AgeCategory": "55-59", - "Race": "White", - "Diabetic": "Yes", - "PhysicalActivity": "Yes", - "GenHealth": "Good", - "SleepTime": 6.0, - "Asthma": "Yes", - "KidneyDisease": "No", - "SkinCancer": "No", - "State": null, - "GeneralHealth": null, - "PhysicalHealthDays": null, - "MentalHealthDays": null, - "LastCheckupTime": null, - "PhysicalActivities": null, - "SleepHours": null, - "RemovedTeeth": null, - "HadHeartAttack": null, - "HadAngina": null, - "HadStroke": null, - "HadAsthma": null, - "HadSkinCancer": null, - "HadCOPD": null, - "HadDepressiveDisorder": null, - "HadKidneyDisease": null, - "HadArthritis": null, - "HadDiabetes": null, - "DeafOrHardOfHearing": null, - "BlindOrVisionDifficulty": null, - 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"features": { - "HeartDisease": null, - "BMI": 22.6, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Male", - "AgeCategory": "Age 65 to 69", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "Wisconsin", - "GeneralHealth": "Good", - "PhysicalHealthDays": 0.0, - "MentalHealthDays": 0.0, - "LastCheckupTime": "Within past year (anytime less than 12 months ago)", - "PhysicalActivities": "No", - "SleepHours": 8.0, - "RemovedTeeth": "1 to 5", - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "No", - "HadSkinCancer": "No", - "HadCOPD": "No", - "HadDepressiveDisorder": "No", - "HadKidneyDisease": "No", - "HadArthritis": "No", - "HadDiabetes": "No", - "DeafOrHardOfHearing": "No", - "BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "No", - "DifficultyWalking": "No", - "DifficultyDressingBathing": "No", - "DifficultyErrands": "No", - "SmokerStatus": "Never smoked", - "ECigaretteUsage": "Never used e-cigarettes in my entire life", - "ChestScan": "No", - "RaceEthnicityCategory": "Hispanic", - "HeightInMeters": 1.68, - "WeightInKilograms": 63.5, - "AlcoholDrinkers": "Yes", - "HIVTesting": "No", - "FluVaxLast12": "Yes", - "PneumoVaxEver": "No", - "TetanusLast10Tdap": "Yes, received tetanus shot but not sure what type", - "HighRiskLastYear": "No", - "CovidPos": "No" - } - }, - { - "scenario_id": "477", - "features": { - "HeartDisease": null, - "BMI": 22.43, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Male", - "AgeCategory": "Age 70 to 74", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "Colorado", - "GeneralHealth": "Fair", - "PhysicalHealthDays": 30.0, - "MentalHealthDays": 0.0, - "LastCheckupTime": "Within past year (anytime less than 12 months ago)", - "PhysicalActivities": "No", - "SleepHours": 8.0, - "RemovedTeeth": "1 to 5", - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "Yes", - "HadSkinCancer": "Yes", - "HadCOPD": "No", - "HadDepressiveDisorder": "Yes", - "HadKidneyDisease": "No", - "HadArthritis": "No", - "HadDiabetes": "No", - "DeafOrHardOfHearing": "No", - "BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "No", - "DifficultyWalking": "Yes", - "DifficultyDressingBathing": "Yes", - "DifficultyErrands": "Yes", - "SmokerStatus": "Never smoked", - "ECigaretteUsage": "Never used e-cigarettes in my entire life", - "ChestScan": "Yes", - "RaceEthnicityCategory": "White only, Non-Hispanic", - "HeightInMeters": 1.85, - "WeightInKilograms": 77.11, - "AlcoholDrinkers": "No", - "HIVTesting": "Yes", - "FluVaxLast12": "Yes", - "PneumoVaxEver": "Yes", - "TetanusLast10Tdap": "Yes, received tetanus shot, but not Tdap", - "HighRiskLastYear": "No", - "CovidPos": "No" - } - }, - { - "scenario_id": "482", - "features": { - "HeartDisease": null, - "BMI": 27.46, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Female", - "AgeCategory": "Age 55 to 59", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "Puerto Rico", - "GeneralHealth": "Fair", - "PhysicalHealthDays": 6.0, - "MentalHealthDays": 6.0, - "LastCheckupTime": "Within past year (anytime less than 12 months ago)", - "PhysicalActivities": "No", - "SleepHours": 6.0, - "RemovedTeeth": "1 to 5", - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "No", - "HadSkinCancer": "No", - "HadCOPD": "No", - "HadDepressiveDisorder": "No", - "HadKidneyDisease": "No", - "HadArthritis": null, - "HadDiabetes": "No, pre-diabetes or borderline diabetes", - "DeafOrHardOfHearing": "No", - "BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "No", - "DifficultyWalking": "No", - "DifficultyDressingBathing": "No", - "DifficultyErrands": "No", - "SmokerStatus": "Never smoked", - "ECigaretteUsage": "Not at all (right now)", - "ChestScan": "No", - "RaceEthnicityCategory": "Hispanic", - "HeightInMeters": 1.63, - "WeightInKilograms": 72.57, - "AlcoholDrinkers": "No", - "HIVTesting": "Yes", - "FluVaxLast12": "No", - "PneumoVaxEver": "No", - "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", - "HighRiskLastYear": "No", - "CovidPos": "Yes" - } - }, - { - "scenario_id": "483", - "features": { - "HeartDisease": null, - "BMI": 27.26, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Male", - "AgeCategory": "Age 18 to 24", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "Rhode Island", - "GeneralHealth": "Fair", - "PhysicalHealthDays": 0.0, - "MentalHealthDays": 4.0, - "LastCheckupTime": "Within past year (anytime less than 12 months ago)", - "PhysicalActivities": "Yes", - "SleepHours": 7.0, - "RemovedTeeth": "None of them", - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "No", - "HadSkinCancer": "No", - "HadCOPD": "No", - "HadDepressiveDisorder": "Yes", - "HadKidneyDisease": "No", - "HadArthritis": "No", - "HadDiabetes": "No", - "DeafOrHardOfHearing": "No", - "BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "Yes", - "DifficultyWalking": "No", - "DifficultyDressingBathing": "No", - "DifficultyErrands": "No", - "SmokerStatus": "Never smoked", - "ECigaretteUsage": "Not at all (right now)", - "ChestScan": "No", - "RaceEthnicityCategory": "Hispanic", - "HeightInMeters": 1.78, - "WeightInKilograms": 86.18, - "AlcoholDrinkers": "Yes", - "HIVTesting": "No", - "FluVaxLast12": "Yes", - "PneumoVaxEver": "Yes", - "TetanusLast10Tdap": "Yes, received Tdap", - "HighRiskLastYear": "No", - "CovidPos": "Yes" - } - }, - { - "scenario_id": "484", - "features": { - "HeartDisease": null, - "BMI": 24.69, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Female", - "AgeCategory": "Age 55 to 59", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "Maryland", - "GeneralHealth": "Good", - "PhysicalHealthDays": 0.0, - "MentalHealthDays": 3.0, - "LastCheckupTime": "Within past 5 years (2 years but less than 5 years ago)", - "PhysicalActivities": "Yes", - "SleepHours": 6.0, - "RemovedTeeth": "None of them", - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "No", - "HadSkinCancer": "No", - "HadCOPD": "No", - "HadDepressiveDisorder": "No", - "HadKidneyDisease": "No", - "HadArthritis": "Yes", - "HadDiabetes": "No", - "DeafOrHardOfHearing": "No", - "BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "No", - "DifficultyWalking": "No", - "DifficultyDressingBathing": "No", - "DifficultyErrands": "No", - "SmokerStatus": "Former smoker", - "ECigaretteUsage": "Never used e-cigarettes in my entire life", - "ChestScan": "No", - "RaceEthnicityCategory": "White only, Non-Hispanic", - "HeightInMeters": 1.57, - "WeightInKilograms": 61.23, - "AlcoholDrinkers": "Yes", - "HIVTesting": "Yes", - "FluVaxLast12": "No", - "PneumoVaxEver": "No", - "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", - "HighRiskLastYear": "No", - "CovidPos": "No" - } - }, - { - "scenario_id": "487", - "features": { - "HeartDisease": null, - "BMI": 37.11, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Female", - "AgeCategory": "Age 40 to 44", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "New York", - "GeneralHealth": "Good", - "PhysicalHealthDays": 7.0, - "MentalHealthDays": 30.0, - "LastCheckupTime": "5 or more years ago", - "PhysicalActivities": "Yes", - "SleepHours": 7.0, - "RemovedTeeth": "None of them", - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "No", - "HadSkinCancer": "No", - "HadCOPD": "No", - "HadDepressiveDisorder": "Yes", - "HadKidneyDisease": "No", - "HadArthritis": "No", - "HadDiabetes": "No", - "DeafOrHardOfHearing": "No", - "BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "Yes", - "DifficultyWalking": "No", - "DifficultyDressingBathing": null, - "DifficultyErrands": null, - "SmokerStatus": null, - "ECigaretteUsage": null, - "ChestScan": null, - "RaceEthnicityCategory": "White only, Non-Hispanic", - "HeightInMeters": 1.52, - "WeightInKilograms": 86.18, - "AlcoholDrinkers": null, - "HIVTesting": null, - "FluVaxLast12": null, - "PneumoVaxEver": null, - "TetanusLast10Tdap": null, - "HighRiskLastYear": null, - "CovidPos": null - } - }, - { - "scenario_id": "491", - "features": { - "HeartDisease": "No", - "BMI": 38.01, - "Smoking": "Yes", - "AlcoholDrinking": "No", - "Stroke": "No", - "PhysicalHealth": 0.0, - "MentalHealth": 0.0, - "DiffWalking": "No", - "Sex": "Male", - "AgeCategory": "35-39", - "Race": "White", - "Diabetic": "No", - "PhysicalActivity": "Yes", - "GenHealth": "Very good", - "SleepTime": 7.0, - "Asthma": "No", - "KidneyDisease": "No", - "SkinCancer": "No", - "State": null, - "GeneralHealth": null, - "PhysicalHealthDays": null, - "MentalHealthDays": null, - "LastCheckupTime": null, - "PhysicalActivities": null, - "SleepHours": null, - "RemovedTeeth": null, - "HadHeartAttack": null, - "HadAngina": null, - "HadStroke": null, - "HadAsthma": null, - "HadSkinCancer": null, - "HadCOPD": null, - "HadDepressiveDisorder": null, - "HadKidneyDisease": null, - "HadArthritis": null, - "HadDiabetes": null, - "DeafOrHardOfHearing": null, - "BlindOrVisionDifficulty": null, - "DifficultyConcentrating": null, - "DifficultyWalking": null, - "DifficultyDressingBathing": null, - "DifficultyErrands": null, - "SmokerStatus": null, - "ECigaretteUsage": null, - "ChestScan": null, - "RaceEthnicityCategory": null, - "HeightInMeters": null, - "WeightInKilograms": null, - "AlcoholDrinkers": null, - "HIVTesting": null, - "FluVaxLast12": null, - "PneumoVaxEver": null, - "TetanusLast10Tdap": null, - "HighRiskLastYear": null, - "CovidPos": null - } - }, - { - "scenario_id": "496", - "features": { - "HeartDisease": null, - "BMI": 20.2, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Female", - "AgeCategory": "Age 55 to 59", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "Alabama", - "GeneralHealth": "Good", - "PhysicalHealthDays": 2.0, - "MentalHealthDays": 0.0, - "LastCheckupTime": "Within past year (anytime less than 12 months ago)", - "PhysicalActivities": "No", - "SleepHours": 7.0, - "RemovedTeeth": "6 or more, but not all", - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "No", - "HadSkinCancer": "No", - "HadCOPD": "No", - "HadDepressiveDisorder": "Yes", - "HadKidneyDisease": "No", - "HadArthritis": "Yes", - "HadDiabetes": "No", - "DeafOrHardOfHearing": "No", - "BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "No", - "DifficultyWalking": "No", - "DifficultyDressingBathing": "No", - "DifficultyErrands": "No", - "SmokerStatus": "Never smoked", - "ECigaretteUsage": "Not at all (right now)", - "ChestScan": "Yes", - "RaceEthnicityCategory": "White only, Non-Hispanic", - "HeightInMeters": 1.7, - "WeightInKilograms": 58.51, - "AlcoholDrinkers": "No", - "HIVTesting": "No", - "FluVaxLast12": "No", - "PneumoVaxEver": "No", - "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", - "HighRiskLastYear": "No", - "CovidPos": "No" - } - }, - { - "scenario_id": "497", - "features": { - "HeartDisease": null, - "BMI": 20.51, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Female", - "AgeCategory": "Age 65 to 69", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "West Virginia", - "GeneralHealth": "Good", - "PhysicalHealthDays": 0.0, - "MentalHealthDays": 0.0, - "LastCheckupTime": "Within past year (anytime less than 12 months ago)", - "PhysicalActivities": "Yes", - "SleepHours": 8.0, - "RemovedTeeth": "None of them", - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "No", - "HadSkinCancer": "No", - "HadCOPD": "No", - "HadDepressiveDisorder": "No", - "HadKidneyDisease": "No", - "HadArthritis": "Yes", - "HadDiabetes": "No", - "DeafOrHardOfHearing": "No", - "BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "No", - "DifficultyWalking": "No", - "DifficultyDressingBathing": "No", - "DifficultyErrands": "No", - "SmokerStatus": "Current smoker - now smokes every day", - "ECigaretteUsage": "Never used e-cigarettes in my entire life", - "ChestScan": "No", - "RaceEthnicityCategory": "White only, Non-Hispanic", - "HeightInMeters": 1.52, - "WeightInKilograms": 47.63, - "AlcoholDrinkers": "No", - "HIVTesting": "No", - "FluVaxLast12": "No", - "PneumoVaxEver": "No", - "TetanusLast10Tdap": "Yes, received tetanus shot but not sure what type", - "HighRiskLastYear": "No", - "CovidPos": "No" - } - }, - { - "scenario_id": "498", - "features": { - "HeartDisease": "No", - "BMI": 25.77, - "Smoking": "Yes", - "AlcoholDrinking": "No", - "Stroke": "No", - "PhysicalHealth": 0.0, - "MentalHealth": 0.0, - "DiffWalking": "No", - "Sex": "Male", - "AgeCategory": "40-44", - "Race": "Hispanic", - "Diabetic": "No", - "PhysicalActivity": "No", - "GenHealth": "Excellent", - "SleepTime": 7.0, - "Asthma": "No", - "KidneyDisease": "No", - "SkinCancer": "No", - "State": null, - "GeneralHealth": null, - "PhysicalHealthDays": null, - "MentalHealthDays": null, - "LastCheckupTime": null, - "PhysicalActivities": null, - "SleepHours": null, - "RemovedTeeth": null, - "HadHeartAttack": null, - "HadAngina": null, - "HadStroke": null, - "HadAsthma": null, - "HadSkinCancer": null, - "HadCOPD": null, - "HadDepressiveDisorder": null, - "HadKidneyDisease": null, - "HadArthritis": null, - "HadDiabetes": null, - "DeafOrHardOfHearing": null, - "BlindOrVisionDifficulty": null, - "DifficultyConcentrating": null, - "DifficultyWalking": null, - "DifficultyDressingBathing": null, - "DifficultyErrands": null, - "SmokerStatus": null, - "ECigaretteUsage": null, - "ChestScan": null, - "RaceEthnicityCategory": null, - "HeightInMeters": null, - "WeightInKilograms": null, - "AlcoholDrinkers": null, - "HIVTesting": null, - "FluVaxLast12": null, - "PneumoVaxEver": null, - "TetanusLast10Tdap": null, - "HighRiskLastYear": null, - "CovidPos": null - } - }, - { - "scenario_id": "499", - "features": { - "HeartDisease": "Yes", - "BMI": 34.87, - "Smoking": "No", - "AlcoholDrinking": "No", - "Stroke": "No", - "PhysicalHealth": 3.0, - "MentalHealth": 0.0, - "DiffWalking": "Yes", - "Sex": "Male", - "AgeCategory": "65-69", - "Race": "White", - "Diabetic": "Yes", - "PhysicalActivity": "Yes", - "GenHealth": "Fair", - "SleepTime": 6.0, - "Asthma": "No", - "KidneyDisease": "Yes", - "SkinCancer": "No", - "State": null, - "GeneralHealth": null, - "PhysicalHealthDays": null, - "MentalHealthDays": null, - "LastCheckupTime": null, - "PhysicalActivities": null, - "SleepHours": null, - "RemovedTeeth": null, - "HadHeartAttack": null, - "HadAngina": null, - "HadStroke": null, - "HadAsthma": null, - "HadSkinCancer": null, - "HadCOPD": null, - "HadDepressiveDisorder": null, - "HadKidneyDisease": null, - "HadArthritis": null, - "HadDiabetes": null, - "DeafOrHardOfHearing": null, - "BlindOrVisionDifficulty": null, - "DifficultyConcentrating": null, - "DifficultyWalking": null, - "DifficultyDressingBathing": null, - "DifficultyErrands": null, - "SmokerStatus": null, - "ECigaretteUsage": null, - "ChestScan": null, - "RaceEthnicityCategory": null, - "HeightInMeters": null, - "WeightInKilograms": null, - "AlcoholDrinkers": null, - "HIVTesting": null, - "FluVaxLast12": null, - "PneumoVaxEver": null, - "TetanusLast10Tdap": null, - "HighRiskLastYear": null, - "CovidPos": null - } - } - ], - "target_column": "GeneralHealth", - "task_sub_type": "classification", - "ranking_ground_truth": { - "top_k_ids": [ - "042", - "121", - "122", - "174", - "212", - "245", - "258", - "277", - "313", - "353", - "391", - "439", - "450", - "474", - "497" - ] - }, - "active_filters": [ - { - "col": "SmokerStatus", - "op": "==", - "val": "Current smoker - now smokes every day" - } - ], - "target_class_value": "good" - }, - "response": "", - "evaluation_score": {} -} \ No newline at end of file + "active_filters": [ + { + "col": "SmokerStatus", + "op": "==", + "val": "Current smoker - now smokes every day" + } + ], + "target_class_value": "good" + }, + "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_006/current.csv b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_006/current.csv index 66dc904b2cf2e0e050ec784fff25a96fe3e3055c..2f40fe6d14e73f9713124d5bd251b2d5a9d66f67 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_006/current.csv +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_006/current.csv @@ -1,201 +1,201 @@ -HeartDisease,BMI,Smoking,AlcoholDrinking,Stroke,PhysicalHealth,MentalHealth,DiffWalking,Sex,AgeCategory,Race,Diabetic,PhysicalActivity,GenHealth,SleepTime,Asthma,KidneyDisease,SkinCancer,State,PhysicalHealthDays,MentalHealthDays,LastCheckupTime,PhysicalActivities,SleepHours,RemovedTeeth,HadHeartAttack,HadAngina,HadStroke,HadAsthma,HadSkinCancer,HadCOPD,HadDepressiveDisorder,HadKidneyDisease,HadArthritis,HadDiabetes,DeafOrHardOfHearing,BlindOrVisionDifficulty,DifficultyConcentrating,DifficultyWalking,DifficultyDressingBathing,DifficultyErrands,SmokerStatus,ECigaretteUsage,ChestScan,RaceEthnicityCategory,HeightInMeters,WeightInKilograms,AlcoholDrinkers,HIVTesting,FluVaxLast12,PneumoVaxEver,TetanusLast10Tdap,HighRiskLastYear,CovidPos -No,35.44,Yes,No,No,30.0,10.0,No,Male,55-59,White,No,Yes,Poor,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,44.63,,,,,,,Female,Age 55 to 59,,,,,,,,,South Dakota,,2.0,Within past year (anytime less than 12 months ago),No,6.0,"6 or more, but not all",No,No,No,No,No,No,No,Yes,Yes,Yes,No,No,No,,,,,,,"Multiracial, Non-Hispanic",1.63,117.93,,,,,,, -No,20.36,Yes,Yes,No,0.0,2.0,No,Female,70-74,White,No,Yes,Excellent,8.0,Yes,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,21.02,No,No,No,1.0,0.0,No,Male,18-24,White,No,Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,30.34,No,No,No,0.0,0.0,No,Male,35-39,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,25.09,Yes,No,No,0.0,0.0,No,Male,40-44,Other,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,,,,,,,,Female,Age 30 to 34,,,,,,,,,Missouri,0.0,30.0,Within past 2 years (1 year but less than 2 years ago),No,4.0,None of them,No,No,No,Yes,No,No,Yes,No,No,No,No,No,Yes,No,No,Yes,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.52,,No,No,No,Yes,"Yes, received Tdap",No,No -,44.93,,,,,,,Female,Age 65 to 69,,,,,,,,,Florida,5.0,10.0,Within past year (anytime less than 12 months ago),Yes,10.0,1 to 5,No,No,No,No,Yes,No,Yes,No,Yes,Yes,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,122.47,No,Yes,Yes,No,"Yes, received Tdap",No,Tested positive using home test without a health professional -,29.12,,,,,,,Female,Age 70 to 74,,,,,,,,,New York,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,79.38,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,21.14,,,,,,,Female,Age 75 to 79,,,,,,,,,Washington,0.0,15.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,No,Yes,No,Yes,Yes,No,No,No,Yes,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,61.23,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,24.75,,,,,,,Female,Age 70 to 74,,,,,,,,,Hawaii,10.0,0.0,Within past year (anytime less than 12 months ago),Yes,10.0,1 to 5,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.7,71.67,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -No,28.13,No,No,No,5.0,5.0,Yes,Female,65-69,Black,No,Yes,Fair,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,24.28,,,,,,,Male,Age 75 to 79,,,,,,,,,Vermont,3.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,1 to 5,Yes,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,70.31,Yes,No,Yes,No,,No,No -,32.5,,,,,,,Female,Age 70 to 74,,,,,,,,,West Virginia,0.0,5.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.55,78.02,Yes,No,Yes,Yes,"Yes, received Tdap",No,No -,29.29,,,,,,,Female,Age 55 to 59,,,,,,,,,New York,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,Hispanic,1.55,70.31,No,Yes,Yes,Yes,"Yes, received Tdap",No,No -,,,,,,,,Male,Age 40 to 44,,,,,,,,,Florida,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, -No,38.39,No,No,No,30.0,0.0,No,Male,60-64,White,No,Yes,Excellent,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,24.59,,,,,,,Female,Age 65 to 69,,,,,,,,,Nevada,14.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,,No,No,No,Yes,No,No,No,Yes,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,71.21,Yes,Yes,No,Yes,"Yes, received tetanus shot but not sure what type",No,Yes -,27.41,,,,,,,Male,Age 80 or older,,,,,,,,,Alabama,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,Yes,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.7,79.38,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,34.37,,,,,,,Male,Age 55 to 59,,,,,,,,,Iowa,1.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.91,124.74,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -,33.47,,,,,,,Female,Age 35 to 39,,,,,,,,,Nebraska,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Not at all (right now),No,"White only, Non-Hispanic",1.63,88.45,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -,22.85,,,,,,,Female,Age 75 to 79,,,,,,,,,Montana,30.0,30.0,Within past year (anytime less than 12 months ago),No,7.0,All,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.52,53.07,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes -,37.76,,,,,,,Male,Age 60 to 64,,,,,,,,,North Dakota,30.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,All,Yes,No,No,No,No,Yes,Yes,No,Yes,No,No,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,99.79,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -No,31.17,No,Yes,No,2.0,0.0,No,Male,40-44,White,No,Yes,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,24.72,,,,,,,Male,Age 55 to 59,,,,,,,,,Pennsylvania,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,Yes,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),No,"White only, Non-Hispanic",2.03,102.06,No,Yes,Yes,No,"Yes, received tetanus shot but not sure what type",No,Yes -,35.87,,,,,,,Male,Age 45 to 49,,,,,,,,,Wisconsin,10.0,5.0,Within past year (anytime less than 12 months ago),No,6.0,None of them,Yes,No,No,Yes,No,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,113.4,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,28.35,,,,,,,Female,Age 80 or older,,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.57,70.31,Yes,No,Yes,Yes,"Yes, received Tdap",No,No -,31.09,,,,,,,Female,Age 70 to 74,,,,,,,,,Vermont,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,Yes,No,No,No,No,No,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.57,77.11,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,28.34,,,,,,,Female,Age 70 to 74,,,,,,,,,Massachusetts,15.0,10.0,Within past year (anytime less than 12 months ago),No,6.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.55,68.04,No,No,No,Yes,"Yes, received tetanus shot but not sure what type",No,No -,,,,,,,,Female,,,,,,,,,,Missouri,7.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,1 to 5,No,No,No,Yes,No,No,No,No,No,No,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, -No,54.82,Yes,Yes,No,0.0,0.0,No,Male,50-54,White,No,Yes,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,44.29,Yes,No,No,0.0,5.0,No,Male,35-39,White,No,No,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,27.05,No,No,No,1.0,0.0,No,Male,45-49,White,No,Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,24.8,,,,,,,Female,Age 35 to 39,,,,,,,,,Minnesota,30.0,30.0,Within past year (anytime less than 12 months ago),Yes,5.0,1 to 5,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.6,63.5,No,Yes,Yes,No,"Yes, received Tdap",No,No -,25.09,,,,,,,Male,Age 70 to 74,,,,,,,,,West Virginia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,83.91,No,No,Yes,Yes,"Yes, received Tdap",No,No -,21.03,,,,,,,Female,Age 18 to 24,,,,,,,,,Hawaii,3.0,12.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Use them some days,No,"White only, Non-Hispanic",1.57,52.16,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -No,25.32,Yes,No,No,3.0,2.0,No,Female,55-59,White,Yes,No,Fair,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,22.31,No,No,No,0.0,0.0,No,Female,80 or older,White,No,Yes,Very good,8.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,21.79,,,,,,,Female,Age 45 to 49,,,,,,,,,Vermont,,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.68,61.23,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,28.12,,,,,,,Male,Age 70 to 74,,,,,,,,,Florida,0.0,30.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,Yes,No,Yes,No,Yes,No,Yes,No,No,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.93,104.78,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,23.57,,,,,,,Female,Age 18 to 24,,,,,,,,,Missouri,0.0,14.0,Within past 2 years (1 year but less than 2 years ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.73,70.31,Yes,No,No,No,"Yes, received Tdap",No,No -,27.26,,,,,,,Male,Age 30 to 34,,,,,,,,,Montana,0.0,30.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,Yes,No,No,Yes,Yes,No,No,Yes,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.78,86.18,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No -,32.11,,,,,,,Male,Age 70 to 74,,,,,,,,,South Dakota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,92.99,Yes,No,Yes,Yes,"Yes, received Tdap",No,No -,49.78,,,,,,,Female,Age 35 to 39,,,,,,,,,California,15.0,7.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Multiracial, Non-Hispanic",1.63,131.54,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,No -,29.18,,,,,,,Female,Age 70 to 74,,,,,,,,,Idaho,3.0,1.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,Yes,No,Yes,No,Yes,No,No,Yes,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,77.11,No,No,Yes,Yes,"Yes, received Tdap",No,No -No,27.44,No,No,No,0.0,0.0,No,Female,70-74,Hispanic,No,No,Excellent,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Yes,28.63,No,No,No,10.0,0.0,Yes,Male,55-59,White,Yes,Yes,Good,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,22.96,,,,,,,Male,Age 80 or older,,,,,,,,,Washington,14.0,5.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,Yes,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,72.57,Yes,No,Yes,Yes,"Yes, received Tdap",No,No -,31.38,,,,,,,Male,Age 55 to 59,,,,,,,,,Kansas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,Yes,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.8,102.06,Yes,No,No,Yes,"Yes, received tetanus shot but not sure what type",No,Yes -No,21.7,No,No,No,0.0,0.0,No,Male,75-79,Hispanic,Yes,Yes,Fair,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,27.71,,,,,,,Male,Age 45 to 49,,,,,,,,,Maine,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Use them some days,No,"White only, Non-Hispanic",1.85,95.25,No,Yes,Yes,Yes,"Yes, received Tdap",No,No -,31.71,,,,,,,Female,Age 75 to 79,,,,,,,,,Colorado,0.0,30.0,Within past year (anytime less than 12 months ago),Yes,5.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Use them some days,No,"White only, Non-Hispanic",1.6,81.19,No,No,No,Yes,"Yes, received tetanus shot but not sure what type",No,Yes -,26.61,,,,,,,Male,Age 75 to 79,,,,,,,,,Idaho,7.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,79.38,No,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No -,,,,,,,,Female,Age 75 to 79,,,,,,,,,Arizona,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, -No,25.84,No,No,No,0.0,0.0,No,Female,45-49,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,28.19,No,No,Yes,5.0,10.0,No,Female,30-34,White,No,Yes,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,28.29,,,,,,,Female,Age 65 to 69,,,,,,,,,Kansas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,77.11,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -No,23.17,No,No,No,0.0,1.0,No,Female,30-34,White,No,Yes,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,29.84,No,No,No,0.0,0.0,No,Male,45-49,White,No,Yes,Excellent,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,38.52,,,,,,,Male,Age 18 to 24,,,,,,,,,Nevada,3.0,3.0,5 or more years ago,Yes,5.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.88,136.08,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,No -No,25.4,Yes,No,No,30.0,0.0,Yes,Female,75-79,White,No,Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,26.63,,,,,,,Female,Age 35 to 39,,,,,,,,,South Dakota,10.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.65,72.57,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -No,19.77,No,No,No,2.0,10.0,No,Female,18-24,White,No,Yes,Excellent,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,36.18,Yes,No,No,0.0,7.0,Yes,Male,45-49,Other,No,Yes,Excellent,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,33.09,,,,,,,Male,Age 40 to 44,,,,,,,,,Mississippi,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Use them some days,,"White only, Non-Hispanic",1.68,92.99,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -,19.67,,,,,,,Male,Age 35 to 39,,,,,,,,,Wisconsin,0.0,0.0,5 or more years ago,No,5.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.83,65.77,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No -,25.83,,,,,,,Male,Age 35 to 39,,,,,,,,,Alabama,0.0,2.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,81.65,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",Yes,No -,28.31,,,,,,,Male,Age 65 to 69,,,,,,,,,Maryland,5.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,No,Yes,No,Yes,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,92.08,No,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,Yes -,17.63,,,,,,,Male,Age 70 to 74,,,,,,,,,Colorado,,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,All,No,No,No,No,No,Yes,No,No,Yes,No,No,No,No,Yes,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.83,58.97,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,No -,28.23,,,,,,,Male,Age 50 to 54,,,,,,,,,Alabama,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.85,97.07,No,No,No,No,"Yes, received Tdap",No,No -No,30.41,Yes,Yes,No,1.0,0.0,No,Female,50-54,White,Yes,No,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,43.75,,,,,,,Male,Age 65 to 69,,,,,,,,,Missouri,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,Yes,Yes,Yes,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.91,158.76,Yes,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes -,34.46,,,,,,,Male,Age 80 or older,,,,,,,,,Massachusetts,1.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,"6 or more, but not all",No,No,No,No,Yes,No,No,No,Yes,Yes,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,99.79,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -No,21.89,No,No,No,0.0,0.0,No,Female,40-44,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,27.02,Yes,No,No,0.0,0.0,No,Female,35-39,American Indian/Alaskan Native,No,Yes,Excellent,8.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,24.03,,,,,,,Female,Age 18 to 24,,,,,,,,,Virginia,6.0,14.0,Within past 2 years (1 year but less than 2 years ago),Yes,11.0,None of them,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.63,63.5,Yes,No,No,No,"Yes, received Tdap",No,No -,,,,,,,,Female,Age 75 to 79,,,,,,,,,Utah,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.52,,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,28.15,,,,,,,Female,Age 70 to 74,,,,,,,,,Colorado,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,74.39,Yes,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No -,20.53,,,,,,,Female,Age 50 to 54,,,,,,,,,Michigan,0.0,5.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,61.23,No,Yes,Yes,No,"Yes, received Tdap",No,No -,25.84,,,,,,,Female,Age 65 to 69,,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,"6 or more, but not all",No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,79.38,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,21.93,,,,,,,Female,Age 80 or older,,,,,,,,,Connecticut,0.0,0.0,Within past year (anytime less than 12 months ago),No,9.0,None of them,No,No,No,No,Yes,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,63.5,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -No,28.34,Yes,Yes,No,0.0,15.0,No,Female,40-44,White,No,Yes,Good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,30.68,No,No,No,0.0,0.0,No,Male,25-29,Hispanic,No,Yes,Excellent,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,31.01,,,,,,,Male,Age 50 to 54,,,,,,,,,Arkansas,1.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,1 to 5,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.75,95.25,No,No,No,Yes,"Yes, received tetanus shot but not sure what type",No,Yes -No,24.11,No,No,No,2.0,5.0,No,Male,18-24,White,No,Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,23.01,,,,,,,Male,Age 25 to 29,,,,,,,,,New York,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.8,74.84,Yes,Yes,Yes,No,"Yes, received Tdap",No,Yes -,27.26,,,,,,,Male,Age 50 to 54,,,,,,,,,Vermont,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.78,86.18,Yes,No,Yes,No,"Yes, received tetanus shot, but not Tdap",No,Yes -,24.41,,,,,,,Male,Age 60 to 64,,,,,,,,,Maryland,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.83,81.65,,,,,,, -,48.71,,,,,,,Female,Age 70 to 74,,,,,,,,,West Virginia,10.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,All,No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,124.74,No,No,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,28.25,,,,,,,Male,Age 65 to 69,,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.68,79.38,Yes,No,No,Yes,"Yes, received Tdap",No,No -,30.13,,,,,,,Male,Age 70 to 74,,,,,,,,,Maine,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,Yes,Yes,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.78,95.25,No,No,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,42.57,,,,,,,Female,Age 45 to 49,,,,,,,,,Minnesota,30.0,15.0,Within past year (anytime less than 12 months ago),No,6.0,"6 or more, but not all",No,No,No,No,No,No,Yes,No,Yes,"No, pre-diabetes or borderline diabetes",No,No,No,Yes,Yes,No,Current smoker - now smokes every day,Use them some days,Yes,"White only, Non-Hispanic",1.73,127.01,No,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,19.67,,,,,,,Male,Age 18 to 24,,,,,,,,,Ohio,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,65.77,No,No,Yes,No,"Yes, received Tdap",No,No -,30.81,,,,,,,Male,Age 50 to 54,,,,,,,,,Minnesota,0.0,5.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),No,"White only, Non-Hispanic",1.88,108.86,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -No,22.71,Yes,No,No,0.0,5.0,No,Female,35-39,White,No,No,Very good,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,23.06,,,,,,,Male,Age 18 to 24,,,,,,,,,Nebraska,0.0,4.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,77.11,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,22.92,,,,,,,Female,Age 80 or older,,,,,,,,,Wyoming,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,None of them,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.68,64.41,No,No,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,,,,,,,,Female,Age 75 to 79,,,,,,,,,Maryland,2.0,10.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,Yes,No,No,Yes,No,Yes,No,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, -No,22.96,Yes,No,No,0.0,0.0,No,Male,65-69,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,24.33,No,No,No,0.0,2.0,No,Male,35-39,White,No,Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,27.27,,,,,,,Male,Age 70 to 74,,,,,,,,,Virginia,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,,No,No,No,No,No,Yes,No,No,Yes,Yes,Yes,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.96,104.33,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -Yes,27.44,No,No,No,0.0,0.0,No,Female,80 or older,White,No,Yes,Good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,29.03,,,,,,,Male,Age 40 to 44,,,,,,,,,Texas,0.0,30.0,Within past 5 years (2 years but less than 5 years ago),No,4.0,1 to 5,Yes,Yes,No,No,No,Yes,Yes,No,Yes,No,Yes,No,Yes,No,No,Yes,Current smoker - now smokes every day,Use them some days,Yes,"White only, Non-Hispanic",1.85,99.79,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No -,31.31,,,,,,,Female,Age 65 to 69,,,,,,,,,Minnesota,15.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.5,70.31,No,No,Yes,No,"Yes, received Tdap",No,No -,21.63,,,,,,,Female,Age 80 or older,,,,,,,,,Ohio,6.0,,Within past year (anytime less than 12 months ago),No,7.0,1 to 5,No,No,No,No,No,No,Yes,No,Yes,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,58.97,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,,,,,,,,Female,Age 55 to 59,,,,,,,,,Tennessee,1.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,None of them,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,"Black only, Non-Hispanic",,,,,,,,, -No,25.58,Yes,No,No,15.0,15.0,Yes,Female,75-79,American Indian/Alaskan Native,Yes,No,Fair,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,34.01,No,No,No,0.0,0.0,Yes,Female,80 or older,White,No,No,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,28.32,,,,,,,Female,Age 80 or older,,,,,,,,,Nebraska,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,1 to 5,No,No,No,No,Yes,Yes,No,No,Yes,No,No,Yes,No,Yes,Yes,Yes,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.52,65.77,No,No,No,Yes,"Yes, received tetanus shot, but not Tdap",No,No -,35.15,,,,,,,Female,Age 55 to 59,,,,,,,,,Indiana,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,Yes,Yes,No,No,Yes,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,,1.52,81.65,No,,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -,42.77,,,,,,,Female,Age 35 to 39,,,,,,,,,Florida,1.0,4.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,120.2,No,,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -No,28.7,Yes,No,No,1.0,0.0,No,Male,80 or older,Hispanic,Yes,Yes,Good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,23.99,,,,,,,Female,Age 80 or older,,,,,,,,,New Hampshire,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.8,78.02,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,21.52,,,,,,,Female,Age 60 to 64,,,,,,,,,Washington,14.0,5.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,Yes,No,Yes,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,68.04,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -,28.8,,,,,,,Female,Age 30 to 34,,,,,,,,,Nebraska,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,88.45,Yes,No,No,No,"Yes, received Tdap",No,Yes -No,32.08,No,No,No,1.0,3.0,No,Male,18-24,Black,No,Yes,Good,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,19.37,,,,,,,Female,Age 45 to 49,,,,,,,,,Rhode Island,0.0,7.0,Within past year (anytime less than 12 months ago),Yes,4.0,1 to 5,No,No,No,No,No,No,Yes,No,Yes,"No, pre-diabetes or borderline diabetes",No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,54.43,No,No,Yes,Yes,"Yes, received Tdap",No,No -,31.19,,,,,,,Male,Age 70 to 74,,,,,,,,,Colorado,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,All,No,No,No,No,No,No,Yes,No,Yes,"No, pre-diabetes or borderline diabetes",Yes,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,,1.83,104.33,Yes,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,29.23,,,,,,,Male,Age 55 to 59,,,,,,,,,Indiana,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,74.84,Yes,,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,29.69,,,,,,,Female,Age 75 to 79,,,,,,,,,New York,2.0,3.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,Yes,No,Yes,Yes,No,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.5,66.68,No,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Tested positive using home test without a health professional -,26.89,,,,,,,Female,Age 75 to 79,,,,,,,,,Puerto Rico,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Not at all (right now),No,Hispanic,1.57,66.68,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,22.96,,,,,,,Male,Age 80 or older,,,,,,,,,Arizona,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,72.57,Yes,No,No,Yes,"Yes, received tetanus shot but not sure what type",No,No -,21.7,,,,,,,Male,Age 40 to 44,,,,,,,,,Georgia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.83,72.57,No,Yes,Yes,No,"Yes, received tetanus shot but not sure what type",No,Yes -,27.89,,,,,,,Male,Age 65 to 69,,,,,,,,,Alaska,30.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,90.72,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No -No,34.95,No,No,No,0.0,0.0,No,Female,55-59,White,No,Yes,Very good,7.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,31.53,No,No,No,3.0,0.0,No,Female,60-64,Hispanic,No,Yes,Fair,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,25.66,Yes,No,No,0.0,0.0,No,Female,70-74,Black,Yes,Yes,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,33.0,Yes,No,No,0.0,1.0,No,Male,70-74,White,No,Yes,Very good,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,20.09,Yes,No,No,0.0,4.0,No,Male,30-34,White,No,Yes,Good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,25.42,,,,,,,Female,Age 40 to 44,,,,,,,,,Nevada,0.0,30.0,Within past 5 years (2 years but less than 5 years ago),No,4.0,1 to 5,No,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,No,Hispanic,1.57,63.05,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,30.41,,,,,,,Female,Age 75 to 79,,,,,,,,,Kansas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,90.72,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -No,38.41,Yes,Yes,No,0.0,14.0,Yes,Male,65-69,White,No,Yes,Good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,20.41,Yes,No,No,0.0,0.0,No,Female,70-74,White,No,Yes,Excellent,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,25.84,,,,,,,Male,Age 25 to 29,,,,,,,,,Massachusetts,0.0,1.0,5 or more years ago,Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"Other race only, Non-Hispanic",1.75,79.38,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -,27.8,,,,,,,Male,Age 70 to 74,,,,,,,,,New Hampshire,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,92.99,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,No -,19.94,,,,,,,Male,Age 55 to 59,,,,,,,,,Pennsylvania,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,9.0,All,No,No,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,61.23,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,No -,24.13,,,,,,,Female,Age 80 or older,,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,65.77,No,No,Yes,Yes,"Yes, received Tdap",No,No -No,24.28,Yes,No,No,0.0,2.0,No,Female,65-69,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,28.7,,,,,,,Female,Age 60 to 64,,,,,,,,,Minnesota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,Yes,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,73.48,No,Yes,Yes,No,"Yes, received Tdap",No,No -Yes,23.74,Yes,No,No,0.0,0.0,No,Male,65-69,Black,No,Yes,Good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,45.76,No,No,No,0.0,0.0,Yes,Female,55-59,Hispanic,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,29.86,No,No,No,0.0,4.0,No,Female,60-64,White,No,Yes,Excellent,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,26.54,Yes,No,No,0.0,0.0,No,Male,75-79,White,No,No,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,24.96,No,No,No,0.0,3.0,No,Female,25-29,White,No,Yes,Very good,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,23.43,,,,,,,Male,Age 60 to 64,,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.8,76.2,Yes,No,No,,"Yes, received Tdap",No,Tested positive using home test without a health professional -,23.63,,,,,,,Male,Age 50 to 54,,,,,,,,,Wyoming,3.0,0.0,5 or more years ago,Yes,8.0,1 to 5,No,No,No,Yes,No,No,Yes,No,Yes,No,No,No,No,,,,,,,"White only, Non-Hispanic",1.75,72.57,,,,,,, -,31.01,,,,,,,Male,Age 50 to 54,,,,,,,,,North Dakota,0.0,0.0,5 or more years ago,Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,95.25,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -No,23.4,Yes,No,No,0.0,0.0,No,Female,60-64,White,No,Yes,Excellent,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,29.05,,,,,,,Male,Age 40 to 44,,,,,,,,,Michigan,1.0,3.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"Other race only, Non-Hispanic",1.68,81.65,Yes,Yes,No,Yes,"Yes, received tetanus shot but not sure what type",No,No -No,23.06,No,No,No,0.0,0.0,No,Male,30-34,Black,No,Yes,Excellent,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,,,,,,,,Female,Age 40 to 44,,,,,,,,,Washington,1.0,2.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,90.72,No,Yes,Yes,,"Yes, received Tdap",Yes,Yes -No,28.25,Yes,No,No,0.0,0.0,No,Male,60-64,White,No,Yes,Very good,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,25.77,,,,,,,Male,Age 75 to 79,,,,,,,,,Kansas,30.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.83,86.18,No,Yes,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No -,25.09,,,,,,,Male,Age 65 to 69,,,,,,,,,South Carolina,30.0,,Within past 5 years (2 years but less than 5 years ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,Yes,Yes,No,No,Yes,Yes,Yes,Former smoker,Not at all (right now),Yes,"Black only, Non-Hispanic",1.73,74.84,No,Yes,Yes,Yes,"Yes, received Tdap",No,Yes -,23.38,,,,,,,Female,Age 80 or older,,,,,,,,,Massachusetts,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,Yes,No,No,Yes,No,Yes,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,59.87,Yes,No,Yes,Yes,"Yes, received Tdap",No,Yes -No,22.31,No,No,No,0.0,0.0,No,Female,18-24,White,No,Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,22.32,,,,,,,Male,Age 70 to 74,,,,,,,,,Colorado,1.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,72.57,Yes,No,No,Yes,"Yes, received tetanus shot but not sure what type",No,No -,27.26,,,,,,,Male,Age 75 to 79,,,,,,,,,California,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,86.18,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -No,35.15,No,No,No,0.0,0.0,Yes,Female,80 or older,White,No,No,Good,8.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,34.33,,,,,,,Female,Age 75 to 79,,,,,,,,,Maine,,0.0,Within past year (anytime less than 12 months ago),No,8.0,All,No,No,No,No,,No,No,No,Yes,Yes,Yes,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,,1.63,90.72,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,22.31,,,,,,,Female,Age 65 to 69,,,,,,,,,Nebraska,5.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,58.97,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,26.61,,,,,,,Male,Age 80 or older,,,,,,,,,California,0.0,20.0,Within past year (anytime less than 12 months ago),No,,"6 or more, but not all",No,No,No,No,,No,No,No,,Yes,No,Yes,No,No,No,Yes,Former smoker,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.73,79.38,Yes,,Yes,Yes,"Yes, received Tdap",No,No -No,26.96,No,No,No,0.0,0.0,No,Female,40-44,White,No,Yes,Excellent,8.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,32.28,No,No,No,0.0,0.0,No,Male,60-64,White,No,Yes,Good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,28.34,,,,,,,Female,Age 70 to 74,,,,,,,,,Rhode Island,0.0,5.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,Yes,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.6,72.57,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,33.47,,,,,,,Male,Age 75 to 79,,,,,,,,,Kansas,15.0,15.0,Within past year (anytime less than 12 months ago),No,9.0,"6 or more, but not all",Yes,Yes,No,No,No,No,No,No,Yes,Yes,Yes,Yes,Yes,Yes,Yes,Yes,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,108.86,Yes,No,Yes,Yes,"Yes, received Tdap",No,No -,23.75,,,,,,,Male,Age 25 to 29,,,,,,,,,Ohio,3.0,2.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Use them every day,No,"White only, Non-Hispanic",1.88,83.91,Yes,No,No,,"Yes, received Tdap",No,Yes -,25.83,,,,,,,Male,Age 65 to 69,,,,,,,,,Iowa,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,81.65,No,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,Tested positive using home test without a health professional -,23.72,,,,,,,Male,Age 18 to 24,,,,,,,,,Missouri,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,,1.73,70.76,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -No,31.01,Yes,Yes,No,0.0,0.0,No,Male,50-54,Hispanic,No,No,Good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,36.85,,,,,,,Female,Age 35 to 39,,,,,,,,,Arkansas,0.0,2.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,94.35,Yes,No,Yes,No,"Yes, received Tdap",No,No -,27.46,,,,,,,Male,Age 60 to 64,,,,,,,,,Indiana,0.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,74.84,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No -,37.59,,,,,,,Female,Age 65 to 69,,,,,,,,,Wisconsin,3.0,30.0,Within past year (anytime less than 12 months ago),Yes,12.0,1 to 5,No,No,No,No,No,No,Yes,Yes,Yes,No,No,No,Yes,Yes,No,No,Current smoker - now smokes some days,Not at all (right now),Yes,"White only, Non-Hispanic",1.7,108.86,No,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -No,24.37,No,No,No,0.0,0.0,No,Female,35-39,White,No,Yes,Excellent,8.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,27.71,,,,,,,Male,Age 65 to 69,,,,,,,,,Maine,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,Yes,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.85,95.25,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,30.9,,,,,,,Female,Age 25 to 29,,,,,,,,,Montana,10.0,5.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"Other race only, Non-Hispanic",1.63,81.65,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,18.65,,,,,,,Female,Age 45 to 49,,,,,,,,,California,0.0,5.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,Current smoker - now smokes every day,Use them every day,No,"White only, Non-Hispanic",1.78,58.97,No,Yes,No,Yes,"Yes, received tetanus shot but not sure what type",No,No -,28.48,,,,,,,Male,Age 25 to 29,,,,,,,,,Texas,0.0,0.0,,No,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.83,95.25,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",Yes,Yes -,35.95,,,,,,,Male,Age 75 to 79,,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,Yes,Yes,Yes,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.88,127.01,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,42.05,,,,,,,Female,Age 50 to 54,,,,,,,,,California,15.0,10.0,Within past year (anytime less than 12 months ago),No,6.0,1 to 5,No,No,No,Yes,No,Yes,Yes,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,111.13,Yes,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -No,24.21,No,No,No,0.0,0.0,Yes,Female,80 or older,White,No,No,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,46.79,Yes,No,No,30.0,3.0,Yes,Male,55-59,White,No,No,Very good,4.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,32.89,No,No,No,0.0,0.0,No,Male,75-79,Asian,Yes,Yes,Very good,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Yes,29.12,Yes,No,No,5.0,1.0,Yes,Female,50-54,White,No,Yes,Fair,4.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,,,,,,,,Female,Age 65 to 69,,,,,,,,,Ohio,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,All,No,No,No,No,No,No,No,No,No,Yes,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, -,21.87,,,,,,,Female,Age 65 to 69,,,,,,,,,New York,2.0,4.0,Within past year (anytime less than 12 months ago),Yes,5.0,"6 or more, but not all",No,No,No,No,,No,No,No,Yes,No,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.52,50.8,No,,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,33.54,,,,,,,Female,Age 55 to 59,,,,,,,,,New Mexico,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.45,70.31,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,22.31,,,,,,,Female,Age 18 to 24,,,,,,,,,Connecticut,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.63,58.97,Yes,,Yes,,"Yes, received tetanus shot but not sure what type",No, -,27.81,,,,,,,Female,Age 60 to 64,,,,,,,,,South Carolina,14.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,5.0,1 to 5,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.63,73.48,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,22.6,,,,,,,Male,Age 65 to 69,,,,,,,,,Wisconsin,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.68,63.5,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -,22.43,,,,,,,Male,Age 70 to 74,,,,,,,,,Colorado,30.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,Yes,Yes,No,Yes,No,No,No,No,No,No,Yes,Yes,Yes,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.85,77.11,No,Yes,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No -,27.46,,,,,,,Female,Age 55 to 59,,,,,,,,,Puerto Rico,6.0,6.0,Within past year (anytime less than 12 months ago),No,6.0,1 to 5,No,No,No,No,No,No,No,No,,"No, pre-diabetes or borderline diabetes",No,No,No,No,No,No,Never smoked,Not at all (right now),No,Hispanic,1.63,72.57,No,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,27.26,,,,,,,Male,Age 18 to 24,,,,,,,,,Rhode Island,0.0,4.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,Yes,No,No,No,Never smoked,Not at all (right now),No,Hispanic,1.78,86.18,Yes,No,Yes,Yes,"Yes, received Tdap",No,Yes -,24.69,,,,,,,Female,Age 55 to 59,,,,,,,,,Maryland,0.0,3.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,61.23,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,37.11,,,,,,,Female,Age 40 to 44,,,,,,,,,New York,7.0,30.0,5 or more years ago,Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,Yes,No,,,,,,"White only, Non-Hispanic",1.52,86.18,,,,,,, -No,38.01,Yes,No,No,0.0,0.0,No,Male,35-39,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,20.2,,,,,,,Female,Age 55 to 59,,,,,,,,,Alabama,2.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,"6 or more, but not all",No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.7,58.51,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,20.51,,,,,,,Female,Age 65 to 69,,,,,,,,,West Virginia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.52,47.63,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No -No,25.77,Yes,No,No,0.0,0.0,No,Male,40-44,Hispanic,No,No,Excellent,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Yes,34.87,No,No,No,3.0,0.0,Yes,Male,65-69,White,Yes,Yes,Fair,6.0,No,Yes,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +HeartDisease,BMI,Smoking,AlcoholDrinking,Stroke,PhysicalHealth,MentalHealth,DiffWalking,Sex,AgeCategory,Race,Diabetic,PhysicalActivity,SleepTime,Asthma,KidneyDisease,SkinCancer,State,PhysicalHealthDays,MentalHealthDays,LastCheckupTime,PhysicalActivities,SleepHours,RemovedTeeth,HadHeartAttack,HadAngina,HadStroke,HadAsthma,HadSkinCancer,HadCOPD,HadDepressiveDisorder,HadKidneyDisease,HadArthritis,HadDiabetes,DeafOrHardOfHearing,BlindOrVisionDifficulty,DifficultyConcentrating,DifficultyWalking,DifficultyDressingBathing,DifficultyErrands,SmokerStatus,ECigaretteUsage,ChestScan,RaceEthnicityCategory,HeightInMeters,WeightInKilograms,AlcoholDrinkers,HIVTesting,FluVaxLast12,PneumoVaxEver,TetanusLast10Tdap,HighRiskLastYear,CovidPos +No,35.44,Yes,No,No,30.0,10.0,No,Male,55-59,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,44.63,,,,,,,Female,Age 55 to 59,,,,,,,,South Dakota,,2.0,Within past year (anytime less than 12 months ago),No,6.0,"6 or more, but not all",No,No,No,No,No,No,No,Yes,Yes,Yes,No,No,No,,,,,,,"Multiracial, Non-Hispanic",1.63,117.93,,,,,,, +No,20.36,Yes,Yes,No,0.0,2.0,No,Female,70-74,White,No,Yes,8.0,Yes,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,21.02,No,No,No,1.0,0.0,No,Male,18-24,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,30.34,No,No,No,0.0,0.0,No,Male,35-39,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,25.09,Yes,No,No,0.0,0.0,No,Male,40-44,Other,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,,,,,,,,Female,Age 30 to 34,,,,,,,,Missouri,0.0,30.0,Within past 2 years (1 year but less than 2 years ago),No,4.0,None of them,No,No,No,Yes,No,No,Yes,No,No,No,No,No,Yes,No,No,Yes,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.52,,No,No,No,Yes,"Yes, received Tdap",No,No +,44.93,,,,,,,Female,Age 65 to 69,,,,,,,,Florida,5.0,10.0,Within past year (anytime less than 12 months ago),Yes,10.0,1 to 5,No,No,No,No,Yes,No,Yes,No,Yes,Yes,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,122.47,No,Yes,Yes,No,"Yes, received Tdap",No,Tested positive using home test without a health professional +,29.12,,,,,,,Female,Age 70 to 74,,,,,,,,New York,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,79.38,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,21.14,,,,,,,Female,Age 75 to 79,,,,,,,,Washington,0.0,15.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,No,Yes,No,Yes,Yes,No,No,No,Yes,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,61.23,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,24.75,,,,,,,Female,Age 70 to 74,,,,,,,,Hawaii,10.0,0.0,Within past year (anytime less than 12 months ago),Yes,10.0,1 to 5,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.7,71.67,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,28.13,No,No,No,5.0,5.0,Yes,Female,65-69,Black,No,Yes,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,24.28,,,,,,,Male,Age 75 to 79,,,,,,,,Vermont,3.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,1 to 5,Yes,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,70.31,Yes,No,Yes,No,,No,No +,32.5,,,,,,,Female,Age 70 to 74,,,,,,,,West Virginia,0.0,5.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.55,78.02,Yes,No,Yes,Yes,"Yes, received Tdap",No,No +,29.29,,,,,,,Female,Age 55 to 59,,,,,,,,New York,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,Hispanic,1.55,70.31,No,Yes,Yes,Yes,"Yes, received Tdap",No,No +,,,,,,,,Male,Age 40 to 44,,,,,,,,Florida,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, +No,38.39,No,No,No,30.0,0.0,No,Male,60-64,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,24.59,,,,,,,Female,Age 65 to 69,,,,,,,,Nevada,14.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,,No,No,No,Yes,No,No,No,Yes,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,71.21,Yes,Yes,No,Yes,"Yes, received tetanus shot but not sure what type",No,Yes +,27.41,,,,,,,Male,Age 80 or older,,,,,,,,Alabama,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,Yes,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.7,79.38,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,34.37,,,,,,,Male,Age 55 to 59,,,,,,,,Iowa,1.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.91,124.74,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +,33.47,,,,,,,Female,Age 35 to 39,,,,,,,,Nebraska,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Not at all (right now),No,"White only, Non-Hispanic",1.63,88.45,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +,22.85,,,,,,,Female,Age 75 to 79,,,,,,,,Montana,30.0,30.0,Within past year (anytime less than 12 months ago),No,7.0,All,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.52,53.07,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes +,37.76,,,,,,,Male,Age 60 to 64,,,,,,,,North Dakota,30.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,All,Yes,No,No,No,No,Yes,Yes,No,Yes,No,No,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,99.79,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +No,31.17,No,Yes,No,2.0,0.0,No,Male,40-44,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,24.72,,,,,,,Male,Age 55 to 59,,,,,,,,Pennsylvania,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,Yes,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),No,"White only, Non-Hispanic",2.03,102.06,No,Yes,Yes,No,"Yes, received tetanus shot but not sure what type",No,Yes +,35.87,,,,,,,Male,Age 45 to 49,,,,,,,,Wisconsin,10.0,5.0,Within past year (anytime less than 12 months ago),No,6.0,None of them,Yes,No,No,Yes,No,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,113.4,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,28.35,,,,,,,Female,Age 80 or older,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.57,70.31,Yes,No,Yes,Yes,"Yes, received Tdap",No,No +,31.09,,,,,,,Female,Age 70 to 74,,,,,,,,Vermont,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,Yes,No,No,No,No,No,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.57,77.11,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,28.34,,,,,,,Female,Age 70 to 74,,,,,,,,Massachusetts,15.0,10.0,Within past year (anytime less than 12 months ago),No,6.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.55,68.04,No,No,No,Yes,"Yes, received tetanus shot but not sure what type",No,No +,,,,,,,,Female,,,,,,,,,Missouri,7.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,1 to 5,No,No,No,Yes,No,No,No,No,No,No,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, +No,54.82,Yes,Yes,No,0.0,0.0,No,Male,50-54,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,44.29,Yes,No,No,0.0,5.0,No,Male,35-39,White,No,No,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,27.05,No,No,No,1.0,0.0,No,Male,45-49,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,24.8,,,,,,,Female,Age 35 to 39,,,,,,,,Minnesota,30.0,30.0,Within past year (anytime less than 12 months ago),Yes,5.0,1 to 5,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.6,63.5,No,Yes,Yes,No,"Yes, received Tdap",No,No +,25.09,,,,,,,Male,Age 70 to 74,,,,,,,,West Virginia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,83.91,No,No,Yes,Yes,"Yes, received Tdap",No,No +,21.03,,,,,,,Female,Age 18 to 24,,,,,,,,Hawaii,3.0,12.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Use them some days,No,"White only, Non-Hispanic",1.57,52.16,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +No,25.32,Yes,No,No,3.0,2.0,No,Female,55-59,White,Yes,No,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,22.31,No,No,No,0.0,0.0,No,Female,80 or older,White,No,Yes,8.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,21.79,,,,,,,Female,Age 45 to 49,,,,,,,,Vermont,,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.68,61.23,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,28.12,,,,,,,Male,Age 70 to 74,,,,,,,,Florida,0.0,30.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,Yes,No,Yes,No,Yes,No,Yes,No,No,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.93,104.78,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,23.57,,,,,,,Female,Age 18 to 24,,,,,,,,Missouri,0.0,14.0,Within past 2 years (1 year but less than 2 years ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.73,70.31,Yes,No,No,No,"Yes, received Tdap",No,No +,27.26,,,,,,,Male,Age 30 to 34,,,,,,,,Montana,0.0,30.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,Yes,No,No,Yes,Yes,No,No,Yes,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.78,86.18,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No +,32.11,,,,,,,Male,Age 70 to 74,,,,,,,,South Dakota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,92.99,Yes,No,Yes,Yes,"Yes, received Tdap",No,No +,49.78,,,,,,,Female,Age 35 to 39,,,,,,,,California,15.0,7.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Multiracial, Non-Hispanic",1.63,131.54,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,No +,29.18,,,,,,,Female,Age 70 to 74,,,,,,,,Idaho,3.0,1.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,Yes,No,Yes,No,Yes,No,No,Yes,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,77.11,No,No,Yes,Yes,"Yes, received Tdap",No,No +No,27.44,No,No,No,0.0,0.0,No,Female,70-74,Hispanic,No,No,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Yes,28.63,No,No,No,10.0,0.0,Yes,Male,55-59,White,Yes,Yes,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,22.96,,,,,,,Male,Age 80 or older,,,,,,,,Washington,14.0,5.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,Yes,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,72.57,Yes,No,Yes,Yes,"Yes, received Tdap",No,No +,31.38,,,,,,,Male,Age 55 to 59,,,,,,,,Kansas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,Yes,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.8,102.06,Yes,No,No,Yes,"Yes, received tetanus shot but not sure what type",No,Yes +No,21.7,No,No,No,0.0,0.0,No,Male,75-79,Hispanic,Yes,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,27.71,,,,,,,Male,Age 45 to 49,,,,,,,,Maine,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Use them some days,No,"White only, Non-Hispanic",1.85,95.25,No,Yes,Yes,Yes,"Yes, received Tdap",No,No +,31.71,,,,,,,Female,Age 75 to 79,,,,,,,,Colorado,0.0,30.0,Within past year (anytime less than 12 months ago),Yes,5.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Use them some days,No,"White only, Non-Hispanic",1.6,81.19,No,No,No,Yes,"Yes, received tetanus shot but not sure what type",No,Yes +,26.61,,,,,,,Male,Age 75 to 79,,,,,,,,Idaho,7.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,79.38,No,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No +,,,,,,,,Female,Age 75 to 79,,,,,,,,Arizona,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, +No,25.84,No,No,No,0.0,0.0,No,Female,45-49,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,28.19,No,No,Yes,5.0,10.0,No,Female,30-34,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,28.29,,,,,,,Female,Age 65 to 69,,,,,,,,Kansas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,77.11,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +No,23.17,No,No,No,0.0,1.0,No,Female,30-34,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,29.84,No,No,No,0.0,0.0,No,Male,45-49,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,38.52,,,,,,,Male,Age 18 to 24,,,,,,,,Nevada,3.0,3.0,5 or more years ago,Yes,5.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.88,136.08,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,No +No,25.4,Yes,No,No,30.0,0.0,Yes,Female,75-79,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,26.63,,,,,,,Female,Age 35 to 39,,,,,,,,South Dakota,10.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.65,72.57,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +No,19.77,No,No,No,2.0,10.0,No,Female,18-24,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,36.18,Yes,No,No,0.0,7.0,Yes,Male,45-49,Other,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,33.09,,,,,,,Male,Age 40 to 44,,,,,,,,Mississippi,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Use them some days,,"White only, Non-Hispanic",1.68,92.99,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +,19.67,,,,,,,Male,Age 35 to 39,,,,,,,,Wisconsin,0.0,0.0,5 or more years ago,No,5.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.83,65.77,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No +,25.83,,,,,,,Male,Age 35 to 39,,,,,,,,Alabama,0.0,2.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,81.65,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",Yes,No +,28.31,,,,,,,Male,Age 65 to 69,,,,,,,,Maryland,5.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,No,Yes,No,Yes,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,92.08,No,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,Yes +,17.63,,,,,,,Male,Age 70 to 74,,,,,,,,Colorado,,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,All,No,No,No,No,No,Yes,No,No,Yes,No,No,No,No,Yes,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.83,58.97,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,No +,28.23,,,,,,,Male,Age 50 to 54,,,,,,,,Alabama,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.85,97.07,No,No,No,No,"Yes, received Tdap",No,No +No,30.41,Yes,Yes,No,1.0,0.0,No,Female,50-54,White,Yes,No,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,43.75,,,,,,,Male,Age 65 to 69,,,,,,,,Missouri,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,Yes,Yes,Yes,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.91,158.76,Yes,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes +,34.46,,,,,,,Male,Age 80 or older,,,,,,,,Massachusetts,1.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,"6 or more, but not all",No,No,No,No,Yes,No,No,No,Yes,Yes,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,99.79,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +No,21.89,No,No,No,0.0,0.0,No,Female,40-44,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,27.02,Yes,No,No,0.0,0.0,No,Female,35-39,American Indian/Alaskan Native,No,Yes,8.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,24.03,,,,,,,Female,Age 18 to 24,,,,,,,,Virginia,6.0,14.0,Within past 2 years (1 year but less than 2 years ago),Yes,11.0,None of them,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.63,63.5,Yes,No,No,No,"Yes, received Tdap",No,No +,,,,,,,,Female,Age 75 to 79,,,,,,,,Utah,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.52,,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,28.15,,,,,,,Female,Age 70 to 74,,,,,,,,Colorado,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,74.39,Yes,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No +,20.53,,,,,,,Female,Age 50 to 54,,,,,,,,Michigan,0.0,5.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,61.23,No,Yes,Yes,No,"Yes, received Tdap",No,No +,25.84,,,,,,,Female,Age 65 to 69,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,"6 or more, but not all",No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,79.38,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,21.93,,,,,,,Female,Age 80 or older,,,,,,,,Connecticut,0.0,0.0,Within past year (anytime less than 12 months ago),No,9.0,None of them,No,No,No,No,Yes,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,63.5,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,28.34,Yes,Yes,No,0.0,15.0,No,Female,40-44,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,30.68,No,No,No,0.0,0.0,No,Male,25-29,Hispanic,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,31.01,,,,,,,Male,Age 50 to 54,,,,,,,,Arkansas,1.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,1 to 5,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.75,95.25,No,No,No,Yes,"Yes, received tetanus shot but not sure what type",No,Yes +No,24.11,No,No,No,2.0,5.0,No,Male,18-24,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,23.01,,,,,,,Male,Age 25 to 29,,,,,,,,New York,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.8,74.84,Yes,Yes,Yes,No,"Yes, received Tdap",No,Yes +,27.26,,,,,,,Male,Age 50 to 54,,,,,,,,Vermont,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.78,86.18,Yes,No,Yes,No,"Yes, received tetanus shot, but not Tdap",No,Yes +,24.41,,,,,,,Male,Age 60 to 64,,,,,,,,Maryland,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.83,81.65,,,,,,, +,48.71,,,,,,,Female,Age 70 to 74,,,,,,,,West Virginia,10.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,All,No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,124.74,No,No,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,28.25,,,,,,,Male,Age 65 to 69,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.68,79.38,Yes,No,No,Yes,"Yes, received Tdap",No,No +,30.13,,,,,,,Male,Age 70 to 74,,,,,,,,Maine,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,Yes,Yes,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.78,95.25,No,No,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,42.57,,,,,,,Female,Age 45 to 49,,,,,,,,Minnesota,30.0,15.0,Within past year (anytime less than 12 months ago),No,6.0,"6 or more, but not all",No,No,No,No,No,No,Yes,No,Yes,"No, pre-diabetes or borderline diabetes",No,No,No,Yes,Yes,No,Current smoker - now smokes every day,Use them some days,Yes,"White only, Non-Hispanic",1.73,127.01,No,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,19.67,,,,,,,Male,Age 18 to 24,,,,,,,,Ohio,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,65.77,No,No,Yes,No,"Yes, received Tdap",No,No +,30.81,,,,,,,Male,Age 50 to 54,,,,,,,,Minnesota,0.0,5.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),No,"White only, Non-Hispanic",1.88,108.86,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +No,22.71,Yes,No,No,0.0,5.0,No,Female,35-39,White,No,No,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,23.06,,,,,,,Male,Age 18 to 24,,,,,,,,Nebraska,0.0,4.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,77.11,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,22.92,,,,,,,Female,Age 80 or older,,,,,,,,Wyoming,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,None of them,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.68,64.41,No,No,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,,,,,,,,Female,Age 75 to 79,,,,,,,,Maryland,2.0,10.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,Yes,No,No,Yes,No,Yes,No,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, +No,22.96,Yes,No,No,0.0,0.0,No,Male,65-69,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,24.33,No,No,No,0.0,2.0,No,Male,35-39,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,27.27,,,,,,,Male,Age 70 to 74,,,,,,,,Virginia,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,,No,No,No,No,No,Yes,No,No,Yes,Yes,Yes,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.96,104.33,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +Yes,27.44,No,No,No,0.0,0.0,No,Female,80 or older,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,29.03,,,,,,,Male,Age 40 to 44,,,,,,,,Texas,0.0,30.0,Within past 5 years (2 years but less than 5 years ago),No,4.0,1 to 5,Yes,Yes,No,No,No,Yes,Yes,No,Yes,No,Yes,No,Yes,No,No,Yes,Current smoker - now smokes every day,Use them some days,Yes,"White only, Non-Hispanic",1.85,99.79,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No +,31.31,,,,,,,Female,Age 65 to 69,,,,,,,,Minnesota,15.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.5,70.31,No,No,Yes,No,"Yes, received Tdap",No,No +,21.63,,,,,,,Female,Age 80 or older,,,,,,,,Ohio,6.0,,Within past year (anytime less than 12 months ago),No,7.0,1 to 5,No,No,No,No,No,No,Yes,No,Yes,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,58.97,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,,,,,,,,Female,Age 55 to 59,,,,,,,,Tennessee,1.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,None of them,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,"Black only, Non-Hispanic",,,,,,,,, +No,25.58,Yes,No,No,15.0,15.0,Yes,Female,75-79,American Indian/Alaskan Native,Yes,No,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,34.01,No,No,No,0.0,0.0,Yes,Female,80 or older,White,No,No,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,28.32,,,,,,,Female,Age 80 or older,,,,,,,,Nebraska,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,1 to 5,No,No,No,No,Yes,Yes,No,No,Yes,No,No,Yes,No,Yes,Yes,Yes,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.52,65.77,No,No,No,Yes,"Yes, received tetanus shot, but not Tdap",No,No +,35.15,,,,,,,Female,Age 55 to 59,,,,,,,,Indiana,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,Yes,Yes,No,No,Yes,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,,1.52,81.65,No,,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +,42.77,,,,,,,Female,Age 35 to 39,,,,,,,,Florida,1.0,4.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,120.2,No,,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,28.7,Yes,No,No,1.0,0.0,No,Male,80 or older,Hispanic,Yes,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,23.99,,,,,,,Female,Age 80 or older,,,,,,,,New Hampshire,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.8,78.02,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,21.52,,,,,,,Female,Age 60 to 64,,,,,,,,Washington,14.0,5.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,Yes,No,Yes,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,68.04,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +,28.8,,,,,,,Female,Age 30 to 34,,,,,,,,Nebraska,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,88.45,Yes,No,No,No,"Yes, received Tdap",No,Yes +No,32.08,No,No,No,1.0,3.0,No,Male,18-24,Black,No,Yes,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,19.37,,,,,,,Female,Age 45 to 49,,,,,,,,Rhode Island,0.0,7.0,Within past year (anytime less than 12 months ago),Yes,4.0,1 to 5,No,No,No,No,No,No,Yes,No,Yes,"No, pre-diabetes or borderline diabetes",No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,54.43,No,No,Yes,Yes,"Yes, received Tdap",No,No +,31.19,,,,,,,Male,Age 70 to 74,,,,,,,,Colorado,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,All,No,No,No,No,No,No,Yes,No,Yes,"No, pre-diabetes or borderline diabetes",Yes,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,,1.83,104.33,Yes,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,29.23,,,,,,,Male,Age 55 to 59,,,,,,,,Indiana,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,74.84,Yes,,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,29.69,,,,,,,Female,Age 75 to 79,,,,,,,,New York,2.0,3.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,Yes,No,Yes,Yes,No,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.5,66.68,No,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Tested positive using home test without a health professional +,26.89,,,,,,,Female,Age 75 to 79,,,,,,,,Puerto Rico,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Not at all (right now),No,Hispanic,1.57,66.68,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,22.96,,,,,,,Male,Age 80 or older,,,,,,,,Arizona,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,72.57,Yes,No,No,Yes,"Yes, received tetanus shot but not sure what type",No,No +,21.7,,,,,,,Male,Age 40 to 44,,,,,,,,Georgia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.83,72.57,No,Yes,Yes,No,"Yes, received tetanus shot but not sure what type",No,Yes +,27.89,,,,,,,Male,Age 65 to 69,,,,,,,,Alaska,30.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,90.72,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No +No,34.95,No,No,No,0.0,0.0,No,Female,55-59,White,No,Yes,7.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,31.53,No,No,No,3.0,0.0,No,Female,60-64,Hispanic,No,Yes,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,25.66,Yes,No,No,0.0,0.0,No,Female,70-74,Black,Yes,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,33.0,Yes,No,No,0.0,1.0,No,Male,70-74,White,No,Yes,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,20.09,Yes,No,No,0.0,4.0,No,Male,30-34,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,25.42,,,,,,,Female,Age 40 to 44,,,,,,,,Nevada,0.0,30.0,Within past 5 years (2 years but less than 5 years ago),No,4.0,1 to 5,No,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,No,Hispanic,1.57,63.05,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,30.41,,,,,,,Female,Age 75 to 79,,,,,,,,Kansas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,90.72,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,38.41,Yes,Yes,No,0.0,14.0,Yes,Male,65-69,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,20.41,Yes,No,No,0.0,0.0,No,Female,70-74,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,25.84,,,,,,,Male,Age 25 to 29,,,,,,,,Massachusetts,0.0,1.0,5 or more years ago,Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"Other race only, Non-Hispanic",1.75,79.38,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +,27.8,,,,,,,Male,Age 70 to 74,,,,,,,,New Hampshire,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,92.99,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,No +,19.94,,,,,,,Male,Age 55 to 59,,,,,,,,Pennsylvania,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,9.0,All,No,No,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,61.23,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,No +,24.13,,,,,,,Female,Age 80 or older,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,65.77,No,No,Yes,Yes,"Yes, received Tdap",No,No +No,24.28,Yes,No,No,0.0,2.0,No,Female,65-69,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,28.7,,,,,,,Female,Age 60 to 64,,,,,,,,Minnesota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,Yes,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,73.48,No,Yes,Yes,No,"Yes, received Tdap",No,No +Yes,23.74,Yes,No,No,0.0,0.0,No,Male,65-69,Black,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,45.76,No,No,No,0.0,0.0,Yes,Female,55-59,Hispanic,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,29.86,No,No,No,0.0,4.0,No,Female,60-64,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,26.54,Yes,No,No,0.0,0.0,No,Male,75-79,White,No,No,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,24.96,No,No,No,0.0,3.0,No,Female,25-29,White,No,Yes,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,23.43,,,,,,,Male,Age 60 to 64,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.8,76.2,Yes,No,No,,"Yes, received Tdap",No,Tested positive using home test without a health professional +,23.63,,,,,,,Male,Age 50 to 54,,,,,,,,Wyoming,3.0,0.0,5 or more years ago,Yes,8.0,1 to 5,No,No,No,Yes,No,No,Yes,No,Yes,No,No,No,No,,,,,,,"White only, Non-Hispanic",1.75,72.57,,,,,,, +,31.01,,,,,,,Male,Age 50 to 54,,,,,,,,North Dakota,0.0,0.0,5 or more years ago,Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,95.25,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,23.4,Yes,No,No,0.0,0.0,No,Female,60-64,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,29.05,,,,,,,Male,Age 40 to 44,,,,,,,,Michigan,1.0,3.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"Other race only, Non-Hispanic",1.68,81.65,Yes,Yes,No,Yes,"Yes, received tetanus shot but not sure what type",No,No +No,23.06,No,No,No,0.0,0.0,No,Male,30-34,Black,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,,,,,,,,Female,Age 40 to 44,,,,,,,,Washington,1.0,2.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,90.72,No,Yes,Yes,,"Yes, received Tdap",Yes,Yes +No,28.25,Yes,No,No,0.0,0.0,No,Male,60-64,White,No,Yes,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,25.77,,,,,,,Male,Age 75 to 79,,,,,,,,Kansas,30.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.83,86.18,No,Yes,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No +,33.89,,,,,,,Female,Age 65 to 69,,,,,,,,New Hampshire,1.0,2.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,"6 or more, but not all",No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,95.25,Yes,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes +,23.38,,,,,,,Female,Age 80 or older,,,,,,,,Massachusetts,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,Yes,No,No,Yes,No,Yes,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,59.87,Yes,No,Yes,Yes,"Yes, received Tdap",No,Yes +No,22.31,No,No,No,0.0,0.0,No,Female,18-24,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,22.32,,,,,,,Male,Age 70 to 74,,,,,,,,Colorado,1.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,72.57,Yes,No,No,Yes,"Yes, received tetanus shot but not sure what type",No,No +,27.26,,,,,,,Male,Age 75 to 79,,,,,,,,California,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,86.18,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +No,35.15,No,No,No,0.0,0.0,Yes,Female,80 or older,White,No,No,8.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,34.33,,,,,,,Female,Age 75 to 79,,,,,,,,Maine,,0.0,Within past year (anytime less than 12 months ago),No,8.0,All,No,No,No,No,,No,No,No,Yes,Yes,Yes,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,,1.63,90.72,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,22.31,,,,,,,Female,Age 65 to 69,,,,,,,,Nebraska,5.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,58.97,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,26.61,,,,,,,Male,Age 80 or older,,,,,,,,California,0.0,20.0,Within past year (anytime less than 12 months ago),No,,"6 or more, but not all",No,No,No,No,,No,No,No,,Yes,No,Yes,No,No,No,Yes,Former smoker,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.73,79.38,Yes,,Yes,Yes,"Yes, received Tdap",No,No +No,26.96,No,No,No,0.0,0.0,No,Female,40-44,White,No,Yes,8.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,32.28,No,No,No,0.0,0.0,No,Male,60-64,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,28.34,,,,,,,Female,Age 70 to 74,,,,,,,,Rhode Island,0.0,5.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,Yes,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.6,72.57,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,33.47,,,,,,,Male,Age 75 to 79,,,,,,,,Kansas,15.0,15.0,Within past year (anytime less than 12 months ago),No,9.0,"6 or more, but not all",Yes,Yes,No,No,No,No,No,No,Yes,Yes,Yes,Yes,Yes,Yes,Yes,Yes,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,108.86,Yes,No,Yes,Yes,"Yes, received Tdap",No,No +,23.75,,,,,,,Male,Age 25 to 29,,,,,,,,Ohio,3.0,2.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Use them every day,No,"White only, Non-Hispanic",1.88,83.91,Yes,No,No,,"Yes, received Tdap",No,Yes +,25.83,,,,,,,Male,Age 65 to 69,,,,,,,,Iowa,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,81.65,No,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,Tested positive using home test without a health professional +,23.72,,,,,,,Male,Age 18 to 24,,,,,,,,Missouri,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,,1.73,70.76,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +No,31.01,Yes,Yes,No,0.0,0.0,No,Male,50-54,Hispanic,No,No,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,36.85,,,,,,,Female,Age 35 to 39,,,,,,,,Arkansas,0.0,2.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,94.35,Yes,No,Yes,No,"Yes, received Tdap",No,No +,27.46,,,,,,,Male,Age 60 to 64,,,,,,,,Indiana,0.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,74.84,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No +,37.59,,,,,,,Female,Age 65 to 69,,,,,,,,Wisconsin,3.0,30.0,Within past year (anytime less than 12 months ago),Yes,12.0,1 to 5,No,No,No,No,No,No,Yes,Yes,Yes,No,No,No,Yes,Yes,No,No,Current smoker - now smokes some days,Not at all (right now),Yes,"White only, Non-Hispanic",1.7,108.86,No,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +No,24.37,No,No,No,0.0,0.0,No,Female,35-39,White,No,Yes,8.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,27.71,,,,,,,Male,Age 65 to 69,,,,,,,,Maine,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,Yes,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.85,95.25,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,30.9,,,,,,,Female,Age 25 to 29,,,,,,,,Montana,10.0,5.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"Other race only, Non-Hispanic",1.63,81.65,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,18.65,,,,,,,Female,Age 45 to 49,,,,,,,,California,0.0,5.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,Current smoker - now smokes every day,Use them every day,No,"White only, Non-Hispanic",1.78,58.97,No,Yes,No,Yes,"Yes, received tetanus shot but not sure what type",No,No +,28.48,,,,,,,Male,Age 25 to 29,,,,,,,,Texas,0.0,0.0,,No,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.83,95.25,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",Yes,Yes +,35.95,,,,,,,Male,Age 75 to 79,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,Yes,Yes,Yes,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.88,127.01,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,42.05,,,,,,,Female,Age 50 to 54,,,,,,,,California,15.0,10.0,Within past year (anytime less than 12 months ago),No,6.0,1 to 5,No,No,No,Yes,No,Yes,Yes,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,111.13,Yes,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,24.21,No,No,No,0.0,0.0,Yes,Female,80 or older,White,No,No,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,46.79,Yes,No,No,30.0,3.0,Yes,Male,55-59,White,No,No,4.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,32.89,No,No,No,0.0,0.0,No,Male,75-79,Asian,Yes,Yes,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Yes,29.12,Yes,No,No,5.0,1.0,Yes,Female,50-54,White,No,Yes,4.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,,,,,,,,Female,Age 65 to 69,,,,,,,,Ohio,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,All,No,No,No,No,No,No,No,No,No,Yes,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, +,21.87,,,,,,,Female,Age 65 to 69,,,,,,,,New York,2.0,4.0,Within past year (anytime less than 12 months ago),Yes,5.0,"6 or more, but not all",No,No,No,No,,No,No,No,Yes,No,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.52,50.8,No,,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,33.54,,,,,,,Female,Age 55 to 59,,,,,,,,New Mexico,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.45,70.31,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,22.31,,,,,,,Female,Age 18 to 24,,,,,,,,Connecticut,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.63,58.97,Yes,,Yes,,"Yes, received tetanus shot but not sure what type",No, +,27.81,,,,,,,Female,Age 60 to 64,,,,,,,,South Carolina,14.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,5.0,1 to 5,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.63,73.48,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,22.6,,,,,,,Male,Age 65 to 69,,,,,,,,Wisconsin,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.68,63.5,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +,22.43,,,,,,,Male,Age 70 to 74,,,,,,,,Colorado,30.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,Yes,Yes,No,Yes,No,No,No,No,No,No,Yes,Yes,Yes,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.85,77.11,No,Yes,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No +,27.46,,,,,,,Female,Age 55 to 59,,,,,,,,Puerto Rico,6.0,6.0,Within past year (anytime less than 12 months ago),No,6.0,1 to 5,No,No,No,No,No,No,No,No,,"No, pre-diabetes or borderline diabetes",No,No,No,No,No,No,Never smoked,Not at all (right now),No,Hispanic,1.63,72.57,No,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,27.26,,,,,,,Male,Age 18 to 24,,,,,,,,Rhode Island,0.0,4.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,Yes,No,No,No,Never smoked,Not at all (right now),No,Hispanic,1.78,86.18,Yes,No,Yes,Yes,"Yes, received Tdap",No,Yes +,24.69,,,,,,,Female,Age 55 to 59,,,,,,,,Maryland,0.0,3.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,61.23,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,37.11,,,,,,,Female,Age 40 to 44,,,,,,,,New York,7.0,30.0,5 or more years ago,Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,Yes,No,,,,,,"White only, Non-Hispanic",1.52,86.18,,,,,,, +No,38.01,Yes,No,No,0.0,0.0,No,Male,35-39,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,20.2,,,,,,,Female,Age 55 to 59,,,,,,,,Alabama,2.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,"6 or more, but not all",No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.7,58.51,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,20.51,,,,,,,Female,Age 65 to 69,,,,,,,,West Virginia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.52,47.63,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No +No,25.77,Yes,No,No,0.0,0.0,No,Male,40-44,Hispanic,No,No,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Yes,34.87,No,No,No,3.0,0.0,Yes,Male,65-69,White,Yes,Yes,6.0,No,Yes,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_006/history.csv b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_006/history.csv index a9b104c592ad25546795fcd5c31bfd741671a573..cd85ba28f3ab6f5b57d63a703b2f34d097c0ce0d 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_006/history.csv +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_006/history.csv @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:b46075750bea58ff23c4d7949025a9ce5ed0953f249a6d2def426fb9e5267972 -size 271613311 +oid sha256:cb3ba31d5e18d100aaf9f61a75cebc9d716494aa22c54721bf837b51568fb236 +size 270591908 diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_006/info.json b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_006/info.json index 53272e3074451c673817a05ef4da5ee438d07863..c876b7e9aa18ec2175d72a1a6b59bf487c77e98c 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_006/info.json +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_006/info.json @@ -1,401 +1,393 @@ -{ - "name": "Indicators of Heart Disease (2022 UPDATE)", - "source": "https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease/data", - "data_intro": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", - "is_splited": false, - "overall_size": 1010949, - "train_size": 0, - "test_size": 0, - "c_classes": 46, - "n_classes": 9, - "cat_feature_intro": { - "HeartDisease": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", - "Smoking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", - "AlcoholDrinking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", - "Stroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", - "DiffWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", - "Sex": "Sex is a biological factor that can influence the risk of heart disease. Men and women may experience different symptoms and risk factors for heart disease.", - "AgeCategory": "Age is a significant risk factor for heart disease. As people age, their risk of developing heart disease increases. It is important to monitor heart health as individuals get older.", - "Race": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", - "Diabetic": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", - "PhysicalActivity": "Regular physical activity is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", - "GenHealth": "General health status can influence the risk of heart disease. It is important to maintain overall health and address any underlying health issues to reduce the risk of heart-related complications.", - "Asthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", - "KidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", - "SkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", - "State": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", - "GeneralHealth": "General health status can influence the risk of heart disease. It is important to maintain overall health and address any underlying health issues to reduce the risk of heart-related complications.", - "LastCheckupTime": "The time since the last medical checkup can be an important factor in heart health. Regular checkups can help identify and address risk factors for heart disease early.", - "PhysicalActivities": "Engaging in regular physical activities is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", - "RemovedTeeth": "The number of removed teeth can be an indicator of overall health, including heart health. Poor oral health has been linked to an increased risk of heart disease.", - "HadHeartAttack": "As described above, the original dataset of nearly 300 variables was reduced to 40 variables. In addition to classical EDA, this dataset can be used to apply a number of machine learning methods, especially classifier models (logistic regression, SVM, random forest, etc.). You should treat the variable \"HadHeartAttack\" as binary (\"Yes\" - respondent had heart disease; \"No\" - respondent did not have heart disease). Note, however, that the classes are unbalanced, so the classic approach of applying a model is not advisable. Fixing the weights/undersampling should yield much better results. Based on the data set, I built a logistic regression model and embedded it in an application that might inspire you: https://share.streamlit.io/kamilpytlak/heart-condition-checker/main/app.py. Can you indicate which variables have a significant effect on the likelihood of heart disease?", - "HadAngina": "Angina is a type of chest pain caused by reduced blood flow to the heart. It is a symptom of coronary artery disease and can indicate an increased risk of heart disease.", - "HadStroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", - "HadAsthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", - "HadSkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", - "HadCOPD": "Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease that causes obstructed airflow from the lungs. It can increase the risk of heart disease.", - "HadDepressiveDisorder": "Depressive disorders can affect overall health and increase the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", - "HadKidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", - "HadArthritis": "Arthritis is a condition that causes inflammation of the joints. It can affect overall health and increase the risk of heart disease.", - "HadDiabetes": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", - "DeafOrHardOfHearing": "Hearing loss can affect overall health and quality of life. It is important to address and manage hearing issues to maintain overall health and reduce the risk of heart disease.", - "BlindOrVisionDifficulty": "Vision difficulties can affect overall health and quality of life. It is important to address and manage vision issues to maintain overall health and reduce the risk of heart disease.", - "DifficultyConcentrating": "Difficulty concentrating can be a sign of underlying health issues, including heart disease. It is important to address cognitive issues to improve overall health and reduce the risk of heart-related complications.", - "DifficultyWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", - "DifficultyDressingBathing": "Difficulty with daily activities such as dressing and bathing can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", - "DifficultyErrands": "Difficulty running errands can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", - "SmokerStatus": "Smoking is a major risk factor for heart disease. It is important to address and manage smoking habits to reduce the risk of heart-related complications.", - "ECigaretteUsage": "E-cigarette usage can affect heart health. It is important to monitor and manage e-cigarette usage to reduce the risk of heart disease.", - "ChestScan": "Chest scans can help identify underlying health issues, including heart disease. It is important to monitor and address any abnormalities found in chest scans to maintain overall health.", - "RaceEthnicityCategory": "Race and ethnicity can influence the risk of heart disease. It is important to consider these factors when assessing and managing heart health.", - "AlcoholDrinkers": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", - "HIVTesting": "HIV testing is important for overall health. It is important to address and manage HIV status to reduce the risk of heart-related complications.", - "FluVaxLast12": "Receiving a flu vaccine in the last 12 months can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", - "PneumoVaxEver": "Receiving a pneumococcal vaccine can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", - "TetanusLast10Tdap": "Receiving a tetanus vaccine in the last 10 years can help reduce the risk of infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", - "HighRiskLastYear": "Being at high risk for health issues in the last year can affect overall health and increase the risk of heart disease. It is important to address and manage high-risk factors to reduce the risk of heart-related complications.", - "CovidPos": "Testing positive for COVID-19 can affect overall health and increase the risk of heart disease. It is important to monitor and manage COVID-19 symptoms to reduce the risk of heart-related complications." - }, - "num_feature_intro": { - "BMI": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", - "PhysicalHealth": "Physical health is an important factor in overall well-being and can influence the risk of heart disease. It is important to maintain good physical health to reduce the risk of heart-related complications.", - "MentalHealth": "Mental health is an important factor in overall well-being and can influence the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", - "SleepTime": "Sleep time is an important factor in overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", - "PhysicalHealthDays": "The number of days with good physical health can be an indicator of overall well-being. It is important to maintain good physical health to reduce the risk of heart-related complications.", - "MentalHealthDays": "The number of days with good mental health can be an indicator of overall well-being. It is important to address and manage mental health to reduce the risk of heart-related complications.", - "SleepHours": "The number of hours of sleep can affect overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", - "HeightInMeters": "Height can be an indicator of overall health. It is important to monitor and maintain a healthy height-to-weight ratio to reduce the risk of heart disease.", - "WeightInKilograms": "Weight can be an indicator of overall health. It is important to maintain a healthy weight to reduce the risk of heart disease." - }, - "evaluation_metric": "The evaluation metric for this dataset can include accuracy, precision, recall, F1-score, and AUC-ROC. These metrics can help assess the performance of machine learning models in predicting heart disease.", - "task_type": "classification", - "target": "GeneralHealth", - "num_feature_value": { - "BMI": [ - 12.02, - 99.64 - ], - "HeightInMeters": [ - 0.91, - 2.41 - ], - "MentalHealth": [ - 0.0, - 30.0 - ], - "MentalHealthDays": [ - 0.0, - 30.0 - ], - "PhysicalHealth": [ - 0.0, - 30.0 - ], - "PhysicalHealthDays": [ - 0.0, - 30.0 - ], - "SleepHours": [ - 1.0, - 24.0 - ], - "SleepTime": [ - 1.0, - 24.0 - ], - "WeightInKilograms": [ - 22.68, - 292.57 - ] - }, - "cat_feature_value": { - "AgeCategory": [ - "18-24", - "25-29", - "30-34", - "35-39", - "40-44", - "45-49", - "50-54", - "55-59", - "60-64", - "65-69", - "70-74", - "75-79", - "80 or older", - "Age 18 to 24", - "Age 25 to 29", - "Age 30 to 34", - "Age 35 to 39", - "Age 40 to 44", - "Age 45 to 49", - "Age 50 to 54", - "Age 55 to 59", - "Age 60 to 64", - "Age 65 to 69", - "Age 70 to 74", - "Age 75 to 79", - "Age 80 or older" - ], - "AlcoholDrinkers": [ - "No", - "Yes" - ], - "AlcoholDrinking": [ - "No", - "Yes" - ], - "Asthma": [ - "No", - "Yes" - ], - "BlindOrVisionDifficulty": [ - "No", - "Yes" - ], - "ChestScan": [ - "No", - "Yes" - ], - "CovidPos": [ - "No", - "Tested positive using home test without a health professional", - "Yes" - ], - "DeafOrHardOfHearing": [ - "No", - "Yes" - ], - "Diabetic": [ - "No", - "No, borderline diabetes", - "Yes", - "Yes (during pregnancy)" - ], - "DiffWalking": [ - "No", - "Yes" - ], - "DifficultyConcentrating": [ - "No", - "Yes" - ], - "DifficultyDressingBathing": [ - "No", - "Yes" - ], - "DifficultyErrands": [ - "No", - "Yes" - ], - "DifficultyWalking": [ - "No", - "Yes" - ], - "ECigaretteUsage": [ - "Never used e-cigarettes in my entire life", - "Not at all (right now)", - "Use them every day", - "Use them some days" - ], - "FluVaxLast12": [ - "No", - "Yes" - ], - "GenHealth": [ - "Excellent", - "Fair", - "Good", - "Poor", - "Very good" - ], - "GeneralHealth": [ - "Excellent", - "Fair", - "Good", - "Poor", - "Very good" - ], - "HIVTesting": [ - "No", - "Yes" - ], - "HadAngina": [ - "No", - "Yes" - ], - "HadArthritis": [ - "No", - "Yes" - ], - "HadAsthma": [ - "No", - "Yes" - ], - "HadCOPD": [ - "No", - "Yes" - ], - "HadDepressiveDisorder": [ - "No", - "Yes" - ], - "HadDiabetes": [ - "No", - "No, pre-diabetes or borderline diabetes", - "Yes", - "Yes, but only during pregnancy (female)" - ], - "HadHeartAttack": [ - "No", - "Yes" - ], - "HadKidneyDisease": [ - "No", - "Yes" - ], - "HadSkinCancer": [ - "No", - "Yes" - ], - "HadStroke": [ - "No", - "Yes" - ], - "HeartDisease": [ - "No", - "Yes" - ], - "HighRiskLastYear": [ - "No", - "Yes" - ], - "KidneyDisease": [ - "No", - "Yes" - ], - "LastCheckupTime": [ - "5 or more years ago", - "Within past 2 years (1 year but less than 2 years ago)", - "Within past 5 years (2 years but less than 5 years ago)", - "Within past year (anytime less than 12 months ago)" - ], - "PhysicalActivities": [ - "No", - "Yes" - ], - "PhysicalActivity": [ - "No", - "Yes" - ], - "PneumoVaxEver": [ - "No", - "Yes" - ], - "Race": [ - "American Indian/Alaskan Native", - "Asian", - "Black", - "Hispanic", - "Other", - "White" - ], - "RaceEthnicityCategory": [ - "Black only, Non-Hispanic", - "Hispanic", - "Multiracial, Non-Hispanic", - "Other race only, Non-Hispanic", - "White only, Non-Hispanic" - ], - "RemovedTeeth": [ - "1 to 5", - "6 or more, but not all", - "All", - "None of them" - ], - "Sex": [ - "Female", - "Male" - ], - "SkinCancer": [ - "No", - "Yes" - ], - "SmokerStatus": [ - "Current smoker - now smokes every day", - "Current smoker - now smokes some days", - "Former smoker", - "Never smoked" - ], - "Smoking": [ - "No", - "Yes" - ], - "State": [ - "Alabama", - "Alaska", - "Arizona", - "Arkansas", - "California", - "Colorado", - "Connecticut", - "Delaware", - "District of Columbia", - "Florida", - "Georgia", - "Guam", - "Hawaii", - "Idaho", - "Illinois", - "Indiana", - "Iowa", - "Kansas", - "Kentucky", - "Louisiana", - "Maine", - "Maryland", - "Massachusetts", - "Michigan", - "Minnesota", - "Mississippi", - "Missouri", - "Montana", - "Nebraska", - "Nevada", - "New Hampshire", - "New Jersey", - "New Mexico", - "New York", - "North Carolina", - "North Dakota", - "Ohio", - "Oklahoma", - "Oregon", - "Pennsylvania", - "Puerto Rico", - "Rhode Island", - "South Carolina", - "South Dakota", - "Tennessee", - "Texas", - "Utah", - "Vermont", - "Virgin Islands", - "Virginia", - "Washington", - "West Virginia", - "Wisconsin", - "Wyoming" - ], - "Stroke": [ - "No", - "Yes" - ], - "TetanusLast10Tdap": [ - "No, did not receive any tetanus shot in the past 10 years", - "Yes, received Tdap", - "Yes, received tetanus shot but not sure what type", - "Yes, received tetanus shot, but not Tdap" - ] - } -} \ No newline at end of file +{ + "name": "Indicators of Heart Disease (2022 UPDATE)", + "source": "https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease/data", + "data_intro": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", + "is_splited": false, + "overall_size": 1010949, + "train_size": 0, + "test_size": 0, + "c_classes": 45, + "n_classes": 9, + "cat_feature_intro": { + "HeartDisease": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", + "Smoking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", + "AlcoholDrinking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", + "Stroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", + "DiffWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", + "Sex": "Sex is a biological factor that can influence the risk of heart disease. Men and women may experience different symptoms and risk factors for heart disease.", + "AgeCategory": "Age is a significant risk factor for heart disease. As people age, their risk of developing heart disease increases. It is important to monitor heart health as individuals get older.", + "Race": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", + "Diabetic": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", + "PhysicalActivity": "Regular physical activity is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", + "Asthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", + "KidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", + "SkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", + "State": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", + "GeneralHealth": "Self-reported general health from BRFSS GENHLTH: Excellent, Very good, Good, Fair, or Poor.", + "LastCheckupTime": "The time since the last medical checkup can be an important factor in heart health. Regular checkups can help identify and address risk factors for heart disease early.", + "PhysicalActivities": "Engaging in regular physical activities is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", + "RemovedTeeth": "The number of removed teeth can be an indicator of overall health, including heart health. Poor oral health has been linked to an increased risk of heart disease.", + "HadHeartAttack": "As described above, the original dataset of nearly 300 variables was reduced to 40 variables. In addition to classical EDA, this dataset can be used to apply a number of machine learning methods, especially classifier models (logistic regression, SVM, random forest, etc.). You should treat the variable \"HadHeartAttack\" as binary (\"Yes\" - respondent had heart disease; \"No\" - respondent did not have heart disease). Note, however, that the classes are unbalanced, so the classic approach of applying a model is not advisable. Fixing the weights/undersampling should yield much better results. Based on the data set, I built a logistic regression model and embedded it in an application that might inspire you: https://share.streamlit.io/kamilpytlak/heart-condition-checker/main/app.py. Can you indicate which variables have a significant effect on the likelihood of heart disease?", + "HadAngina": "Angina is a type of chest pain caused by reduced blood flow to the heart. It is a symptom of coronary artery disease and can indicate an increased risk of heart disease.", + "HadStroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", + "HadAsthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", + "HadSkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", + "HadCOPD": "Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease that causes obstructed airflow from the lungs. It can increase the risk of heart disease.", + "HadDepressiveDisorder": "Depressive disorders can affect overall health and increase the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", + "HadKidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", + "HadArthritis": "Arthritis is a condition that causes inflammation of the joints. It can affect overall health and increase the risk of heart disease.", + "HadDiabetes": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", + "DeafOrHardOfHearing": "Hearing loss can affect overall health and quality of life. It is important to address and manage hearing issues to maintain overall health and reduce the risk of heart disease.", + "BlindOrVisionDifficulty": "Vision difficulties can affect overall health and quality of life. It is important to address and manage vision issues to maintain overall health and reduce the risk of heart disease.", + "DifficultyConcentrating": "Difficulty concentrating can be a sign of underlying health issues, including heart disease. It is important to address cognitive issues to improve overall health and reduce the risk of heart-related complications.", + "DifficultyWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", + "DifficultyDressingBathing": "Difficulty with daily activities such as dressing and bathing can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", + "DifficultyErrands": "Difficulty running errands can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", + "SmokerStatus": "Smoking is a major risk factor for heart disease. It is important to address and manage smoking habits to reduce the risk of heart-related complications.", + "ECigaretteUsage": "E-cigarette usage can affect heart health. It is important to monitor and manage e-cigarette usage to reduce the risk of heart disease.", + "ChestScan": "Chest scans can help identify underlying health issues, including heart disease. It is important to monitor and address any abnormalities found in chest scans to maintain overall health.", + "RaceEthnicityCategory": "Race and ethnicity can influence the risk of heart disease. It is important to consider these factors when assessing and managing heart health.", + "AlcoholDrinkers": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", + "HIVTesting": "Whether the respondent has ever been tested for HIV (Yes/No), not the result of that test.", + "FluVaxLast12": "Receiving a flu vaccine in the last 12 months can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", + "PneumoVaxEver": "Receiving a pneumococcal vaccine can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", + "TetanusLast10Tdap": "Receiving a tetanus vaccine in the last 10 years can help reduce the risk of infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", + "HighRiskLastYear": "Being at high risk for health issues in the last year can affect overall health and increase the risk of heart disease. It is important to address and manage high-risk factors to reduce the risk of heart-related complications.", + "CovidPos": "Testing positive for COVID-19 can affect overall health and increase the risk of heart disease. It is important to monitor and manage COVID-19 symptoms to reduce the risk of heart-related complications." + }, + "num_feature_intro": { + "BMI": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", + "PhysicalHealth": "Physical health is an important factor in overall well-being and can influence the risk of heart disease. It is important to maintain good physical health to reduce the risk of heart-related complications.", + "MentalHealth": "Mental health is an important factor in overall well-being and can influence the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", + "SleepTime": "Sleep time is an important factor in overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", + "PhysicalHealthDays": "The number of days with good physical health can be an indicator of overall well-being. It is important to maintain good physical health to reduce the risk of heart-related complications.", + "MentalHealthDays": "The number of days with good mental health can be an indicator of overall well-being. It is important to address and manage mental health to reduce the risk of heart-related complications.", + "SleepHours": "The number of hours of sleep can affect overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", + "HeightInMeters": "Height can be an indicator of overall health. It is important to monitor and maintain a healthy height-to-weight ratio to reduce the risk of heart disease.", + "WeightInKilograms": "Weight can be an indicator of overall health. It is important to maintain a healthy weight to reduce the risk of heart disease." + }, + "evaluation_metric": "The evaluation metric for this dataset can include accuracy, precision, recall, F1-score, and AUC-ROC. These metrics can help assess the performance of machine learning models in predicting heart disease.", + "task_type": "classification", + "target": "GeneralHealth", + "num_feature_value": { + "BMI": [ + 12.02, + 99.64 + ], + "HeightInMeters": [ + 0.91, + 2.41 + ], + "MentalHealth": [ + 0.0, + 30.0 + ], + "MentalHealthDays": [ + 0.0, + 30.0 + ], + "PhysicalHealth": [ + 0.0, + 30.0 + ], + "PhysicalHealthDays": [ + 0.0, + 30.0 + ], + "SleepHours": [ + 1.0, + 24.0 + ], + "SleepTime": [ + 1.0, + 24.0 + ], + "WeightInKilograms": [ + 22.68, + 292.57 + ] + }, + "cat_feature_value": { + "AgeCategory": [ + "18-24", + "25-29", + "30-34", + "35-39", + "40-44", + "45-49", + "50-54", + "55-59", + "60-64", + "65-69", + "70-74", + "75-79", + "80 or older", + "Age 18 to 24", + "Age 25 to 29", + "Age 30 to 34", + "Age 35 to 39", + "Age 40 to 44", + "Age 45 to 49", + "Age 50 to 54", + "Age 55 to 59", + "Age 60 to 64", + "Age 65 to 69", + "Age 70 to 74", + "Age 75 to 79", + "Age 80 or older" + ], + "AlcoholDrinkers": [ + "No", + "Yes" + ], + "AlcoholDrinking": [ + "No", + "Yes" + ], + "Asthma": [ + "No", + "Yes" + ], + "BlindOrVisionDifficulty": [ + "No", + "Yes" + ], + "ChestScan": [ + "No", + "Yes" + ], + "CovidPos": [ + "No", + "Tested positive using home test without a health professional", + "Yes" + ], + "DeafOrHardOfHearing": [ + "No", + "Yes" + ], + "Diabetic": [ + "No", + "No, borderline diabetes", + "Yes", + "Yes (during pregnancy)" + ], + "DiffWalking": [ + "No", + "Yes" + ], + "DifficultyConcentrating": [ + "No", + "Yes" + ], + "DifficultyDressingBathing": [ + "No", + "Yes" + ], + "DifficultyErrands": [ + "No", + "Yes" + ], + "DifficultyWalking": [ + "No", + "Yes" + ], + "ECigaretteUsage": [ + "Never used e-cigarettes in my entire life", + "Not at all (right now)", + "Use them every day", + "Use them some days" + ], + "FluVaxLast12": [ + "No", + "Yes" + ], + "GeneralHealth": [ + "Excellent", + "Very good", + "Good", + "Fair", + "Poor" + ], + "HIVTesting": [ + "No", + "Yes" + ], + "HadAngina": [ + "No", + "Yes" + ], + "HadArthritis": [ + "No", + "Yes" + ], + "HadAsthma": [ + "No", + "Yes" + ], + "HadCOPD": [ + "No", + "Yes" + ], + "HadDepressiveDisorder": [ + "No", + "Yes" + ], + "HadDiabetes": [ + "No", + "No, pre-diabetes or borderline diabetes", + "Yes", + "Yes, but only during pregnancy (female)" + ], + "HadHeartAttack": [ + "No", + "Yes" + ], + "HadKidneyDisease": [ + "No", + "Yes" + ], + "HadSkinCancer": [ + "No", + "Yes" + ], + "HadStroke": [ + "No", + "Yes" + ], + "HeartDisease": [ + "No", + "Yes" + ], + "HighRiskLastYear": [ + "No", + "Yes" + ], + "KidneyDisease": [ + "No", + "Yes" + ], + "LastCheckupTime": [ + "5 or more years ago", + "Within past 2 years (1 year but less than 2 years ago)", + "Within past 5 years (2 years but less than 5 years ago)", + "Within past year (anytime less than 12 months ago)" + ], + "PhysicalActivities": [ + "No", + "Yes" + ], + "PhysicalActivity": [ + "No", + "Yes" + ], + "PneumoVaxEver": [ + "No", + "Yes" + ], + "Race": [ + "American Indian/Alaskan Native", + "Asian", + "Black", + "Hispanic", + "Other", + "White" + ], + "RaceEthnicityCategory": [ + "Black only, Non-Hispanic", + "Hispanic", + "Multiracial, Non-Hispanic", + "Other race only, Non-Hispanic", + "White only, Non-Hispanic" + ], + "RemovedTeeth": [ + "1 to 5", + "6 or more, but not all", + "All", + "None of them" + ], + "Sex": [ + "Female", + "Male" + ], + "SkinCancer": [ + "No", + "Yes" + ], + "SmokerStatus": [ + "Current smoker - now smokes every day", + "Current smoker - now smokes some days", + "Former smoker", + "Never smoked" + ], + "Smoking": [ + "No", + "Yes" + ], + "State": [ + "Alabama", + "Alaska", + "Arizona", + "Arkansas", + "California", + "Colorado", + "Connecticut", + "Delaware", + "District of Columbia", + "Florida", + "Georgia", + "Guam", + "Hawaii", + "Idaho", + "Illinois", + "Indiana", + "Iowa", + "Kansas", + "Kentucky", + "Louisiana", + "Maine", + "Maryland", + "Massachusetts", + "Michigan", + "Minnesota", + "Mississippi", + "Missouri", + "Montana", + "Nebraska", + "Nevada", + "New Hampshire", + "New Jersey", + "New Mexico", + "New York", + "North Carolina", + "North Dakota", + "Ohio", + "Oklahoma", + "Oregon", + "Pennsylvania", + "Puerto Rico", + "Rhode Island", + "South Carolina", + "South Dakota", + "Tennessee", + "Texas", + "Utah", + "Vermont", + "Virgin Islands", + "Virginia", + "Washington", + "West Virginia", + "Wisconsin", + "Wyoming" + ], + "Stroke": [ + "No", + "Yes" + ], + "TetanusLast10Tdap": [ + "No, did not receive any tetanus shot in the past 10 years", + "Yes, received Tdap", + "Yes, received tetanus shot but not sure what type", + "Yes, received tetanus shot, but not Tdap" + ] + } +} diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_006/test_current.csv b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_006/test_current.csv index 4015514fdc4db4b7d19adbd6399b51e4fd6ec56e..74608c71144c486cbbc2110d11a600eba7647601 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_006/test_current.csv +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_006/test_current.csv @@ -1,16 +1,16 @@ -HeartDisease,BMI,Smoking,AlcoholDrinking,Stroke,PhysicalHealth,MentalHealth,DiffWalking,Sex,AgeCategory,Race,Diabetic,PhysicalActivity,GenHealth,SleepTime,Asthma,KidneyDisease,SkinCancer,State,GeneralHealth,PhysicalHealthDays,MentalHealthDays,LastCheckupTime,PhysicalActivities,SleepHours,RemovedTeeth,HadHeartAttack,HadAngina,HadStroke,HadAsthma,HadSkinCancer,HadCOPD,HadDepressiveDisorder,HadKidneyDisease,HadArthritis,HadDiabetes,DeafOrHardOfHearing,BlindOrVisionDifficulty,DifficultyConcentrating,DifficultyWalking,DifficultyDressingBathing,DifficultyErrands,SmokerStatus,ECigaretteUsage,ChestScan,RaceEthnicityCategory,HeightInMeters,WeightInKilograms,AlcoholDrinkers,HIVTesting,FluVaxLast12,PneumoVaxEver,TetanusLast10Tdap,HighRiskLastYear,CovidPos -,34.37,,,,,,,Male,Age 55 to 59,,,,,,,,,Iowa,Good,1.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.91,124.74,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -,27.71,,,,,,,Male,Age 45 to 49,,,,,,,,,Maine,Good,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Use them some days,No,"White only, Non-Hispanic",1.85,95.25,No,Yes,Yes,Yes,"Yes, received Tdap",No,No -,31.71,,,,,,,Female,Age 75 to 79,,,,,,,,,Colorado,Good,0.0,30.0,Within past year (anytime less than 12 months ago),Yes,5.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Use them some days,No,"White only, Non-Hispanic",1.6,81.19,No,No,No,Yes,"Yes, received tetanus shot but not sure what type",No,Yes -,17.63,,,,,,,Male,Age 70 to 74,,,,,,,,,Colorado,Good,,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,All,No,No,No,No,No,Yes,No,No,Yes,No,No,No,No,Yes,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.83,58.97,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,No -,25.84,,,,,,,Female,Age 65 to 69,,,,,,,,,Washington,Good,0.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,"6 or more, but not all",No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,79.38,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,24.41,,,,,,,Male,Age 60 to 64,,,,,,,,,Maryland,Good,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.83,81.65,,,,,,, -,30.81,,,,,,,Male,Age 50 to 54,,,,,,,,,Minnesota,Good,0.0,5.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),No,"White only, Non-Hispanic",1.88,108.86,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -,29.03,,,,,,,Male,Age 40 to 44,,,,,,,,,Texas,Good,0.0,30.0,Within past 5 years (2 years but less than 5 years ago),No,4.0,1 to 5,Yes,Yes,No,No,No,Yes,Yes,No,Yes,No,Yes,No,Yes,No,No,Yes,Current smoker - now smokes every day,Use them some days,Yes,"White only, Non-Hispanic",1.85,99.79,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No -,19.37,,,,,,,Female,Age 45 to 49,,,,,,,,,Rhode Island,Good,0.0,7.0,Within past year (anytime less than 12 months ago),Yes,4.0,1 to 5,No,No,No,No,No,No,Yes,No,Yes,"No, pre-diabetes or borderline diabetes",No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,54.43,No,No,Yes,Yes,"Yes, received Tdap",No,No -,19.94,,,,,,,Male,Age 55 to 59,,,,,,,,,Pennsylvania,Good,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,9.0,All,No,No,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,61.23,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,No -,27.26,,,,,,,Male,Age 75 to 79,,,,,,,,,California,Good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,86.18,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -,27.46,,,,,,,Male,Age 60 to 64,,,,,,,,,Indiana,Good,0.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,74.84,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No -,18.65,,,,,,,Female,Age 45 to 49,,,,,,,,,California,Good,0.0,5.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,Current smoker - now smokes every day,Use them every day,No,"White only, Non-Hispanic",1.78,58.97,No,Yes,No,Yes,"Yes, received tetanus shot but not sure what type",No,No -,27.81,,,,,,,Female,Age 60 to 64,,,,,,,,,South Carolina,Good,14.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,5.0,1 to 5,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.63,73.48,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,20.51,,,,,,,Female,Age 65 to 69,,,,,,,,,West Virginia,Good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.52,47.63,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No +HeartDisease,BMI,Smoking,AlcoholDrinking,Stroke,PhysicalHealth,MentalHealth,DiffWalking,Sex,AgeCategory,Race,Diabetic,PhysicalActivity,SleepTime,Asthma,KidneyDisease,SkinCancer,State,GeneralHealth,PhysicalHealthDays,MentalHealthDays,LastCheckupTime,PhysicalActivities,SleepHours,RemovedTeeth,HadHeartAttack,HadAngina,HadStroke,HadAsthma,HadSkinCancer,HadCOPD,HadDepressiveDisorder,HadKidneyDisease,HadArthritis,HadDiabetes,DeafOrHardOfHearing,BlindOrVisionDifficulty,DifficultyConcentrating,DifficultyWalking,DifficultyDressingBathing,DifficultyErrands,SmokerStatus,ECigaretteUsage,ChestScan,RaceEthnicityCategory,HeightInMeters,WeightInKilograms,AlcoholDrinkers,HIVTesting,FluVaxLast12,PneumoVaxEver,TetanusLast10Tdap,HighRiskLastYear,CovidPos +,34.37,,,,,,,Male,Age 55 to 59,,,,,,,,Iowa,Good,1.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.91,124.74,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +,27.71,,,,,,,Male,Age 45 to 49,,,,,,,,Maine,Good,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Use them some days,No,"White only, Non-Hispanic",1.85,95.25,No,Yes,Yes,Yes,"Yes, received Tdap",No,No +,31.71,,,,,,,Female,Age 75 to 79,,,,,,,,Colorado,Good,0.0,30.0,Within past year (anytime less than 12 months ago),Yes,5.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Use them some days,No,"White only, Non-Hispanic",1.6,81.19,No,No,No,Yes,"Yes, received tetanus shot but not sure what type",No,Yes +,17.63,,,,,,,Male,Age 70 to 74,,,,,,,,Colorado,Good,,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,All,No,No,No,No,No,Yes,No,No,Yes,No,No,No,No,Yes,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.83,58.97,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,No +,25.84,,,,,,,Female,Age 65 to 69,,,,,,,,Washington,Good,0.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,"6 or more, but not all",No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.75,79.38,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,24.41,,,,,,,Male,Age 60 to 64,,,,,,,,Maryland,Good,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.83,81.65,,,,,,, +,30.81,,,,,,,Male,Age 50 to 54,,,,,,,,Minnesota,Good,0.0,5.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),No,"White only, Non-Hispanic",1.88,108.86,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +,29.03,,,,,,,Male,Age 40 to 44,,,,,,,,Texas,Good,0.0,30.0,Within past 5 years (2 years but less than 5 years ago),No,4.0,1 to 5,Yes,Yes,No,No,No,Yes,Yes,No,Yes,No,Yes,No,Yes,No,No,Yes,Current smoker - now smokes every day,Use them some days,Yes,"White only, Non-Hispanic",1.85,99.79,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No +,19.37,,,,,,,Female,Age 45 to 49,,,,,,,,Rhode Island,Good,0.0,7.0,Within past year (anytime less than 12 months ago),Yes,4.0,1 to 5,No,No,No,No,No,No,Yes,No,Yes,"No, pre-diabetes or borderline diabetes",No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,54.43,No,No,Yes,Yes,"Yes, received Tdap",No,No +,19.94,,,,,,,Male,Age 55 to 59,,,,,,,,Pennsylvania,Good,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,9.0,All,No,No,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,61.23,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,No +,27.26,,,,,,,Male,Age 75 to 79,,,,,,,,California,Good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,86.18,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +,27.46,,,,,,,Male,Age 60 to 64,,,,,,,,Indiana,Good,0.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,74.84,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No +,18.65,,,,,,,Female,Age 45 to 49,,,,,,,,California,Good,0.0,5.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,Current smoker - now smokes every day,Use them every day,No,"White only, Non-Hispanic",1.78,58.97,No,Yes,No,Yes,"Yes, received tetanus shot but not sure what type",No,No +,27.81,,,,,,,Female,Age 60 to 64,,,,,,,,South Carolina,Good,14.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,5.0,1 to 5,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.63,73.48,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,20.51,,,,,,,Female,Age 65 to 69,,,,,,,,West Virginia,Good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.52,47.63,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_007/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_007_current.json b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_007/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_007_current.json index 06a98ce096054de989dc39b50c1caf6991971e7d..a27da49ffb531cb988e0baf67a2cac9d10736b72 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_007/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_007_current.json +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_007/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_007_current.json @@ -1,12049 +1,11849 @@ { - "id": "007", - "task_type": "B4", - "subtask_type": "filter", - "perspective": "data_holder", - "dataset_name": "Indicators of Heart Disease (2022 UPDATE)", - "table_path": "output/medical/classification/Indicators of Heart Disease (2022 UPDATE)_B4/Indicators of Heart Disease (2022 UPDATE)_B4_007/test.csv", - "query": "I need to analyze the state-level data from the archive I provided. Please isolate all records from the state of Texas and identify which individuals are most likely to rate their health as 'Very good'. This is for regional health benchmarking. Ensure this analysis strictly targets the new data batch for tasks.", - "meta_info": { - "domain": "medical" + "id": "007", + "task_type": "B4", + "subtask_type": "filter", + "perspective": "data_holder", + "dataset_name": "Indicators of Heart Disease (2022 UPDATE)", + "table_path": "output/medical/classification/Indicators of Heart Disease (2022 UPDATE)_B4/Indicators of Heart Disease (2022 UPDATE)_B4_007/test.csv", + "query": "I need to analyze the state-level data from the archive I provided. Please isolate all records from the state of Texas and identify which individuals are most likely to rate their health as 'Very good'. This is for regional health benchmarking. Ensure this analysis strictly targets the new data batch for tasks.", + "meta_info": { + "domain": "medical" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "HeartDisease": null, + "BMI": 24.13, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 18 to 24", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Iowa", + "GeneralHealth": "Excellent", + "PhysicalHealthDays": 4.0, + "MentalHealthDays": 2.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 9.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "No", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.65, + "WeightInKilograms": 65.77, + "AlcoholDrinkers": "No", + "HIVTesting": "No", + "FluVaxLast12": "No", + "PneumoVaxEver": "No", + "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", + "HighRiskLastYear": "No", + "CovidPos": "Yes" + } + }, + { + "scenario_id": "002", + "features": { + "HeartDisease": null, + "BMI": 28.7, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 40 to 44", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Colorado", + "GeneralHealth": "Excellent", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "5 or more years ago", + "PhysicalActivities": "Yes", + "SleepHours": 7.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "Yes", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.78, + "WeightInKilograms": 90.72, + "AlcoholDrinkers": "No", + "HIVTesting": "Yes", + "FluVaxLast12": "Yes", + "PneumoVaxEver": "Yes", + "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "004", + "features": { + "HeartDisease": "No", + "BMI": 23.62, + "Smoking": "No", + "AlcoholDrinking": "No", + "Stroke": "No", + "PhysicalHealth": 0.0, + "MentalHealth": 0.0, + "DiffWalking": "No", + "Sex": "Female", + "AgeCategory": "30-34", + "Race": "White", + "Diabetic": "Yes (during pregnancy)", + "PhysicalActivity": "Yes", + "SleepTime": 8.0, + "Asthma": "No", + "KidneyDisease": "No", + "SkinCancer": "No", + "State": null, + "GeneralHealth": "Very good", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + "LastCheckupTime": null, + "PhysicalActivities": null, + "SleepHours": null, + "RemovedTeeth": null, + "HadHeartAttack": null, + "HadAngina": null, + "HadStroke": null, + "HadAsthma": null, + "HadSkinCancer": null, + "HadCOPD": null, + "HadDepressiveDisorder": null, + "HadKidneyDisease": null, + "HadArthritis": null, + "HadDiabetes": null, + "DeafOrHardOfHearing": null, + "BlindOrVisionDifficulty": null, + "DifficultyConcentrating": null, + "DifficultyWalking": null, + "DifficultyDressingBathing": null, + "DifficultyErrands": null, + "SmokerStatus": null, + "ECigaretteUsage": null, + "ChestScan": null, + "RaceEthnicityCategory": null, + "HeightInMeters": null, + "WeightInKilograms": null, + "AlcoholDrinkers": null, + "HIVTesting": null, + "FluVaxLast12": null, + "PneumoVaxEver": null, + "TetanusLast10Tdap": null, + "HighRiskLastYear": null, + "CovidPos": null + } + }, + { + "scenario_id": "006", + "features": { + "HeartDisease": null, + "BMI": 33.72, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 60 to 64", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Nevada", + "GeneralHealth": "Good", + "PhysicalHealthDays": 7.0, + "MentalHealthDays": 7.0, + "LastCheckupTime": "Within past 5 years (2 years but less than 5 years ago)", + "PhysicalActivities": "Yes", + "SleepHours": 6.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Former smoker", + "ECigaretteUsage": "Not at all (right now)", + "ChestScan": "No", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.78, + "WeightInKilograms": 106.59, + "AlcoholDrinkers": "Yes", + "HIVTesting": "No", + "FluVaxLast12": "No", + "PneumoVaxEver": "No", + "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", + "HighRiskLastYear": null, + "CovidPos": null + } + }, + { + "scenario_id": "008", + "features": { + "HeartDisease": null, + "BMI": 29.18, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 40 to 44", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Washington", + "GeneralHealth": "Very good", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 2.0, + "LastCheckupTime": "Within past 5 years (2 years but less than 5 years ago)", + "PhysicalActivities": "Yes", + "SleepHours": 8.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "Yes", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "Yes", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "No", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.63, + "WeightInKilograms": 77.11, + "AlcoholDrinkers": "No", + "HIVTesting": "Yes", + "FluVaxLast12": "No", + "PneumoVaxEver": "Yes", + "TetanusLast10Tdap": "Yes, received tetanus shot, but not Tdap", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "010", + "features": { + "HeartDisease": "No", + "BMI": 24.21, + "Smoking": "Yes", + "AlcoholDrinking": "No", + "Stroke": "No", + "PhysicalHealth": 0.0, + "MentalHealth": 0.0, + "DiffWalking": "No", + "Sex": "Female", + "AgeCategory": "70-74", + "Race": "White", + "Diabetic": "No", + "PhysicalActivity": "Yes", + "SleepTime": 7.0, + "Asthma": "No", + "KidneyDisease": "No", + "SkinCancer": "Yes", + "State": null, + "GeneralHealth": "Excellent", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + "LastCheckupTime": null, + "PhysicalActivities": null, + "SleepHours": null, + "RemovedTeeth": null, + "HadHeartAttack": null, + "HadAngina": null, + "HadStroke": null, + "HadAsthma": null, + "HadSkinCancer": null, + "HadCOPD": null, + "HadDepressiveDisorder": null, + "HadKidneyDisease": null, + "HadArthritis": null, + "HadDiabetes": null, + "DeafOrHardOfHearing": null, + "BlindOrVisionDifficulty": null, + "DifficultyConcentrating": null, + "DifficultyWalking": null, + "DifficultyDressingBathing": null, + "DifficultyErrands": null, + "SmokerStatus": null, + "ECigaretteUsage": null, + "ChestScan": null, + "RaceEthnicityCategory": null, + "HeightInMeters": null, + "WeightInKilograms": null, + "AlcoholDrinkers": null, + "HIVTesting": null, + "FluVaxLast12": null, + "PneumoVaxEver": null, + "TetanusLast10Tdap": null, + "HighRiskLastYear": null, + "CovidPos": null + } + }, + { + "scenario_id": "016", + "features": { + "HeartDisease": null, + "BMI": 30.9, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 40 to 44", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "New Jersey", + "GeneralHealth": "Good", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": null, + "PhysicalActivities": "No", + 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+ "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 60 to 64", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "West Virginia", + "GeneralHealth": "Good", + "PhysicalHealthDays": 15.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 8.0, + "RemovedTeeth": "All", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "Yes", + "HadDiabetes": "Yes", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Current smoker - now smokes every day", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "No", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.68, + "WeightInKilograms": 80.74, + "AlcoholDrinkers": "Yes", + "HIVTesting": "No", + "FluVaxLast12": "No", + "PneumoVaxEver": "No", + "TetanusLast10Tdap": "Yes, received tetanus shot but not sure what type", + "HighRiskLastYear": "No", + "CovidPos": "Tested positive using home test without a health professional" + } + }, + { + "scenario_id": "042", + "features": { + "HeartDisease": null, + "BMI": 27.4, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 40 to 44", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Massachusetts", + 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did not receive any tetanus shot in the past 10 years", + "HighRiskLastYear": "Yes", + "CovidPos": "No" + } + }, + { + "scenario_id": "084", + "features": { + "HeartDisease": null, + "BMI": 26.96, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 75 to 79", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "New York", + "GeneralHealth": "Good", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 6.0, + "RemovedTeeth": "1 to 5", + "HadHeartAttack": "Yes", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + 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+ "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Former smoker", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "Yes", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.68, + "WeightInKilograms": 71.67, + "AlcoholDrinkers": "Yes", + "HIVTesting": "No", + "FluVaxLast12": "Yes", + "PneumoVaxEver": "No", + "TetanusLast10Tdap": "Yes, received Tdap", + "HighRiskLastYear": "No", + "CovidPos": "Yes" + } + }, + { + "scenario_id": "092", + "features": { + "HeartDisease": null, + "BMI": 19.37, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 75 to 79", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Georgia", + "GeneralHealth": "Fair", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 9.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "Yes", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "Yes", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "Yes", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Former smoker", + "ECigaretteUsage": "Not at all (right now)", + "ChestScan": "Yes", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.68, + "WeightInKilograms": 54.43, + "AlcoholDrinkers": "Yes", + "HIVTesting": "No", + "FluVaxLast12": "Yes", + "PneumoVaxEver": "Yes", + "TetanusLast10Tdap": "Yes, received tetanus shot but not sure what type", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "094", + "features": { + "HeartDisease": null, + "BMI": 24.41, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 45 to 49", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Connecticut", + "GeneralHealth": "Good", + "PhysicalHealthDays": 3.0, + "MentalHealthDays": 4.0, + "LastCheckupTime": "5 or more years ago", + 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a health professional" + } + }, + { + "scenario_id": "099", + "features": { + "HeartDisease": null, + "BMI": 21.29, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 35 to 39", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Ohio", + "GeneralHealth": "Fair", + "PhysicalHealthDays": 1.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "5 or more years ago", + "PhysicalActivities": "Yes", + "SleepHours": 6.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "Yes", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "Yes", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.73, + "WeightInKilograms": 63.5, + "AlcoholDrinkers": "No", + "HIVTesting": "No", + "FluVaxLast12": "No", + "PneumoVaxEver": "No", + "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "104", + "features": { + "HeartDisease": null, + "BMI": 20.8, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 35 to 39", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "New York", + "GeneralHealth": "Good", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 5.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "No", + "SleepHours": 7.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "No", + "RaceEthnicityCategory": "Black only, Non-Hispanic", + "HeightInMeters": 1.65, + "WeightInKilograms": 56.7, + "AlcoholDrinkers": "No", + "HIVTesting": "Yes", + "FluVaxLast12": "No", + "PneumoVaxEver": "No", + "TetanusLast10Tdap": "Yes, received tetanus shot, but not Tdap", + "HighRiskLastYear": "No", + "CovidPos": "Yes" + } + }, + { + "scenario_id": "105", + "features": { + "HeartDisease": null, + "BMI": 25.83, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 40 to 44", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "West Virginia", + "GeneralHealth": "Excellent", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past 5 years (2 years but less than 5 years ago)", + "PhysicalActivities": "Yes", + "SleepHours": 8.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "No", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.78, + "WeightInKilograms": 81.65, + "AlcoholDrinkers": "No", + "HIVTesting": "No", + "FluVaxLast12": "Yes", + "PneumoVaxEver": "No", + "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "106", + "features": { + "HeartDisease": null, + "BMI": 33.91, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + 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"BMI": 23.63, + "Smoking": "Yes", + "AlcoholDrinking": "No", + "Stroke": "No", + "PhysicalHealth": 0.0, + "MentalHealth": 0.0, + "DiffWalking": "No", + "Sex": "Male", + "AgeCategory": "60-64", + "Race": "American Indian/Alaskan Native", + "Diabetic": "No", + "PhysicalActivity": "Yes", + "SleepTime": 6.0, + "Asthma": "No", + "KidneyDisease": "No", + "SkinCancer": "No", + "State": null, + "GeneralHealth": "Good", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + "LastCheckupTime": null, + "PhysicalActivities": null, + "SleepHours": null, + "RemovedTeeth": null, + "HadHeartAttack": null, + "HadAngina": null, + "HadStroke": null, + "HadAsthma": null, + "HadSkinCancer": null, + "HadCOPD": null, + "HadDepressiveDisorder": null, + "HadKidneyDisease": null, + "HadArthritis": null, + "HadDiabetes": null, + "DeafOrHardOfHearing": null, + "BlindOrVisionDifficulty": null, + "DifficultyConcentrating": null, + "DifficultyWalking": null, + "DifficultyDressingBathing": null, + 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"DifficultyErrands": "No", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "No", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.7, + "WeightInKilograms": 73.94, + "AlcoholDrinkers": "No", + "HIVTesting": "No", + "FluVaxLast12": "Yes", + "PneumoVaxEver": "Yes", + "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "486", + "features": { + "HeartDisease": null, + "BMI": 19.77, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 60 to 64", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Minnesota", + "GeneralHealth": "Good", + "PhysicalHealthDays": 0.0, 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tetanus shot, but not Tdap", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "487", + "features": { + "HeartDisease": null, + "BMI": 31.25, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 65 to 69", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Georgia", + "GeneralHealth": "Very good", + "PhysicalHealthDays": 1.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 8.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "Yes", + 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"PneumoVaxEver": null, + "TetanusLast10Tdap": null, + "HighRiskLastYear": null, + "CovidPos": null + } + }, + { + "scenario_id": "494", + "features": { + "HeartDisease": null, + "BMI": null, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 55 to 59", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Utah", + "GeneralHealth": "Fair", + "PhysicalHealthDays": 30.0, + "MentalHealthDays": 15.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "No", + "SleepHours": 4.0, + "RemovedTeeth": "All", + "HadHeartAttack": "No", + "HadAngina": "Yes", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "Yes", + "HadKidneyDisease": "Yes", + "HadArthritis": 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"SkinCancer": null, + "State": "District of Columbia", + "GeneralHealth": "Good", + "PhysicalHealthDays": 30.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 5.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "Yes", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "Yes", + "HadKidneyDisease": "No", + "HadArthritis": "Yes", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Current smoker - now smokes every day", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "No", + "RaceEthnicityCategory": "Black only, Non-Hispanic", + "HeightInMeters": 1.6, + "WeightInKilograms": 90.72, + "AlcoholDrinkers": "No", + "HIVTesting": "Yes", + "FluVaxLast12": "Yes", + "PneumoVaxEver": "Yes", + "TetanusLast10Tdap": "Yes, received Tdap", + "HighRiskLastYear": "No", + "CovidPos": "Yes" + } + }, + { + "scenario_id": "498", + "features": { + "HeartDisease": null, + "BMI": 34.95, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 60 to 64", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Louisiana", + "GeneralHealth": "Very good", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "5 or more years ago", + "PhysicalActivities": "No", + "SleepHours": 6.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "No", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.65, + "WeightInKilograms": 95.25, + "AlcoholDrinkers": "Yes", + "HIVTesting": "No", + "FluVaxLast12": "No", + "PneumoVaxEver": "No", + "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "499", + "features": { + "HeartDisease": "No", + "BMI": 28.34, + "Smoking": "No", + "AlcoholDrinking": "No", + "Stroke": "No", + "PhysicalHealth": 0.0, + "MentalHealth": 0.0, + "DiffWalking": "No", + "Sex": "Female", + "AgeCategory": "50-54", + "Race": "White", + "Diabetic": "No", + "PhysicalActivity": "Yes", + "SleepTime": 6.0, + "Asthma": "No", + "KidneyDisease": "No", + "SkinCancer": "No", + "State": null, + "GeneralHealth": "Good", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + "LastCheckupTime": null, + "PhysicalActivities": null, + "SleepHours": null, + "RemovedTeeth": null, + "HadHeartAttack": null, + "HadAngina": null, + "HadStroke": null, + "HadAsthma": null, + "HadSkinCancer": null, + "HadCOPD": null, + "HadDepressiveDisorder": null, + "HadKidneyDisease": null, + "HadArthritis": null, + "HadDiabetes": null, + "DeafOrHardOfHearing": null, + "BlindOrVisionDifficulty": null, + "DifficultyConcentrating": null, + "DifficultyWalking": null, + "DifficultyDressingBathing": null, + "DifficultyErrands": null, + "SmokerStatus": null, + "ECigaretteUsage": null, + "ChestScan": null, + "RaceEthnicityCategory": null, + "HeightInMeters": null, + "WeightInKilograms": null, + "AlcoholDrinkers": null, + "HIVTesting": null, + "FluVaxLast12": null, + "PneumoVaxEver": null, + "TetanusLast10Tdap": null, + "HighRiskLastYear": null, + "CovidPos": null + } + }, + { + "scenario_id": "500", + "features": { + "HeartDisease": null, + "BMI": 19.2, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 75 to 79", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Florida", + "GeneralHealth": "Good", + "PhysicalHealthDays": 10.0, + "MentalHealthDays": 10.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 12.0, + "RemovedTeeth": "6 or more, but not all", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "Yes", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "Yes", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "Yes", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "Yes", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.57, + "WeightInKilograms": 47.63, + "AlcoholDrinkers": "No", + "HIVTesting": "No", + "FluVaxLast12": "Yes", + "PneumoVaxEver": "Yes", + "TetanusLast10Tdap": "Yes, received tetanus shot but not sure what type", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + } + ], + "target_column": "GeneralHealth", + "task_sub_type": "classification", + "ranking_ground_truth": { + "top_k_ids": [ + "060", + "079", + "133", + "145", + "170", + "173", + "205", + "215", + "316", + "357", + "379", + "383", + "400", + "403", + "474" + ] }, - "final_decision": {}, - "what_if": {}, - "ground_truth": { - "extracted_features": [ - { - "scenario_id": "001", - "features": { - "HeartDisease": null, - "BMI": 24.13, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Female", - "AgeCategory": "Age 18 to 24", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "Iowa", - "GeneralHealth": "Excellent", - "PhysicalHealthDays": 4.0, - "MentalHealthDays": 2.0, - "LastCheckupTime": "Within past year (anytime less than 12 months ago)", - "PhysicalActivities": "Yes", - "SleepHours": 9.0, - "RemovedTeeth": "None of them", - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "No", - "HadSkinCancer": "No", - "HadCOPD": "No", - "HadDepressiveDisorder": "No", - "HadKidneyDisease": "No", - "HadArthritis": "No", - "HadDiabetes": "No", - "DeafOrHardOfHearing": "No", - "BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "No", - "DifficultyWalking": "No", - "DifficultyDressingBathing": "No", - "DifficultyErrands": "No", - "SmokerStatus": "Never smoked", - "ECigaretteUsage": "Never used e-cigarettes in my entire life", - "ChestScan": "No", - "RaceEthnicityCategory": "White only, Non-Hispanic", - "HeightInMeters": 1.65, - "WeightInKilograms": 65.77, - "AlcoholDrinkers": "No", - "HIVTesting": "No", - "FluVaxLast12": "No", - "PneumoVaxEver": "No", - "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", - "HighRiskLastYear": "No", - "CovidPos": "Yes" - } - }, - { - "scenario_id": "002", - "features": { - "HeartDisease": null, - "BMI": 28.7, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - 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Tdap", - "HighRiskLastYear": "No", - "CovidPos": "No" - } - }, - { - "scenario_id": "010", - "features": { - "HeartDisease": "No", - "BMI": 24.21, - "Smoking": "Yes", - "AlcoholDrinking": "No", - "Stroke": "No", - "PhysicalHealth": 0.0, - "MentalHealth": 0.0, - "DiffWalking": "No", - "Sex": "Female", - "AgeCategory": "70-74", - "Race": "White", - "Diabetic": "No", - "PhysicalActivity": "Yes", - "GenHealth": "Excellent", - "SleepTime": 7.0, - "Asthma": "No", - "KidneyDisease": "No", - "SkinCancer": "Yes", - "State": null, - "GeneralHealth": null, - "PhysicalHealthDays": null, - "MentalHealthDays": null, - "LastCheckupTime": null, - "PhysicalActivities": null, - "SleepHours": null, - "RemovedTeeth": null, - "HadHeartAttack": null, - "HadAngina": null, - "HadStroke": null, - "HadAsthma": null, - "HadSkinCancer": null, - "HadCOPD": null, - "HadDepressiveDisorder": null, - "HadKidneyDisease": null, - "HadArthritis": null, - "HadDiabetes": null, - "DeafOrHardOfHearing": null, - 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"HighRiskLastYear": "No", - "CovidPos": "No" - } - }, - { - "scenario_id": "017", - "features": { - "HeartDisease": null, - "BMI": 51.65, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Male", - "AgeCategory": "Age 30 to 34", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "South Carolina", - "GeneralHealth": "Good", - "PhysicalHealthDays": 0.0, - "MentalHealthDays": 6.0, - "LastCheckupTime": "Within past year (anytime less than 12 months ago)", - "PhysicalActivities": "No", - "SleepHours": 6.0, - "RemovedTeeth": "None of them", - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "Yes", - "HadSkinCancer": "No", - "HadCOPD": "No", - "HadDepressiveDisorder": "No", - "HadKidneyDisease": "No", - "HadArthritis": "No", - "HadDiabetes": "No", - "DeafOrHardOfHearing": "No", - "BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "Yes", - "DifficultyWalking": "No", - "DifficultyDressingBathing": "No", - "DifficultyErrands": "No", - "SmokerStatus": "Never smoked", - "ECigaretteUsage": "Never used e-cigarettes in my entire life", - "ChestScan": "No", - "RaceEthnicityCategory": "White only, Non-Hispanic", - "HeightInMeters": 1.68, - "WeightInKilograms": 145.15, - "AlcoholDrinkers": "No", - "HIVTesting": "No", - "FluVaxLast12": "Yes", - "PneumoVaxEver": "No", - "TetanusLast10Tdap": "Yes, received Tdap", - "HighRiskLastYear": "No", - "CovidPos": "No" - } - }, - { - "scenario_id": "020", - "features": { - "HeartDisease": null, - "BMI": 27.2, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Female", - "AgeCategory": "Age 30 to 34", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - 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"RaceEthnicityCategory": "White only, Non-Hispanic", - "HeightInMeters": 1.6, - "WeightInKilograms": 68.04, - "AlcoholDrinkers": "Yes", - "HIVTesting": "Yes", - "FluVaxLast12": "Yes", - "PneumoVaxEver": "No", - "TetanusLast10Tdap": "Yes, received Tdap", - "HighRiskLastYear": "No", - "CovidPos": "Tested positive using home test without a health professional" - } - }, - { - "scenario_id": "028", - "features": { - "HeartDisease": null, - "BMI": 27.48, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Male", - "AgeCategory": "Age 55 to 59", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "Kansas", - "GeneralHealth": "Very good", - "PhysicalHealthDays": 0.0, - "MentalHealthDays": 5.0, - "LastCheckupTime": "5 or more years ago", - "PhysicalActivities": "Yes", - 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28.97, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Female", - "AgeCategory": "Age 70 to 74", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "New Hampshire", - "GeneralHealth": "Fair", - "PhysicalHealthDays": 1.0, - "MentalHealthDays": 1.0, - "LastCheckupTime": "Within past year (anytime less than 12 months ago)", - "PhysicalActivities": "No", - "SleepHours": 3.0, - "RemovedTeeth": null, - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "No", - "HadSkinCancer": "No", - "HadCOPD": "No", - "HadDepressiveDisorder": "No", - "HadKidneyDisease": "Yes", - "HadArthritis": "No", - "HadDiabetes": "Yes", - "DeafOrHardOfHearing": "No", - "BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "No", - "DifficultyWalking": "No", 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"TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", - "HighRiskLastYear": "No", - "CovidPos": "No" - } - }, - { - "scenario_id": "031", - "features": { - "HeartDisease": null, - "BMI": 28.73, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Male", - "AgeCategory": "Age 60 to 64", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "West Virginia", - "GeneralHealth": "Good", - "PhysicalHealthDays": 15.0, - "MentalHealthDays": 0.0, - "LastCheckupTime": "Within past year (anytime less than 12 months ago)", - "PhysicalActivities": "Yes", - "SleepHours": 8.0, - "RemovedTeeth": "All", - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "No", - "HadSkinCancer": "No", - "HadCOPD": "No", - 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{ - "scenario_id": "135", - "features": { - "HeartDisease": null, - "BMI": 23.56, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Female", - "AgeCategory": "Age 70 to 74", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "Indiana", - "GeneralHealth": "Very good", - "PhysicalHealthDays": 6.0, - "MentalHealthDays": 14.0, - "LastCheckupTime": "Within past year (anytime less than 12 months ago)", - "PhysicalActivities": "Yes", - "SleepHours": 7.0, - "RemovedTeeth": "None of them", - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "No", - "HadSkinCancer": "No", - "HadCOPD": "No", - "HadDepressiveDisorder": "No", - "HadKidneyDisease": "No", - "HadArthritis": "No", - "HadDiabetes": "No", - "DeafOrHardOfHearing": "No", - 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"No", - "HadDepressiveDisorder": "No", - "HadKidneyDisease": "No", - "HadArthritis": "Yes", - "HadDiabetes": "No", - "DeafOrHardOfHearing": "No", - "BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "No", - "DifficultyWalking": "No", - "DifficultyDressingBathing": "No", - "DifficultyErrands": "No", - "SmokerStatus": "Never smoked", - "ECigaretteUsage": "Never used e-cigarettes in my entire life", - "ChestScan": "Yes", - "RaceEthnicityCategory": "White only, Non-Hispanic", - "HeightInMeters": 1.52, - "WeightInKilograms": 72.57, - "AlcoholDrinkers": "No", - "HIVTesting": "No", - "FluVaxLast12": "Yes", - "PneumoVaxEver": "Yes", - "TetanusLast10Tdap": "Yes, received Tdap", - "HighRiskLastYear": "No", - "CovidPos": "No" - } - }, - { - "scenario_id": "488", - "features": { - "HeartDisease": "No", - "BMI": 33.3, - "Smoking": "Yes", - "AlcoholDrinking": "No", - "Stroke": "No", - "PhysicalHealth": 30.0, - "MentalHealth": 5.0, - "DiffWalking": "Yes", - "Sex": "Female", - "AgeCategory": "70-74", - "Race": "American Indian/Alaskan Native", - "Diabetic": "No", - "PhysicalActivity": "Yes", - "GenHealth": "Fair", - "SleepTime": 5.0, - "Asthma": "Yes", - "KidneyDisease": "No", - "SkinCancer": "No", - "State": null, - "GeneralHealth": null, - "PhysicalHealthDays": null, - "MentalHealthDays": null, - "LastCheckupTime": null, - "PhysicalActivities": null, - "SleepHours": null, - "RemovedTeeth": null, - "HadHeartAttack": null, - "HadAngina": null, - "HadStroke": null, - "HadAsthma": null, - "HadSkinCancer": null, - "HadCOPD": null, - "HadDepressiveDisorder": null, - "HadKidneyDisease": null, - "HadArthritis": null, - "HadDiabetes": null, - "DeafOrHardOfHearing": null, - "BlindOrVisionDifficulty": null, - "DifficultyConcentrating": null, - "DifficultyWalking": null, - "DifficultyDressingBathing": null, - "DifficultyErrands": null, - "SmokerStatus": null, - "ECigaretteUsage": null, - "ChestScan": null, - "RaceEthnicityCategory": null, - "HeightInMeters": null, - "WeightInKilograms": null, - "AlcoholDrinkers": null, - "HIVTesting": null, - "FluVaxLast12": null, - "PneumoVaxEver": null, - "TetanusLast10Tdap": null, - "HighRiskLastYear": null, - "CovidPos": null - } - }, - { - "scenario_id": "494", - "features": { - "HeartDisease": null, - "BMI": null, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Female", - "AgeCategory": "Age 55 to 59", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "Utah", - "GeneralHealth": "Fair", - "PhysicalHealthDays": 30.0, - "MentalHealthDays": 15.0, - "LastCheckupTime": "Within past year (anytime less than 12 months ago)", - "PhysicalActivities": "No", - "SleepHours": 4.0, - "RemovedTeeth": "All", - "HadHeartAttack": "No", - "HadAngina": "Yes", - "HadStroke": "No", - "HadAsthma": "No", - "HadSkinCancer": "No", - "HadCOPD": "No", - "HadDepressiveDisorder": "Yes", - "HadKidneyDisease": "Yes", - "HadArthritis": "Yes", - "HadDiabetes": "Yes", - "DeafOrHardOfHearing": null, - "BlindOrVisionDifficulty": null, - "DifficultyConcentrating": null, - "DifficultyWalking": null, - "DifficultyDressingBathing": null, - "DifficultyErrands": null, - "SmokerStatus": null, - "ECigaretteUsage": null, - "ChestScan": null, - "RaceEthnicityCategory": "White only, Non-Hispanic", - "HeightInMeters": null, - "WeightInKilograms": null, - "AlcoholDrinkers": null, - "HIVTesting": null, - "FluVaxLast12": null, - "PneumoVaxEver": null, - "TetanusLast10Tdap": null, - "HighRiskLastYear": null, - "CovidPos": null - } - }, - { - "scenario_id": "497", - "features": { - "HeartDisease": null, - "BMI": 35.43, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Female", - "AgeCategory": "Age 35 to 39", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "District of Columbia", - "GeneralHealth": "Good", - "PhysicalHealthDays": 30.0, - "MentalHealthDays": 0.0, - "LastCheckupTime": "Within past year (anytime less than 12 months ago)", - "PhysicalActivities": "Yes", - "SleepHours": 5.0, - "RemovedTeeth": "None of them", - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "Yes", - "HadSkinCancer": "No", - "HadCOPD": "No", - "HadDepressiveDisorder": "Yes", - "HadKidneyDisease": "No", - "HadArthritis": "Yes", - "HadDiabetes": "No", - "DeafOrHardOfHearing": "No", - "BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "No", - "DifficultyWalking": "No", - "DifficultyDressingBathing": "No", - "DifficultyErrands": "No", - "SmokerStatus": "Current smoker - now smokes every day", - "ECigaretteUsage": "Never used e-cigarettes in my entire life", - "ChestScan": "No", - "RaceEthnicityCategory": "Black only, Non-Hispanic", - "HeightInMeters": 1.6, - "WeightInKilograms": 90.72, - "AlcoholDrinkers": "No", - "HIVTesting": "Yes", - "FluVaxLast12": "Yes", - "PneumoVaxEver": "Yes", - "TetanusLast10Tdap": "Yes, received Tdap", - "HighRiskLastYear": "No", - "CovidPos": "Yes" - } - }, - { - "scenario_id": "498", - "features": { - "HeartDisease": null, - "BMI": 34.95, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Female", - "AgeCategory": "Age 60 to 64", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "Louisiana", - "GeneralHealth": "Very good", - "PhysicalHealthDays": 0.0, - "MentalHealthDays": 0.0, - "LastCheckupTime": "5 or more years ago", - "PhysicalActivities": "No", - "SleepHours": 6.0, - "RemovedTeeth": "None of them", - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "No", - "HadSkinCancer": "No", - "HadCOPD": "No", - "HadDepressiveDisorder": "No", - "HadKidneyDisease": "No", - "HadArthritis": "No", - "HadDiabetes": "No", - "DeafOrHardOfHearing": "No", - "BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "No", - "DifficultyWalking": "No", - "DifficultyDressingBathing": "No", - "DifficultyErrands": "No", - "SmokerStatus": "Never smoked", - "ECigaretteUsage": "Never used e-cigarettes in my entire life", - "ChestScan": "No", - "RaceEthnicityCategory": "White only, Non-Hispanic", - "HeightInMeters": 1.65, - "WeightInKilograms": 95.25, - "AlcoholDrinkers": "Yes", - "HIVTesting": "No", - "FluVaxLast12": "No", - "PneumoVaxEver": "No", - "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", - "HighRiskLastYear": "No", - "CovidPos": "No" - } - }, - { - "scenario_id": "499", - "features": { - "HeartDisease": "No", - "BMI": 28.34, - "Smoking": "No", - "AlcoholDrinking": "No", - "Stroke": "No", - "PhysicalHealth": 0.0, - "MentalHealth": 0.0, - "DiffWalking": "No", - "Sex": "Female", - "AgeCategory": "50-54", - "Race": "White", - "Diabetic": "No", - "PhysicalActivity": "Yes", - "GenHealth": "Good", - "SleepTime": 6.0, - "Asthma": "No", - "KidneyDisease": "No", - "SkinCancer": "No", - "State": null, - "GeneralHealth": null, - "PhysicalHealthDays": null, - "MentalHealthDays": null, - "LastCheckupTime": null, - "PhysicalActivities": null, - "SleepHours": null, - "RemovedTeeth": null, - "HadHeartAttack": null, - "HadAngina": null, - "HadStroke": null, - "HadAsthma": null, - "HadSkinCancer": null, - "HadCOPD": null, - "HadDepressiveDisorder": null, - "HadKidneyDisease": null, - "HadArthritis": null, - "HadDiabetes": null, - "DeafOrHardOfHearing": null, - "BlindOrVisionDifficulty": null, - "DifficultyConcentrating": null, - "DifficultyWalking": null, - "DifficultyDressingBathing": null, - "DifficultyErrands": null, - "SmokerStatus": null, - "ECigaretteUsage": null, - "ChestScan": null, - "RaceEthnicityCategory": null, - "HeightInMeters": null, - "WeightInKilograms": null, - "AlcoholDrinkers": null, - "HIVTesting": null, - "FluVaxLast12": null, - "PneumoVaxEver": null, - "TetanusLast10Tdap": null, - "HighRiskLastYear": null, - "CovidPos": null - } - }, - { - "scenario_id": "500", - "features": { - "HeartDisease": null, - "BMI": 19.2, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Female", - "AgeCategory": "Age 75 to 79", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "Florida", - "GeneralHealth": "Good", - "PhysicalHealthDays": 10.0, - "MentalHealthDays": 10.0, - "LastCheckupTime": "Within past year (anytime less than 12 months ago)", - "PhysicalActivities": "Yes", - "SleepHours": 12.0, - "RemovedTeeth": "6 or more, but not all", - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "No", - "HadSkinCancer": "No", - "HadCOPD": "Yes", - "HadDepressiveDisorder": "No", - "HadKidneyDisease": "No", - "HadArthritis": "Yes", - "HadDiabetes": "No", - "DeafOrHardOfHearing": "No", - "BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "No", - "DifficultyWalking": "No", - "DifficultyDressingBathing": "No", - "DifficultyErrands": "Yes", - "SmokerStatus": "Never smoked", - "ECigaretteUsage": "Never used e-cigarettes in my entire life", - "ChestScan": "Yes", - "RaceEthnicityCategory": "White only, Non-Hispanic", - "HeightInMeters": 1.57, - "WeightInKilograms": 47.63, - "AlcoholDrinkers": "No", - "HIVTesting": "No", - "FluVaxLast12": "Yes", - "PneumoVaxEver": "Yes", - "TetanusLast10Tdap": "Yes, received tetanus shot but not sure what type", - "HighRiskLastYear": "No", - "CovidPos": "No" - } - } - ], - "target_column": "GeneralHealth", - "task_sub_type": "classification", - "ranking_ground_truth": { - "top_k_ids": [ - "060", - "079", - "133", - "145", - "170", - "173", - "205", - "215", - "316", - "357", - "379", - "383", - "400", - "403", - "474" - ] - }, - "active_filters": [ - { - "col": "State", - "op": "==", - "val": "Texas" - } - ], - "target_class_value": "very good" - }, - "response": "", - "evaluation_score": {} -} \ No newline at end of file + "active_filters": [ + { + "col": "State", + "op": "==", + "val": "Texas" + } + ], + "target_class_value": "very good" + }, + "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_007/current.csv b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_007/current.csv index e54bf61fb98aeed3a9db700b2e3235667cfa3d9d..242f4ef3080b813a26b13194e2a1d5444000d0df 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_007/current.csv +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_007/current.csv @@ -1,201 +1,201 @@ -HeartDisease,BMI,Smoking,AlcoholDrinking,Stroke,PhysicalHealth,MentalHealth,DiffWalking,Sex,AgeCategory,Race,Diabetic,PhysicalActivity,GenHealth,SleepTime,Asthma,KidneyDisease,SkinCancer,State,PhysicalHealthDays,MentalHealthDays,LastCheckupTime,PhysicalActivities,SleepHours,RemovedTeeth,HadHeartAttack,HadAngina,HadStroke,HadAsthma,HadSkinCancer,HadCOPD,HadDepressiveDisorder,HadKidneyDisease,HadArthritis,HadDiabetes,DeafOrHardOfHearing,BlindOrVisionDifficulty,DifficultyConcentrating,DifficultyWalking,DifficultyDressingBathing,DifficultyErrands,SmokerStatus,ECigaretteUsage,ChestScan,RaceEthnicityCategory,HeightInMeters,WeightInKilograms,AlcoholDrinkers,HIVTesting,FluVaxLast12,PneumoVaxEver,TetanusLast10Tdap,HighRiskLastYear,CovidPos -,24.13,,,,,,,Female,Age 18 to 24,,,,,,,,,Iowa,4.0,2.0,Within past year (anytime less than 12 months ago),Yes,9.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,65.77,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,28.7,,,,,,,Male,Age 40 to 44,,,,,,,,,Colorado,0.0,0.0,5 or more years ago,Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,90.72,No,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -No,23.62,No,No,No,0.0,0.0,No,Female,30-34,White,Yes (during pregnancy),Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,33.72,,,,,,,Male,Age 60 to 64,,,,,,,,,Nevada,7.0,7.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.78,106.59,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",, -,29.18,,,,,,,Female,Age 40 to 44,,,,,,,,,Washington,0.0,2.0,Within past 5 years (2 years but less than 5 years ago),Yes,8.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,77.11,No,Yes,No,Yes,"Yes, received tetanus shot, but not Tdap",No,No -No,24.21,Yes,No,No,0.0,0.0,No,Female,70-74,White,No,Yes,Excellent,7.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,30.9,,,,,,,Male,Age 40 to 44,,,,,,,,,New Jersey,0.0,0.0,,No,6.0,1 to 5,No,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,Hispanic,1.63,81.65,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,51.65,,,,,,,Male,Age 30 to 34,,,,,,,,,South Carolina,0.0,6.0,Within past year (anytime less than 12 months ago),No,6.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,145.15,No,No,Yes,No,"Yes, received Tdap",No,No -,27.2,,,,,,,Female,Age 30 to 34,,,,,,,,,Illinois,5.0,10.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.8,88.45,Yes,Yes,No,No,"Yes, received Tdap",No,Yes -No,27.46,No,No,Yes,7.0,3.0,No,Female,30-34,White,No,Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,26.57,,,,,,,Female,Age 45 to 49,,,,,,,,,Washington,0.0,6.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,68.04,Yes,Yes,Yes,No,"Yes, received Tdap",No,Tested positive using home test without a health professional -,27.48,,,,,,,Male,Age 55 to 59,,,,,,,,,Kansas,0.0,5.0,5 or more years ago,Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.88,97.07,No,Yes,No,No,"Yes, received Tdap",No,No -,28.97,,,,,,,Female,Age 70 to 74,,,,,,,,,New Hampshire,1.0,1.0,Within past year (anytime less than 12 months ago),No,3.0,,No,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,83.91,No,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,30.67,,,,,,,Male,Age 60 to 64,,,,,,,,,New Jersey,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.68,86.18,No,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,28.73,,,,,,,Male,Age 60 to 64,,,,,,,,,West Virginia,15.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,All,No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,80.74,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Tested positive using home test without a health professional -,27.4,,,,,,,Female,Age 40 to 44,,,,,,,,,Massachusetts,0.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,1 to 5,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.55,65.77,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,23.65,,,,,,,Female,Age 60 to 64,,,,,,,,,Rhode Island,2.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,1 to 5,No,No,Yes,Yes,No,No,No,No,No,No,No,No,Yes,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,68.49,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,No -,42.57,,,,,,,Female,Age 45 to 49,,,,,,,,,Delaware,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,127.01,No,Yes,Yes,No,"Yes, received Tdap",No,Yes -No,32.49,Yes,No,No,30.0,15.0,Yes,Female,50-54,White,No,Yes,Very good,3.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,24.37,,,,,,,Male,Age 55 to 59,,,,,,,,,Minnesota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.91,88.45,Yes,Yes,Yes,No,"Yes, received Tdap",No,No -,33.0,,,,,,,Male,Age 35 to 39,,,,,,,,,Oregon,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,Yes,No,No,No,No,No,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,104.33,No,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,,,,,,,,Female,Age 35 to 39,,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,Hispanic,,,,,,,,, -No,23.62,No,Yes,No,0.0,19.0,No,Female,18-24,Hispanic,No,Yes,Very good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,26.63,,,,,,,Female,Age 55 to 59,,,,,,,,,Nebraska,15.0,5.0,Within past year (anytime less than 12 months ago),No,7.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,72.57,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,No -No,25.61,No,No,No,0.0,0.0,No,Female,30-34,Hispanic,No,Yes,Very good,8.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,28.97,No,No,No,0.0,0.0,No,Female,55-59,White,No,Yes,Excellent,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,23.13,,,,,,,Male,Age 35 to 39,,,,,,,,,Virginia,4.0,10.0,5 or more years ago,Yes,5.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.93,86.18,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,33.76,,,,,,,Female,Age 50 to 54,,,,,,,,,California,0.0,10.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.45,70.76,Yes,No,No,No,"Yes, received tetanus shot, but not Tdap",No,Tested positive using home test without a health professional -No,23.91,Yes,No,No,0.0,15.0,No,Female,30-34,White,No,Yes,Excellent,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,25.1,,,,,,,Male,Age 80 or older,,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,1 to 5,No,No,No,No,Yes,No,No,Yes,Yes,No,Yes,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.8,81.65,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -No,34.72,Yes,No,No,7.0,7.0,Yes,Female,45-49,White,No,Yes,Fair,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,32.28,,,,,,,Female,Age 55 to 59,,,,,,,,,Rhode Island,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,90.72,No,Yes,Yes,Yes,"Yes, received Tdap",No,Yes -,24.28,,,,,,,Male,Age 25 to 29,,,,,,,,,Vermont,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,8.0,None of them,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.83,81.19,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",Yes,No -,26.96,,,,,,,Female,Age 75 to 79,,,,,,,,,New York,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,Yes,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,73.48,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,27.44,,,,,,,Female,Age 40 to 44,,,,,,,,,Texas,0.0,1.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.57,68.04,No,,,,,, -,25.5,,,,,,,Female,Age 40 to 44,,,,,,,,,Nebraska,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,71.67,Yes,No,Yes,No,"Yes, received Tdap",No,Yes -,19.37,,,,,,,Male,Age 75 to 79,,,,,,,,,Georgia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,None of them,Yes,No,No,No,Yes,No,No,No,No,No,No,Yes,No,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.68,54.43,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,24.41,,,,,,,Male,Age 45 to 49,,,,,,,,,Connecticut,3.0,4.0,5 or more years ago,Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.85,83.91,Yes,No,No,,"No, did not receive any tetanus shot in the past 10 years",No,Tested positive using home test without a health professional -,21.29,,,,,,,Male,Age 35 to 39,,,,,,,,,Ohio,1.0,0.0,5 or more years ago,Yes,6.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,63.5,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,20.8,,,,,,,Female,Age 35 to 39,,,,,,,,,New York,0.0,5.0,Within past year (anytime less than 12 months ago),No,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.65,56.7,No,Yes,No,No,"Yes, received tetanus shot, but not Tdap",No,Yes -,25.83,,,,,,,Female,Age 40 to 44,,,,,,,,,West Virginia,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,81.65,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,33.91,,,,,,,Female,Age 70 to 74,,,,,,,,,Maine,1.0,4.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,Yes,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.83,113.4,No,No,Yes,Yes,"Yes, received Tdap",No,No -,32.55,,,,,,,Male,Age 50 to 54,,,,,,,,,Oklahoma,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,,No,No,No,No,No,No,No,No,Yes,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Other race only, Non-Hispanic",1.83,108.86,,No,Yes,No,"Yes, received Tdap",No,No -Yes,23.63,Yes,No,No,0.0,0.0,No,Male,60-64,American Indian/Alaskan Native,No,Yes,Good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,,,,,,,,Female,Age 45 to 49,,,,,,,,,Louisiana,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),No,5.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.5,,Yes,,No,,"No, did not receive any tetanus shot in the past 10 years",No,Tested positive using home test without a health professional -No,27.79,Yes,No,No,0.0,0.0,Yes,Female,55-59,White,No,Yes,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,26.39,No,No,No,0.0,0.0,No,Male,30-34,White,No,Yes,Excellent,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,,,,,,,,Male,Age 60 to 64,,,,,,,,,Connecticut,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),No,6.0,"6 or more, but not all",No,No,No,No,No,No,No,Yes,Yes,Yes,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,,72.12,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,37.31,,,,,,,Male,Age 30 to 34,,,,,,,,,Ohio,0.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,Hispanic,1.78,117.93,Yes,No,Yes,No,"Yes, received Tdap",No,No -,24.41,,,,,,,Male,Age 65 to 69,,,,,,,,,Massachusetts,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,"6 or more, but not all",No,No,No,Yes,Yes,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.8,79.38,Yes,Yes,No,Yes,"Yes, received tetanus shot but not sure what type",No,Yes -No,26.31,No,No,No,0.0,0.0,No,Male,55-59,White,No,Yes,Good,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,29.53,,,,,,,Male,Age 50 to 54,,,,,,,,,Oregon,0.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,1 to 5,No,No,No,No,No,No,No,No,No,"No, pre-diabetes or borderline diabetes",No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,Hispanic,1.75,90.72,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,26.63,,,,,,,Female,Age 70 to 74,,,,,,,,,Minnesota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,77.11,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -,22.81,,,,,,,Male,Age 65 to 69,,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,Yes,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,68.04,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,23.56,,,,,,,Female,Age 70 to 74,,,,,,,,,Indiana,6.0,14.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,66.22,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,35.87,,,,,,,Male,Age 55 to 59,,,,,,,,,Montana,3.0,20.0,Within past year (anytime less than 12 months ago),No,5.0,1 to 5,No,No,No,No,No,No,Yes,No,No,Yes,Yes,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,113.4,Yes,No,Yes,No,"Yes, received Tdap",No,Yes -,39.33,,,,,,,Male,Age 55 to 59,,,,,,,,,Kentucky,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.83,131.54,Yes,Yes,Yes,,"Yes, received Tdap",No,Yes -,37.31,,,,,,,Female,Age 50 to 54,,,,,,,,,Minnesota,2.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.78,117.93,Yes,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -No,45.49,No,No,No,2.0,7.0,No,Female,55-59,Black,Yes,No,Good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Yes,24.41,No,No,No,0.0,0.0,No,Male,80 or older,White,No,Yes,Very good,8.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,34.44,,,,,,,Female,,,,,,,,,,Massachusetts,2.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,Yes,Yes,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,108.86,Yes,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No -,21.11,,,,,,,Male,Age 40 to 44,,,,,,,,,Texas,0.0,5.0,5 or more years ago,Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,72.57,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,24.03,,,,,,,Female,Age 75 to 79,,,,,,,,,North Carolina,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,Yes,No,Yes,No,Yes,"No, pre-diabetes or borderline diabetes",No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,63.5,Yes,No,Yes,Yes,"Yes, received Tdap",No,No -No,18.6,Yes,No,No,0.0,0.0,No,Female,65-69,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,27.44,,,,,,,Female,Age 55 to 59,,,,,,,,,Florida,1.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,Yes,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,77.11,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,33.12,,,,,,,Male,Age 75 to 79,,,,,,,,,South Dakota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.91,120.2,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,25.69,,,,,,,Female,Age 18 to 24,,,,,,,,,Oregon,5.0,1.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.6,65.77,Yes,Yes,No,No,"Yes, received Tdap",Yes,No -No,29.84,No,No,No,0.0,0.0,No,Male,25-29,White,No,Yes,Good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,39.32,Yes,No,No,23.0,23.0,Yes,Female,60-64,White,Yes,No,Poor,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,32.28,,,,,,,Female,Age 45 to 49,,,,,,,,,Texas,0.0,5.0,Within past year (anytime less than 12 months ago),No,5.0,None of them,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,90.72,Yes,No,No,No,"Yes, received tetanus shot, but not Tdap",No,No -,27.44,,,,,,,Female,Age 80 or older,,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,All,No,No,No,Yes,No,Yes,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.57,68.04,No,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -No,30.13,No,No,No,0.0,0.0,No,Male,50-54,White,No,Yes,Very good,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,26.46,,,,,,,Male,Age 75 to 79,,,,,,,,,Tennessee,10.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,Yes,No,Yes,No,No,No,Yes,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.73,78.93,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,27.34,,,,,,,Female,Age 70 to 74,,,,,,,,,Oklahoma,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.52,63.5,No,No,Yes,Yes,"Yes, received Tdap",No,Yes -No,22.96,Yes,No,No,0.0,0.0,No,Male,65-69,White,No,Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,32.1,,,,,,,Male,Age 35 to 39,,,,,,,,,Maryland,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.88,113.4,Yes,No,No,No,,No,Yes -,23.63,,,,,,,Female,Age 55 to 59,,,,,,,,,Maryland,0.0,2.0,Within past year (anytime less than 12 months ago),Yes,7.0,"6 or more, but not all",Yes,Yes,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,64.41,No,No,Yes,Yes,"Yes, received Tdap",No,No -,30.67,,,,,,,Female,Age 65 to 69,,,,,,,,,Pennsylvania,2.0,3.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,86.18,Yes,No,Yes,Yes,"Yes, received Tdap",No,Yes -,34.75,,,,,,,Female,Age 60 to 64,,,,,,,,,Pennsylvania,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,All,No,No,No,No,No,No,No,No,No,Yes,No,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,86.18,Yes,No,Yes,Yes,"Yes, received Tdap",No,Yes -,20.12,,,,,,,Female,Age 80 or older,,,,,,,,,Vermont,2.0,0.0,Within past year (anytime less than 12 months ago),Yes,,1 to 5,No,,No,No,Yes,Yes,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.57,49.9,Yes,No,Yes,,"Yes, received tetanus shot but not sure what type",No,No -,27.98,,,,,,,Male,Age 35 to 39,,,,,,,,,West Virginia,2.0,30.0,Within past year (anytime less than 12 months ago),Yes,3.0,1 to 5,No,No,No,No,No,No,Yes,No,No,No,No,No,Yes,No,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.73,83.46,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",Yes,No -No,26.57,No,No,No,2.0,5.0,No,Female,65-69,White,No,No,Good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,33.96,,,,,,,Female,Age 50 to 54,,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.75,104.33,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,35.26,,,,,,,Male,Age 80 or older,,,,,,,,,South Dakota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,No,No,,No,No,No,No,Yes,No,No,No,No,No,No,No,,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.83,117.93,No,,Yes,No,"Yes, received tetanus shot but not sure what type",No, -,34.3,,,,,,,Male,Age 65 to 69,,,,,,,,,New Hampshire,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,117.93,Yes,Yes,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -No,25.82,No,No,No,0.0,30.0,No,Female,18-24,Black,No,No,Fair,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,34.33,No,No,No,0.0,5.0,No,Male,18-24,Hispanic,No,Yes,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,17.36,,,,,,,Female,Age 65 to 69,,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,44.45,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,31.42,,,,,,,Male,Age 40 to 44,,,,,,,,,Massachusetts,0.0,6.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,Yes,No,No,"No, pre-diabetes or borderline diabetes",No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.96,120.2,Yes,Yes,Yes,No,"Yes, received Tdap",No,No -,,,,,,,,Female,Age 65 to 69,,,,,,,,,Washington,12.0,2.0,Within past year (anytime less than 12 months ago),No,7.0,None of them,No,No,No,No,No,No,No,No,No,Yes,No,No,No,,,,,,,"White only, Non-Hispanic",1.6,,,,,,,, -No,33.23,Yes,No,No,0.0,0.0,No,Male,60-64,White,No,No,Good,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,28.25,,,,,,,Male,Age 40 to 44,,,,,,,,,Nebraska,2.0,5.0,Within past 2 years (1 year but less than 2 years ago),No,7.0,None of them,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.88,99.79,No,No,Yes,No,"Yes, received Tdap",No,No -No,23.71,No,No,No,0.0,0.0,No,Male,70-74,White,No,Yes,Good,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,26.45,,,,,,,Male,Age 55 to 59,,,,,,,,,Massachusetts,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,88.45,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,No -No,27.2,No,No,No,10.0,0.0,No,Male,60-64,White,Yes,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,34.17,No,No,No,0.0,0.0,No,Male,50-54,White,Yes,Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,35.51,,,,,,,Female,Age 45 to 49,,,,,,,,,Michigan,0.0,1.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,99.79,No,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,22.81,,,,,,,Female,Age 35 to 39,,,,,,,,,Utah,0.0,20.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,68.04,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,26.39,,,,,,,Female,Age 55 to 59,,,,,,,,,Minnesota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,67.59,Yes,No,No,No,"Yes, received Tdap",No,Yes -,21.95,,,,,,,Female,Age 50 to 54,,,,,,,,,Minnesota,30.0,30.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,Yes,No,Yes,Yes,No,Yes,No,No,No,Yes,Yes,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.57,54.43,Yes,Yes,No,Yes,"Yes, received tetanus shot but not sure what type",No,No -,31.85,,,,,,,Male,Age 75 to 79,,,,,,,,,South Dakota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,Yes,Yes,No,No,No,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,100.7,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes -,34.15,,,,,,,Male,Age 65 to 69,,,,,,,,,West Virginia,30.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,Yes,Yes,No,No,No,No,No,Yes,Yes,Yes,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,107.95,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -No,31.57,Yes,No,No,0.0,3.0,No,Male,35-39,White,No,Yes,Very good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,30.04,Yes,No,No,0.0,3.0,No,Male,35-39,White,No,Yes,Good,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,23.01,No,Yes,No,2.0,0.0,Yes,Male,75-79,White,No,Yes,Fair,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,27.89,Yes,No,No,0.0,0.0,Yes,Male,65-69,Black,No,Yes,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,21.26,No,No,No,27.0,0.0,No,Female,80 or older,White,"No, borderline diabetes",Yes,Good,8.0,Yes,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,23.09,Yes,Yes,No,0.0,0.0,No,Male,35-39,White,No,Yes,Excellent,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,19.37,,,,,,,Female,Age 40 to 44,,,,,,,,,Virginia,0.0,4.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.68,54.43,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,31.28,,,,,,,Female,Age 40 to 44,,,,,,,,,Minnesota,0.0,4.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,98.88,Yes,Yes,Yes,No,"Yes, received Tdap",No,No -,25.84,,,,,,,Male,Age 80 or older,,,,,,,,,Connecticut,2.0,10.0,Within past year (anytime less than 12 months ago),No,12.0,1 to 5,No,No,No,No,No,No,Yes,No,Yes,No,Yes,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,79.38,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,25.75,,,,,,,Female,Age 60 to 64,,,,,,,,,Louisiana,0.0,10.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,Yes,No,Yes,No,No,Yes,No,Yes,No,No,No,No,Yes,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"Black only, Non-Hispanic",1.63,68.04,Yes,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,,,,,,,,Male,Age 50 to 54,,,,,,,,,New York,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,Yes,No,Never smoked,Not at all (right now),Yes,Hispanic,,,Yes,Yes,Yes,No,,No,Yes -,32.49,,,,,,,Male,Age 50 to 54,,,,,,,,,California,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),No,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,,Hispanic,1.75,99.79,,,,,,, -,23.3,,,,,,,Male,Age 65 to 69,,,,,,,,,Massachusetts,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.65,63.5,Yes,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Tested positive using home test without a health professional -,32.1,,,,,,,Female,Age 55 to 59,,,,,,,,,New Hampshire,0.0,30.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,84.82,No,No,Yes,,"Yes, received Tdap",No,Yes -No,37.3,No,No,No,0.0,14.0,No,Male,60-64,White,No,Yes,Good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,22.05,,,,,,,Female,Age 30 to 34,,,,,,,,,District of Columbia,4.0,10.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,65.77,No,Yes,No,No,"Yes, received Tdap",No,Yes -,,,,,,,,Female,Age 80 or older,,,,,,,,,Wisconsin,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,,,No,No,No,No,No,No,No,Yes,Yes,No,No,No,Yes,No,Yes,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.45,,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -No,35.26,Yes,No,No,0.0,7.0,No,Male,65-69,White,No,Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,29.29,,,,,,,Male,Age 45 to 49,,,,,,,,,Alaska,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.8,95.25,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,,,,,,,,Female,Age 35 to 39,,,,,,,,,Virginia,7.0,16.0,Within past year (anytime less than 12 months ago),No,6.0,None of them,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,Yes,Yes,No,Former smoker,Not at all (right now),No,"Multiracial, Non-Hispanic",1.73,,Yes,No,No,No,"Yes, received Tdap",No,Yes -No,32.78,Yes,No,No,0.0,0.0,No,Male,45-49,White,No,Yes,Excellent,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,28.37,,,,,,,Male,Age 70 to 74,,,,,,,,,Washington,14.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,Yes,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Multiracial, Non-Hispanic",1.88,100.24,Yes,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes -,37.12,,,,,,,Female,Age 18 to 24,,,,,,,,,New York,0.0,3.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,Yes,No,No,Yes,No,No,No,No,Yes,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.68,104.33,No,Yes,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -,41.15,,,,,,,Female,Age 25 to 29,,,,,,,,,Massachusetts,5.0,5.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,,1.57,102.06,Yes,,,,,, -,24.13,,,,,,,Female,Age 80 or older,,,,,,,,,New Jersey,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,65.77,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,27.44,,,,,,,Female,Age 30 to 34,,,,,,,,,Texas,2.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,77.11,Yes,Yes,No,No,"Yes, received tetanus shot, but not Tdap",No,Yes -,39.16,,,,,,,Male,Age 55 to 59,,,,,,,,,Maine,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,Yes,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,113.4,No,No,Yes,No,,No,No -,27.12,,,,,,,Male,Age 40 to 44,,,,,,,,,Montana,0.0,15.0,Within past 2 years (1 year but less than 2 years ago),Yes,4.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.83,90.72,Yes,No,No,No,"Yes, received Tdap",No,Yes -,32.44,,,,,,,Female,Age 25 to 29,,,,,,,,,Ohio,30.0,30.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,No,No,Yes,Yes,No,,No,No,Yes,Yes,No,No,Yes,Current smoker - now smokes every day,Use them every day,No,"White only, Non-Hispanic",1.63,85.73,No,Yes,Yes,No,"Yes, received tetanus shot but not sure what type",Yes,No -No,24.09,Yes,No,No,0.0,0.0,No,Female,55-59,White,No,No,Good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,31.12,,,,,,,Female,Age 65 to 69,,,,,,,,,South Dakota,14.0,30.0,Within past year (anytime less than 12 months ago),No,12.0,All,Yes,Yes,Yes,No,No,No,Yes,No,Yes,No,No,No,Yes,Yes,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,Yes,"Other race only, Non-Hispanic",1.65,84.82,No,Yes,Yes,No,"Yes, received Tdap",No,Yes -No,20.97,No,No,No,0.0,0.0,No,Female,80 or older,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,32.22,,,,,,,Female,Age 70 to 74,,,,,,,,,New York,30.0,2.0,Within past year (anytime less than 12 months ago),No,8.0,All,Yes,Yes,No,Yes,Yes,Yes,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.52,74.84,Yes,Yes,Yes,Yes,,No,No -,30.68,,,,,,,Male,Age 50 to 54,,,,,,,,,Indiana,15.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,99.79,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -,21.8,,,,,,,Female,Age 70 to 74,,,,,,,,,West Virginia,,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,All,Yes,No,No,No,No,No,No,Yes,Yes,Yes,Yes,Yes,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,57.61,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -No,18.69,No,No,No,0.0,0.0,No,Female,75-79,Other,No,Yes,Good,8.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,20.98,,,,,,,Male,Age 75 to 79,,,,,,,,,Tennessee,30.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,All,Yes,No,No,No,Yes,Yes,No,No,Yes,No,No,No,No,Yes,Yes,Yes,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,58.97,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,32.55,,,,,,,Male,Age 60 to 64,,,,,,,,,South Carolina,0.0,0.0,,Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.83,108.86,No,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -,22.46,,,,,,,Female,Age 75 to 79,,,,,,,,,Ohio,1.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.52,52.16,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,21.58,,,,,,,Female,Age 80 or older,,,,,,,,,Texas,0.0,2.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,No,Yes,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,53.52,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,24.13,,,,,,,Female,Age 18 to 24,,,,,,,,,North Carolina,0.0,30.0,Within past year (anytime less than 12 months ago),No,5.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,Yes,No,No,No,Never smoked,Use them every day,No,Hispanic,1.65,65.77,Yes,Yes,Yes,No,"Yes, received tetanus shot, but not Tdap",No,Yes -No,20.6,No,No,No,0.0,0.0,No,Female,65-69,White,No,Yes,Excellent,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,22.15,No,No,No,0.0,0.0,No,Female,70-74,White,No,Yes,Excellent,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,24.37,,,,,,,Male,Age 25 to 29,,,,,,,,,North Carolina,7.0,0.0,5 or more years ago,Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.75,74.84,Yes,No,No,Yes,"Yes, received Tdap",No,Yes -No,25.09,No,No,No,0.0,0.0,No,Male,70-74,White,No,No,Excellent,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,31.09,No,No,No,2.0,0.0,No,Female,70-74,White,No,Yes,Very good,7.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,35.15,Yes,No,No,30.0,30.0,Yes,Female,60-64,White,No,Yes,Poor,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,24.87,,,,,,,Female,Age 65 to 69,,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,Yes,No,No,No,Yes,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.57,61.69,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,33.66,,,,,,,Female,Age 40 to 44,,,,,,,,,Texas,1.0,2.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.6,86.18,Yes,,Yes,No,"Yes, received Tdap",No,Yes -No,24.96,No,No,No,0.0,0.0,No,Female,45-49,Black,No,Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,38.41,,,,,,,Female,Age 65 to 69,,,,,,,,,Georgia,0.0,,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,Yes,No,Yes,,Yes,Yes,No,Yes,,Not at all (right now),Yes,"Black only, Non-Hispanic",1.57,95.25,No,Yes,No,No,"Yes, received Tdap",No,No -,30.79,,,,,,,Female,Age 45 to 49,,,,,,,,,Virginia,2.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,83.91,Yes,Yes,No,No,,No,Tested positive using home test without a health professional -No,29.29,No,No,No,0.0,0.0,No,Female,75-79,Other,Yes,Yes,Good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,20.98,,,,,,,Female,Age 80 or older,,,,,,,,,Rhode Island,10.0,15.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,Yes,No,No,Yes,No,Yes,No,Yes,Yes,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,62.6,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,23.41,,,,,,,Female,Age 18 to 24,,,,,,,,,Tennessee,3.0,2.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,58.06,No,No,No,No,"Yes, received Tdap",No,Yes -No,24.03,No,No,No,0.0,0.0,No,Female,70-74,Asian,Yes,Yes,Good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,23.17,,,,,,,Female,Age 18 to 24,,,,,,,,,Hawaii,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),Yes,"Other race only, Non-Hispanic",1.63,61.23,No,No,Yes,No,"Yes, received Tdap",No,Yes -,40.39,,,,,,,Female,Age 45 to 49,,,,,,,,,Tennessee,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,"Yes, but only during pregnancy (female)",No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,103.42,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,24.69,,,,,,,Female,Age 45 to 49,,,,,,,,,Texas,2.0,15.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,61.23,Yes,Yes,Yes,No,"Yes, received Tdap",Yes,Yes -No,29.7,Yes,No,No,0.0,0.0,No,Female,55-59,White,No,Yes,Very good,4.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,33.0,,,,,,,Male,Age 35 to 39,,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,104.33,Yes,Yes,No,No,"Yes, received Tdap",No,No -,26.61,,,,,,,Female,Age 60 to 64,,,,,,,,,Indiana,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,79.38,No,No,No,No,"Yes, received Tdap",No,No -,25.61,,,,,,,Female,Age 80 or older,,,,,,,,,Maryland,,0.0,Within past year (anytime less than 12 months ago),Yes,3.0,1 to 5,Yes,Yes,No,No,No,No,No,Yes,Yes,No,,,,,,,,,,"Black only, Non-Hispanic",1.57,63.5,,,,,,, -No,23.34,Yes,No,No,0.0,0.0,No,Female,75-79,White,No,Yes,Excellent,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Yes,30.34,Yes,No,No,0.0,0.0,No,Male,70-74,White,Yes,No,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,28.06,No,No,No,0.0,0.0,No,Male,80 or older,White,No,Yes,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,,,,,,,,Female,Age 35 to 39,,,,,,,,,Ohio,3.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, -No,23.24,No,No,No,0.0,0.0,No,Female,55-59,Hispanic,No,Yes,Good,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,26.58,Yes,No,No,3.0,0.0,No,Male,55-59,Other,No,Yes,Good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,29.13,No,No,No,0.0,0.0,No,Male,25-29,Hispanic,No,Yes,Excellent,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,33.52,,,,,,,Male,Age 75 to 79,,,,,,,,,Ohio,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.7,97.07,No,No,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -No,31.57,Yes,No,No,0.0,0.0,No,Male,35-39,Hispanic,No,No,Good,3.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,26.39,,,,,,,Male,Age 40 to 44,,,,,,,,,Colorado,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.85,90.72,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No -,28.07,,,,,,,Male,Age 65 to 69,,,,,,,,,New Hampshire,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.83,93.89,No,No,No,,"Yes, received tetanus shot but not sure what type",No,No -,27.89,,,,,,,Male,Age 18 to 24,,,,,,,,,Hawaii,1.0,10.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"Multiracial, Non-Hispanic",1.8,90.72,Yes,,No,Yes,"Yes, received tetanus shot but not sure what type",Yes,No -,31.32,,,,,,,Male,Age 80 or older,,,,,,,,,Kansas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,All,No,No,No,Yes,No,Yes,Yes,No,Yes,No,Yes,No,No,Yes,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.7,90.72,No,No,Yes,Yes,,No,Yes -No,27.44,No,No,No,30.0,0.0,No,Female,45-49,White,No,No,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,25.59,,,,,,,Male,Age 75 to 79,,,,,,,,,Maryland,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,Yes,Yes,No,No,Yes,No,No,No,Yes,No,Yes,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.85,88.0,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -No,31.16,No,No,No,0.0,0.0,No,Female,65-69,Black,No,Yes,Good,5.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,,,,,,,,Male,Age 40 to 44,,,,,,,,,Florida,0.0,14.0,Within past 2 years (1 year but less than 2 years ago),No,9.0,"6 or more, but not all",No,No,No,No,No,Yes,Yes,No,Yes,No,,,,,,,,,,"Black only, Non-Hispanic",,,,,,,,, -,49.78,,,,,,,Female,Age 65 to 69,,,,,,,,,Nebraska,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,131.54,Yes,No,Yes,Yes,"Yes, received Tdap",No,No -,39.58,,,,,,,Male,Age 65 to 69,,,,,,,,,Maine,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.85,136.08,Yes,No,Yes,Yes,,No,No -No,25.1,No,No,No,0.0,0.0,No,Male,65-69,White,No,Yes,Very good,6.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,33.23,,,,,,,Male,Age 50 to 54,,,,,,,,,South Dakota,3.0,0.0,5 or more years ago,Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,,Yes,"White only, Non-Hispanic",1.75,102.06,Yes,No,No,No,"Yes, received tetanus shot, but not Tdap",No,No -,24.39,,,,,,,Male,Age 55 to 59,,,,,,,,,Massachusetts,2.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.88,86.18,Yes,No,No,Yes,,No,No -,28.71,,,,,,,Male,Age 55 to 59,,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,Hispanic,1.69,82.0,Yes,Yes,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,24.68,,,,,,,Male,Age 18 to 24,,,,,,,,,California,5.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,Yes,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.78,78.02,No,No,No,,,No,No -No,27.41,Yes,No,No,0.0,2.0,No,Female,65-69,White,No,Yes,Very good,7.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,27.34,No,No,No,0.0,0.0,No,Female,55-59,White,No,Yes,Excellent,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,25.53,,,,,,,Female,Age 80 or older,,,,,,,,,Iowa,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,None of them,No,No,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,73.94,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,19.77,,,,,,,Female,Age 60 to 64,,,,,,,,,Minnesota,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,58.97,No,No,No,No,"Yes, received tetanus shot, but not Tdap",No,No -,31.25,,,,,,,Female,Age 65 to 69,,,,,,,,,Georgia,1.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.52,72.57,No,No,Yes,Yes,"Yes, received Tdap",No,No -No,33.3,Yes,No,No,30.0,5.0,Yes,Female,70-74,American Indian/Alaskan Native,No,Yes,Fair,5.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,,,,,,,,Female,Age 55 to 59,,,,,,,,,Utah,30.0,15.0,Within past year (anytime less than 12 months ago),No,4.0,All,No,Yes,No,No,No,No,Yes,Yes,Yes,Yes,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, -,35.43,,,,,,,Female,Age 35 to 39,,,,,,,,,District of Columbia,30.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,Yes,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.6,90.72,No,Yes,Yes,Yes,"Yes, received Tdap",No,Yes -,34.95,,,,,,,Female,Age 60 to 64,,,,,,,,,Louisiana,0.0,0.0,5 or more years ago,No,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,95.25,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -No,28.34,No,No,No,0.0,0.0,No,Female,50-54,White,No,Yes,Good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,19.2,,,,,,,Female,Age 75 to 79,,,,,,,,,Florida,10.0,10.0,Within past year (anytime less than 12 months ago),Yes,12.0,"6 or more, but not all",No,No,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,Yes,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.57,47.63,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +HeartDisease,BMI,Smoking,AlcoholDrinking,Stroke,PhysicalHealth,MentalHealth,DiffWalking,Sex,AgeCategory,Race,Diabetic,PhysicalActivity,SleepTime,Asthma,KidneyDisease,SkinCancer,State,PhysicalHealthDays,MentalHealthDays,LastCheckupTime,PhysicalActivities,SleepHours,RemovedTeeth,HadHeartAttack,HadAngina,HadStroke,HadAsthma,HadSkinCancer,HadCOPD,HadDepressiveDisorder,HadKidneyDisease,HadArthritis,HadDiabetes,DeafOrHardOfHearing,BlindOrVisionDifficulty,DifficultyConcentrating,DifficultyWalking,DifficultyDressingBathing,DifficultyErrands,SmokerStatus,ECigaretteUsage,ChestScan,RaceEthnicityCategory,HeightInMeters,WeightInKilograms,AlcoholDrinkers,HIVTesting,FluVaxLast12,PneumoVaxEver,TetanusLast10Tdap,HighRiskLastYear,CovidPos +,24.13,,,,,,,Female,Age 18 to 24,,,,,,,,Iowa,4.0,2.0,Within past year (anytime less than 12 months ago),Yes,9.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,65.77,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,28.7,,,,,,,Male,Age 40 to 44,,,,,,,,Colorado,0.0,0.0,5 or more years ago,Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,90.72,No,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +No,23.62,No,No,No,0.0,0.0,No,Female,30-34,White,Yes (during pregnancy),Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,33.72,,,,,,,Male,Age 60 to 64,,,,,,,,Nevada,7.0,7.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.78,106.59,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",, +,29.18,,,,,,,Female,Age 40 to 44,,,,,,,,Washington,0.0,2.0,Within past 5 years (2 years but less than 5 years ago),Yes,8.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,77.11,No,Yes,No,Yes,"Yes, received tetanus shot, but not Tdap",No,No +No,24.21,Yes,No,No,0.0,0.0,No,Female,70-74,White,No,Yes,7.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,30.9,,,,,,,Male,Age 40 to 44,,,,,,,,New Jersey,0.0,0.0,,No,6.0,1 to 5,No,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,Hispanic,1.63,81.65,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,51.65,,,,,,,Male,Age 30 to 34,,,,,,,,South Carolina,0.0,6.0,Within past year (anytime less than 12 months ago),No,6.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,145.15,No,No,Yes,No,"Yes, received Tdap",No,No +,27.2,,,,,,,Female,Age 30 to 34,,,,,,,,Illinois,5.0,10.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.8,88.45,Yes,Yes,No,No,"Yes, received Tdap",No,Yes +No,27.46,No,No,Yes,7.0,3.0,No,Female,30-34,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,26.57,,,,,,,Female,Age 45 to 49,,,,,,,,Washington,0.0,6.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,68.04,Yes,Yes,Yes,No,"Yes, received Tdap",No,Tested positive using home test without a health professional +,27.48,,,,,,,Male,Age 55 to 59,,,,,,,,Kansas,0.0,5.0,5 or more years ago,Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.88,97.07,No,Yes,No,No,"Yes, received Tdap",No,No +,28.97,,,,,,,Female,Age 70 to 74,,,,,,,,New Hampshire,1.0,1.0,Within past year (anytime less than 12 months ago),No,3.0,,No,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,83.91,No,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,30.67,,,,,,,Male,Age 60 to 64,,,,,,,,New Jersey,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.68,86.18,No,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,28.73,,,,,,,Male,Age 60 to 64,,,,,,,,West Virginia,15.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,All,No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,80.74,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Tested positive using home test without a health professional +,27.4,,,,,,,Female,Age 40 to 44,,,,,,,,Massachusetts,0.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,1 to 5,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.55,65.77,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,23.65,,,,,,,Female,Age 60 to 64,,,,,,,,Rhode Island,2.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,1 to 5,No,No,Yes,Yes,No,No,No,No,No,No,No,No,Yes,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,68.49,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,No +,42.57,,,,,,,Female,Age 45 to 49,,,,,,,,Delaware,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,127.01,No,Yes,Yes,No,"Yes, received Tdap",No,Yes +No,32.49,Yes,No,No,30.0,15.0,Yes,Female,50-54,White,No,Yes,3.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,24.37,,,,,,,Male,Age 55 to 59,,,,,,,,Minnesota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.91,88.45,Yes,Yes,Yes,No,"Yes, received Tdap",No,No +,33.0,,,,,,,Male,Age 35 to 39,,,,,,,,Oregon,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,Yes,No,No,No,No,No,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,104.33,No,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,,,,,,,,Female,Age 35 to 39,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,Hispanic,,,,,,,,, +No,23.62,No,Yes,No,0.0,19.0,No,Female,18-24,Hispanic,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,26.63,,,,,,,Female,Age 55 to 59,,,,,,,,Nebraska,15.0,5.0,Within past year (anytime less than 12 months ago),No,7.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,72.57,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,No +No,25.61,No,No,No,0.0,0.0,No,Female,30-34,Hispanic,No,Yes,8.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,28.97,No,No,No,0.0,0.0,No,Female,55-59,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,23.13,,,,,,,Male,Age 35 to 39,,,,,,,,Virginia,4.0,10.0,5 or more years ago,Yes,5.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.93,86.18,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,33.76,,,,,,,Female,Age 50 to 54,,,,,,,,California,0.0,10.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.45,70.76,Yes,No,No,No,"Yes, received tetanus shot, but not Tdap",No,Tested positive using home test without a health professional +No,23.91,Yes,No,No,0.0,15.0,No,Female,30-34,White,No,Yes,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,25.1,,,,,,,Male,Age 80 or older,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,1 to 5,No,No,No,No,Yes,No,No,Yes,Yes,No,Yes,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.8,81.65,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +No,34.72,Yes,No,No,7.0,7.0,Yes,Female,45-49,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,32.28,,,,,,,Female,Age 55 to 59,,,,,,,,Rhode Island,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,90.72,No,Yes,Yes,Yes,"Yes, received Tdap",No,Yes +,24.28,,,,,,,Male,Age 25 to 29,,,,,,,,Vermont,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,8.0,None of them,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.83,81.19,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",Yes,No +,26.96,,,,,,,Female,Age 75 to 79,,,,,,,,New York,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,Yes,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,73.48,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,27.44,,,,,,,Female,Age 40 to 44,,,,,,,,Texas,0.0,1.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.57,68.04,No,,,,,, +,25.5,,,,,,,Female,Age 40 to 44,,,,,,,,Nebraska,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,71.67,Yes,No,Yes,No,"Yes, received Tdap",No,Yes +,19.37,,,,,,,Male,Age 75 to 79,,,,,,,,Georgia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,None of them,Yes,No,No,No,Yes,No,No,No,No,No,No,Yes,No,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.68,54.43,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,24.41,,,,,,,Male,Age 45 to 49,,,,,,,,Connecticut,3.0,4.0,5 or more years ago,Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.85,83.91,Yes,No,No,,"No, did not receive any tetanus shot in the past 10 years",No,Tested positive using home test without a health professional +,21.29,,,,,,,Male,Age 35 to 39,,,,,,,,Ohio,1.0,0.0,5 or more years ago,Yes,6.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,63.5,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,20.8,,,,,,,Female,Age 35 to 39,,,,,,,,New York,0.0,5.0,Within past year (anytime less than 12 months ago),No,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.65,56.7,No,Yes,No,No,"Yes, received tetanus shot, but not Tdap",No,Yes +,25.83,,,,,,,Female,Age 40 to 44,,,,,,,,West Virginia,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,81.65,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,33.91,,,,,,,Female,Age 70 to 74,,,,,,,,Maine,1.0,4.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,Yes,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.83,113.4,No,No,Yes,Yes,"Yes, received Tdap",No,No +,32.55,,,,,,,Male,Age 50 to 54,,,,,,,,Oklahoma,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,,No,No,No,No,No,No,No,No,Yes,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Other race only, Non-Hispanic",1.83,108.86,,No,Yes,No,"Yes, received Tdap",No,No +Yes,23.63,Yes,No,No,0.0,0.0,No,Male,60-64,American Indian/Alaskan Native,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,,,,,,,,Female,Age 45 to 49,,,,,,,,Louisiana,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),No,5.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.5,,Yes,,No,,"No, did not receive any tetanus shot in the past 10 years",No,Tested positive using home test without a health professional +No,27.79,Yes,No,No,0.0,0.0,Yes,Female,55-59,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,26.39,No,No,No,0.0,0.0,No,Male,30-34,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,,,,,,,,Male,Age 60 to 64,,,,,,,,Connecticut,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),No,6.0,"6 or more, but not all",No,No,No,No,No,No,No,Yes,Yes,Yes,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,,72.12,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,37.31,,,,,,,Male,Age 30 to 34,,,,,,,,Ohio,0.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,Hispanic,1.78,117.93,Yes,No,Yes,No,"Yes, received Tdap",No,No +,24.41,,,,,,,Male,Age 65 to 69,,,,,,,,Massachusetts,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,"6 or more, but not all",No,No,No,Yes,Yes,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.8,79.38,Yes,Yes,No,Yes,"Yes, received tetanus shot but not sure what type",No,Yes +No,26.31,No,No,No,0.0,0.0,No,Male,55-59,White,No,Yes,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,29.53,,,,,,,Male,Age 50 to 54,,,,,,,,Oregon,0.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,1 to 5,No,No,No,No,No,No,No,No,No,"No, pre-diabetes or borderline diabetes",No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,Hispanic,1.75,90.72,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,26.63,,,,,,,Female,Age 70 to 74,,,,,,,,Minnesota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,77.11,Yes,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +,22.81,,,,,,,Male,Age 65 to 69,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,Yes,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,68.04,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,23.56,,,,,,,Female,Age 70 to 74,,,,,,,,Indiana,6.0,14.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,66.22,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,35.87,,,,,,,Male,Age 55 to 59,,,,,,,,Montana,3.0,20.0,Within past year (anytime less than 12 months ago),No,5.0,1 to 5,No,No,No,No,No,No,Yes,No,No,Yes,Yes,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,113.4,Yes,No,Yes,No,"Yes, received Tdap",No,Yes +,39.33,,,,,,,Male,Age 55 to 59,,,,,,,,Kentucky,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.83,131.54,Yes,Yes,Yes,,"Yes, received Tdap",No,Yes +,37.31,,,,,,,Female,Age 50 to 54,,,,,,,,Minnesota,2.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.78,117.93,Yes,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,45.49,No,No,No,2.0,7.0,No,Female,55-59,Black,Yes,No,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Yes,24.41,No,No,No,0.0,0.0,No,Male,80 or older,White,No,Yes,8.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,34.44,,,,,,,Female,,,,,,,,,Massachusetts,2.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,Yes,Yes,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,108.86,Yes,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No +,21.11,,,,,,,Male,Age 40 to 44,,,,,,,,Texas,0.0,5.0,5 or more years ago,Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,72.57,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,24.03,,,,,,,Female,Age 75 to 79,,,,,,,,North Carolina,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,Yes,No,Yes,No,Yes,"No, pre-diabetes or borderline diabetes",No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,63.5,Yes,No,Yes,Yes,"Yes, received Tdap",No,No +No,18.6,Yes,No,No,0.0,0.0,No,Female,65-69,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,27.44,,,,,,,Female,Age 55 to 59,,,,,,,,Florida,1.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,Yes,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,77.11,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,33.12,,,,,,,Male,Age 75 to 79,,,,,,,,South Dakota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.91,120.2,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,25.69,,,,,,,Female,Age 18 to 24,,,,,,,,Oregon,5.0,1.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.6,65.77,Yes,Yes,No,No,"Yes, received Tdap",Yes,No +No,29.84,No,No,No,0.0,0.0,No,Male,25-29,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,39.32,Yes,No,No,23.0,23.0,Yes,Female,60-64,White,Yes,No,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,32.28,,,,,,,Female,Age 45 to 49,,,,,,,,Texas,0.0,5.0,Within past year (anytime less than 12 months ago),No,5.0,None of them,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,90.72,Yes,No,No,No,"Yes, received tetanus shot, but not Tdap",No,No +,27.44,,,,,,,Female,Age 80 or older,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,All,No,No,No,Yes,No,Yes,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.57,68.04,No,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +No,30.13,No,No,No,0.0,0.0,No,Male,50-54,White,No,Yes,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,26.46,,,,,,,Male,Age 75 to 79,,,,,,,,Tennessee,10.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,Yes,No,Yes,No,No,No,Yes,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.73,78.93,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,27.34,,,,,,,Female,Age 70 to 74,,,,,,,,Oklahoma,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.52,63.5,No,No,Yes,Yes,"Yes, received Tdap",No,Yes +No,22.96,Yes,No,No,0.0,0.0,No,Male,65-69,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,32.1,,,,,,,Male,Age 35 to 39,,,,,,,,Maryland,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.88,113.4,Yes,No,No,No,,No,Yes +,23.63,,,,,,,Female,Age 55 to 59,,,,,,,,Maryland,0.0,2.0,Within past year (anytime less than 12 months ago),Yes,7.0,"6 or more, but not all",Yes,Yes,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,64.41,No,No,Yes,Yes,"Yes, received Tdap",No,No +,30.67,,,,,,,Female,Age 65 to 69,,,,,,,,Pennsylvania,2.0,3.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,86.18,Yes,No,Yes,Yes,"Yes, received Tdap",No,Yes +,34.75,,,,,,,Female,Age 60 to 64,,,,,,,,Pennsylvania,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,All,No,No,No,No,No,No,No,No,No,Yes,No,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,86.18,Yes,No,Yes,Yes,"Yes, received Tdap",No,Yes +,20.12,,,,,,,Female,Age 80 or older,,,,,,,,Vermont,2.0,0.0,Within past year (anytime less than 12 months ago),Yes,,1 to 5,No,,No,No,Yes,Yes,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.57,49.9,Yes,No,Yes,,"Yes, received tetanus shot but not sure what type",No,No +,27.98,,,,,,,Male,Age 35 to 39,,,,,,,,West Virginia,2.0,30.0,Within past year (anytime less than 12 months ago),Yes,3.0,1 to 5,No,No,No,No,No,No,Yes,No,No,No,No,No,Yes,No,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.73,83.46,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",Yes,No +No,26.57,No,No,No,2.0,5.0,No,Female,65-69,White,No,No,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,33.96,,,,,,,Female,Age 50 to 54,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.75,104.33,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,35.26,,,,,,,Male,Age 80 or older,,,,,,,,South Dakota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,No,No,,No,No,No,No,Yes,No,No,No,No,No,No,No,,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.83,117.93,No,,Yes,No,"Yes, received tetanus shot but not sure what type",No, +,34.3,,,,,,,Male,Age 65 to 69,,,,,,,,New Hampshire,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,117.93,Yes,Yes,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +No,25.82,No,No,No,0.0,30.0,No,Female,18-24,Black,No,No,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,34.33,No,No,No,0.0,5.0,No,Male,18-24,Hispanic,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,17.36,,,,,,,Female,Age 65 to 69,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,44.45,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,31.42,,,,,,,Male,Age 40 to 44,,,,,,,,Massachusetts,0.0,6.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,Yes,No,No,"No, pre-diabetes or borderline diabetes",No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.96,120.2,Yes,Yes,Yes,No,"Yes, received Tdap",No,No +,,,,,,,,Female,Age 65 to 69,,,,,,,,Washington,12.0,2.0,Within past year (anytime less than 12 months ago),No,7.0,None of them,No,No,No,No,No,No,No,No,No,Yes,No,No,No,,,,,,,"White only, Non-Hispanic",1.6,,,,,,,, +No,33.23,Yes,No,No,0.0,0.0,No,Male,60-64,White,No,No,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,28.25,,,,,,,Male,Age 40 to 44,,,,,,,,Nebraska,2.0,5.0,Within past 2 years (1 year but less than 2 years ago),No,7.0,None of them,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.88,99.79,No,No,Yes,No,"Yes, received Tdap",No,No +No,23.71,No,No,No,0.0,0.0,No,Male,70-74,White,No,Yes,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,26.45,,,,,,,Male,Age 55 to 59,,,,,,,,Massachusetts,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,88.45,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,No +No,27.2,No,No,No,10.0,0.0,No,Male,60-64,White,Yes,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,34.17,No,No,No,0.0,0.0,No,Male,50-54,White,Yes,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,35.51,,,,,,,Female,Age 45 to 49,,,,,,,,Michigan,0.0,1.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,99.79,No,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,22.81,,,,,,,Female,Age 35 to 39,,,,,,,,Utah,0.0,20.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,68.04,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,26.39,,,,,,,Female,Age 55 to 59,,,,,,,,Minnesota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,67.59,Yes,No,No,No,"Yes, received Tdap",No,Yes +,21.95,,,,,,,Female,Age 50 to 54,,,,,,,,Minnesota,30.0,30.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,Yes,No,Yes,Yes,No,Yes,No,No,No,Yes,Yes,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.57,54.43,Yes,Yes,No,Yes,"Yes, received tetanus shot but not sure what type",No,No +,31.85,,,,,,,Male,Age 75 to 79,,,,,,,,South Dakota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,Yes,Yes,No,No,No,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,100.7,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes +,34.15,,,,,,,Male,Age 65 to 69,,,,,,,,West Virginia,30.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,Yes,Yes,No,No,No,No,No,Yes,Yes,Yes,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,107.95,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +No,31.57,Yes,No,No,0.0,3.0,No,Male,35-39,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,30.04,Yes,No,No,0.0,3.0,No,Male,35-39,White,No,Yes,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,23.01,No,Yes,No,2.0,0.0,Yes,Male,75-79,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,27.89,Yes,No,No,0.0,0.0,Yes,Male,65-69,Black,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,21.26,No,No,No,27.0,0.0,No,Female,80 or older,White,"No, borderline diabetes",Yes,8.0,Yes,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,23.09,Yes,Yes,No,0.0,0.0,No,Male,35-39,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,19.37,,,,,,,Female,Age 40 to 44,,,,,,,,Virginia,0.0,4.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.68,54.43,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,31.28,,,,,,,Female,Age 40 to 44,,,,,,,,Minnesota,0.0,4.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,98.88,Yes,Yes,Yes,No,"Yes, received Tdap",No,No +,25.84,,,,,,,Male,Age 80 or older,,,,,,,,Connecticut,2.0,10.0,Within past year (anytime less than 12 months ago),No,12.0,1 to 5,No,No,No,No,No,No,Yes,No,Yes,No,Yes,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,79.38,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,25.75,,,,,,,Female,Age 60 to 64,,,,,,,,Louisiana,0.0,10.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,Yes,No,Yes,No,No,Yes,No,Yes,No,No,No,No,Yes,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"Black only, Non-Hispanic",1.63,68.04,Yes,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,,,,,,,,Male,Age 50 to 54,,,,,,,,New York,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,Yes,No,Never smoked,Not at all (right now),Yes,Hispanic,,,Yes,Yes,Yes,No,,No,Yes +,32.49,,,,,,,Male,Age 50 to 54,,,,,,,,California,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),No,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,,Hispanic,1.75,99.79,,,,,,, +,23.3,,,,,,,Male,Age 65 to 69,,,,,,,,Massachusetts,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.65,63.5,Yes,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Tested positive using home test without a health professional +,32.1,,,,,,,Female,Age 55 to 59,,,,,,,,New Hampshire,0.0,30.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,84.82,No,No,Yes,,"Yes, received Tdap",No,Yes +No,37.3,No,No,No,0.0,14.0,No,Male,60-64,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,22.05,,,,,,,Female,Age 30 to 34,,,,,,,,District of Columbia,4.0,10.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,65.77,No,Yes,No,No,"Yes, received Tdap",No,Yes +,,,,,,,,Female,Age 80 or older,,,,,,,,Wisconsin,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,,,No,No,No,No,No,No,No,Yes,Yes,No,No,No,Yes,No,Yes,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.45,,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +No,35.26,Yes,No,No,0.0,7.0,No,Male,65-69,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,29.29,,,,,,,Male,Age 45 to 49,,,,,,,,Alaska,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.8,95.25,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,,,,,,,,Female,Age 35 to 39,,,,,,,,Virginia,7.0,16.0,Within past year (anytime less than 12 months ago),No,6.0,None of them,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,Yes,Yes,No,Former smoker,Not at all (right now),No,"Multiracial, Non-Hispanic",1.73,,Yes,No,No,No,"Yes, received Tdap",No,Yes +No,32.78,Yes,No,No,0.0,0.0,No,Male,45-49,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,28.37,,,,,,,Male,Age 70 to 74,,,,,,,,Washington,14.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,Yes,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Multiracial, Non-Hispanic",1.88,100.24,Yes,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes +,37.12,,,,,,,Female,Age 18 to 24,,,,,,,,New York,0.0,3.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,Yes,No,No,Yes,No,No,No,No,Yes,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.68,104.33,No,Yes,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +,41.15,,,,,,,Female,Age 25 to 29,,,,,,,,Massachusetts,5.0,5.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,,1.57,102.06,Yes,,,,,, +,24.13,,,,,,,Female,Age 80 or older,,,,,,,,New Jersey,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,65.77,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,27.44,,,,,,,Female,Age 30 to 34,,,,,,,,Texas,2.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,77.11,Yes,Yes,No,No,"Yes, received tetanus shot, but not Tdap",No,Yes +,39.16,,,,,,,Male,Age 55 to 59,,,,,,,,Maine,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,Yes,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,113.4,No,No,Yes,No,,No,No +,27.12,,,,,,,Male,Age 40 to 44,,,,,,,,Montana,0.0,15.0,Within past 2 years (1 year but less than 2 years ago),Yes,4.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.83,90.72,Yes,No,No,No,"Yes, received Tdap",No,Yes +,32.44,,,,,,,Female,Age 25 to 29,,,,,,,,Ohio,30.0,30.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,No,No,Yes,Yes,No,,No,No,Yes,Yes,No,No,Yes,Current smoker - now smokes every day,Use them every day,No,"White only, Non-Hispanic",1.63,85.73,No,Yes,Yes,No,"Yes, received tetanus shot but not sure what type",Yes,No +No,24.09,Yes,No,No,0.0,0.0,No,Female,55-59,White,No,No,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,31.12,,,,,,,Female,Age 65 to 69,,,,,,,,South Dakota,14.0,30.0,Within past year (anytime less than 12 months ago),No,12.0,All,Yes,Yes,Yes,No,No,No,Yes,No,Yes,No,No,No,Yes,Yes,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,Yes,"Other race only, Non-Hispanic",1.65,84.82,No,Yes,Yes,No,"Yes, received Tdap",No,Yes +No,20.97,No,No,No,0.0,0.0,No,Female,80 or older,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,32.22,,,,,,,Female,Age 70 to 74,,,,,,,,New York,30.0,2.0,Within past year (anytime less than 12 months ago),No,8.0,All,Yes,Yes,No,Yes,Yes,Yes,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.52,74.84,Yes,Yes,Yes,Yes,,No,No +,30.68,,,,,,,Male,Age 50 to 54,,,,,,,,Indiana,15.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,99.79,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +,21.8,,,,,,,Female,Age 70 to 74,,,,,,,,West Virginia,,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,All,Yes,No,No,No,No,No,No,Yes,Yes,Yes,Yes,Yes,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,57.61,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,18.69,No,No,No,0.0,0.0,No,Female,75-79,Other,No,Yes,8.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,20.98,,,,,,,Male,Age 75 to 79,,,,,,,,Tennessee,30.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,All,Yes,No,No,No,Yes,Yes,No,No,Yes,No,No,No,No,Yes,Yes,Yes,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,58.97,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,32.55,,,,,,,Male,Age 60 to 64,,,,,,,,South Carolina,0.0,0.0,,Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.83,108.86,No,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +,22.46,,,,,,,Female,Age 75 to 79,,,,,,,,Ohio,1.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.52,52.16,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,21.58,,,,,,,Female,Age 80 or older,,,,,,,,Texas,0.0,2.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,No,Yes,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,53.52,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,24.13,,,,,,,Female,Age 18 to 24,,,,,,,,North Carolina,0.0,30.0,Within past year (anytime less than 12 months ago),No,5.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,Yes,No,No,No,Never smoked,Use them every day,No,Hispanic,1.65,65.77,Yes,Yes,Yes,No,"Yes, received tetanus shot, but not Tdap",No,Yes +No,20.6,No,No,No,0.0,0.0,No,Female,65-69,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,22.15,No,No,No,0.0,0.0,No,Female,70-74,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,24.37,,,,,,,Male,Age 25 to 29,,,,,,,,North Carolina,7.0,0.0,5 or more years ago,Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.75,74.84,Yes,No,No,Yes,"Yes, received Tdap",No,Yes +No,25.09,No,No,No,0.0,0.0,No,Male,70-74,White,No,No,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,31.09,No,No,No,2.0,0.0,No,Female,70-74,White,No,Yes,7.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,35.15,Yes,No,No,30.0,30.0,Yes,Female,60-64,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,24.87,,,,,,,Female,Age 65 to 69,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,Yes,No,No,No,Yes,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.57,61.69,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,33.66,,,,,,,Female,Age 40 to 44,,,,,,,,Texas,1.0,2.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.6,86.18,Yes,,Yes,No,"Yes, received Tdap",No,Yes +No,24.96,No,No,No,0.0,0.0,No,Female,45-49,Black,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,38.41,,,,,,,Female,Age 65 to 69,,,,,,,,Georgia,0.0,,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,Yes,No,Yes,,Yes,Yes,No,Yes,,Not at all (right now),Yes,"Black only, Non-Hispanic",1.57,95.25,No,Yes,No,No,"Yes, received Tdap",No,No +,30.79,,,,,,,Female,Age 45 to 49,,,,,,,,Virginia,2.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,83.91,Yes,Yes,No,No,,No,Tested positive using home test without a health professional +No,29.29,No,No,No,0.0,0.0,No,Female,75-79,Other,Yes,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,20.98,,,,,,,Female,Age 80 or older,,,,,,,,Rhode Island,10.0,15.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,Yes,No,No,Yes,No,Yes,No,Yes,Yes,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,62.6,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,23.41,,,,,,,Female,Age 18 to 24,,,,,,,,Tennessee,3.0,2.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,58.06,No,No,No,No,"Yes, received Tdap",No,Yes +No,24.03,No,No,No,0.0,0.0,No,Female,70-74,Asian,Yes,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,23.17,,,,,,,Female,Age 18 to 24,,,,,,,,Hawaii,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),Yes,"Other race only, Non-Hispanic",1.63,61.23,No,No,Yes,No,"Yes, received Tdap",No,Yes +,40.39,,,,,,,Female,Age 45 to 49,,,,,,,,Tennessee,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,"Yes, but only during pregnancy (female)",No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,103.42,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,24.69,,,,,,,Female,Age 45 to 49,,,,,,,,Texas,2.0,15.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,61.23,Yes,Yes,Yes,No,"Yes, received Tdap",Yes,Yes +No,29.7,Yes,No,No,0.0,0.0,No,Female,55-59,White,No,Yes,4.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,33.0,,,,,,,Male,Age 35 to 39,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,104.33,Yes,Yes,No,No,"Yes, received Tdap",No,No +,26.61,,,,,,,Female,Age 60 to 64,,,,,,,,Indiana,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,79.38,No,No,No,No,"Yes, received Tdap",No,No +,25.61,,,,,,,Female,Age 80 or older,,,,,,,,Maryland,,0.0,Within past year (anytime less than 12 months ago),Yes,3.0,1 to 5,Yes,Yes,No,No,No,No,No,Yes,Yes,No,,,,,,,,,,"Black only, Non-Hispanic",1.57,63.5,,,,,,, +No,23.34,Yes,No,No,0.0,0.0,No,Female,75-79,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Yes,30.34,Yes,No,No,0.0,0.0,No,Male,70-74,White,Yes,No,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,28.06,No,No,No,0.0,0.0,No,Male,80 or older,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,,,,,,,,Female,Age 35 to 39,,,,,,,,Ohio,3.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, +No,23.24,No,No,No,0.0,0.0,No,Female,55-59,Hispanic,No,Yes,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,26.58,Yes,No,No,3.0,0.0,No,Male,55-59,Other,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,29.13,No,No,No,0.0,0.0,No,Male,25-29,Hispanic,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,33.52,,,,,,,Male,Age 75 to 79,,,,,,,,Ohio,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.7,97.07,No,No,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +No,31.57,Yes,No,No,0.0,0.0,No,Male,35-39,Hispanic,No,No,3.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,26.39,,,,,,,Male,Age 40 to 44,,,,,,,,Colorado,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.85,90.72,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No +,28.07,,,,,,,Male,Age 65 to 69,,,,,,,,New Hampshire,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.83,93.89,No,No,No,,"Yes, received tetanus shot but not sure what type",No,No +,27.89,,,,,,,Male,Age 18 to 24,,,,,,,,Hawaii,1.0,10.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"Multiracial, Non-Hispanic",1.8,90.72,Yes,,No,Yes,"Yes, received tetanus shot but not sure what type",Yes,No +,31.32,,,,,,,Male,Age 80 or older,,,,,,,,Kansas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,All,No,No,No,Yes,No,Yes,Yes,No,Yes,No,Yes,No,No,Yes,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.7,90.72,No,No,Yes,Yes,,No,Yes +No,27.44,No,No,No,30.0,0.0,No,Female,45-49,White,No,No,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,25.59,,,,,,,Male,Age 75 to 79,,,,,,,,Maryland,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,Yes,Yes,No,No,Yes,No,No,No,Yes,No,Yes,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.85,88.0,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +No,31.16,No,No,No,0.0,0.0,No,Female,65-69,Black,No,Yes,5.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,,,,,,,,Male,Age 40 to 44,,,,,,,,Florida,0.0,14.0,Within past 2 years (1 year but less than 2 years ago),No,9.0,"6 or more, but not all",No,No,No,No,No,Yes,Yes,No,Yes,No,,,,,,,,,,"Black only, Non-Hispanic",,,,,,,,, +,49.78,,,,,,,Female,Age 65 to 69,,,,,,,,Nebraska,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,131.54,Yes,No,Yes,Yes,"Yes, received Tdap",No,No +,39.58,,,,,,,Male,Age 65 to 69,,,,,,,,Maine,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,,"White only, Non-Hispanic",1.85,136.08,Yes,No,Yes,Yes,,No,No +No,25.1,No,No,No,0.0,0.0,No,Male,65-69,White,No,Yes,6.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,33.23,,,,,,,Male,Age 50 to 54,,,,,,,,South Dakota,3.0,0.0,5 or more years ago,Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,,Yes,"White only, Non-Hispanic",1.75,102.06,Yes,No,No,No,"Yes, received tetanus shot, but not Tdap",No,No +,24.39,,,,,,,Male,Age 55 to 59,,,,,,,,Massachusetts,2.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.88,86.18,Yes,No,No,Yes,,No,No +,28.71,,,,,,,Male,Age 55 to 59,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,Hispanic,1.69,82.0,Yes,Yes,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,24.68,,,,,,,Male,Age 18 to 24,,,,,,,,California,5.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,Yes,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.78,78.02,No,No,No,,,No,No +No,27.41,Yes,No,No,0.0,2.0,No,Female,65-69,White,No,Yes,7.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,27.34,No,No,No,0.0,0.0,No,Female,55-59,White,No,Yes,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,25.53,,,,,,,Female,Age 80 or older,,,,,,,,Iowa,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,None of them,No,No,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,73.94,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,19.77,,,,,,,Female,Age 60 to 64,,,,,,,,Minnesota,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,58.97,No,No,No,No,"Yes, received tetanus shot, but not Tdap",No,No +,31.25,,,,,,,Female,Age 65 to 69,,,,,,,,Georgia,1.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.52,72.57,No,No,Yes,Yes,"Yes, received Tdap",No,No +No,33.3,Yes,No,No,30.0,5.0,Yes,Female,70-74,American Indian/Alaskan Native,No,Yes,5.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,,,,,,,,Female,Age 55 to 59,,,,,,,,Utah,30.0,15.0,Within past year (anytime less than 12 months ago),No,4.0,All,No,Yes,No,No,No,No,Yes,Yes,Yes,Yes,,,,,,,,,,"White only, Non-Hispanic",,,,,,,,, +,35.43,,,,,,,Female,Age 35 to 39,,,,,,,,District of Columbia,30.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,Yes,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.6,90.72,No,Yes,Yes,Yes,"Yes, received Tdap",No,Yes +,34.95,,,,,,,Female,Age 60 to 64,,,,,,,,Louisiana,0.0,0.0,5 or more years ago,No,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,95.25,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,28.34,No,No,No,0.0,0.0,No,Female,50-54,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,19.2,,,,,,,Female,Age 75 to 79,,,,,,,,Florida,10.0,10.0,Within past year (anytime less than 12 months ago),Yes,12.0,"6 or more, but not all",No,No,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,Yes,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.57,47.63,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_007/history.csv b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_007/history.csv index b2e106fe182c8541a9087c88fe9e48b76c2ff803..341e3d45583d267952a48e52b023db4f707d1bc8 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_007/history.csv +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_007/history.csv @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:75b60e71a83fcd44ed68d1ed7aa13d80db6c57a3d2e6f72af022c17a620e757f -size 271615431 +oid sha256:909505b745890aff132f888cd7ca00f56ac9807396d019320e105d7446632aed +size 270594777 diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_007/info.json b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_007/info.json index 53272e3074451c673817a05ef4da5ee438d07863..c876b7e9aa18ec2175d72a1a6b59bf487c77e98c 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_007/info.json +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_007/info.json @@ -1,401 +1,393 @@ -{ - "name": "Indicators of Heart Disease (2022 UPDATE)", - "source": "https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease/data", - "data_intro": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", - "is_splited": false, - "overall_size": 1010949, - "train_size": 0, - "test_size": 0, - "c_classes": 46, - "n_classes": 9, - "cat_feature_intro": { - "HeartDisease": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", - "Smoking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", - "AlcoholDrinking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", - "Stroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", - "DiffWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", - "Sex": "Sex is a biological factor that can influence the risk of heart disease. Men and women may experience different symptoms and risk factors for heart disease.", - "AgeCategory": "Age is a significant risk factor for heart disease. As people age, their risk of developing heart disease increases. It is important to monitor heart health as individuals get older.", - "Race": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", - "Diabetic": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", - "PhysicalActivity": "Regular physical activity is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", - "GenHealth": "General health status can influence the risk of heart disease. It is important to maintain overall health and address any underlying health issues to reduce the risk of heart-related complications.", - "Asthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", - "KidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", - "SkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", - "State": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", - "GeneralHealth": "General health status can influence the risk of heart disease. It is important to maintain overall health and address any underlying health issues to reduce the risk of heart-related complications.", - "LastCheckupTime": "The time since the last medical checkup can be an important factor in heart health. Regular checkups can help identify and address risk factors for heart disease early.", - "PhysicalActivities": "Engaging in regular physical activities is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", - "RemovedTeeth": "The number of removed teeth can be an indicator of overall health, including heart health. Poor oral health has been linked to an increased risk of heart disease.", - "HadHeartAttack": "As described above, the original dataset of nearly 300 variables was reduced to 40 variables. In addition to classical EDA, this dataset can be used to apply a number of machine learning methods, especially classifier models (logistic regression, SVM, random forest, etc.). You should treat the variable \"HadHeartAttack\" as binary (\"Yes\" - respondent had heart disease; \"No\" - respondent did not have heart disease). Note, however, that the classes are unbalanced, so the classic approach of applying a model is not advisable. Fixing the weights/undersampling should yield much better results. Based on the data set, I built a logistic regression model and embedded it in an application that might inspire you: https://share.streamlit.io/kamilpytlak/heart-condition-checker/main/app.py. Can you indicate which variables have a significant effect on the likelihood of heart disease?", - "HadAngina": "Angina is a type of chest pain caused by reduced blood flow to the heart. It is a symptom of coronary artery disease and can indicate an increased risk of heart disease.", - "HadStroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", - "HadAsthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", - "HadSkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", - "HadCOPD": "Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease that causes obstructed airflow from the lungs. It can increase the risk of heart disease.", - "HadDepressiveDisorder": "Depressive disorders can affect overall health and increase the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", - "HadKidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", - "HadArthritis": "Arthritis is a condition that causes inflammation of the joints. It can affect overall health and increase the risk of heart disease.", - "HadDiabetes": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", - "DeafOrHardOfHearing": "Hearing loss can affect overall health and quality of life. It is important to address and manage hearing issues to maintain overall health and reduce the risk of heart disease.", - "BlindOrVisionDifficulty": "Vision difficulties can affect overall health and quality of life. It is important to address and manage vision issues to maintain overall health and reduce the risk of heart disease.", - "DifficultyConcentrating": "Difficulty concentrating can be a sign of underlying health issues, including heart disease. It is important to address cognitive issues to improve overall health and reduce the risk of heart-related complications.", - "DifficultyWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", - "DifficultyDressingBathing": "Difficulty with daily activities such as dressing and bathing can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", - "DifficultyErrands": "Difficulty running errands can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", - "SmokerStatus": "Smoking is a major risk factor for heart disease. It is important to address and manage smoking habits to reduce the risk of heart-related complications.", - "ECigaretteUsage": "E-cigarette usage can affect heart health. It is important to monitor and manage e-cigarette usage to reduce the risk of heart disease.", - "ChestScan": "Chest scans can help identify underlying health issues, including heart disease. It is important to monitor and address any abnormalities found in chest scans to maintain overall health.", - "RaceEthnicityCategory": "Race and ethnicity can influence the risk of heart disease. It is important to consider these factors when assessing and managing heart health.", - "AlcoholDrinkers": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", - "HIVTesting": "HIV testing is important for overall health. It is important to address and manage HIV status to reduce the risk of heart-related complications.", - "FluVaxLast12": "Receiving a flu vaccine in the last 12 months can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", - "PneumoVaxEver": "Receiving a pneumococcal vaccine can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", - "TetanusLast10Tdap": "Receiving a tetanus vaccine in the last 10 years can help reduce the risk of infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", - "HighRiskLastYear": "Being at high risk for health issues in the last year can affect overall health and increase the risk of heart disease. It is important to address and manage high-risk factors to reduce the risk of heart-related complications.", - "CovidPos": "Testing positive for COVID-19 can affect overall health and increase the risk of heart disease. It is important to monitor and manage COVID-19 symptoms to reduce the risk of heart-related complications." - }, - "num_feature_intro": { - "BMI": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", - "PhysicalHealth": "Physical health is an important factor in overall well-being and can influence the risk of heart disease. It is important to maintain good physical health to reduce the risk of heart-related complications.", - "MentalHealth": "Mental health is an important factor in overall well-being and can influence the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", - "SleepTime": "Sleep time is an important factor in overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", - "PhysicalHealthDays": "The number of days with good physical health can be an indicator of overall well-being. It is important to maintain good physical health to reduce the risk of heart-related complications.", - "MentalHealthDays": "The number of days with good mental health can be an indicator of overall well-being. It is important to address and manage mental health to reduce the risk of heart-related complications.", - "SleepHours": "The number of hours of sleep can affect overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", - "HeightInMeters": "Height can be an indicator of overall health. It is important to monitor and maintain a healthy height-to-weight ratio to reduce the risk of heart disease.", - "WeightInKilograms": "Weight can be an indicator of overall health. It is important to maintain a healthy weight to reduce the risk of heart disease." - }, - "evaluation_metric": "The evaluation metric for this dataset can include accuracy, precision, recall, F1-score, and AUC-ROC. These metrics can help assess the performance of machine learning models in predicting heart disease.", - "task_type": "classification", - "target": "GeneralHealth", - "num_feature_value": { - "BMI": [ - 12.02, - 99.64 - ], - "HeightInMeters": [ - 0.91, - 2.41 - ], - "MentalHealth": [ - 0.0, - 30.0 - ], - "MentalHealthDays": [ - 0.0, - 30.0 - ], - "PhysicalHealth": [ - 0.0, - 30.0 - ], - "PhysicalHealthDays": [ - 0.0, - 30.0 - ], - "SleepHours": [ - 1.0, - 24.0 - ], - "SleepTime": [ - 1.0, - 24.0 - ], - "WeightInKilograms": [ - 22.68, - 292.57 - ] - }, - "cat_feature_value": { - "AgeCategory": [ - "18-24", - "25-29", - "30-34", - "35-39", - "40-44", - "45-49", - "50-54", - "55-59", - "60-64", - "65-69", - "70-74", - "75-79", - "80 or older", - "Age 18 to 24", - "Age 25 to 29", - "Age 30 to 34", - "Age 35 to 39", - "Age 40 to 44", - "Age 45 to 49", - "Age 50 to 54", - "Age 55 to 59", - "Age 60 to 64", - "Age 65 to 69", - "Age 70 to 74", - "Age 75 to 79", - "Age 80 or older" - ], - "AlcoholDrinkers": [ - "No", - "Yes" - ], - "AlcoholDrinking": [ - "No", - "Yes" - ], - "Asthma": [ - "No", - "Yes" - ], - "BlindOrVisionDifficulty": [ - "No", - "Yes" - ], - "ChestScan": [ - "No", - "Yes" - ], - "CovidPos": [ - "No", - "Tested positive using home test without a health professional", - "Yes" - ], - "DeafOrHardOfHearing": [ - "No", - "Yes" - ], - "Diabetic": [ - "No", - "No, borderline diabetes", - "Yes", - "Yes (during pregnancy)" - ], - "DiffWalking": [ - "No", - "Yes" - ], - "DifficultyConcentrating": [ - "No", - "Yes" - ], - "DifficultyDressingBathing": [ - "No", - "Yes" - ], - "DifficultyErrands": [ - "No", - "Yes" - ], - "DifficultyWalking": [ - "No", - "Yes" - ], - "ECigaretteUsage": [ - "Never used e-cigarettes in my entire life", - "Not at all (right now)", - "Use them every day", - "Use them some days" - ], - "FluVaxLast12": [ - "No", - "Yes" - ], - "GenHealth": [ - "Excellent", - "Fair", - "Good", - "Poor", - "Very good" - ], - "GeneralHealth": [ - "Excellent", - "Fair", - "Good", - "Poor", - "Very good" - ], - "HIVTesting": [ - "No", - "Yes" - ], - "HadAngina": [ - "No", - "Yes" - ], - "HadArthritis": [ - "No", - "Yes" - ], - "HadAsthma": [ - "No", - "Yes" - ], - "HadCOPD": [ - "No", - "Yes" - ], - "HadDepressiveDisorder": [ - "No", - "Yes" - ], - "HadDiabetes": [ - "No", - "No, pre-diabetes or borderline diabetes", - "Yes", - "Yes, but only during pregnancy (female)" - ], - "HadHeartAttack": [ - "No", - "Yes" - ], - "HadKidneyDisease": [ - "No", - "Yes" - ], - "HadSkinCancer": [ - "No", - "Yes" - ], - "HadStroke": [ - "No", - "Yes" - ], - "HeartDisease": [ - "No", - "Yes" - ], - "HighRiskLastYear": [ - "No", - "Yes" - ], - "KidneyDisease": [ - "No", - "Yes" - ], - "LastCheckupTime": [ - "5 or more years ago", - "Within past 2 years (1 year but less than 2 years ago)", - "Within past 5 years (2 years but less than 5 years ago)", - "Within past year (anytime less than 12 months ago)" - ], - "PhysicalActivities": [ - "No", - "Yes" - ], - "PhysicalActivity": [ - "No", - "Yes" - ], - "PneumoVaxEver": [ - "No", - "Yes" - ], - "Race": [ - "American Indian/Alaskan Native", - "Asian", - "Black", - "Hispanic", - "Other", - "White" - ], - "RaceEthnicityCategory": [ - "Black only, Non-Hispanic", - "Hispanic", - "Multiracial, Non-Hispanic", - "Other race only, Non-Hispanic", - "White only, Non-Hispanic" - ], - "RemovedTeeth": [ - "1 to 5", - "6 or more, but not all", - "All", - "None of them" - ], - "Sex": [ - "Female", - "Male" - ], - "SkinCancer": [ - "No", - "Yes" - ], - "SmokerStatus": [ - "Current smoker - now smokes every day", - "Current smoker - now smokes some days", - "Former smoker", - "Never smoked" - ], - "Smoking": [ - "No", - "Yes" - ], - "State": [ - "Alabama", - "Alaska", - "Arizona", - "Arkansas", - "California", - "Colorado", - "Connecticut", - "Delaware", - "District of Columbia", - "Florida", - "Georgia", - "Guam", - "Hawaii", - "Idaho", - "Illinois", - "Indiana", - "Iowa", - "Kansas", - "Kentucky", - "Louisiana", - "Maine", - "Maryland", - "Massachusetts", - "Michigan", - "Minnesota", - "Mississippi", - "Missouri", - "Montana", - "Nebraska", - "Nevada", - "New Hampshire", - "New Jersey", - "New Mexico", - "New York", - "North Carolina", - "North Dakota", - "Ohio", - "Oklahoma", - "Oregon", - "Pennsylvania", - "Puerto Rico", - "Rhode Island", - "South Carolina", - "South Dakota", - "Tennessee", - "Texas", - "Utah", - "Vermont", - "Virgin Islands", - "Virginia", - "Washington", - "West Virginia", - "Wisconsin", - "Wyoming" - ], - "Stroke": [ - "No", - "Yes" - ], - "TetanusLast10Tdap": [ - "No, did not receive any tetanus shot in the past 10 years", - "Yes, received Tdap", - "Yes, received tetanus shot but not sure what type", - "Yes, received tetanus shot, but not Tdap" - ] - } -} \ No newline at end of file +{ + "name": "Indicators of Heart Disease (2022 UPDATE)", + "source": "https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease/data", + "data_intro": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", + "is_splited": false, + "overall_size": 1010949, + "train_size": 0, + "test_size": 0, + "c_classes": 45, + "n_classes": 9, + "cat_feature_intro": { + "HeartDisease": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", + "Smoking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", + "AlcoholDrinking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", + "Stroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", + "DiffWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", + "Sex": "Sex is a biological factor that can influence the risk of heart disease. Men and women may experience different symptoms and risk factors for heart disease.", + "AgeCategory": "Age is a significant risk factor for heart disease. As people age, their risk of developing heart disease increases. It is important to monitor heart health as individuals get older.", + "Race": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", + "Diabetic": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", + "PhysicalActivity": "Regular physical activity is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", + "Asthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", + "KidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", + "SkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", + "State": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", + "GeneralHealth": "Self-reported general health from BRFSS GENHLTH: Excellent, Very good, Good, Fair, or Poor.", + "LastCheckupTime": "The time since the last medical checkup can be an important factor in heart health. Regular checkups can help identify and address risk factors for heart disease early.", + "PhysicalActivities": "Engaging in regular physical activities is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", + "RemovedTeeth": "The number of removed teeth can be an indicator of overall health, including heart health. Poor oral health has been linked to an increased risk of heart disease.", + "HadHeartAttack": "As described above, the original dataset of nearly 300 variables was reduced to 40 variables. In addition to classical EDA, this dataset can be used to apply a number of machine learning methods, especially classifier models (logistic regression, SVM, random forest, etc.). You should treat the variable \"HadHeartAttack\" as binary (\"Yes\" - respondent had heart disease; \"No\" - respondent did not have heart disease). Note, however, that the classes are unbalanced, so the classic approach of applying a model is not advisable. Fixing the weights/undersampling should yield much better results. Based on the data set, I built a logistic regression model and embedded it in an application that might inspire you: https://share.streamlit.io/kamilpytlak/heart-condition-checker/main/app.py. Can you indicate which variables have a significant effect on the likelihood of heart disease?", + "HadAngina": "Angina is a type of chest pain caused by reduced blood flow to the heart. It is a symptom of coronary artery disease and can indicate an increased risk of heart disease.", + "HadStroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", + "HadAsthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", + "HadSkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", + "HadCOPD": "Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease that causes obstructed airflow from the lungs. It can increase the risk of heart disease.", + "HadDepressiveDisorder": "Depressive disorders can affect overall health and increase the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", + "HadKidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", + "HadArthritis": "Arthritis is a condition that causes inflammation of the joints. It can affect overall health and increase the risk of heart disease.", + "HadDiabetes": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", + "DeafOrHardOfHearing": "Hearing loss can affect overall health and quality of life. It is important to address and manage hearing issues to maintain overall health and reduce the risk of heart disease.", + "BlindOrVisionDifficulty": "Vision difficulties can affect overall health and quality of life. It is important to address and manage vision issues to maintain overall health and reduce the risk of heart disease.", + "DifficultyConcentrating": "Difficulty concentrating can be a sign of underlying health issues, including heart disease. It is important to address cognitive issues to improve overall health and reduce the risk of heart-related complications.", + "DifficultyWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", + "DifficultyDressingBathing": "Difficulty with daily activities such as dressing and bathing can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", + "DifficultyErrands": "Difficulty running errands can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", + "SmokerStatus": "Smoking is a major risk factor for heart disease. It is important to address and manage smoking habits to reduce the risk of heart-related complications.", + "ECigaretteUsage": "E-cigarette usage can affect heart health. It is important to monitor and manage e-cigarette usage to reduce the risk of heart disease.", + "ChestScan": "Chest scans can help identify underlying health issues, including heart disease. It is important to monitor and address any abnormalities found in chest scans to maintain overall health.", + "RaceEthnicityCategory": "Race and ethnicity can influence the risk of heart disease. It is important to consider these factors when assessing and managing heart health.", + "AlcoholDrinkers": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", + "HIVTesting": "Whether the respondent has ever been tested for HIV (Yes/No), not the result of that test.", + "FluVaxLast12": "Receiving a flu vaccine in the last 12 months can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", + "PneumoVaxEver": "Receiving a pneumococcal vaccine can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", + "TetanusLast10Tdap": "Receiving a tetanus vaccine in the last 10 years can help reduce the risk of infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", + "HighRiskLastYear": "Being at high risk for health issues in the last year can affect overall health and increase the risk of heart disease. It is important to address and manage high-risk factors to reduce the risk of heart-related complications.", + "CovidPos": "Testing positive for COVID-19 can affect overall health and increase the risk of heart disease. It is important to monitor and manage COVID-19 symptoms to reduce the risk of heart-related complications." + }, + "num_feature_intro": { + "BMI": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", + "PhysicalHealth": "Physical health is an important factor in overall well-being and can influence the risk of heart disease. It is important to maintain good physical health to reduce the risk of heart-related complications.", + "MentalHealth": "Mental health is an important factor in overall well-being and can influence the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", + "SleepTime": "Sleep time is an important factor in overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", + "PhysicalHealthDays": "The number of days with good physical health can be an indicator of overall well-being. It is important to maintain good physical health to reduce the risk of heart-related complications.", + "MentalHealthDays": "The number of days with good mental health can be an indicator of overall well-being. It is important to address and manage mental health to reduce the risk of heart-related complications.", + "SleepHours": "The number of hours of sleep can affect overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", + "HeightInMeters": "Height can be an indicator of overall health. It is important to monitor and maintain a healthy height-to-weight ratio to reduce the risk of heart disease.", + "WeightInKilograms": "Weight can be an indicator of overall health. It is important to maintain a healthy weight to reduce the risk of heart disease." + }, + "evaluation_metric": "The evaluation metric for this dataset can include accuracy, precision, recall, F1-score, and AUC-ROC. These metrics can help assess the performance of machine learning models in predicting heart disease.", + "task_type": "classification", + "target": "GeneralHealth", + "num_feature_value": { + "BMI": [ + 12.02, + 99.64 + ], + "HeightInMeters": [ + 0.91, + 2.41 + ], + "MentalHealth": [ + 0.0, + 30.0 + ], + "MentalHealthDays": [ + 0.0, + 30.0 + ], + "PhysicalHealth": [ + 0.0, + 30.0 + ], + "PhysicalHealthDays": [ + 0.0, + 30.0 + ], + "SleepHours": [ + 1.0, + 24.0 + ], + "SleepTime": [ + 1.0, + 24.0 + ], + "WeightInKilograms": [ + 22.68, + 292.57 + ] + }, + "cat_feature_value": { + "AgeCategory": [ + "18-24", + "25-29", + "30-34", + "35-39", + "40-44", + "45-49", + "50-54", + "55-59", + "60-64", + "65-69", + "70-74", + "75-79", + "80 or older", + "Age 18 to 24", + "Age 25 to 29", + "Age 30 to 34", + "Age 35 to 39", + "Age 40 to 44", + "Age 45 to 49", + "Age 50 to 54", + "Age 55 to 59", + "Age 60 to 64", + "Age 65 to 69", + "Age 70 to 74", + "Age 75 to 79", + "Age 80 or older" + ], + "AlcoholDrinkers": [ + "No", + "Yes" + ], + "AlcoholDrinking": [ + "No", + "Yes" + ], + "Asthma": [ + "No", + "Yes" + ], + "BlindOrVisionDifficulty": [ + "No", + "Yes" + ], + "ChestScan": [ + "No", + "Yes" + ], + "CovidPos": [ + "No", + "Tested positive using home test without a health professional", + "Yes" + ], + "DeafOrHardOfHearing": [ + "No", + "Yes" + ], + "Diabetic": [ + "No", + "No, borderline diabetes", + "Yes", + "Yes (during pregnancy)" + ], + "DiffWalking": [ + "No", + "Yes" + ], + "DifficultyConcentrating": [ + "No", + "Yes" + ], + "DifficultyDressingBathing": [ + "No", + "Yes" + ], + "DifficultyErrands": [ + "No", + "Yes" + ], + "DifficultyWalking": [ + "No", + "Yes" + ], + "ECigaretteUsage": [ + "Never used e-cigarettes in my entire life", + "Not at all (right now)", + "Use them every day", + "Use them some days" + ], + "FluVaxLast12": [ + "No", + "Yes" + ], + "GeneralHealth": [ + "Excellent", + "Very good", + "Good", + "Fair", + "Poor" + ], + "HIVTesting": [ + "No", + "Yes" + ], + "HadAngina": [ + "No", + "Yes" + ], + "HadArthritis": [ + "No", + "Yes" + ], + "HadAsthma": [ + "No", + "Yes" + ], + "HadCOPD": [ + "No", + "Yes" + ], + "HadDepressiveDisorder": [ + "No", + "Yes" + ], + "HadDiabetes": [ + "No", + "No, pre-diabetes or borderline diabetes", + "Yes", + "Yes, but only during pregnancy (female)" + ], + "HadHeartAttack": [ + "No", + "Yes" + ], + "HadKidneyDisease": [ + "No", + "Yes" + ], + "HadSkinCancer": [ + "No", + "Yes" + ], + "HadStroke": [ + "No", + "Yes" + ], + "HeartDisease": [ + "No", + "Yes" + ], + "HighRiskLastYear": [ + "No", + "Yes" + ], + "KidneyDisease": [ + "No", + "Yes" + ], + "LastCheckupTime": [ + "5 or more years ago", + "Within past 2 years (1 year but less than 2 years ago)", + "Within past 5 years (2 years but less than 5 years ago)", + "Within past year (anytime less than 12 months ago)" + ], + "PhysicalActivities": [ + "No", + "Yes" + ], + "PhysicalActivity": [ + "No", + "Yes" + ], + "PneumoVaxEver": [ + "No", + "Yes" + ], + "Race": [ + "American Indian/Alaskan Native", + "Asian", + "Black", + "Hispanic", + "Other", + "White" + ], + "RaceEthnicityCategory": [ + "Black only, Non-Hispanic", + "Hispanic", + "Multiracial, Non-Hispanic", + "Other race only, Non-Hispanic", + "White only, Non-Hispanic" + ], + "RemovedTeeth": [ + "1 to 5", + "6 or more, but not all", + "All", + "None of them" + ], + "Sex": [ + "Female", + "Male" + ], + "SkinCancer": [ + "No", + "Yes" + ], + "SmokerStatus": [ + "Current smoker - now smokes every day", + "Current smoker - now smokes some days", + "Former smoker", + "Never smoked" + ], + "Smoking": [ + "No", + "Yes" + ], + "State": [ + "Alabama", + "Alaska", + "Arizona", + "Arkansas", + "California", + "Colorado", + "Connecticut", + "Delaware", + "District of Columbia", + "Florida", + "Georgia", + "Guam", + "Hawaii", + "Idaho", + "Illinois", + "Indiana", + "Iowa", + "Kansas", + "Kentucky", + "Louisiana", + "Maine", + "Maryland", + "Massachusetts", + "Michigan", + "Minnesota", + "Mississippi", + "Missouri", + "Montana", + "Nebraska", + "Nevada", + "New Hampshire", + "New Jersey", + "New Mexico", + "New York", + "North Carolina", + "North Dakota", + "Ohio", + "Oklahoma", + "Oregon", + "Pennsylvania", + "Puerto Rico", + "Rhode Island", + "South Carolina", + "South Dakota", + "Tennessee", + "Texas", + "Utah", + "Vermont", + "Virgin Islands", + "Virginia", + "Washington", + "West Virginia", + "Wisconsin", + "Wyoming" + ], + "Stroke": [ + "No", + "Yes" + ], + "TetanusLast10Tdap": [ + "No, did not receive any tetanus shot in the past 10 years", + "Yes, received Tdap", + "Yes, received tetanus shot but not sure what type", + "Yes, received tetanus shot, but not Tdap" + ] + } +} diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_007/test_current.csv b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_007/test_current.csv index e8d3c5108cf4461eff6a5c50e8880bbe6324db7b..770474c9dc8a1259737516ca486694e739479ea8 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_007/test_current.csv +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_007/test_current.csv @@ -1,16 +1,16 @@ -HeartDisease,BMI,Smoking,AlcoholDrinking,Stroke,PhysicalHealth,MentalHealth,DiffWalking,Sex,AgeCategory,Race,Diabetic,PhysicalActivity,GenHealth,SleepTime,Asthma,KidneyDisease,SkinCancer,State,GeneralHealth,PhysicalHealthDays,MentalHealthDays,LastCheckupTime,PhysicalActivities,SleepHours,RemovedTeeth,HadHeartAttack,HadAngina,HadStroke,HadAsthma,HadSkinCancer,HadCOPD,HadDepressiveDisorder,HadKidneyDisease,HadArthritis,HadDiabetes,DeafOrHardOfHearing,BlindOrVisionDifficulty,DifficultyConcentrating,DifficultyWalking,DifficultyDressingBathing,DifficultyErrands,SmokerStatus,ECigaretteUsage,ChestScan,RaceEthnicityCategory,HeightInMeters,WeightInKilograms,AlcoholDrinkers,HIVTesting,FluVaxLast12,PneumoVaxEver,TetanusLast10Tdap,HighRiskLastYear,CovidPos -,,,,,,,,Female,Age 35 to 39,,,,,,,,,Texas,Very good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,Hispanic,,,,,,,,, -,25.1,,,,,,,Male,Age 80 or older,,,,,,,,,Texas,Very good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,1 to 5,No,No,No,No,Yes,No,No,Yes,Yes,No,Yes,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.8,81.65,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,22.81,,,,,,,Male,Age 65 to 69,,,,,,,,,Texas,Very good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,Yes,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,68.04,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,21.11,,,,,,,Male,Age 40 to 44,,,,,,,,,Texas,Very good,0.0,5.0,5 or more years ago,Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,72.57,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,32.28,,,,,,,Female,Age 45 to 49,,,,,,,,,Texas,Very good,0.0,5.0,Within past year (anytime less than 12 months ago),No,5.0,None of them,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,90.72,Yes,No,No,No,"Yes, received tetanus shot, but not Tdap",No,No -,27.44,,,,,,,Female,Age 80 or older,,,,,,,,,Texas,Very good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,All,No,No,No,Yes,No,Yes,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.57,68.04,No,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,33.96,,,,,,,Female,Age 50 to 54,,,,,,,,,Texas,Very good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.75,104.33,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,17.36,,,,,,,Female,Age 65 to 69,,,,,,,,,Texas,Very good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,44.45,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,27.44,,,,,,,Female,Age 30 to 34,,,,,,,,,Texas,Very good,2.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,77.11,Yes,Yes,No,No,"Yes, received tetanus shot, but not Tdap",No,Yes -,21.58,,,,,,,Female,Age 80 or older,,,,,,,,,Texas,Very good,0.0,2.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,No,Yes,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,53.52,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,24.87,,,,,,,Female,Age 65 to 69,,,,,,,,,Texas,Very good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,Yes,No,No,No,Yes,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.57,61.69,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,33.66,,,,,,,Female,Age 40 to 44,,,,,,,,,Texas,Very good,1.0,2.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.6,86.18,Yes,,Yes,No,"Yes, received Tdap",No,Yes -,24.69,,,,,,,Female,Age 45 to 49,,,,,,,,,Texas,Very good,2.0,15.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,61.23,Yes,Yes,Yes,No,"Yes, received Tdap",Yes,Yes -,33.0,,,,,,,Male,Age 35 to 39,,,,,,,,,Texas,Very good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,104.33,Yes,Yes,No,No,"Yes, received Tdap",No,No -,28.71,,,,,,,Male,Age 55 to 59,,,,,,,,,Texas,Very good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,Hispanic,1.69,82.0,Yes,Yes,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +HeartDisease,BMI,Smoking,AlcoholDrinking,Stroke,PhysicalHealth,MentalHealth,DiffWalking,Sex,AgeCategory,Race,Diabetic,PhysicalActivity,SleepTime,Asthma,KidneyDisease,SkinCancer,State,GeneralHealth,PhysicalHealthDays,MentalHealthDays,LastCheckupTime,PhysicalActivities,SleepHours,RemovedTeeth,HadHeartAttack,HadAngina,HadStroke,HadAsthma,HadSkinCancer,HadCOPD,HadDepressiveDisorder,HadKidneyDisease,HadArthritis,HadDiabetes,DeafOrHardOfHearing,BlindOrVisionDifficulty,DifficultyConcentrating,DifficultyWalking,DifficultyDressingBathing,DifficultyErrands,SmokerStatus,ECigaretteUsage,ChestScan,RaceEthnicityCategory,HeightInMeters,WeightInKilograms,AlcoholDrinkers,HIVTesting,FluVaxLast12,PneumoVaxEver,TetanusLast10Tdap,HighRiskLastYear,CovidPos +,,,,,,,,Female,Age 35 to 39,,,,,,,,Texas,Very good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,Hispanic,,,,,,,,, +,25.1,,,,,,,Male,Age 80 or older,,,,,,,,Texas,Very good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,1 to 5,No,No,No,No,Yes,No,No,Yes,Yes,No,Yes,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.8,81.65,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,22.81,,,,,,,Male,Age 65 to 69,,,,,,,,Texas,Very good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,Yes,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,68.04,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,21.11,,,,,,,Male,Age 40 to 44,,,,,,,,Texas,Very good,0.0,5.0,5 or more years ago,Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,72.57,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,32.28,,,,,,,Female,Age 45 to 49,,,,,,,,Texas,Very good,0.0,5.0,Within past year (anytime less than 12 months ago),No,5.0,None of them,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,90.72,Yes,No,No,No,"Yes, received tetanus shot, but not Tdap",No,No +,27.44,,,,,,,Female,Age 80 or older,,,,,,,,Texas,Very good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,All,No,No,No,Yes,No,Yes,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.57,68.04,No,Yes,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,33.96,,,,,,,Female,Age 50 to 54,,,,,,,,Texas,Very good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.75,104.33,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,17.36,,,,,,,Female,Age 65 to 69,,,,,,,,Texas,Very good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,44.45,No,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,27.44,,,,,,,Female,Age 30 to 34,,,,,,,,Texas,Very good,2.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,77.11,Yes,Yes,No,No,"Yes, received tetanus shot, but not Tdap",No,Yes +,21.58,,,,,,,Female,Age 80 or older,,,,,,,,Texas,Very good,0.0,2.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,No,Yes,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,53.52,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,24.87,,,,,,,Female,Age 65 to 69,,,,,,,,Texas,Very good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,Yes,No,No,No,Yes,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.57,61.69,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,33.66,,,,,,,Female,Age 40 to 44,,,,,,,,Texas,Very good,1.0,2.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.6,86.18,Yes,,Yes,No,"Yes, received Tdap",No,Yes +,24.69,,,,,,,Female,Age 45 to 49,,,,,,,,Texas,Very good,2.0,15.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,61.23,Yes,Yes,Yes,No,"Yes, received Tdap",Yes,Yes +,33.0,,,,,,,Male,Age 35 to 39,,,,,,,,Texas,Very good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,104.33,Yes,Yes,No,No,"Yes, received Tdap",No,No +,28.71,,,,,,,Male,Age 55 to 59,,,,,,,,Texas,Very good,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,Hispanic,1.69,82.0,Yes,Yes,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_008/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_008_current.json b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_008/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_008_current.json index 7ca08e3b347d9b1450b95daeee72e8da1b4c2aca..03ac479954debff5f3102052e8a4389e6c23510f 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_008/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_008_current.json +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_008/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_008_current.json @@ -1,12049 +1,11852 @@ { - "id": "008", - "task_type": "B4", - "subtask_type": "filter", - "perspective": "data_holder", - "dataset_name": "Indicators of Heart Disease (2022 UPDATE)", - "table_path": "output/medical/classification/Indicators of Heart Disease (2022 UPDATE)_B4/Indicators of Heart Disease (2022 UPDATE)_B4_008/test.csv", - "query": "From the dataset of past survey responses I provided, I need to flag individuals with BMI over 30 who nevertheless report excellent general health in the NEW batch of data. These cases might reveal interesting insights about body positivity or alternative health metrics.", - "meta_info": { - "domain": "medical" + "id": "008", + "task_type": "B4", + "subtask_type": "filter", + "perspective": "data_holder", + "dataset_name": "Indicators of Heart Disease (2022 UPDATE)", + "table_path": "output/medical/classification/Indicators of Heart Disease (2022 UPDATE)_B4/Indicators of Heart Disease (2022 UPDATE)_B4_008/test.csv", + "query": "From the dataset of past survey responses I provided, I need to flag individuals with BMI over 30 who nevertheless report excellent general health in the NEW batch of data. These cases might reveal interesting insights about body positivity or alternative health metrics.", + "meta_info": { + "domain": "medical" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "HeartDisease": null, + "BMI": 25.82, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 80 or older", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Utah", + "GeneralHealth": "Good", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "No", + "SleepHours": 8.0, + "RemovedTeeth": "1 to 5", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "Yes", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "Yes", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "Yes", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "Yes", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "Yes", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.68, + "WeightInKilograms": 72.57, + "AlcoholDrinkers": "Yes", + "HIVTesting": "No", + "FluVaxLast12": "No", + "PneumoVaxEver": "No", + "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "002", + "features": { + "HeartDisease": null, + "BMI": 21.79, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 45 to 49", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Virginia", + "GeneralHealth": "Good", + "PhysicalHealthDays": 7.0, + "MentalHealthDays": 3.0, + "LastCheckupTime": "Within past 2 years (1 year but less than 2 years ago)", + "PhysicalActivities": "Yes", + "SleepHours": 4.0, + "RemovedTeeth": "All", + "HadHeartAttack": "Yes", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "Yes", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "Yes", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Current smoker - now smokes some days", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "Yes", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.68, + "WeightInKilograms": 61.23, + "AlcoholDrinkers": "No", + "HIVTesting": "Yes", + "FluVaxLast12": "No", + "PneumoVaxEver": "No", + "TetanusLast10Tdap": "Yes, received tetanus shot but not sure what type", + "HighRiskLastYear": "No", + "CovidPos": "Yes" + } + }, + { + "scenario_id": "004", + "features": { + "HeartDisease": "No", + "BMI": 23.4, + "Smoking": "No", + "AlcoholDrinking": "Yes", + "Stroke": "No", + "PhysicalHealth": 15.0, + "MentalHealth": 20.0, + "DiffWalking": "No", + "Sex": "Female", + "AgeCategory": "18-24", + "Race": "White", + "Diabetic": "No", + "PhysicalActivity": "Yes", + "SleepTime": 7.0, + "Asthma": "No", + "KidneyDisease": "No", + "SkinCancer": "No", + "State": null, + "GeneralHealth": "Very good", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + "LastCheckupTime": null, + "PhysicalActivities": null, + "SleepHours": null, + "RemovedTeeth": null, + "HadHeartAttack": null, + "HadAngina": null, + "HadStroke": null, + "HadAsthma": null, + "HadSkinCancer": null, + "HadCOPD": null, + "HadDepressiveDisorder": null, + "HadKidneyDisease": null, + "HadArthritis": null, + "HadDiabetes": null, + "DeafOrHardOfHearing": null, + "BlindOrVisionDifficulty": null, + "DifficultyConcentrating": null, + "DifficultyWalking": null, + "DifficultyDressingBathing": null, + "DifficultyErrands": null, + "SmokerStatus": null, + "ECigaretteUsage": null, + "ChestScan": null, + "RaceEthnicityCategory": null, + "HeightInMeters": null, + "WeightInKilograms": null, + "AlcoholDrinkers": null, + "HIVTesting": null, + "FluVaxLast12": null, + "PneumoVaxEver": null, + "TetanusLast10Tdap": null, + "HighRiskLastYear": null, + "CovidPos": null + } + }, + { + "scenario_id": "005", + "features": { + "HeartDisease": "No", + "BMI": 29.76, + "Smoking": "No", + "AlcoholDrinking": "No", + "Stroke": "No", + "PhysicalHealth": 2.0, + "MentalHealth": 8.0, + "DiffWalking": "No", + "Sex": "Female", + "AgeCategory": "35-39", + "Race": "White", + "Diabetic": "No", + "PhysicalActivity": "Yes", + "SleepTime": 6.0, + "Asthma": "No", + "KidneyDisease": "No", + "SkinCancer": "No", + "State": null, + "GeneralHealth": "Excellent", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + "LastCheckupTime": null, + "PhysicalActivities": null, + "SleepHours": null, + "RemovedTeeth": null, + "HadHeartAttack": null, + "HadAngina": null, + "HadStroke": null, + "HadAsthma": null, + "HadSkinCancer": null, + "HadCOPD": null, + "HadDepressiveDisorder": null, + "HadKidneyDisease": null, + "HadArthritis": null, + "HadDiabetes": null, + "DeafOrHardOfHearing": null, + "BlindOrVisionDifficulty": null, + "DifficultyConcentrating": null, + "DifficultyWalking": null, + "DifficultyDressingBathing": null, + "DifficultyErrands": null, + "SmokerStatus": null, + "ECigaretteUsage": null, + "ChestScan": null, + "RaceEthnicityCategory": null, + "HeightInMeters": null, + "WeightInKilograms": null, + "AlcoholDrinkers": null, + "HIVTesting": null, + "FluVaxLast12": null, + "PneumoVaxEver": null, + "TetanusLast10Tdap": null, + "HighRiskLastYear": null, + "CovidPos": null + } + }, + { + "scenario_id": "007", + "features": { + "HeartDisease": null, + "BMI": 33.45, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 80 or older", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Georgia", + "GeneralHealth": "Fair", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 3.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 8.0, + "RemovedTeeth": "6 or more, but not all", + "HadHeartAttack": "Yes", + "HadAngina": "Yes", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "Yes", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "Yes", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Former smoker", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "Yes", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.73, + "WeightInKilograms": 99.79, + "AlcoholDrinkers": "Yes", + "HIVTesting": null, + "FluVaxLast12": null, + "PneumoVaxEver": null, + "TetanusLast10Tdap": null, + "HighRiskLastYear": null, + "CovidPos": null + } + }, + { + "scenario_id": "009", + "features": { + "HeartDisease": null, + "BMI": 19.53, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 60 to 64", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Florida", + "GeneralHealth": "Very good", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 6.0, + "RemovedTeeth": "6 or more, but not all", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "Yes", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Former smoker", + "ECigaretteUsage": "Not at all (right now)", + "ChestScan": "Yes", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.8, + "WeightInKilograms": 63.5, + "AlcoholDrinkers": "Yes", + "HIVTesting": "No", + "FluVaxLast12": "Yes", + "PneumoVaxEver": "Yes", + "TetanusLast10Tdap": "Yes, received Tdap", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "010", + "features": { + "HeartDisease": "No", + "BMI": 30.99, + "Smoking": "No", + "AlcoholDrinking": "No", + "Stroke": "No", + "PhysicalHealth": 0.0, + "MentalHealth": 0.0, + "DiffWalking": "No", + "Sex": "Female", + "AgeCategory": "45-49", + "Race": "White", + "Diabetic": "No", + "PhysicalActivity": "No", + "SleepTime": 10.0, + "Asthma": "No", + "KidneyDisease": "No", + "SkinCancer": "No", + "State": null, + "GeneralHealth": "Excellent", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + "LastCheckupTime": null, + "PhysicalActivities": null, + "SleepHours": null, + "RemovedTeeth": null, + "HadHeartAttack": null, + "HadAngina": null, + "HadStroke": null, + "HadAsthma": null, + "HadSkinCancer": null, + "HadCOPD": null, + "HadDepressiveDisorder": null, + "HadKidneyDisease": null, + "HadArthritis": null, + "HadDiabetes": null, + "DeafOrHardOfHearing": null, + "BlindOrVisionDifficulty": null, + "DifficultyConcentrating": null, + "DifficultyWalking": null, + "DifficultyDressingBathing": null, + "DifficultyErrands": null, + "SmokerStatus": null, + "ECigaretteUsage": null, + "ChestScan": null, + "RaceEthnicityCategory": null, + "HeightInMeters": null, + "WeightInKilograms": null, + "AlcoholDrinkers": null, + "HIVTesting": null, + "FluVaxLast12": null, + "PneumoVaxEver": null, + "TetanusLast10Tdap": null, + "HighRiskLastYear": null, + "CovidPos": null + } + }, + { + "scenario_id": "013", + "features": { + "HeartDisease": "No", + "BMI": 25.32, + "Smoking": "No", + "AlcoholDrinking": "No", + "Stroke": "No", + "PhysicalHealth": 0.0, + "MentalHealth": 0.0, + "DiffWalking": "No", + "Sex": "Female", + "AgeCategory": "55-59", + "Race": "White", + "Diabetic": "No", + "PhysicalActivity": "Yes", + "SleepTime": 7.0, + "Asthma": "No", + "KidneyDisease": "No", + "SkinCancer": "No", + "State": null, + "GeneralHealth": "Very good", + "PhysicalHealthDays": null, + "MentalHealthDays": null, + "LastCheckupTime": null, + "PhysicalActivities": null, + "SleepHours": null, + "RemovedTeeth": null, + "HadHeartAttack": null, + "HadAngina": null, + "HadStroke": null, + "HadAsthma": null, + "HadSkinCancer": null, + "HadCOPD": null, + "HadDepressiveDisorder": null, + "HadKidneyDisease": null, + "HadArthritis": null, + "HadDiabetes": null, + "DeafOrHardOfHearing": null, + "BlindOrVisionDifficulty": null, + "DifficultyConcentrating": null, + "DifficultyWalking": null, + "DifficultyDressingBathing": null, + "DifficultyErrands": null, + "SmokerStatus": null, + "ECigaretteUsage": null, + "ChestScan": null, + "RaceEthnicityCategory": null, + "HeightInMeters": null, + "WeightInKilograms": null, + "AlcoholDrinkers": null, + "HIVTesting": null, + "FluVaxLast12": null, + "PneumoVaxEver": null, + "TetanusLast10Tdap": null, + "HighRiskLastYear": null, + "CovidPos": null + } + }, + { + "scenario_id": "014", + "features": { + "HeartDisease": null, + "BMI": 27.46, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 45 to 49", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Illinois", + "GeneralHealth": "Very good", + "PhysicalHealthDays": 3.0, + "MentalHealthDays": 6.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 7.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "Yes", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Current smoker - now smokes some days", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "No", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.63, + "WeightInKilograms": 72.57, + "AlcoholDrinkers": "Yes", + "HIVTesting": "Yes", + "FluVaxLast12": "Yes", + "PneumoVaxEver": "No", + "TetanusLast10Tdap": "Yes, received tetanus shot but not sure what type", + "HighRiskLastYear": "No", + "CovidPos": "Yes" + } + }, + { + "scenario_id": "016", + "features": { + "HeartDisease": null, + "BMI": 27.46, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 25 to 29", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Washington", + "GeneralHealth": "Very good", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 6.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "No", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.63, + "WeightInKilograms": 72.57, + "AlcoholDrinkers": "Yes", + "HIVTesting": "No", + "FluVaxLast12": "Yes", + "PneumoVaxEver": "No", + "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "017", + "features": { + "HeartDisease": null, + "BMI": 48.81, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 55 to 59", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Arkansas", + "GeneralHealth": "Good", + "PhysicalHealthDays": 15.0, + "MentalHealthDays": 20.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "No", + "SleepHours": 7.0, + "RemovedTeeth": "6 or more, but not all", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "Yes", + "HadDiabetes": "Yes", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "Yes", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Never smoked", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "No", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.8, + "WeightInKilograms": 158.76, + "AlcoholDrinkers": "No", + "HIVTesting": "Yes", + "FluVaxLast12": "Yes", + "PneumoVaxEver": "Yes", + "TetanusLast10Tdap": "Yes, received Tdap", + "HighRiskLastYear": "No", + "CovidPos": "Yes" + } + }, + { + "scenario_id": "018", + "features": { + "HeartDisease": null, + "BMI": 29.53, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 65 to 69", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Nebraska", + "GeneralHealth": "Good", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "No", + "SleepHours": 7.0, + "RemovedTeeth": "1 to 5", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "Yes", + "HadDiabetes": "Yes", + "DeafOrHardOfHearing": "No", + "BlindOrVisionDifficulty": "No", + "DifficultyConcentrating": "No", + "DifficultyWalking": "No", + "DifficultyDressingBathing": "No", + "DifficultyErrands": "No", + "SmokerStatus": "Current smoker - now smokes every day", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "Yes", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.75, + "WeightInKilograms": 90.72, + "AlcoholDrinkers": "Yes", + "HIVTesting": "No", + "FluVaxLast12": "No", + "PneumoVaxEver": "Yes", + "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", + "HighRiskLastYear": "No", + "CovidPos": "No" + } + }, + { + "scenario_id": "019", + "features": { + "HeartDisease": null, + "BMI": 25.4, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 25 to 29", + "Race": null, + "Diabetic": null, + 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"TetanusLast10Tdap": null, + "HighRiskLastYear": null, + "CovidPos": null + } + }, + { + "scenario_id": "137", + "features": { + "HeartDisease": null, + "BMI": 37.79, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Female", + "AgeCategory": "Age 55 to 59", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Maine", + "GeneralHealth": "Fair", + "PhysicalHealthDays": 20.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 10.0, + "RemovedTeeth": "6 or more, but not all", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "Yes", 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"No" + } + }, + { + "scenario_id": "238", + "features": { + "HeartDisease": null, + "BMI": 22.81, + "Smoking": null, + "AlcoholDrinking": null, + "Stroke": null, + "PhysicalHealth": null, + "MentalHealth": null, + "DiffWalking": null, + "Sex": "Male", + "AgeCategory": "Age 25 to 29", + "Race": null, + "Diabetic": null, + "PhysicalActivity": null, + "SleepTime": null, + "Asthma": null, + "KidneyDisease": null, + "SkinCancer": null, + "State": "Oklahoma", + "GeneralHealth": "Good", + "PhysicalHealthDays": 0.0, + "MentalHealthDays": 0.0, + "LastCheckupTime": "Within past year (anytime less than 12 months ago)", + "PhysicalActivities": "Yes", + "SleepHours": 6.0, + "RemovedTeeth": "None of them", + "HadHeartAttack": "No", + "HadAngina": "No", + "HadStroke": "No", + "HadAsthma": "No", + "HadSkinCancer": "No", + "HadCOPD": "No", + "HadDepressiveDisorder": "No", + "HadKidneyDisease": "No", + "HadArthritis": "No", + "HadDiabetes": "No", + "DeafOrHardOfHearing": "No", + 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"SmokerStatus": "Never smoked", + "ECigaretteUsage": "Never used e-cigarettes in my entire life", + "ChestScan": "No", + "RaceEthnicityCategory": "White only, Non-Hispanic", + "HeightInMeters": 1.55, + "WeightInKilograms": 73.03, + "AlcoholDrinkers": "Yes", + "HIVTesting": "No", + "FluVaxLast12": "No", + "PneumoVaxEver": "Yes", + "TetanusLast10Tdap": "Yes, received Tdap", + "HighRiskLastYear": "No", + "CovidPos": "Yes" + } + } + ], + "target_column": "GeneralHealth", + "task_sub_type": "classification", + "ranking_ground_truth": { + "top_k_ids": [ + "010", + "050", + "053", + "081", + "096", + "118", + "152", + "160", + "163", + "220", + "270", + "300", + "306", + "310", + "399", + "416", + "480", + "499" + ] }, - "final_decision": {}, - "what_if": {}, - "ground_truth": { - "extracted_features": [ - { - "scenario_id": "001", - "features": { - "HeartDisease": null, - "BMI": 25.82, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": 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- "BMI": 19.26, - "Smoking": "Yes", - "AlcoholDrinking": "No", - "Stroke": "No", - "PhysicalHealth": 0.0, - "MentalHealth": 0.0, - "DiffWalking": "No", - "Sex": "Male", - "AgeCategory": "50-54", - "Race": "White", - "Diabetic": "No", - "PhysicalActivity": "Yes", - "GenHealth": "Good", - "SleepTime": 6.0, - "Asthma": "No", - "KidneyDisease": "No", - "SkinCancer": "No", - "State": null, - "GeneralHealth": null, - "PhysicalHealthDays": null, - "MentalHealthDays": null, - "LastCheckupTime": null, - "PhysicalActivities": null, - "SleepHours": null, - "RemovedTeeth": null, - "HadHeartAttack": null, - "HadAngina": null, - "HadStroke": null, - "HadAsthma": null, - "HadSkinCancer": null, - "HadCOPD": null, - "HadDepressiveDisorder": null, - "HadKidneyDisease": null, - "HadArthritis": null, - "HadDiabetes": null, - "DeafOrHardOfHearing": null, - "BlindOrVisionDifficulty": null, - "DifficultyConcentrating": null, - "DifficultyWalking": null, - "DifficultyDressingBathing": null, - 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"scenario_id": "164", - "features": { - "HeartDisease": null, - "BMI": 21.02, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Male", - "AgeCategory": "Age 55 to 59", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "Iowa", - "GeneralHealth": "Good", - "PhysicalHealthDays": 7.0, - "MentalHealthDays": 0.0, - "LastCheckupTime": "Within past 2 years (1 year but less than 2 years ago)", - "PhysicalActivities": "Yes", - "SleepHours": 7.0, - "RemovedTeeth": "6 or more, but not all", - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "No", - "HadSkinCancer": "No", - "HadCOPD": "No", - "HadDepressiveDisorder": "No", - "HadKidneyDisease": "No", - "HadArthritis": "No", - "HadDiabetes": "No", - "DeafOrHardOfHearing": "No", - 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"HighRiskLastYear": "No", - "CovidPos": "Yes" - } - }, - { - "scenario_id": "341", - "features": { - "HeartDisease": "No", - "BMI": 37.68, - "Smoking": "No", - "AlcoholDrinking": "No", - "Stroke": "No", - "PhysicalHealth": 5.0, - "MentalHealth": 0.0, - "DiffWalking": "No", - "Sex": "Female", - "AgeCategory": "50-54", - "Race": "Black", - "Diabetic": "No", - "PhysicalActivity": "No", - "GenHealth": "Good", - "SleepTime": 6.0, - "Asthma": "No", - "KidneyDisease": "No", - "SkinCancer": "No", - "State": null, - "GeneralHealth": null, - "PhysicalHealthDays": null, - "MentalHealthDays": null, - "LastCheckupTime": null, - "PhysicalActivities": null, - "SleepHours": null, - "RemovedTeeth": null, - "HadHeartAttack": null, - "HadAngina": null, - "HadStroke": null, - "HadAsthma": null, - "HadSkinCancer": null, - "HadCOPD": null, - "HadDepressiveDisorder": null, - "HadKidneyDisease": null, - "HadArthritis": null, - "HadDiabetes": null, - "DeafOrHardOfHearing": null, - "BlindOrVisionDifficulty": 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"No", - "CovidPos": "Yes" - } - }, - { - "scenario_id": "368", - "features": { - "HeartDisease": null, - "BMI": 43.24, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Male", - "AgeCategory": "Age 60 to 64", - "Race": null, - "Diabetic": null, - "PhysicalActivity": null, - "GenHealth": null, - "SleepTime": null, - "Asthma": null, - "KidneyDisease": null, - "SkinCancer": null, - "State": "New Mexico", - "GeneralHealth": "Good", - "PhysicalHealthDays": 5.0, - "MentalHealthDays": 0.0, - "LastCheckupTime": "Within past year (anytime less than 12 months ago)", - "PhysicalActivities": "No", - "SleepHours": 8.0, - "RemovedTeeth": "1 to 5", - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "No", - "HadSkinCancer": "No", - "HadCOPD": "No", - "HadDepressiveDisorder": "No", - "HadKidneyDisease": "No", - "HadArthritis": "No", - "HadDiabetes": "Yes", - "DeafOrHardOfHearing": "No", - "BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "No", - "DifficultyWalking": "Yes", - "DifficultyDressingBathing": "No", - "DifficultyErrands": "No", - "SmokerStatus": "Former smoker", - "ECigaretteUsage": "Never used e-cigarettes in my entire life", - "ChestScan": "Yes", - "RaceEthnicityCategory": "Other race only, Non-Hispanic", - "HeightInMeters": 1.8, - "WeightInKilograms": 140.61, - "AlcoholDrinkers": "No", - "HIVTesting": null, - "FluVaxLast12": "No", - "PneumoVaxEver": "Yes", - "TetanusLast10Tdap": "No, did not receive any tetanus shot in the past 10 years", - "HighRiskLastYear": "No", - "CovidPos": "Yes" - } - }, - { - "scenario_id": "369", - "features": { - "HeartDisease": null, - "BMI": 18.36, - "Smoking": null, - "AlcoholDrinking": null, - "Stroke": null, - "PhysicalHealth": null, - "MentalHealth": null, - "DiffWalking": null, - "Sex": "Female", - "AgeCategory": "Age 30 to 34", - "Race": null, - "Diabetic": null, - 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"RemovedTeeth": "None of them", - "HadHeartAttack": "No", - "HadAngina": "No", - "HadStroke": "No", - "HadAsthma": "No", - "HadSkinCancer": "No", - "HadCOPD": "No", - "HadDepressiveDisorder": "No", - "HadKidneyDisease": "No", - "HadArthritis": "No", - "HadDiabetes": "No", - "DeafOrHardOfHearing": "No", - "BlindOrVisionDifficulty": "No", - "DifficultyConcentrating": "No", - "DifficultyWalking": "No", - "DifficultyDressingBathing": "No", - "DifficultyErrands": "No", - "SmokerStatus": "Never smoked", - "ECigaretteUsage": "Never used e-cigarettes in my entire life", - "ChestScan": "No", - "RaceEthnicityCategory": "White only, Non-Hispanic", - "HeightInMeters": 1.68, - "WeightInKilograms": 61.23, - "AlcoholDrinkers": "Yes", - "HIVTesting": "No", - "FluVaxLast12": "No", - "PneumoVaxEver": "No", - "TetanusLast10Tdap": "Yes, received Tdap", - "HighRiskLastYear": "No", - "CovidPos": "No" - } - }, - { - "scenario_id": "373", - "features": { - "HeartDisease": null, - "BMI": 34.96, - "Smoking": 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"ECigaretteUsage": "Never used e-cigarettes in my entire life", - "ChestScan": "No", - "RaceEthnicityCategory": "White only, Non-Hispanic", - "HeightInMeters": 1.55, - "WeightInKilograms": 73.03, - "AlcoholDrinkers": "Yes", - "HIVTesting": "No", - "FluVaxLast12": "No", - "PneumoVaxEver": "Yes", - "TetanusLast10Tdap": "Yes, received Tdap", - "HighRiskLastYear": "No", - "CovidPos": "Yes" - } - } - ], - "target_column": "GeneralHealth", - "task_sub_type": "classification", - "ranking_ground_truth": { - "top_k_ids": [ - "050", - "053", - "081", - "096", - "118", - "160", - "163", - "220", - "270", - "300", - "306", - "310", - "399", - "480", - "499" - ] - }, - "active_filters": [ - { - "col": "BMI", - "op": ">", - "val": 30 - } - ], - "target_class_value": "excellent" - }, - "response": "", - "evaluation_score": {} -} \ No newline at end of file + "active_filters": [ + { + "col": "BMI", + "op": ">", + "val": 30 + } + ], + "target_class_value": "excellent" + }, + "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_008/current.csv b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_008/current.csv index 11a6c44ff9d29aee17e140e934ef934b1308000c..45795793875d55bbaed84caee49c83ed5b483f97 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_008/current.csv +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_008/current.csv @@ -1,201 +1,201 @@ -HeartDisease,BMI,Smoking,AlcoholDrinking,Stroke,PhysicalHealth,MentalHealth,DiffWalking,Sex,AgeCategory,Race,Diabetic,PhysicalActivity,GenHealth,SleepTime,Asthma,KidneyDisease,SkinCancer,State,PhysicalHealthDays,MentalHealthDays,LastCheckupTime,PhysicalActivities,SleepHours,RemovedTeeth,HadHeartAttack,HadAngina,HadStroke,HadAsthma,HadSkinCancer,HadCOPD,HadDepressiveDisorder,HadKidneyDisease,HadArthritis,HadDiabetes,DeafOrHardOfHearing,BlindOrVisionDifficulty,DifficultyConcentrating,DifficultyWalking,DifficultyDressingBathing,DifficultyErrands,SmokerStatus,ECigaretteUsage,ChestScan,RaceEthnicityCategory,HeightInMeters,WeightInKilograms,AlcoholDrinkers,HIVTesting,FluVaxLast12,PneumoVaxEver,TetanusLast10Tdap,HighRiskLastYear,CovidPos -,25.82,,,,,,,Male,Age 80 or older,,,,,,,,,Utah,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,No,Yes,No,No,No,Yes,No,Yes,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,72.57,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,21.79,,,,,,,Female,Age 45 to 49,,,,,,,,,Virginia,7.0,3.0,Within past 2 years (1 year but less than 2 years ago),Yes,4.0,All,Yes,No,No,No,No,No,No,No,Yes,No,No,Yes,No,No,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,61.23,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -No,23.4,No,Yes,No,15.0,20.0,No,Female,18-24,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,29.76,No,No,No,2.0,8.0,No,Female,35-39,White,No,Yes,Excellent,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,33.45,,,,,,,Male,Age 80 or older,,,,,,,,,Georgia,0.0,3.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",Yes,Yes,No,No,No,No,Yes,No,No,No,No,No,Yes,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,99.79,Yes,,,,,, -,19.53,,,,,,,Female,Age 60 to 64,,,,,,,,,Florida,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.8,63.5,Yes,No,Yes,Yes,"Yes, received Tdap",No,No -No,30.99,No,No,No,0.0,0.0,No,Female,45-49,White,No,No,Excellent,10.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,25.32,No,No,No,0.0,0.0,No,Female,55-59,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,27.46,,,,,,,Female,Age 45 to 49,,,,,,,,,Illinois,3.0,6.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,72.57,Yes,Yes,Yes,No,"Yes, received tetanus shot but not sure what type",No,Yes -,27.46,,,,,,,Female,Age 25 to 29,,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,72.57,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,48.81,,,,,,,Male,Age 55 to 59,,,,,,,,,Arkansas,15.0,20.0,Within past year (anytime less than 12 months ago),No,7.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.8,158.76,No,Yes,Yes,Yes,"Yes, received Tdap",No,Yes -,29.53,,,,,,,Male,Age 65 to 69,,,,,,,,,Nebraska,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,1 to 5,No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,90.72,Yes,No,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,25.4,,,,,,,Male,Age 25 to 29,,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.75,78.02,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Tested positive using home test without a health professional -No,29.21,No,No,No,0.0,3.0,No,Male,70-74,White,No,Yes,Very good,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,26.63,,,,,,,Female,Age 30 to 34,,,,,,,,,South Carolina,0.0,2.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,77.11,Yes,Yes,Yes,,"No, did not receive any tetanus shot in the past 10 years",No,Tested positive using home test without a health professional -,24.03,,,,,,,Female,Age 18 to 24,,,,,,,,,Puerto Rico,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.5,53.98,No,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -,,,,,,,,Female,Age 40 to 44,,,,,,,,,Hawaii,8.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",,,No,Yes,No,No,,No,No -,,,,,,,,Female,Age 60 to 64,,,,,,,,,Utah,14.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,,No,No,No,No,"Yes, received Tdap",No,No -,27.8,,,,,,,Male,Age 30 to 34,,,,,,,,,New York,0.0,5.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.83,92.99,Yes,No,No,,"Yes, received tetanus shot but not sure what type",No,No -No,34.06,No,No,No,0.0,0.0,No,Male,50-54,Hispanic,No,Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,38.1,Yes,No,No,1.0,0.0,No,Female,35-39,White,No,Yes,Very good,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,31.53,No,No,No,30.0,0.0,Yes,Female,80 or older,White,No,No,Fair,6.0,Yes,Yes,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,30.23,,,,,,,Female,Age 55 to 59,,,,,,,,,Delaware,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.55,72.57,Yes,No,,Yes,"Yes, received Tdap",No,No -No,31.89,No,No,No,4.0,0.0,No,Female,60-64,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,32.01,,,,,,,Female,Age 60 to 64,,,,,,,,,Tennessee,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,Yes,No,,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,,1.57,79.38,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",, -No,28.0,Yes,No,No,30.0,0.0,No,Male,65-69,White,No,Yes,Very good,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,22.25,No,No,No,2.0,2.0,No,Male,18-24,White,No,Yes,Very good,8.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,17.33,Yes,No,No,0.0,0.0,No,Male,65-69,White,No,Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,34.17,,,,,,,Male,Age 25 to 29,,,,,,,,,Texas,2.0,9.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.8,111.13,No,No,No,Yes,"Yes, received Tdap",No,Yes -,26.61,,,,,,,Male,Age 70 to 74,,,,,,,,,New York,30.0,30.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,No,No,No,Yes,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,79.38,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -No,32.61,No,No,No,0.0,5.0,No,Female,35-39,White,No,Yes,Good,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,22.6,,,,,,,Male,Age 80 or older,,,,,,,,,Wyoming,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,Yes,No,No,Yes,Yes,Yes,No,Yes,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,63.5,No,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No -,27.26,,,,,,,Male,Age 18 to 24,,,,,,,,,Missouri,0.0,2.0,5 or more years ago,Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Use them every day,No,"White only, Non-Hispanic",1.78,86.18,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,22.6,,,,,,,Female,Age 70 to 74,,,,,,,,,Kansas,2.0,2.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,63.5,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No -,37.11,,,,,,,Female,Age 40 to 44,,,,,,,,,California,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.52,86.18,No,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,Yes -No,26.29,No,No,No,0.0,0.0,No,Female,60-64,White,No,Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,,,,,,,,Male,Age 65 to 69,,,,,,,,,Ohio,2.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,,No,No,Yes,,"Yes, received tetanus shot but not sure what type",No,No -,34.3,,,,,,,Male,Age 40 to 44,,,,,,,,,Oregon,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,117.93,Yes,Yes,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -No,23.67,No,No,No,3.0,2.0,No,Male,18-24,Black,No,Yes,Excellent,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,25.06,,,,,,,Male,Age 55 to 59,,,,,,,,,Oregon,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,Yes,No,Yes,No,No,No,"No, pre-diabetes or borderline diabetes",No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,72.57,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -No,31.46,No,No,No,3.0,20.0,No,Male,65-69,Black,No,Yes,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,26.54,,,,,,,Female,Age 80 or older,,,,,,,,,Tennessee,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,All,Yes,Yes,Yes,No,No,Yes,No,No,No,No,No,No,No,No,Yes,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,83.91,,,,,,, -,27.89,,,,,,,Male,Age 25 to 29,,,,,,,,,Colorado,0.0,0.0,5 or more years ago,Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,Hispanic,1.8,90.72,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -No,29.76,No,No,No,0.0,1.0,No,Female,55-59,White,No,Yes,Very good,7.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,26.61,Yes,No,No,0.0,0.0,No,Male,80 or older,White,No,Yes,Excellent,15.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,32.28,,,,,,,Female,Age 80 or older,,,,,,,,,Missouri,30.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,"6 or more, but not all",No,Yes,No,Yes,Yes,Yes,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,90.72,Yes,No,Yes,Yes,,No,No -No,20.78,No,No,No,0.0,5.0,No,Female,30-34,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,31.64,,,,,,,Female,Age 18 to 24,,,,,,,,,Utah,0.0,10.0,Within past year (anytime less than 12 months ago),Yes,9.0,None of them,No,No,No,No,No,No,No,No,No,"Yes, but only during pregnancy (female)",No,No,No,No,No,No,Never smoked,Use them some days,Yes,Hispanic,1.52,73.48,No,No,No,No,"Yes, received Tdap",No,No -No,30.45,Yes,No,No,0.0,0.0,No,Female,65-69,White,Yes,Yes,Good,8.0,No,Yes,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,34.21,No,No,No,0.0,3.0,No,Male,35-39,White,No,Yes,Good,5.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,37.79,,,,,,,Female,Age 55 to 59,,,,,,,,,Maine,20.0,0.0,Within past year (anytime less than 12 months ago),Yes,10.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.55,90.72,No,,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Tested positive using home test without a health professional -,20.34,,,,,,,Male,Age 80 or older,,,,,,,,,Kansas,,0.0,Within past 2 years (1 year but less than 2 years ago),No,8.0,All,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,68.04,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,28.75,,,,,,,Male,Age 18 to 24,,,,,,,,,Ohio,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.83,96.16,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,15.04,,,,,,,Female,Age 30 to 34,,,,,,,,,California,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,Never smoked,Not at all (right now),Yes,"Other race only, Non-Hispanic",1.65,41.0,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -No,30.54,No,No,No,0.0,14.0,No,Male,50-54,White,No,No,Excellent,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,25.39,,,,,,,Male,Age 55 to 59,,,,,,,,,California,30.0,4.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,75.75,Yes,Yes,Yes,No,"Yes, received Tdap",No,No -,32.32,,,,,,,Male,Age 50 to 54,,,,,,,,,Illinois,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,111.13,Yes,No,Yes,No,"Yes, received Tdap",No,No -No,19.26,Yes,No,No,0.0,0.0,No,Male,50-54,White,No,Yes,Good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,36.49,,,,,,,Male,Age 40 to 44,,,,,,,,,Arkansas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.73,108.86,No,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,21.02,,,,,,,Male,Age 55 to 59,,,,,,,,,Iowa,7.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.83,70.31,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,19.47,,,,,,,Female,Age 55 to 59,,,,,,,,,Georgia,1.0,2.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Multiracial, Non-Hispanic",1.65,53.07,Yes,No,No,No,"Yes, received Tdap",No,No -,35.24,,,,,,,Male,Age 25 to 29,,,,,,,,,Colorado,0.0,7.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.7,102.06,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No -,17.72,,,,,,,Female,Age 50 to 54,,,,,,,,,Colorado,1.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.75,54.43,No,Yes,No,No,"Yes, received Tdap",No,No -,24.54,,,,,,,Female,Age 60 to 64,,,,,,,,,Colorado,0.0,20.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,1 to 5,No,No,No,No,No,No,Yes,No,Yes,No,No,,No,No,No,Yes,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.85,84.37,Yes,No,No,Yes,"Yes, received Tdap",No,No -,27.57,,,,,,,Male,Age 80 or older,,,,,,,,,Nebraska,10.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,Yes,No,No,Yes,No,No,No,No,"No, pre-diabetes or borderline diabetes",No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,79.83,No,No,No,Yes,"Yes, received tetanus shot but not sure what type",No,Yes -No,27.5,No,No,No,0.0,0.0,No,Male,55-59,White,No,Yes,Good,5.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,22.22,Yes,No,No,0.0,14.0,No,Female,45-49,American Indian/Alaskan Native,No,Yes,Good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,24.96,,,,,,,Male,Age 18 to 24,,,,,,,,,Minnesota,0.0,14.0,Within past 5 years (2 years but less than 5 years ago),No,6.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.65,68.04,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,22.24,,,,,,,Male,Age 65 to 69,,,,,,,,,Ohio,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,70.31,Yes,No,Yes,Yes,"Yes, received Tdap",No,No -No,23.91,No,No,No,0.0,1.0,No,Female,40-44,White,No,Yes,Very good,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,38.74,,,,,,,Male,Age 70 to 74,,,,,,,,,Ohio,3.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,Yes,Yes,No,No,Yes,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.68,108.86,No,Yes,Yes,Yes,"Yes, received Tdap",No,No -No,26.26,Yes,No,No,0.0,0.0,No,Female,70-74,White,No,Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,24.59,,,,,,,Male,Age 60 to 64,,,,,,,,,Illinois,,,Within past year (anytime less than 12 months ago),Yes,4.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.7,71.21,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -Yes,24.37,Yes,No,No,0.0,0.0,No,Male,75-79,White,No,Yes,Good,8.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,36.26,,,,,,,Female,Age 50 to 54,,,,,,,,,Georgia,10.0,5.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.8,117.93,No,No,Yes,No,"Yes, received Tdap",No,Yes -,28.06,,,,,,,Male,Age 75 to 79,,,,,,,,,Wisconsin,3.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,Yes,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,86.18,Yes,No,Yes,Yes,"Yes, received Tdap",No,No -,30.29,,,,,,,Female,Age 45 to 49,,,,,,,,,Indiana,0.0,1.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,82.55,Yes,No,Yes,Yes,"Yes, received Tdap",No,No -No,24.59,No,No,No,0.0,5.0,No,Male,55-59,White,No,Yes,Excellent,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,30.34,,,,,,,Male,Age 80 or older,,,,,,,,,Kansas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,Yes,No,No,No,No,No,No,Yes,,No,No,No,Yes,No,No,Former smoker,Use them some days,No,,1.85,104.33,Yes,No,No,Yes,"Yes, received tetanus shot but not sure what type",No,No -,32.55,,,,,,,Male,Age 60 to 64,,,,,,,,,Nevada,2.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,,,,,,,"White only, Non-Hispanic",1.83,108.86,,,,,,, -No,33.2,No,No,No,21.0,0.0,No,Male,65-69,American Indian/Alaskan Native,Yes,Yes,Fair,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,30.9,,,,,,,Female,Age 30 to 34,,,,,,,,,Indiana,0.0,2.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,81.65,Yes,Yes,No,No,"Yes, received Tdap",No,No -,21.95,,,,,,,Female,Age 70 to 74,,,,,,,,,Florida,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,54.43,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,No -,31.66,,,,,,,Male,Age 70 to 74,,,,,,,,,Ohio,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,1 to 5,No,No,No,No,Yes,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.85,108.86,No,No,No,No,,No,No -,26.58,,,,,,,Male,Age 50 to 54,,,,,,,,,Virginia,3.0,20.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,81.65,Yes,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,22.81,,,,,,,Male,Age 25 to 29,,,,,,,,,Oklahoma,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.73,68.04,Yes,No,,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,36.81,,,,,,,Male,Age 80 or older,,,,,,,,,Florida,30.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,All,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Yes,Yes,Yes,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,106.59,Yes,No,No,No,"Yes, received Tdap",No,No -,26.57,,,,,,,Female,Age 65 to 69,,,,,,,,,South Dakota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,68.04,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Tested positive using home test without a health professional -,23.05,,,,,,,Female,Age 75 to 79,,,,,,,,,Nebraska,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,None of them,No,No,No,No,Yes,No,No,No,Yes,"No, pre-diabetes or borderline diabetes",No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.55,55.34,,,,,,, -No,29.6,No,No,No,0.0,0.0,No,Female,45-49,Hispanic,No,Yes,Fair,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,20.6,No,Yes,No,0.0,30.0,No,Female,40-44,White,No,Yes,Excellent,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,35.88,,,,,,,Male,Age 50 to 54,,,,,,,,,Florida,7.0,10.0,Within past year (anytime less than 12 months ago),No,8.0,"6 or more, but not all",Yes,Yes,No,No,No,No,No,No,No,Yes,No,No,Yes,Yes,No,No,Former smoker,Use them some days,Yes,"White only, Non-Hispanic",1.73,107.05,No,Yes,Yes,No,"Yes, received tetanus shot, but not Tdap",No,No -,21.63,,,,,,,Male,Age 75 to 79,,,,,,,,,Wisconsin,1.0,1.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,Hispanic,1.65,58.97,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -No,24.33,Yes,No,No,0.0,0.0,No,Female,60-64,White,No,Yes,Good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,30.68,Yes,No,No,0.0,0.0,No,Male,65-69,White,No,Yes,Very good,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,29.53,,,,,,,Male,Age 50 to 54,,,,,,,,,Ohio,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.88,104.33,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,,,,,,,,Female,Age 75 to 79,,,,,,,,,Indiana,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,"Other race only, Non-Hispanic",,,,,,,,, -,32.78,,,,,,,Male,Age 35 to 39,,,,,,,,,South Dakota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,106.59,Yes,Yes,No,Yes,"Yes, received tetanus shot but not sure what type",No,Yes -,21.13,,,,,,,Female,Age 70 to 74,,,,,,,,,District of Columbia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.65,57.61,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,Yes -No,15.96,No,No,No,30.0,15.0,Yes,Female,70-74,White,No,No,Poor,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,21.95,,,,,,,Female,,,,,,,,,,New Mexico,0.0,7.0,5 or more years ago,Yes,8.0,1 to 5,No,No,No,Yes,No,No,Yes,No,No,No,No,Yes,No,No,No,No,,,,,1.57,54.43,,,,,,, -No,21.7,No,No,No,0.0,5.0,No,Male,18-24,White,No,Yes,Excellent,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,30.79,,,,,,,Female,Age 75 to 79,,,,,,,,,Maine,0.0,0.0,Within past year (anytime less than 12 months ago),No,9.0,"6 or more, but not all",Yes,Yes,No,No,No,Yes,No,No,Yes,"Yes, but only during pregnancy (female)",No,No,No,No,No,Yes,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,83.91,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes -Yes,20.54,Yes,No,No,0.0,0.0,No,Male,80 or older,Asian,No,Yes,Excellent,8.0,Yes,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,34.54,,,,,,,Female,Age 55 to 59,,,,,,,,,North Carolina,0.0,10.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.6,88.45,Yes,Yes,Yes,No,"Yes, received Tdap",No,Yes -,32.69,,,,,,,Female,Age 55 to 59,,,,,,,,,Wisconsin,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",Yes,Yes,No,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),No,"White only, Non-Hispanic",1.73,97.52,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No -,,,,,,,,Female,Age 60 to 64,,,,,,,,,Minnesota,,1.0,Within past year (anytime less than 12 months ago),Yes,4.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,No -,27.8,,,,,,,Male,Age 25 to 29,,,,,,,,,Arkansas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.83,92.99,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,No -No,21.63,Yes,No,No,0.0,0.0,No,Male,30-34,Hispanic,No,No,Good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,29.29,,,,,,,Female,Age 75 to 79,,,,,,,,,Massachusetts,0.0,8.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.52,68.04,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -Yes,27.88,No,No,No,0.0,0.0,No,Male,75-79,White,No,Yes,Very good,9.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,31.89,Yes,No,No,2.0,5.0,No,Female,60-64,White,No,Yes,Good,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,28.07,,,,,,,Male,Age 35 to 39,,,,,,,,,Ohio,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"Black only, Non-Hispanic",1.83,93.89,No,,Yes,No,"Yes, received Tdap",, -,32.26,,,,,,,Female,Age 30 to 34,,,,,,,,,Texas,0.0,7.0,Within past year (anytime less than 12 months ago),No,7.0,None of them,No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,Yes,Yes,Yes,Former smoker,Never used e-cigarettes in my entire life,Yes,Hispanic,1.7,93.44,No,Yes,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,26.54,,,,,,,Male,Age 50 to 54,,,,,,,,,Mississippi,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,83.91,Yes,Yes,No,Yes,"Yes, received tetanus shot but not sure what type",No,Yes -,31.01,,,,,,,Female,Age 80 or older,,,,,,,,,New Hampshire,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,1 to 5,No,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,95.25,No,No,Yes,Yes,"Yes, received Tdap",No,No -,15.81,,,,,,,Female,Age 65 to 69,,,,,,,,,Florida,30.0,14.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,Yes,No,No,Yes,Yes,No,Yes,Yes,No,No,Yes,Yes,Yes,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.65,43.09,No,Yes,Yes,Yes,"Yes, received Tdap",No,No -,25.1,,,,,,,Male,Age 18 to 24,,,,,,,,,New Jersey,0.0,1.0,Within past 2 years (1 year but less than 2 years ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,77.11,Yes,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,34.75,,,,,,,Female,Age 55 to 59,,,,,,,,,Ohio,0.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,86.18,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,25.77,,,,,,,Male,Age 70 to 74,,,,,,,,,Louisiana,30.0,0.0,Within past year (anytime less than 12 months ago),Yes,10.0,"6 or more, but not all",No,No,Yes,No,Yes,No,No,No,Yes,Yes,No,No,Yes,Yes,No,Yes,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.83,86.18,No,No,Yes,Yes,,No,No -No,28.75,Yes,Yes,No,0.0,2.0,No,Male,50-54,White,No,Yes,Very good,5.0,No,Yes,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,30.9,,,,,,,Female,Age 18 to 24,,,,,,,,,Indiana,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,Yes,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.63,81.65,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,46.59,,,,,,,Female,Age 40 to 44,,,,,,,,,West Virginia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,Former smoker,Use them every day,Yes,"White only, Non-Hispanic",1.65,127.01,No,Yes,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,,,,,,,,Male,Age 40 to 44,,,,,,,,,Washington,0.0,0.0,5 or more years ago,Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,Hispanic,,81.65,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -No,30.85,No,No,No,0.0,0.0,Yes,Male,65-69,White,No,Yes,Good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,25.54,No,No,No,5.0,0.0,No,Male,60-64,White,No,No,Good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,20.6,No,No,No,0.0,0.0,No,Female,30-34,White,No,Yes,Excellent,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,29.57,No,No,No,30.0,0.0,Yes,Female,75-79,White,Yes,No,Poor,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,20.98,,,,,,,Female,Age 65 to 69,,,,,,,,,Washington,0.0,0.0,5 or more years ago,Yes,8.0,All,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,58.97,No,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -No,40.62,No,No,No,0.0,30.0,Yes,Female,45-49,Other,Yes,Yes,Fair,4.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,33.91,,,,,,,Male,Age 45 to 49,,,,,,,,,Indiana,5.0,3.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,113.4,Yes,No,Yes,Yes,"Yes, received Tdap",No,No -No,27.27,Yes,No,No,0.0,0.0,No,Male,50-54,White,No,Yes,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,28.7,,,,,,,Male,Age 60 to 64,,,,,,,,,Ohio,0.0,2.0,Within past 5 years (2 years but less than 5 years ago),Yes,8.0,1 to 5,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,90.72,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,32.45,,,,,,,Female,Age 30 to 34,,,,,,,,,Texas,7.0,30.0,Within past 2 years (1 year but less than 2 years ago),No,5.0,1 to 5,No,No,No,No,No,No,Yes,No,No,No,Yes,No,Yes,No,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.65,88.45,Yes,Yes,No,Yes,"Yes, received tetanus shot but not sure what type",No,Yes -,30.13,,,,,,,Male,Age 80 or older,,,,,,,,,South Dakota,30.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,4.0,1 to 5,No,No,No,No,Yes,No,No,Yes,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,95.25,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes -No,37.68,No,No,No,5.0,0.0,No,Female,50-54,Black,No,No,Good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,30.54,,,,,,,Female,Age 45 to 49,,,,,,,,,California,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Other race only, Non-Hispanic",1.57,75.75,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,26.64,,,,,,,Male,Age 65 to 69,,,,,,,,,New Hampshire,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,86.64,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,29.18,,,,,,,Female,Age 45 to 49,,,,,,,,,Nebraska,3.0,3.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,77.11,Yes,Yes,No,Yes,"Yes, received Tdap",No,No -,24.96,,,,,,,Female,Age 60 to 64,,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,68.04,No,No,Yes,Yes,"Yes, received Tdap",No,Yes -,26.61,,,,,,,Female,Age 75 to 79,,,,,,,,,Delaware,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.73,79.38,Yes,No,No,Yes,"Yes, received tetanus shot, but not Tdap",No,Tested positive using home test without a health professional -,20.51,,,,,,,Female,Age 18 to 24,,,,,,,,,Nebraska,0.0,0.0,5 or more years ago,No,9.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.52,47.63,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,33.64,,,,,,,Female,Age 65 to 69,,,,,,,,,Maryland,13.0,12.0,Within past year (anytime less than 12 months ago),Yes,9.0,1 to 5,Yes,No,No,No,No,Yes,Yes,No,Yes,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,88.9,Yes,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,29.13,,,,,,,Male,Age 70 to 74,,,,,,,,,Maine,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,None of them,Yes,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,92.08,Yes,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,Yes -,35.67,,,,,,,Female,Age 35 to 39,,,,,,,,,New York,3.0,5.0,Within past 2 years (1 year but less than 2 years ago),Yes,5.0,None of them,No,No,No,No,No,No,No,No,No,"Yes, but only during pregnancy (female)",No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,100.24,Yes,No,No,No,"Yes, received tetanus shot, but not Tdap",No,Yes -,43.24,,,,,,,Male,Age 60 to 64,,,,,,,,,New Mexico,5.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,No,No,No,No,No,No,Yes,No,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"Other race only, Non-Hispanic",1.8,140.61,No,,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,18.36,,,,,,,Female,Age 30 to 34,,,,,,,,,West Virginia,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,1 to 5,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Use them some days,No,"White only, Non-Hispanic",1.52,42.64,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,21.79,,,,,,,Male,Age 30 to 34,,,,,,,,,Colorado,0.0,2.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,61.23,Yes,No,No,No,"Yes, received Tdap",No,No -,34.96,,,,,,,Male,Age 25 to 29,,,,,,,,,Alabama,2.0,15.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Yes,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,120.2,No,Yes,Yes,No,"Yes, received tetanus shot but not sure what type",No,No -No,31.32,Yes,No,No,0.0,0.0,No,Female,55-59,White,No,Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Yes,43.76,No,No,Yes,15.0,30.0,Yes,Male,70-74,White,Yes,Yes,Poor,8.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,21.03,No,No,No,0.0,0.0,No,Female,30-34,Hispanic,No,Yes,Good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,23.26,,,,,,,Male,Age 80 or older,,,,,,,,,Utah,0.0,1.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,,No,No,No,No,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,69.4,No,,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -No,22.15,Yes,No,No,0.0,0.0,No,Male,60-64,White,No,Yes,Very good,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,28.12,,,,,,,Female,Age 75 to 79,,,,,,,,,Louisiana,25.0,10.0,Within past year (anytime less than 12 months ago),No,10.0,,No,No,No,No,Yes,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,76.66,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -,34.87,,,,,,,Male,Age 45 to 49,,,,,,,,,Ohio,0.0,20.0,Within past year (anytime less than 12 months ago),Yes,3.0,1 to 5,No,No,No,No,No,No,Yes,No,No,No,No,No,Yes,Yes,Yes,Yes,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,113.4,No,Yes,No,No,"Yes, received Tdap",No,Yes -No,18.65,No,No,No,1.0,0.0,Yes,Male,35-39,White,No,Yes,Very good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,39.05,,,,,,,Male,Age 25 to 29,,,,,,,,,Montana,0.0,2.0,Within past year (anytime less than 12 months ago),No,5.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,127.01,No,No,No,Yes,,No,No -,27.4,,,,,,,Female,Age 18 to 24,,,,,,,,,Utah,0.0,30.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Use them some days,No,"White only, Non-Hispanic",1.55,65.77,Yes,No,Yes,No,"Yes, received Tdap",No,No -,39.89,,,,,,,Male,Age 45 to 49,,,,,,,,,Utah,1.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,Yes,Yes,No,Yes,Yes,Yes,Yes,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,Hispanic,1.8,129.73,No,Yes,Yes,No,"Yes, received Tdap",No,No -No,19.94,No,No,No,0.0,5.0,No,Female,70-74,White,No,Yes,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,46.59,,,,,,,Female,Age 60 to 64,,,,,,,,,Alabama,0.0,15.0,Within past year (anytime less than 12 months ago),No,6.0,"6 or more, but not all",No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.65,127.01,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,27.09,,,,,,,Male,Age 65 to 69,,,,,,,,,South Carolina,4.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.88,95.71,Yes,Yes,Yes,No,"Yes, received tetanus shot but not sure what type",No,Yes -No,31.53,Yes,No,No,0.0,0.0,No,Female,50-54,White,No,Yes,Excellent,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,36.18,No,No,No,0.0,0.0,No,Male,40-44,Hispanic,No,Yes,Very good,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,27.89,,,,,,,Male,Age 65 to 69,,,,,,,,,Oklahoma,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,Yes,No,No,Yes,Yes,Yes,Yes,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,90.72,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,No -,25.8,,,,,,,Male,Age 75 to 79,,,,,,,,,Michigan,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,83.91,No,No,Yes,Yes,"Yes, received Tdap",No,No -,37.12,,,,,,,Female,Age 60 to 64,,,,,,,,,New Hampshire,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,104.33,No,No,No,No,"Yes, received tetanus shot, but not Tdap",No,No -,26.52,,,,,,,Female,Age 70 to 74,,,,,,,,,New York,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,65.77,Yes,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes -No,31.62,Yes,Yes,No,0.0,0.0,No,Female,35-39,Other,No,Yes,Good,10.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,23.03,No,No,Yes,2.0,3.0,No,Female,80 or older,Black,Yes (during pregnancy),No,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,18.99,No,No,No,2.0,17.0,No,Male,18-24,White,No,Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,22.32,,,,,,,Male,Age 25 to 29,,,,,,,,,Ohio,0.0,30.0,Within past 5 years (2 years but less than 5 years ago),Yes,5.0,1 to 5,No,No,No,No,No,No,Yes,No,No,No,No,No,Yes,No,No,No,Current smoker - now smokes every day,Use them some days,No,"Multiracial, Non-Hispanic",1.8,72.57,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",Yes,Yes -,24.41,,,,,,,Male,Age 25 to 29,,,,,,,,,Wyoming,0.0,20.0,Within past 2 years (1 year but less than 2 years ago),No,6.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,Yes,No,No,Yes,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.83,81.65,No,Yes,No,No,"Yes, received Tdap",No,No -No,20.25,Yes,No,No,0.0,15.0,No,Female,35-39,White,No,No,Very good,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Yes,38.74,Yes,No,No,30.0,15.0,No,Male,70-74,White,Yes,No,Fair,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,34.7,,,,,,,Female,Age 55 to 59,,,,,,,,,New York,30.0,20.0,Within past year (anytime less than 12 months ago),No,12.0,All,No,No,No,Yes,No,No,Yes,No,Yes,No,No,No,Yes,Yes,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.75,106.59,Yes,Yes,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,39.58,,,,,,,Male,Age 50 to 54,,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,136.08,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,29.85,,,,,,,Female,Age 65 to 69,,,,,,,,,Maryland,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),No,9.0,1 to 5,No,No,Yes,No,No,No,Yes,No,No,No,No,Yes,Yes,Yes,No,Yes,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.55,71.67,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -No,22.14,Yes,No,No,5.0,0.0,Yes,Female,30-34,Other,No,Yes,Good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,28.28,,,,,,,Female,Age 70 to 74,,,,,,,,,Mississippi,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,1 to 5,No,Yes,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.5,63.5,No,No,Yes,Yes,"Yes, received Tdap",No,Yes -No,26.39,No,No,No,0.0,0.0,No,Male,35-39,Hispanic,No,Yes,Very good,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,41.6,,,,,,,Female,Age 55 to 59,,,,,,,,,Puerto Rico,30.0,30.0,Within past year (anytime less than 12 months ago),No,5.0,"6 or more, but not all",No,Yes,No,No,No,No,Yes,No,Yes,No,Yes,No,Yes,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,Hispanic,1.65,113.4,No,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,22.71,,,,,,,Female,Age 30 to 34,,,,,,,,,Maine,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.7,65.77,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Tested positive using home test without a health professional -,21.46,,,,,,,Female,Age 80 or older,,,,,,,,,Michigan,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,"Yes, but only during pregnancy (female)",No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,56.7,No,No,Yes,Yes,"Yes, received Tdap",No,No -,29.7,,,,,,,Female,Age 55 to 59,,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,None of them,No,No,No,Yes,No,,Yes,Yes,Yes,No,Yes,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,83.46,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes -,,,,,,,,Female,Age 80 or older,,,,,,,,,Iowa,10.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,Yes,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,,No,No,No,Yes,,No,No -,26.04,,,,,,,Male,Age 45 to 49,,,,,,,,,South Dakota,0.0,0.0,5 or more years ago,No,3.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.83,87.09,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,25.85,,,,,,,Female,Age 70 to 74,,,,,,,,,Georgia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,Yes,Yes,No,No,No,No,No,No,No,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,77.11,Yes,No,Yes,Yes,"Yes, received Tdap",No,No -,32.23,,,,,,,Male,Age 70 to 74,,,,,,,,,Maryland,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,Yes,No,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.88,113.85,No,Yes,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No -,28.32,,,,,,,Female,Age 70 to 74,,,,,,,,,Indiana,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.52,65.77,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No -,26.11,,,,,,,Male,Age 45 to 49,,,,,,,,,New York,0.0,4.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.78,82.55,No,Yes,No,No,"Yes, received Tdap",No,Yes -No,24.21,Yes,No,No,0.0,0.0,No,Female,70-74,White,No,Yes,Excellent,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -No,23.99,Yes,No,No,0.0,0.0,No,Male,35-39,White,No,Yes,Excellent,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,22.24,,,,,,,Female,Age 80 or older,,,,,,,,,New Hampshire,0.0,3.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,64.41,Yes,No,Yes,Yes,"Yes, received Tdap",No,No -No,24.39,No,No,No,0.0,2.0,No,Male,75-79,White,No,Yes,Excellent,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,30.04,,,,,,,Male,Age 55 to 59,,,,,,,,,Idaho,2.0,0.0,Within past 2 years (1 year but less than 2 years ago),No,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,79.38,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No -,23.89,,,,,,,Female,Age 35 to 39,,,,,,,,,New Jersey,10.0,0.0,Within past year (anytime less than 12 months ago),No,5.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,67.13,Yes,Yes,Yes,No,"Yes, received tetanus shot but not sure what type",No,Yes -Yes,37.61,No,No,No,0.0,0.0,No,Male,60-64,White,"No, borderline diabetes",Yes,Good,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,30.42,,,,,,,Female,Age 80 or older,,,,,,,,,Massachusetts,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.55,73.03,Yes,No,No,Yes,"Yes, received Tdap",No,Yes +HeartDisease,BMI,Smoking,AlcoholDrinking,Stroke,PhysicalHealth,MentalHealth,DiffWalking,Sex,AgeCategory,Race,Diabetic,PhysicalActivity,SleepTime,Asthma,KidneyDisease,SkinCancer,State,PhysicalHealthDays,MentalHealthDays,LastCheckupTime,PhysicalActivities,SleepHours,RemovedTeeth,HadHeartAttack,HadAngina,HadStroke,HadAsthma,HadSkinCancer,HadCOPD,HadDepressiveDisorder,HadKidneyDisease,HadArthritis,HadDiabetes,DeafOrHardOfHearing,BlindOrVisionDifficulty,DifficultyConcentrating,DifficultyWalking,DifficultyDressingBathing,DifficultyErrands,SmokerStatus,ECigaretteUsage,ChestScan,RaceEthnicityCategory,HeightInMeters,WeightInKilograms,AlcoholDrinkers,HIVTesting,FluVaxLast12,PneumoVaxEver,TetanusLast10Tdap,HighRiskLastYear,CovidPos +,25.82,,,,,,,Male,Age 80 or older,,,,,,,,Utah,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,No,Yes,No,No,No,Yes,No,Yes,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,72.57,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,21.79,,,,,,,Female,Age 45 to 49,,,,,,,,Virginia,7.0,3.0,Within past 2 years (1 year but less than 2 years ago),Yes,4.0,All,Yes,No,No,No,No,No,No,No,Yes,No,No,Yes,No,No,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,61.23,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +No,23.4,No,Yes,No,15.0,20.0,No,Female,18-24,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,29.76,No,No,No,2.0,8.0,No,Female,35-39,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,33.45,,,,,,,Male,Age 80 or older,,,,,,,,Georgia,0.0,3.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",Yes,Yes,No,No,No,No,Yes,No,No,No,No,No,Yes,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,99.79,Yes,,,,,, +,19.53,,,,,,,Female,Age 60 to 64,,,,,,,,Florida,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.8,63.5,Yes,No,Yes,Yes,"Yes, received Tdap",No,No +No,30.99,No,No,No,0.0,0.0,No,Female,45-49,White,No,No,10.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,25.32,No,No,No,0.0,0.0,No,Female,55-59,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,27.46,,,,,,,Female,Age 45 to 49,,,,,,,,Illinois,3.0,6.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,72.57,Yes,Yes,Yes,No,"Yes, received tetanus shot but not sure what type",No,Yes +,27.46,,,,,,,Female,Age 25 to 29,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,72.57,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,48.81,,,,,,,Male,Age 55 to 59,,,,,,,,Arkansas,15.0,20.0,Within past year (anytime less than 12 months ago),No,7.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.8,158.76,No,Yes,Yes,Yes,"Yes, received Tdap",No,Yes +,29.53,,,,,,,Male,Age 65 to 69,,,,,,,,Nebraska,0.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,1 to 5,No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,90.72,Yes,No,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,25.4,,,,,,,Male,Age 25 to 29,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.75,78.02,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Tested positive using home test without a health professional +No,29.21,No,No,No,0.0,3.0,No,Male,70-74,White,No,Yes,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,26.63,,,,,,,Female,Age 30 to 34,,,,,,,,South Carolina,0.0,2.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.7,77.11,Yes,Yes,Yes,,"No, did not receive any tetanus shot in the past 10 years",No,Tested positive using home test without a health professional +,24.03,,,,,,,Female,Age 18 to 24,,,,,,,,Puerto Rico,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.5,53.98,No,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +,,,,,,,,Female,Age 40 to 44,,,,,,,,Hawaii,8.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",,,No,Yes,No,No,,No,No +,,,,,,,,Female,Age 60 to 64,,,,,,,,Utah,14.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.65,,No,No,No,No,"Yes, received Tdap",No,No +,27.8,,,,,,,Male,Age 30 to 34,,,,,,,,New York,0.0,5.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.83,92.99,Yes,No,No,,"Yes, received tetanus shot but not sure what type",No,No +No,34.06,No,No,No,0.0,0.0,No,Male,50-54,Hispanic,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,38.1,Yes,No,No,1.0,0.0,No,Female,35-39,White,No,Yes,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,31.53,No,No,No,30.0,0.0,Yes,Female,80 or older,White,No,No,6.0,Yes,Yes,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,30.23,,,,,,,Female,Age 55 to 59,,,,,,,,Delaware,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.55,72.57,Yes,No,,Yes,"Yes, received Tdap",No,No +No,31.89,No,No,No,4.0,0.0,No,Female,60-64,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,32.01,,,,,,,Female,Age 60 to 64,,,,,,,,Tennessee,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,Yes,No,,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,,1.57,79.38,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",, +No,28.0,Yes,No,No,30.0,0.0,No,Male,65-69,White,No,Yes,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,22.25,No,No,No,2.0,2.0,No,Male,18-24,White,No,Yes,8.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,17.33,Yes,No,No,0.0,0.0,No,Male,65-69,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,34.17,,,,,,,Male,Age 25 to 29,,,,,,,,Texas,2.0,9.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.8,111.13,No,No,No,Yes,"Yes, received Tdap",No,Yes +,26.61,,,,,,,Male,Age 70 to 74,,,,,,,,New York,30.0,30.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,No,No,No,Yes,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,79.38,Yes,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,32.61,No,No,No,0.0,5.0,No,Female,35-39,White,No,Yes,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,22.6,,,,,,,Male,Age 80 or older,,,,,,,,Wyoming,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,Yes,No,No,Yes,Yes,Yes,No,Yes,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,63.5,No,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No +,27.26,,,,,,,Male,Age 18 to 24,,,,,,,,Missouri,0.0,2.0,5 or more years ago,Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Use them every day,No,"White only, Non-Hispanic",1.78,86.18,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,22.6,,,,,,,Female,Age 70 to 74,,,,,,,,Kansas,2.0,2.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,63.5,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No +,37.11,,,,,,,Female,Age 40 to 44,,,,,,,,California,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.52,86.18,No,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,Yes +No,26.29,No,No,No,0.0,0.0,No,Female,60-64,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,,,,,,,,Male,Age 65 to 69,,,,,,,,Ohio,2.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,,No,No,Yes,,"Yes, received tetanus shot but not sure what type",No,No +,34.3,,,,,,,Male,Age 40 to 44,,,,,,,,Oregon,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,117.93,Yes,Yes,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +No,23.67,No,No,No,3.0,2.0,No,Male,18-24,Black,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,25.06,,,,,,,Male,Age 55 to 59,,,,,,,,Oregon,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,Yes,No,Yes,No,No,No,"No, pre-diabetes or borderline diabetes",No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,72.57,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +No,31.46,No,No,No,3.0,20.0,No,Male,65-69,Black,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,26.54,,,,,,,Female,Age 80 or older,,,,,,,,Tennessee,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,All,Yes,Yes,Yes,No,No,Yes,No,No,No,No,No,No,No,No,Yes,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,83.91,,,,,,, +,27.89,,,,,,,Male,Age 25 to 29,,,,,,,,Colorado,0.0,0.0,5 or more years ago,Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,Hispanic,1.8,90.72,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,29.76,No,No,No,0.0,1.0,No,Female,55-59,White,No,Yes,7.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,26.61,Yes,No,No,0.0,0.0,No,Male,80 or older,White,No,Yes,15.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,32.28,,,,,,,Female,Age 80 or older,,,,,,,,Missouri,30.0,0.0,Within past year (anytime less than 12 months ago),No,7.0,"6 or more, but not all",No,Yes,No,Yes,Yes,Yes,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,90.72,Yes,No,Yes,Yes,,No,No +No,20.78,No,No,No,0.0,5.0,No,Female,30-34,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,31.64,,,,,,,Female,Age 18 to 24,,,,,,,,Utah,0.0,10.0,Within past year (anytime less than 12 months ago),Yes,9.0,None of them,No,No,No,No,No,No,No,No,No,"Yes, but only during pregnancy (female)",No,No,No,No,No,No,Never smoked,Use them some days,Yes,Hispanic,1.52,73.48,No,No,No,No,"Yes, received Tdap",No,No +No,30.45,Yes,No,No,0.0,0.0,No,Female,65-69,White,Yes,Yes,8.0,No,Yes,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,34.21,No,No,No,0.0,3.0,No,Male,35-39,White,No,Yes,5.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,37.79,,,,,,,Female,Age 55 to 59,,,,,,,,Maine,20.0,0.0,Within past year (anytime less than 12 months ago),Yes,10.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.55,90.72,No,,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Tested positive using home test without a health professional +,20.34,,,,,,,Male,Age 80 or older,,,,,,,,Kansas,,0.0,Within past 2 years (1 year but less than 2 years ago),No,8.0,All,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,68.04,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,28.75,,,,,,,Male,Age 18 to 24,,,,,,,,Ohio,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.83,96.16,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,15.04,,,,,,,Female,Age 30 to 34,,,,,,,,California,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,Never smoked,Not at all (right now),Yes,"Other race only, Non-Hispanic",1.65,41.0,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +No,30.54,No,No,No,0.0,14.0,No,Male,50-54,White,No,No,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,25.39,,,,,,,Male,Age 55 to 59,,,,,,,,California,30.0,4.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,75.75,Yes,Yes,Yes,No,"Yes, received Tdap",No,No +,32.32,,,,,,,Male,Age 50 to 54,,,,,,,,Illinois,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,111.13,Yes,No,Yes,No,"Yes, received Tdap",No,No +No,19.26,Yes,No,No,0.0,0.0,No,Male,50-54,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,36.49,,,,,,,Male,Age 40 to 44,,,,,,,,Arkansas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.73,108.86,No,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,21.02,,,,,,,Male,Age 55 to 59,,,,,,,,Iowa,7.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,7.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.83,70.31,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,19.47,,,,,,,Female,Age 55 to 59,,,,,,,,Georgia,1.0,2.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Multiracial, Non-Hispanic",1.65,53.07,Yes,No,No,No,"Yes, received Tdap",No,No +,35.24,,,,,,,Male,Age 25 to 29,,,,,,,,Colorado,0.0,7.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.7,102.06,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No +,17.72,,,,,,,Female,Age 50 to 54,,,,,,,,Colorado,1.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.75,54.43,No,Yes,No,No,"Yes, received Tdap",No,No +,24.54,,,,,,,Female,Age 60 to 64,,,,,,,,Colorado,0.0,20.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,1 to 5,No,No,No,No,No,No,Yes,No,Yes,No,No,,No,No,No,Yes,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.85,84.37,Yes,No,No,Yes,"Yes, received Tdap",No,No +,27.57,,,,,,,Male,Age 80 or older,,,,,,,,Nebraska,10.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,Yes,No,No,Yes,No,No,No,No,"No, pre-diabetes or borderline diabetes",No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,79.83,No,No,No,Yes,"Yes, received tetanus shot but not sure what type",No,Yes +No,27.5,No,No,No,0.0,0.0,No,Male,55-59,White,No,Yes,5.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,22.22,Yes,No,No,0.0,14.0,No,Female,45-49,American Indian/Alaskan Native,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,24.96,,,,,,,Male,Age 18 to 24,,,,,,,,Minnesota,0.0,14.0,Within past 5 years (2 years but less than 5 years ago),No,6.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.65,68.04,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,22.24,,,,,,,Male,Age 65 to 69,,,,,,,,Ohio,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,70.31,Yes,No,Yes,Yes,"Yes, received Tdap",No,No +No,23.91,No,No,No,0.0,1.0,No,Female,40-44,White,No,Yes,7.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,38.74,,,,,,,Male,Age 70 to 74,,,,,,,,Ohio,3.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,"6 or more, but not all",No,No,No,No,No,No,No,No,Yes,Yes,Yes,No,No,Yes,No,No,Never smoked,Not at all (right now),Yes,"White only, Non-Hispanic",1.68,108.86,No,Yes,Yes,Yes,"Yes, received Tdap",No,No +No,26.26,Yes,No,No,0.0,0.0,No,Female,70-74,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,24.59,,,,,,,Male,Age 60 to 64,,,,,,,,Illinois,,,Within past year (anytime less than 12 months ago),Yes,4.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.7,71.21,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +Yes,24.37,Yes,No,No,0.0,0.0,No,Male,75-79,White,No,Yes,8.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,36.26,,,,,,,Female,Age 50 to 54,,,,,,,,Georgia,10.0,5.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.8,117.93,No,No,Yes,No,"Yes, received Tdap",No,Yes +,28.06,,,,,,,Male,Age 75 to 79,,,,,,,,Wisconsin,3.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,Yes,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,86.18,Yes,No,Yes,Yes,"Yes, received Tdap",No,No +,30.29,,,,,,,Female,Age 45 to 49,,,,,,,,Indiana,0.0,1.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,82.55,Yes,No,Yes,Yes,"Yes, received Tdap",No,No +No,24.59,No,No,No,0.0,5.0,No,Male,55-59,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,30.34,,,,,,,Male,Age 80 or older,,,,,,,,Kansas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,Yes,No,No,No,No,No,No,Yes,,No,No,No,Yes,No,No,Former smoker,Use them some days,No,,1.85,104.33,Yes,No,No,Yes,"Yes, received tetanus shot but not sure what type",No,No +,32.55,,,,,,,Male,Age 60 to 64,,,,,,,,Nevada,2.0,0.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,,,,,,,"White only, Non-Hispanic",1.83,108.86,,,,,,, +No,33.2,No,No,No,21.0,0.0,No,Male,65-69,American Indian/Alaskan Native,Yes,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,30.9,,,,,,,Female,Age 30 to 34,,,,,,,,Indiana,0.0,2.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,81.65,Yes,Yes,No,No,"Yes, received Tdap",No,No +,21.95,,,,,,,Female,Age 70 to 74,,,,,,,,Florida,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,54.43,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,No +,31.66,,,,,,,Male,Age 70 to 74,,,,,,,,Ohio,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,7.0,1 to 5,No,No,No,No,Yes,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.85,108.86,No,No,No,No,,No,No +,26.58,,,,,,,Male,Age 50 to 54,,,,,,,,Virginia,3.0,20.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,81.65,Yes,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,22.81,,,,,,,Male,Age 25 to 29,,,,,,,,Oklahoma,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.73,68.04,Yes,No,,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,36.81,,,,,,,Male,Age 80 or older,,,,,,,,Florida,30.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,All,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Yes,Yes,Yes,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,106.59,Yes,No,No,No,"Yes, received Tdap",No,No +,26.57,,,,,,,Female,Age 65 to 69,,,,,,,,South Dakota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.6,68.04,Yes,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Tested positive using home test without a health professional +,23.05,,,,,,,Female,Age 75 to 79,,,,,,,,Nebraska,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,None of them,No,No,No,No,Yes,No,No,No,Yes,"No, pre-diabetes or borderline diabetes",No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.55,55.34,,,,,,, +No,29.6,No,No,No,0.0,0.0,No,Female,45-49,Hispanic,No,Yes,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,20.6,No,Yes,No,0.0,30.0,No,Female,40-44,White,No,Yes,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,35.88,,,,,,,Male,Age 50 to 54,,,,,,,,Florida,7.0,10.0,Within past year (anytime less than 12 months ago),No,8.0,"6 or more, but not all",Yes,Yes,No,No,No,No,No,No,No,Yes,No,No,Yes,Yes,No,No,Former smoker,Use them some days,Yes,"White only, Non-Hispanic",1.73,107.05,No,Yes,Yes,No,"Yes, received tetanus shot, but not Tdap",No,No +,21.63,,,,,,,Male,Age 75 to 79,,,,,,,,Wisconsin,1.0,1.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,Hispanic,1.65,58.97,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +No,24.33,Yes,No,No,0.0,0.0,No,Female,60-64,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,30.68,Yes,No,No,0.0,0.0,No,Male,65-69,White,No,Yes,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,29.53,,,,,,,Male,Age 50 to 54,,,,,,,,Ohio,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.88,104.33,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,,,,,,,,Female,Age 75 to 79,,,,,,,,Indiana,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,,,,,,,,,,"Other race only, Non-Hispanic",,,,,,,,, +,32.78,,,,,,,Male,Age 35 to 39,,,,,,,,South Dakota,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,106.59,Yes,Yes,No,Yes,"Yes, received tetanus shot but not sure what type",No,Yes +,21.13,,,,,,,Female,Age 70 to 74,,,,,,,,District of Columbia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.65,57.61,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,Yes +No,15.96,No,No,No,30.0,15.0,Yes,Female,70-74,White,No,No,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,21.95,,,,,,,Female,,,,,,,,,New Mexico,0.0,7.0,5 or more years ago,Yes,8.0,1 to 5,No,No,No,Yes,No,No,Yes,No,No,No,No,Yes,No,No,No,No,,,,,1.57,54.43,,,,,,, +No,21.7,No,No,No,0.0,5.0,No,Male,18-24,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,30.79,,,,,,,Female,Age 75 to 79,,,,,,,,Maine,0.0,0.0,Within past year (anytime less than 12 months ago),No,9.0,"6 or more, but not all",Yes,Yes,No,No,No,Yes,No,No,Yes,"Yes, but only during pregnancy (female)",No,No,No,No,No,Yes,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,83.91,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes +Yes,20.54,Yes,No,No,0.0,0.0,No,Male,80 or older,Asian,No,Yes,8.0,Yes,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,34.54,,,,,,,Female,Age 55 to 59,,,,,,,,North Carolina,0.0,10.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.6,88.45,Yes,Yes,Yes,No,"Yes, received Tdap",No,Yes +,32.69,,,,,,,Female,Age 55 to 59,,,,,,,,Wisconsin,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",Yes,Yes,No,No,No,No,Yes,No,No,Yes,No,No,No,No,No,No,Current smoker - now smokes every day,Not at all (right now),No,"White only, Non-Hispanic",1.73,97.52,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,No +,,,,,,,,Female,Age 60 to 64,,,,,,,,Minnesota,,1.0,Within past year (anytime less than 12 months ago),Yes,4.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.6,,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,No +,27.8,,,,,,,Male,Age 25 to 29,,,,,,,,Arkansas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.83,92.99,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,No +No,21.63,Yes,No,No,0.0,0.0,No,Male,30-34,Hispanic,No,No,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,29.29,,,,,,,Female,Age 75 to 79,,,,,,,,Massachusetts,0.0,8.0,Within past year (anytime less than 12 months ago),Yes,5.0,None of them,No,No,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.52,68.04,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +Yes,27.88,No,No,No,0.0,0.0,No,Male,75-79,White,No,Yes,9.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,31.89,Yes,No,No,2.0,5.0,No,Female,60-64,White,No,Yes,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,28.07,,,,,,,Male,Age 35 to 39,,,,,,,,Ohio,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"Black only, Non-Hispanic",1.83,93.89,No,,Yes,No,"Yes, received Tdap",, +,32.26,,,,,,,Female,Age 30 to 34,,,,,,,,Texas,0.0,7.0,Within past year (anytime less than 12 months ago),No,7.0,None of them,No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,Yes,Yes,Yes,Former smoker,Never used e-cigarettes in my entire life,Yes,Hispanic,1.7,93.44,No,Yes,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,26.54,,,,,,,Male,Age 50 to 54,,,,,,,,Mississippi,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,83.91,Yes,Yes,No,Yes,"Yes, received tetanus shot but not sure what type",No,Yes +,31.01,,,,,,,Female,Age 80 or older,,,,,,,,New Hampshire,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,1 to 5,No,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,95.25,No,No,Yes,Yes,"Yes, received Tdap",No,No +,15.81,,,,,,,Female,Age 65 to 69,,,,,,,,Florida,30.0,14.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,Yes,No,No,Yes,Yes,No,Yes,Yes,No,No,Yes,Yes,Yes,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.65,43.09,No,Yes,Yes,Yes,"Yes, received Tdap",No,No +,25.1,,,,,,,Male,Age 18 to 24,,,,,,,,New Jersey,0.0,1.0,Within past 2 years (1 year but less than 2 years ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,77.11,Yes,Yes,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,34.75,,,,,,,Female,Age 55 to 59,,,,,,,,Ohio,0.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,86.18,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,25.77,,,,,,,Male,Age 70 to 74,,,,,,,,Louisiana,30.0,0.0,Within past year (anytime less than 12 months ago),Yes,10.0,"6 or more, but not all",No,No,Yes,No,Yes,No,No,No,Yes,Yes,No,No,Yes,Yes,No,Yes,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.83,86.18,No,No,Yes,Yes,,No,No +No,28.75,Yes,Yes,No,0.0,2.0,No,Male,50-54,White,No,Yes,5.0,No,Yes,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,30.9,,,,,,,Female,Age 18 to 24,,,,,,,,Indiana,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,Yes,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.63,81.65,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,46.59,,,,,,,Female,Age 40 to 44,,,,,,,,West Virginia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,Former smoker,Use them every day,Yes,"White only, Non-Hispanic",1.65,127.01,No,Yes,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,,,,,,,,Male,Age 40 to 44,,,,,,,,Washington,0.0,0.0,5 or more years ago,Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,Hispanic,,81.65,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +No,30.85,No,No,No,0.0,0.0,Yes,Male,65-69,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,25.54,No,No,No,5.0,0.0,No,Male,60-64,White,No,No,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,20.6,No,No,No,0.0,0.0,No,Female,30-34,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,29.57,No,No,No,30.0,0.0,Yes,Female,75-79,White,Yes,No,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,20.98,,,,,,,Female,Age 65 to 69,,,,,,,,Washington,0.0,0.0,5 or more years ago,Yes,8.0,All,No,No,No,No,No,No,No,No,Yes,No,No,No,No,Yes,No,No,Current smoker - now smokes some days,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,58.97,No,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +No,40.62,No,No,No,0.0,30.0,Yes,Female,45-49,Other,Yes,Yes,4.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,33.91,,,,,,,Male,Age 45 to 49,,,,,,,,Indiana,5.0,3.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.83,113.4,Yes,No,Yes,Yes,"Yes, received Tdap",No,No +No,27.27,Yes,No,No,0.0,0.0,No,Male,50-54,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,28.7,,,,,,,Male,Age 60 to 64,,,,,,,,Ohio,0.0,2.0,Within past 5 years (2 years but less than 5 years ago),Yes,8.0,1 to 5,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,90.72,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,32.45,,,,,,,Female,Age 30 to 34,,,,,,,,Texas,7.0,30.0,Within past 2 years (1 year but less than 2 years ago),No,5.0,1 to 5,No,No,No,No,No,No,Yes,No,No,No,Yes,No,Yes,No,No,No,Current smoker - now smokes every day,Not at all (right now),Yes,"White only, Non-Hispanic",1.65,88.45,Yes,Yes,No,Yes,"Yes, received tetanus shot but not sure what type",No,Yes +,30.13,,,,,,,Male,Age 80 or older,,,,,,,,South Dakota,30.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,4.0,1 to 5,No,No,No,No,Yes,No,No,Yes,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.78,95.25,No,No,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes +No,37.68,No,No,No,5.0,0.0,No,Female,50-54,Black,No,No,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,30.54,,,,,,,Female,Age 45 to 49,,,,,,,,California,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Other race only, Non-Hispanic",1.57,75.75,Yes,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,26.64,,,,,,,Male,Age 65 to 69,,,,,,,,New Hampshire,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,86.64,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,29.18,,,,,,,Female,Age 45 to 49,,,,,,,,Nebraska,3.0,3.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,77.11,Yes,Yes,No,Yes,"Yes, received Tdap",No,No +,24.96,,,,,,,Female,Age 60 to 64,,,,,,,,Washington,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,1 to 5,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,68.04,No,No,Yes,Yes,"Yes, received Tdap",No,Yes +,26.61,,,,,,,Female,Age 75 to 79,,,,,,,,Delaware,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,Former smoker,Not at all (right now),Yes,"White only, Non-Hispanic",1.73,79.38,Yes,No,No,Yes,"Yes, received tetanus shot, but not Tdap",No,Tested positive using home test without a health professional +,20.51,,,,,,,Female,Age 18 to 24,,,,,,,,Nebraska,0.0,0.0,5 or more years ago,No,9.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Other race only, Non-Hispanic",1.52,47.63,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,33.64,,,,,,,Female,Age 65 to 69,,,,,,,,Maryland,13.0,12.0,Within past year (anytime less than 12 months ago),Yes,9.0,1 to 5,Yes,No,No,No,No,Yes,Yes,No,Yes,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,88.9,Yes,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,29.13,,,,,,,Male,Age 70 to 74,,,,,,,,Maine,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,None of them,Yes,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.78,92.08,Yes,No,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,Yes +,35.67,,,,,,,Female,Age 35 to 39,,,,,,,,New York,3.0,5.0,Within past 2 years (1 year but less than 2 years ago),Yes,5.0,None of them,No,No,No,No,No,No,No,No,No,"Yes, but only during pregnancy (female)",No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,100.24,Yes,No,No,No,"Yes, received tetanus shot, but not Tdap",No,Yes +,43.24,,,,,,,Male,Age 60 to 64,,,,,,,,New Mexico,5.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,No,No,No,No,No,No,Yes,No,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"Other race only, Non-Hispanic",1.8,140.61,No,,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,18.36,,,,,,,Female,Age 30 to 34,,,,,,,,West Virginia,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,1 to 5,No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Use them some days,No,"White only, Non-Hispanic",1.52,42.64,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,21.79,,,,,,,Male,Age 30 to 34,,,,,,,,Colorado,0.0,2.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,61.23,Yes,No,No,No,"Yes, received Tdap",No,No +,34.96,,,,,,,Male,Age 25 to 29,,,,,,,,Alabama,2.0,15.0,Within past year (anytime less than 12 months ago),No,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Yes,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,120.2,No,Yes,Yes,No,"Yes, received tetanus shot but not sure what type",No,No +No,31.32,Yes,No,No,0.0,0.0,No,Female,55-59,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Yes,43.76,No,No,Yes,15.0,30.0,Yes,Male,70-74,White,Yes,Yes,8.0,No,No,Yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,21.03,No,No,No,0.0,0.0,No,Female,30-34,Hispanic,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,23.26,,,,,,,Male,Age 80 or older,,,,,,,,Utah,0.0,1.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,,No,No,No,No,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.73,69.4,No,,Yes,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +No,22.15,Yes,No,No,0.0,0.0,No,Male,60-64,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,28.12,,,,,,,Female,Age 75 to 79,,,,,,,,Louisiana,25.0,10.0,Within past year (anytime less than 12 months ago),No,10.0,,No,No,No,No,Yes,No,No,No,Yes,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.65,76.66,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +,34.87,,,,,,,Male,Age 45 to 49,,,,,,,,Ohio,0.0,20.0,Within past year (anytime less than 12 months ago),Yes,3.0,1 to 5,No,No,No,No,No,No,Yes,No,No,No,No,No,Yes,Yes,Yes,Yes,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,113.4,No,Yes,No,No,"Yes, received Tdap",No,Yes +No,18.65,No,No,No,1.0,0.0,Yes,Male,35-39,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,39.05,,,,,,,Male,Age 25 to 29,,,,,,,,Montana,0.0,2.0,Within past year (anytime less than 12 months ago),No,5.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,127.01,No,No,No,Yes,,No,No +,27.4,,,,,,,Female,Age 18 to 24,,,,,,,,Utah,0.0,30.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Use them some days,No,"White only, Non-Hispanic",1.55,65.77,Yes,No,Yes,No,"Yes, received Tdap",No,No +,39.89,,,,,,,Male,Age 45 to 49,,,,,,,,Utah,1.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,Yes,Yes,No,Yes,Yes,Yes,Yes,No,No,No,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,Hispanic,1.8,129.73,No,Yes,Yes,No,"Yes, received Tdap",No,No +No,19.94,No,No,No,0.0,5.0,No,Female,70-74,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,46.59,,,,,,,Female,Age 60 to 64,,,,,,,,Alabama,0.0,15.0,Within past year (anytime less than 12 months ago),No,6.0,"6 or more, but not all",No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.65,127.01,Yes,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,27.09,,,,,,,Male,Age 65 to 69,,,,,,,,South Carolina,4.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.88,95.71,Yes,Yes,Yes,No,"Yes, received tetanus shot but not sure what type",No,Yes +No,31.53,Yes,No,No,0.0,0.0,No,Female,50-54,White,No,Yes,6.0,Yes,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,36.18,No,No,No,0.0,0.0,No,Male,40-44,Hispanic,No,Yes,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,27.89,,,,,,,Male,Age 65 to 69,,,,,,,,Oklahoma,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,Yes,No,No,Yes,Yes,Yes,Yes,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,90.72,Yes,Yes,Yes,Yes,"Yes, received Tdap",No,No +,25.8,,,,,,,Male,Age 75 to 79,,,,,,,,Michigan,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,83.91,No,No,Yes,Yes,"Yes, received Tdap",No,No +,37.12,,,,,,,Female,Age 60 to 64,,,,,,,,New Hampshire,0.0,0.0,Within past 5 years (2 years but less than 5 years ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,104.33,No,No,No,No,"Yes, received tetanus shot, but not Tdap",No,No +,26.52,,,,,,,Female,Age 70 to 74,,,,,,,,New York,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,65.77,Yes,Yes,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,Yes +No,31.62,Yes,Yes,No,0.0,0.0,No,Female,35-39,Other,No,Yes,10.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,23.03,No,No,Yes,2.0,3.0,No,Female,80 or older,Black,Yes (during pregnancy),No,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,18.99,No,No,No,2.0,17.0,No,Male,18-24,White,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,22.32,,,,,,,Male,Age 25 to 29,,,,,,,,Ohio,0.0,30.0,Within past 5 years (2 years but less than 5 years ago),Yes,5.0,1 to 5,No,No,No,No,No,No,Yes,No,No,No,No,No,Yes,No,No,No,Current smoker - now smokes every day,Use them some days,No,"Multiracial, Non-Hispanic",1.8,72.57,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",Yes,Yes +,24.41,,,,,,,Male,Age 25 to 29,,,,,,,,Wyoming,0.0,20.0,Within past 2 years (1 year but less than 2 years ago),No,6.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,No,Yes,No,No,Yes,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.83,81.65,No,Yes,No,No,"Yes, received Tdap",No,No +No,20.25,Yes,No,No,0.0,15.0,No,Female,35-39,White,No,No,5.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Yes,38.74,Yes,No,No,30.0,15.0,No,Male,70-74,White,Yes,No,9.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,34.7,,,,,,,Female,Age 55 to 59,,,,,,,,New York,30.0,20.0,Within past year (anytime less than 12 months ago),No,12.0,All,No,No,No,Yes,No,No,Yes,No,Yes,No,No,No,Yes,Yes,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.75,106.59,Yes,Yes,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,39.58,,,,,,,Male,Age 50 to 54,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,136.08,No,No,Yes,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,29.85,,,,,,,Female,Age 65 to 69,,,,,,,,Maryland,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),No,9.0,1 to 5,No,No,Yes,No,No,No,Yes,No,No,No,No,Yes,Yes,Yes,No,Yes,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.55,71.67,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +No,22.14,Yes,No,No,5.0,0.0,Yes,Female,30-34,Other,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,28.28,,,,,,,Female,Age 70 to 74,,,,,,,,Mississippi,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,1 to 5,No,Yes,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.5,63.5,No,No,Yes,Yes,"Yes, received Tdap",No,Yes +No,26.39,No,No,No,0.0,0.0,No,Male,35-39,Hispanic,No,Yes,8.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,41.6,,,,,,,Female,Age 55 to 59,,,,,,,,Puerto Rico,30.0,30.0,Within past year (anytime less than 12 months ago),No,5.0,"6 or more, but not all",No,Yes,No,No,No,No,Yes,No,Yes,No,Yes,No,Yes,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,Hispanic,1.65,113.4,No,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,22.71,,,,,,,Female,Age 30 to 34,,,,,,,,Maine,0.0,0.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Not at all (right now),No,"White only, Non-Hispanic",1.7,65.77,Yes,No,No,No,"Yes, received tetanus shot but not sure what type",No,Tested positive using home test without a health professional +,21.46,,,,,,,Female,Age 80 or older,,,,,,,,Michigan,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,"Yes, but only during pregnancy (female)",No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,56.7,No,No,Yes,Yes,"Yes, received Tdap",No,No +,29.7,,,,,,,Female,Age 55 to 59,,,,,,,,Texas,0.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,None of them,No,No,No,Yes,No,,Yes,Yes,Yes,No,Yes,No,No,Yes,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.68,83.46,No,Yes,No,No,"Yes, received tetanus shot but not sure what type",No,Yes +,,,,,,,,Female,Age 80 or older,,,,,,,,Iowa,10.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,Yes,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,,No,No,No,Yes,,No,No +,26.04,,,,,,,Male,Age 45 to 49,,,,,,,,South Dakota,0.0,0.0,5 or more years ago,No,3.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.83,87.09,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,25.85,,,,,,,Female,Age 70 to 74,,,,,,,,Georgia,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,None of them,No,No,Yes,Yes,No,No,No,No,No,No,No,No,No,Yes,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.73,77.11,Yes,No,Yes,Yes,"Yes, received Tdap",No,No +,32.23,,,,,,,Male,Age 70 to 74,,,,,,,,Maryland,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,Yes,No,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.88,113.85,No,Yes,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No +,28.32,,,,,,,Female,Age 70 to 74,,,,,,,,Indiana,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.52,65.77,Yes,No,Yes,Yes,"Yes, received tetanus shot but not sure what type",No,No +,26.11,,,,,,,Male,Age 45 to 49,,,,,,,,New York,0.0,4.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,No,No,Yes,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Not at all (right now),No,"White only, Non-Hispanic",1.78,82.55,No,Yes,No,No,"Yes, received Tdap",No,Yes +No,24.21,Yes,No,No,0.0,0.0,No,Female,70-74,White,No,Yes,6.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +No,23.99,Yes,No,No,0.0,0.0,No,Male,35-39,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,22.24,,,,,,,Female,Age 80 or older,,,,,,,,New Hampshire,0.0,3.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.7,64.41,Yes,No,Yes,Yes,"Yes, received Tdap",No,No +No,24.39,No,No,No,0.0,2.0,No,Male,75-79,White,No,Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,30.04,,,,,,,Male,Age 55 to 59,,,,,,,,Idaho,2.0,0.0,Within past 2 years (1 year but less than 2 years ago),No,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.63,79.38,No,No,No,No,"Yes, received tetanus shot but not sure what type",No,No +,23.89,,,,,,,Female,Age 35 to 39,,,,,,,,New Jersey,10.0,0.0,Within past year (anytime less than 12 months ago),No,5.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Current smoker - now smokes every day,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.68,67.13,Yes,Yes,Yes,No,"Yes, received tetanus shot but not sure what type",No,Yes +Yes,37.61,No,No,No,0.0,0.0,No,Male,60-64,White,"No, borderline diabetes",Yes,7.0,No,No,No,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,30.42,,,,,,,Female,Age 80 or older,,,,,,,,Massachusetts,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.55,73.03,Yes,No,No,Yes,"Yes, received Tdap",No,Yes diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_008/history.csv b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_008/history.csv index 0d7386f5792d391e0321a6aee5f8fabc13ea2a45..3befbd9ad543ed5f2033337139af0c235d2d99cc 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_008/history.csv +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_008/history.csv @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:f8ee1ad707feca7e1a9028dfe2007d845be73e2980f23fda74a0e7c627701529 -size 271612986 +oid sha256:d0e42d757043e7fb63758bd65096cb987d6eb13170297d12bfbebc6e2db220d6 +size 270592337 diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_008/info.json b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_008/info.json index 53272e3074451c673817a05ef4da5ee438d07863..c876b7e9aa18ec2175d72a1a6b59bf487c77e98c 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_008/info.json +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_008/info.json @@ -1,401 +1,393 @@ -{ - "name": "Indicators of Heart Disease (2022 UPDATE)", - "source": "https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease/data", - "data_intro": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", - "is_splited": false, - "overall_size": 1010949, - "train_size": 0, - "test_size": 0, - "c_classes": 46, - "n_classes": 9, - "cat_feature_intro": { - "HeartDisease": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", - "Smoking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", - "AlcoholDrinking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", - "Stroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", - "DiffWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", - "Sex": "Sex is a biological factor that can influence the risk of heart disease. Men and women may experience different symptoms and risk factors for heart disease.", - "AgeCategory": "Age is a significant risk factor for heart disease. As people age, their risk of developing heart disease increases. It is important to monitor heart health as individuals get older.", - "Race": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", - "Diabetic": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", - "PhysicalActivity": "Regular physical activity is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", - "GenHealth": "General health status can influence the risk of heart disease. It is important to maintain overall health and address any underlying health issues to reduce the risk of heart-related complications.", - "Asthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", - "KidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", - "SkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", - "State": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", - "GeneralHealth": "General health status can influence the risk of heart disease. It is important to maintain overall health and address any underlying health issues to reduce the risk of heart-related complications.", - "LastCheckupTime": "The time since the last medical checkup can be an important factor in heart health. Regular checkups can help identify and address risk factors for heart disease early.", - "PhysicalActivities": "Engaging in regular physical activities is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", - "RemovedTeeth": "The number of removed teeth can be an indicator of overall health, including heart health. Poor oral health has been linked to an increased risk of heart disease.", - "HadHeartAttack": "As described above, the original dataset of nearly 300 variables was reduced to 40 variables. In addition to classical EDA, this dataset can be used to apply a number of machine learning methods, especially classifier models (logistic regression, SVM, random forest, etc.). You should treat the variable \"HadHeartAttack\" as binary (\"Yes\" - respondent had heart disease; \"No\" - respondent did not have heart disease). Note, however, that the classes are unbalanced, so the classic approach of applying a model is not advisable. Fixing the weights/undersampling should yield much better results. Based on the data set, I built a logistic regression model and embedded it in an application that might inspire you: https://share.streamlit.io/kamilpytlak/heart-condition-checker/main/app.py. Can you indicate which variables have a significant effect on the likelihood of heart disease?", - "HadAngina": "Angina is a type of chest pain caused by reduced blood flow to the heart. It is a symptom of coronary artery disease and can indicate an increased risk of heart disease.", - "HadStroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", - "HadAsthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", - "HadSkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", - "HadCOPD": "Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease that causes obstructed airflow from the lungs. It can increase the risk of heart disease.", - "HadDepressiveDisorder": "Depressive disorders can affect overall health and increase the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", - "HadKidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", - "HadArthritis": "Arthritis is a condition that causes inflammation of the joints. It can affect overall health and increase the risk of heart disease.", - "HadDiabetes": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", - "DeafOrHardOfHearing": "Hearing loss can affect overall health and quality of life. It is important to address and manage hearing issues to maintain overall health and reduce the risk of heart disease.", - "BlindOrVisionDifficulty": "Vision difficulties can affect overall health and quality of life. It is important to address and manage vision issues to maintain overall health and reduce the risk of heart disease.", - "DifficultyConcentrating": "Difficulty concentrating can be a sign of underlying health issues, including heart disease. It is important to address cognitive issues to improve overall health and reduce the risk of heart-related complications.", - "DifficultyWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", - "DifficultyDressingBathing": "Difficulty with daily activities such as dressing and bathing can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", - "DifficultyErrands": "Difficulty running errands can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", - "SmokerStatus": "Smoking is a major risk factor for heart disease. It is important to address and manage smoking habits to reduce the risk of heart-related complications.", - "ECigaretteUsage": "E-cigarette usage can affect heart health. It is important to monitor and manage e-cigarette usage to reduce the risk of heart disease.", - "ChestScan": "Chest scans can help identify underlying health issues, including heart disease. It is important to monitor and address any abnormalities found in chest scans to maintain overall health.", - "RaceEthnicityCategory": "Race and ethnicity can influence the risk of heart disease. It is important to consider these factors when assessing and managing heart health.", - "AlcoholDrinkers": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", - "HIVTesting": "HIV testing is important for overall health. It is important to address and manage HIV status to reduce the risk of heart-related complications.", - "FluVaxLast12": "Receiving a flu vaccine in the last 12 months can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", - "PneumoVaxEver": "Receiving a pneumococcal vaccine can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", - "TetanusLast10Tdap": "Receiving a tetanus vaccine in the last 10 years can help reduce the risk of infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", - "HighRiskLastYear": "Being at high risk for health issues in the last year can affect overall health and increase the risk of heart disease. It is important to address and manage high-risk factors to reduce the risk of heart-related complications.", - "CovidPos": "Testing positive for COVID-19 can affect overall health and increase the risk of heart disease. It is important to monitor and manage COVID-19 symptoms to reduce the risk of heart-related complications." - }, - "num_feature_intro": { - "BMI": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", - "PhysicalHealth": "Physical health is an important factor in overall well-being and can influence the risk of heart disease. It is important to maintain good physical health to reduce the risk of heart-related complications.", - "MentalHealth": "Mental health is an important factor in overall well-being and can influence the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", - "SleepTime": "Sleep time is an important factor in overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", - "PhysicalHealthDays": "The number of days with good physical health can be an indicator of overall well-being. It is important to maintain good physical health to reduce the risk of heart-related complications.", - "MentalHealthDays": "The number of days with good mental health can be an indicator of overall well-being. It is important to address and manage mental health to reduce the risk of heart-related complications.", - "SleepHours": "The number of hours of sleep can affect overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", - "HeightInMeters": "Height can be an indicator of overall health. It is important to monitor and maintain a healthy height-to-weight ratio to reduce the risk of heart disease.", - "WeightInKilograms": "Weight can be an indicator of overall health. It is important to maintain a healthy weight to reduce the risk of heart disease." - }, - "evaluation_metric": "The evaluation metric for this dataset can include accuracy, precision, recall, F1-score, and AUC-ROC. These metrics can help assess the performance of machine learning models in predicting heart disease.", - "task_type": "classification", - "target": "GeneralHealth", - "num_feature_value": { - "BMI": [ - 12.02, - 99.64 - ], - "HeightInMeters": [ - 0.91, - 2.41 - ], - "MentalHealth": [ - 0.0, - 30.0 - ], - "MentalHealthDays": [ - 0.0, - 30.0 - ], - "PhysicalHealth": [ - 0.0, - 30.0 - ], - "PhysicalHealthDays": [ - 0.0, - 30.0 - ], - "SleepHours": [ - 1.0, - 24.0 - ], - "SleepTime": [ - 1.0, - 24.0 - ], - "WeightInKilograms": [ - 22.68, - 292.57 - ] - }, - "cat_feature_value": { - "AgeCategory": [ - "18-24", - "25-29", - "30-34", - "35-39", - "40-44", - "45-49", - "50-54", - "55-59", - "60-64", - "65-69", - "70-74", - "75-79", - "80 or older", - "Age 18 to 24", - "Age 25 to 29", - "Age 30 to 34", - "Age 35 to 39", - "Age 40 to 44", - "Age 45 to 49", - "Age 50 to 54", - "Age 55 to 59", - "Age 60 to 64", - "Age 65 to 69", - "Age 70 to 74", - "Age 75 to 79", - "Age 80 or older" - ], - "AlcoholDrinkers": [ - "No", - "Yes" - ], - "AlcoholDrinking": [ - "No", - "Yes" - ], - "Asthma": [ - "No", - "Yes" - ], - "BlindOrVisionDifficulty": [ - "No", - "Yes" - ], - "ChestScan": [ - "No", - "Yes" - ], - "CovidPos": [ - "No", - "Tested positive using home test without a health professional", - "Yes" - ], - "DeafOrHardOfHearing": [ - "No", - "Yes" - ], - "Diabetic": [ - "No", - "No, borderline diabetes", - "Yes", - "Yes (during pregnancy)" - ], - "DiffWalking": [ - "No", - "Yes" - ], - "DifficultyConcentrating": [ - "No", - "Yes" - ], - "DifficultyDressingBathing": [ - "No", - "Yes" - ], - "DifficultyErrands": [ - "No", - "Yes" - ], - "DifficultyWalking": [ - "No", - "Yes" - ], - "ECigaretteUsage": [ - "Never used e-cigarettes in my entire life", - "Not at all (right now)", - "Use them every day", - "Use them some days" - ], - "FluVaxLast12": [ - "No", - "Yes" - ], - "GenHealth": [ - "Excellent", - "Fair", - "Good", - "Poor", - "Very good" - ], - "GeneralHealth": [ - "Excellent", - "Fair", - "Good", - "Poor", - "Very good" - ], - "HIVTesting": [ - "No", - "Yes" - ], - "HadAngina": [ - "No", - "Yes" - ], - "HadArthritis": [ - "No", - "Yes" - ], - "HadAsthma": [ - "No", - "Yes" - ], - "HadCOPD": [ - "No", - "Yes" - ], - "HadDepressiveDisorder": [ - "No", - "Yes" - ], - "HadDiabetes": [ - "No", - "No, pre-diabetes or borderline diabetes", - "Yes", - "Yes, but only during pregnancy (female)" - ], - "HadHeartAttack": [ - "No", - "Yes" - ], - "HadKidneyDisease": [ - "No", - "Yes" - ], - "HadSkinCancer": [ - "No", - "Yes" - ], - "HadStroke": [ - "No", - "Yes" - ], - "HeartDisease": [ - "No", - "Yes" - ], - "HighRiskLastYear": [ - "No", - "Yes" - ], - "KidneyDisease": [ - "No", - "Yes" - ], - "LastCheckupTime": [ - "5 or more years ago", - "Within past 2 years (1 year but less than 2 years ago)", - "Within past 5 years (2 years but less than 5 years ago)", - "Within past year (anytime less than 12 months ago)" - ], - "PhysicalActivities": [ - "No", - "Yes" - ], - "PhysicalActivity": [ - "No", - "Yes" - ], - "PneumoVaxEver": [ - "No", - "Yes" - ], - "Race": [ - "American Indian/Alaskan Native", - "Asian", - "Black", - "Hispanic", - "Other", - "White" - ], - "RaceEthnicityCategory": [ - "Black only, Non-Hispanic", - "Hispanic", - "Multiracial, Non-Hispanic", - "Other race only, Non-Hispanic", - "White only, Non-Hispanic" - ], - "RemovedTeeth": [ - "1 to 5", - "6 or more, but not all", - "All", - "None of them" - ], - "Sex": [ - "Female", - "Male" - ], - "SkinCancer": [ - "No", - "Yes" - ], - "SmokerStatus": [ - "Current smoker - now smokes every day", - "Current smoker - now smokes some days", - "Former smoker", - "Never smoked" - ], - "Smoking": [ - "No", - "Yes" - ], - "State": [ - "Alabama", - "Alaska", - "Arizona", - "Arkansas", - "California", - "Colorado", - "Connecticut", - "Delaware", - "District of Columbia", - "Florida", - "Georgia", - "Guam", - "Hawaii", - "Idaho", - "Illinois", - "Indiana", - "Iowa", - "Kansas", - "Kentucky", - "Louisiana", - "Maine", - "Maryland", - "Massachusetts", - "Michigan", - "Minnesota", - "Mississippi", - "Missouri", - "Montana", - "Nebraska", - "Nevada", - "New Hampshire", - "New Jersey", - "New Mexico", - "New York", - "North Carolina", - "North Dakota", - "Ohio", - "Oklahoma", - "Oregon", - "Pennsylvania", - "Puerto Rico", - "Rhode Island", - "South Carolina", - "South Dakota", - "Tennessee", - "Texas", - "Utah", - "Vermont", - "Virgin Islands", - "Virginia", - "Washington", - "West Virginia", - "Wisconsin", - "Wyoming" - ], - "Stroke": [ - "No", - "Yes" - ], - "TetanusLast10Tdap": [ - "No, did not receive any tetanus shot in the past 10 years", - "Yes, received Tdap", - "Yes, received tetanus shot but not sure what type", - "Yes, received tetanus shot, but not Tdap" - ] - } -} \ No newline at end of file +{ + "name": "Indicators of Heart Disease (2022 UPDATE)", + "source": "https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease/data", + "data_intro": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", + "is_splited": false, + "overall_size": 1010949, + "train_size": 0, + "test_size": 0, + "c_classes": 45, + "n_classes": 9, + "cat_feature_intro": { + "HeartDisease": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", + "Smoking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", + "AlcoholDrinking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", + "Stroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", + "DiffWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", + "Sex": "Sex is a biological factor that can influence the risk of heart disease. Men and women may experience different symptoms and risk factors for heart disease.", + "AgeCategory": "Age is a significant risk factor for heart disease. As people age, their risk of developing heart disease increases. It is important to monitor heart health as individuals get older.", + "Race": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", + "Diabetic": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", + "PhysicalActivity": "Regular physical activity is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", + "Asthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", + "KidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", + "SkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", + "State": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", + "GeneralHealth": "Self-reported general health from BRFSS GENHLTH: Excellent, Very good, Good, Fair, or Poor.", + "LastCheckupTime": "The time since the last medical checkup can be an important factor in heart health. Regular checkups can help identify and address risk factors for heart disease early.", + "PhysicalActivities": "Engaging in regular physical activities is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", + "RemovedTeeth": "The number of removed teeth can be an indicator of overall health, including heart health. Poor oral health has been linked to an increased risk of heart disease.", + "HadHeartAttack": "As described above, the original dataset of nearly 300 variables was reduced to 40 variables. In addition to classical EDA, this dataset can be used to apply a number of machine learning methods, especially classifier models (logistic regression, SVM, random forest, etc.). You should treat the variable \"HadHeartAttack\" as binary (\"Yes\" - respondent had heart disease; \"No\" - respondent did not have heart disease). Note, however, that the classes are unbalanced, so the classic approach of applying a model is not advisable. Fixing the weights/undersampling should yield much better results. Based on the data set, I built a logistic regression model and embedded it in an application that might inspire you: https://share.streamlit.io/kamilpytlak/heart-condition-checker/main/app.py. Can you indicate which variables have a significant effect on the likelihood of heart disease?", + "HadAngina": "Angina is a type of chest pain caused by reduced blood flow to the heart. It is a symptom of coronary artery disease and can indicate an increased risk of heart disease.", + "HadStroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", + "HadAsthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", + "HadSkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", + "HadCOPD": "Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease that causes obstructed airflow from the lungs. It can increase the risk of heart disease.", + "HadDepressiveDisorder": "Depressive disorders can affect overall health and increase the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", + "HadKidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", + "HadArthritis": "Arthritis is a condition that causes inflammation of the joints. It can affect overall health and increase the risk of heart disease.", + "HadDiabetes": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", + "DeafOrHardOfHearing": "Hearing loss can affect overall health and quality of life. It is important to address and manage hearing issues to maintain overall health and reduce the risk of heart disease.", + "BlindOrVisionDifficulty": "Vision difficulties can affect overall health and quality of life. It is important to address and manage vision issues to maintain overall health and reduce the risk of heart disease.", + "DifficultyConcentrating": "Difficulty concentrating can be a sign of underlying health issues, including heart disease. It is important to address cognitive issues to improve overall health and reduce the risk of heart-related complications.", + "DifficultyWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", + "DifficultyDressingBathing": "Difficulty with daily activities such as dressing and bathing can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", + "DifficultyErrands": "Difficulty running errands can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", + "SmokerStatus": "Smoking is a major risk factor for heart disease. It is important to address and manage smoking habits to reduce the risk of heart-related complications.", + "ECigaretteUsage": "E-cigarette usage can affect heart health. It is important to monitor and manage e-cigarette usage to reduce the risk of heart disease.", + "ChestScan": "Chest scans can help identify underlying health issues, including heart disease. It is important to monitor and address any abnormalities found in chest scans to maintain overall health.", + "RaceEthnicityCategory": "Race and ethnicity can influence the risk of heart disease. It is important to consider these factors when assessing and managing heart health.", + "AlcoholDrinkers": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", + "HIVTesting": "Whether the respondent has ever been tested for HIV (Yes/No), not the result of that test.", + "FluVaxLast12": "Receiving a flu vaccine in the last 12 months can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", + "PneumoVaxEver": "Receiving a pneumococcal vaccine can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", + "TetanusLast10Tdap": "Receiving a tetanus vaccine in the last 10 years can help reduce the risk of infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", + "HighRiskLastYear": "Being at high risk for health issues in the last year can affect overall health and increase the risk of heart disease. It is important to address and manage high-risk factors to reduce the risk of heart-related complications.", + "CovidPos": "Testing positive for COVID-19 can affect overall health and increase the risk of heart disease. It is important to monitor and manage COVID-19 symptoms to reduce the risk of heart-related complications." + }, + "num_feature_intro": { + "BMI": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", + "PhysicalHealth": "Physical health is an important factor in overall well-being and can influence the risk of heart disease. It is important to maintain good physical health to reduce the risk of heart-related complications.", + "MentalHealth": "Mental health is an important factor in overall well-being and can influence the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", + "SleepTime": "Sleep time is an important factor in overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", + "PhysicalHealthDays": "The number of days with good physical health can be an indicator of overall well-being. It is important to maintain good physical health to reduce the risk of heart-related complications.", + "MentalHealthDays": "The number of days with good mental health can be an indicator of overall well-being. It is important to address and manage mental health to reduce the risk of heart-related complications.", + "SleepHours": "The number of hours of sleep can affect overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", + "HeightInMeters": "Height can be an indicator of overall health. It is important to monitor and maintain a healthy height-to-weight ratio to reduce the risk of heart disease.", + "WeightInKilograms": "Weight can be an indicator of overall health. It is important to maintain a healthy weight to reduce the risk of heart disease." + }, + "evaluation_metric": "The evaluation metric for this dataset can include accuracy, precision, recall, F1-score, and AUC-ROC. These metrics can help assess the performance of machine learning models in predicting heart disease.", + "task_type": "classification", + "target": "GeneralHealth", + "num_feature_value": { + "BMI": [ + 12.02, + 99.64 + ], + "HeightInMeters": [ + 0.91, + 2.41 + ], + "MentalHealth": [ + 0.0, + 30.0 + ], + "MentalHealthDays": [ + 0.0, + 30.0 + ], + "PhysicalHealth": [ + 0.0, + 30.0 + ], + "PhysicalHealthDays": [ + 0.0, + 30.0 + ], + "SleepHours": [ + 1.0, + 24.0 + ], + "SleepTime": [ + 1.0, + 24.0 + ], + "WeightInKilograms": [ + 22.68, + 292.57 + ] + }, + "cat_feature_value": { + "AgeCategory": [ + "18-24", + "25-29", + "30-34", + "35-39", + "40-44", + "45-49", + "50-54", + "55-59", + "60-64", + "65-69", + "70-74", + "75-79", + "80 or older", + "Age 18 to 24", + "Age 25 to 29", + "Age 30 to 34", + "Age 35 to 39", + "Age 40 to 44", + "Age 45 to 49", + "Age 50 to 54", + "Age 55 to 59", + "Age 60 to 64", + "Age 65 to 69", + "Age 70 to 74", + "Age 75 to 79", + "Age 80 or older" + ], + "AlcoholDrinkers": [ + "No", + "Yes" + ], + "AlcoholDrinking": [ + "No", + "Yes" + ], + "Asthma": [ + "No", + "Yes" + ], + "BlindOrVisionDifficulty": [ + "No", + "Yes" + ], + "ChestScan": [ + "No", + "Yes" + ], + "CovidPos": [ + "No", + "Tested positive using home test without a health professional", + "Yes" + ], + "DeafOrHardOfHearing": [ + "No", + "Yes" + ], + "Diabetic": [ + "No", + "No, borderline diabetes", + "Yes", + "Yes (during pregnancy)" + ], + "DiffWalking": [ + "No", + "Yes" + ], + "DifficultyConcentrating": [ + "No", + "Yes" + ], + "DifficultyDressingBathing": [ + "No", + "Yes" + ], + "DifficultyErrands": [ + "No", + "Yes" + ], + "DifficultyWalking": [ + "No", + "Yes" + ], + "ECigaretteUsage": [ + "Never used e-cigarettes in my entire life", + "Not at all (right now)", + "Use them every day", + "Use them some days" + ], + "FluVaxLast12": [ + "No", + "Yes" + ], + "GeneralHealth": [ + "Excellent", + "Very good", + "Good", + "Fair", + "Poor" + ], + "HIVTesting": [ + "No", + "Yes" + ], + "HadAngina": [ + "No", + "Yes" + ], + "HadArthritis": [ + "No", + "Yes" + ], + "HadAsthma": [ + "No", + "Yes" + ], + "HadCOPD": [ + "No", + "Yes" + ], + "HadDepressiveDisorder": [ + "No", + "Yes" + ], + "HadDiabetes": [ + "No", + "No, pre-diabetes or borderline diabetes", + "Yes", + "Yes, but only during pregnancy (female)" + ], + "HadHeartAttack": [ + "No", + "Yes" + ], + "HadKidneyDisease": [ + "No", + "Yes" + ], + "HadSkinCancer": [ + "No", + "Yes" + ], + "HadStroke": [ + "No", + "Yes" + ], + "HeartDisease": [ + "No", + "Yes" + ], + "HighRiskLastYear": [ + "No", + "Yes" + ], + "KidneyDisease": [ + "No", + "Yes" + ], + "LastCheckupTime": [ + "5 or more years ago", + "Within past 2 years (1 year but less than 2 years ago)", + "Within past 5 years (2 years but less than 5 years ago)", + "Within past year (anytime less than 12 months ago)" + ], + "PhysicalActivities": [ + "No", + "Yes" + ], + "PhysicalActivity": [ + "No", + "Yes" + ], + "PneumoVaxEver": [ + "No", + "Yes" + ], + "Race": [ + "American Indian/Alaskan Native", + "Asian", + "Black", + "Hispanic", + "Other", + "White" + ], + "RaceEthnicityCategory": [ + "Black only, Non-Hispanic", + "Hispanic", + "Multiracial, Non-Hispanic", + "Other race only, Non-Hispanic", + "White only, Non-Hispanic" + ], + "RemovedTeeth": [ + "1 to 5", + "6 or more, but not all", + "All", + "None of them" + ], + "Sex": [ + "Female", + "Male" + ], + "SkinCancer": [ + "No", + "Yes" + ], + "SmokerStatus": [ + "Current smoker - now smokes every day", + "Current smoker - now smokes some days", + "Former smoker", + "Never smoked" + ], + "Smoking": [ + "No", + "Yes" + ], + "State": [ + "Alabama", + "Alaska", + "Arizona", + "Arkansas", + "California", + "Colorado", + "Connecticut", + "Delaware", + "District of Columbia", + "Florida", + "Georgia", + "Guam", + "Hawaii", + "Idaho", + "Illinois", + "Indiana", + "Iowa", + "Kansas", + "Kentucky", + "Louisiana", + "Maine", + "Maryland", + "Massachusetts", + "Michigan", + "Minnesota", + "Mississippi", + "Missouri", + "Montana", + "Nebraska", + "Nevada", + "New Hampshire", + "New Jersey", + "New Mexico", + "New York", + "North Carolina", + "North Dakota", + "Ohio", + "Oklahoma", + "Oregon", + "Pennsylvania", + "Puerto Rico", + "Rhode Island", + "South Carolina", + "South Dakota", + "Tennessee", + "Texas", + "Utah", + "Vermont", + "Virgin Islands", + "Virginia", + "Washington", + "West Virginia", + "Wisconsin", + "Wyoming" + ], + "Stroke": [ + "No", + "Yes" + ], + "TetanusLast10Tdap": [ + "No, did not receive any tetanus shot in the past 10 years", + "Yes, received Tdap", + "Yes, received tetanus shot but not sure what type", + "Yes, received tetanus shot, but not Tdap" + ] + } +} diff --git a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_008/test_current.csv b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_008/test_current.csv index f2c7de1cb47a235d7bed85e0bc3d86ad2e7a986c..8f3c9cf5c76831198aa10ce0f1e86c55b581d128 100644 --- a/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_008/test_current.csv +++ b/ranking_and_filtering/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B4/Indicators_of_Heart_Disease_(2022_UPDATE)_B4_008/test_current.csv @@ -1,16 +1,19 @@ -HeartDisease,BMI,Smoking,AlcoholDrinking,Stroke,PhysicalHealth,MentalHealth,DiffWalking,Sex,AgeCategory,Race,Diabetic,PhysicalActivity,GenHealth,SleepTime,Asthma,KidneyDisease,SkinCancer,State,GeneralHealth,PhysicalHealthDays,MentalHealthDays,LastCheckupTime,PhysicalActivities,SleepHours,RemovedTeeth,HadHeartAttack,HadAngina,HadStroke,HadAsthma,HadSkinCancer,HadCOPD,HadDepressiveDisorder,HadKidneyDisease,HadArthritis,HadDiabetes,DeafOrHardOfHearing,BlindOrVisionDifficulty,DifficultyConcentrating,DifficultyWalking,DifficultyDressingBathing,DifficultyErrands,SmokerStatus,ECigaretteUsage,ChestScan,RaceEthnicityCategory,HeightInMeters,WeightInKilograms,AlcoholDrinkers,HIVTesting,FluVaxLast12,PneumoVaxEver,TetanusLast10Tdap,HighRiskLastYear,CovidPos -,30.23,,,,,,,Female,Age 55 to 59,,,,,,,,,Delaware,Excellent,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.55,72.57,Yes,No,,Yes,"Yes, received Tdap",No,No -,32.01,,,,,,,Female,Age 60 to 64,,,,,,,,,Tennessee,Excellent,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,Yes,No,,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,,1.57,79.38,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",, -,37.11,,,,,,,Female,Age 40 to 44,,,,,,,,,California,Excellent,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.52,86.18,No,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,Yes -,34.3,,,,,,,Male,Age 40 to 44,,,,,,,,,Oregon,Excellent,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,117.93,Yes,Yes,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No -,31.64,,,,,,,Female,Age 18 to 24,,,,,,,,,Utah,Excellent,0.0,10.0,Within past year (anytime less than 12 months ago),Yes,9.0,None of them,No,No,No,No,No,No,No,No,No,"Yes, but only during pregnancy (female)",No,No,No,No,No,No,Never smoked,Use them some days,Yes,Hispanic,1.52,73.48,No,No,No,No,"Yes, received Tdap",No,No -,32.32,,,,,,,Male,Age 50 to 54,,,,,,,,,Illinois,Excellent,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,111.13,Yes,No,Yes,No,"Yes, received Tdap",No,No -,36.49,,,,,,,Male,Age 40 to 44,,,,,,,,,Arkansas,Excellent,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.73,108.86,No,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes -,30.9,,,,,,,Female,Age 30 to 34,,,,,,,,,Indiana,Excellent,0.0,2.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,81.65,Yes,Yes,No,No,"Yes, received Tdap",No,No -,34.54,,,,,,,Female,Age 55 to 59,,,,,,,,,North Carolina,Excellent,0.0,10.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.6,88.45,Yes,Yes,Yes,No,"Yes, received Tdap",No,Yes -,31.01,,,,,,,Female,Age 80 or older,,,,,,,,,New Hampshire,Excellent,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,1 to 5,No,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,95.25,No,No,Yes,Yes,"Yes, received Tdap",No,No -,34.75,,,,,,,Female,Age 55 to 59,,,,,,,,,Ohio,Excellent,0.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,86.18,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,30.9,,,,,,,Female,Age 18 to 24,,,,,,,,,Indiana,Excellent,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,Yes,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.63,81.65,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No -,39.05,,,,,,,Male,Age 25 to 29,,,,,,,,,Montana,Excellent,0.0,2.0,Within past year (anytime less than 12 months ago),No,5.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,127.01,No,No,No,Yes,,No,No -,32.23,,,,,,,Male,Age 70 to 74,,,,,,,,,Maryland,Excellent,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,Yes,No,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.88,113.85,No,Yes,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No -,30.42,,,,,,,Female,Age 80 or older,,,,,,,,,Massachusetts,Excellent,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.55,73.03,Yes,No,No,Yes,"Yes, received Tdap",No,Yes +HeartDisease,BMI,Smoking,AlcoholDrinking,Stroke,PhysicalHealth,MentalHealth,DiffWalking,Sex,AgeCategory,Race,Diabetic,PhysicalActivity,SleepTime,Asthma,KidneyDisease,SkinCancer,State,GeneralHealth,PhysicalHealthDays,MentalHealthDays,LastCheckupTime,PhysicalActivities,SleepHours,RemovedTeeth,HadHeartAttack,HadAngina,HadStroke,HadAsthma,HadSkinCancer,HadCOPD,HadDepressiveDisorder,HadKidneyDisease,HadArthritis,HadDiabetes,DeafOrHardOfHearing,BlindOrVisionDifficulty,DifficultyConcentrating,DifficultyWalking,DifficultyDressingBathing,DifficultyErrands,SmokerStatus,ECigaretteUsage,ChestScan,RaceEthnicityCategory,HeightInMeters,WeightInKilograms,AlcoholDrinkers,HIVTesting,FluVaxLast12,PneumoVaxEver,TetanusLast10Tdap,HighRiskLastYear,CovidPos +No,30.99,No,No,No,0.0,0.0,No,Female,45-49,White,No,No,10.0,No,No,No,,Excellent,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,30.23,,,,,,,Female,Age 55 to 59,,,,,,,,Delaware,Excellent,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.55,72.57,Yes,No,,Yes,"Yes, received Tdap",No,No +,32.01,,,,,,,Female,Age 60 to 64,,,,,,,,Tennessee,Excellent,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,"6 or more, but not all",No,No,No,No,No,No,No,No,No,Yes,No,,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,,1.57,79.38,Yes,Yes,No,No,"Yes, received tetanus shot but not sure what type",, +,37.11,,,,,,,Female,Age 40 to 44,,,,,,,,California,Excellent,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,Hispanic,1.52,86.18,No,No,Yes,No,"Yes, received tetanus shot but not sure what type",No,Yes +,34.3,,,,,,,Male,Age 40 to 44,,,,,,,,Oregon,Excellent,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,117.93,Yes,Yes,No,Yes,"No, did not receive any tetanus shot in the past 10 years",No,No +,31.64,,,,,,,Female,Age 18 to 24,,,,,,,,Utah,Excellent,0.0,10.0,Within past year (anytime less than 12 months ago),Yes,9.0,None of them,No,No,No,No,No,No,No,No,No,"Yes, but only during pregnancy (female)",No,No,No,No,No,No,Never smoked,Use them some days,Yes,Hispanic,1.52,73.48,No,No,No,No,"Yes, received Tdap",No,No +No,30.54,No,No,No,0.0,14.0,No,Male,50-54,White,No,No,6.0,No,No,No,,Excellent,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,32.32,,,,,,,Male,Age 50 to 54,,,,,,,,Illinois,Excellent,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,7.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.85,111.13,Yes,No,Yes,No,"Yes, received Tdap",No,No +,36.49,,,,,,,Male,Age 40 to 44,,,,,,,,Arkansas,Excellent,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.73,108.86,No,Yes,No,No,"No, did not receive any tetanus shot in the past 10 years",No,Yes +,30.9,,,,,,,Female,Age 30 to 34,,,,,,,,Indiana,Excellent,0.0,2.0,Within past 2 years (1 year but less than 2 years ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.63,81.65,Yes,Yes,No,No,"Yes, received Tdap",No,No +,34.54,,,,,,,Female,Age 55 to 59,,,,,,,,North Carolina,Excellent,0.0,10.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,Yes,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.6,88.45,Yes,Yes,Yes,No,"Yes, received Tdap",No,Yes +,31.01,,,,,,,Female,Age 80 or older,,,,,,,,New Hampshire,Excellent,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,9.0,1 to 5,No,No,No,No,Yes,No,No,No,No,Yes,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.75,95.25,No,No,Yes,Yes,"Yes, received Tdap",No,No +,34.75,,,,,,,Female,Age 55 to 59,,,,,,,,Ohio,Excellent,0.0,0.0,Within past year (anytime less than 12 months ago),No,6.0,1 to 5,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.57,86.18,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,30.9,,,,,,,Female,Age 18 to 24,,,,,,,,Indiana,Excellent,0.0,0.0,Within past year (anytime less than 12 months ago),No,8.0,None of them,No,No,No,Yes,No,No,No,No,No,No,No,Yes,Yes,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"Black only, Non-Hispanic",1.63,81.65,No,No,No,No,"No, did not receive any tetanus shot in the past 10 years",No,No +,39.05,,,,,,,Male,Age 25 to 29,,,,,,,,Montana,Excellent,0.0,2.0,Within past year (anytime less than 12 months ago),No,5.0,None of them,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,Yes,"White only, Non-Hispanic",1.8,127.01,No,No,No,Yes,,No,No +No,31.53,Yes,No,No,0.0,0.0,No,Female,50-54,White,No,Yes,6.0,Yes,No,No,,Excellent,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,32.23,,,,,,,Male,Age 70 to 74,,,,,,,,Maryland,Excellent,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,Yes,No,No,No,No,No,No,Yes,Yes,No,No,No,No,No,No,Former smoker,Never used e-cigarettes in my entire life,Yes,"Black only, Non-Hispanic",1.88,113.85,No,Yes,Yes,Yes,"Yes, received tetanus shot, but not Tdap",No,No +,30.42,,,,,,,Female,Age 80 or older,,,,,,,,Massachusetts,Excellent,0.0,0.0,Within past year (anytime less than 12 months ago),Yes,8.0,1 to 5,No,No,No,No,No,No,No,No,Yes,No,No,No,No,No,No,No,Never smoked,Never used e-cigarettes in my entire life,No,"White only, Non-Hispanic",1.55,73.03,Yes,No,No,Yes,"Yes, received Tdap",No,Yes diff --git a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_001/Sleep_Health_and_Lifestyle_Dataset_B4_001_current.json b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_001/Sleep_Health_and_Lifestyle_Dataset_B4_001_current.json index 3e63c7f92465b83619d54f3a9cab4a47704cbff1..3b479c2b156a16a2a7d70148093227a395fbe8e0 100644 --- a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_001/Sleep_Health_and_Lifestyle_Dataset_B4_001_current.json +++ b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_001/Sleep_Health_and_Lifestyle_Dataset_B4_001_current.json @@ -1,1375 +1,1375 @@ { - "id": "001", - "task_type": "B4", - "subtask_type": "list", - "perspective": "data_holder", - "dataset_name": "Sleep Health and Lifestyle Dataset", - "table_path": "output/Sleep Health and Lifestyle Dataset_B4/Sleep Health and Lifestyle Dataset_B4_001/test.csv", - "query": "I’ve attached our historical patient logs showing patterns in sleep issues. From this new batch of wellness profiles, can you flag all individuals who are likely experiencing insomnia based on the trends in the file I sent?", - "meta_info": { - "domain": "medical" + "id": "001", + "task_type": "B4", + "subtask_type": "list", + "perspective": "data_holder", + "dataset_name": "Sleep Health and Lifestyle Dataset", + "table_path": "output/Sleep Health and Lifestyle Dataset_B4/Sleep Health and Lifestyle Dataset_B4_001/test.csv", + "query": "I’ve attached our historical patient logs showing patterns in sleep issues. From this new batch of wellness profiles, can you flag all individuals who are likely experiencing insomnia based on the trends in the file I sent?", + "meta_info": { + "domain": "medical" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "Person ID": 130, + "Gender": "Male", + "Age": 38, + "Occupation": "Lawyer", + "Sleep Duration": 7.3, + "Quality of Sleep": 8, + "Physical Activity Level": 60, + "Stress Level": 5, + "BMI Category": "Normal", + "Blood Pressure": "130/85", + "Heart Rate": 68, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "002", + "features": { + "Person ID": 259, + "Gender": "Female", + "Age": 45, + "Occupation": "Teacher", + "Sleep Duration": 6.6, + "Quality of Sleep": 7, + "Physical Activity Level": 45, + "Stress Level": 4, + "BMI Category": "Overweight", + "Blood Pressure": "135/90", + "Heart Rate": 65, + "Daily Steps": 6000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "003", + "features": { + "Person ID": 358, + "Gender": "Female", + "Age": 58, + "Occupation": "Nurse", + "Sleep Duration": 8.0, + "Quality of Sleep": 9, + "Physical Activity Level": 75, + "Stress Level": 3, + "BMI Category": "Overweight", + "Blood Pressure": "140/95", + "Heart Rate": 68, + "Daily Steps": 7000, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "004", + "features": { + "Person ID": 240, + "Gender": "Male", + "Age": 44, + "Occupation": "Salesperson", + "Sleep Duration": 6.4, + "Quality of Sleep": 6, + "Physical Activity Level": 45, + "Stress Level": 7, + "BMI Category": "Overweight", + "Blood Pressure": "130/85", + "Heart Rate": 72, + "Daily Steps": 6000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "005", + "features": { + "Person ID": 36, + "Gender": "Male", + "Age": 31, + "Occupation": "Doctor", + "Sleep Duration": 6.1, + "Quality of Sleep": 6, + "Physical Activity Level": 30, + "Stress Level": 8, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 72, + "Daily Steps": 5000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "006", + "features": { + "Person ID": 145, + "Gender": "Male", + "Age": 38, + "Occupation": "Lawyer", + "Sleep Duration": 7.1, + "Quality of Sleep": 8, + "Physical Activity Level": 60, + "Stress Level": 5, + "BMI Category": "Normal", + "Blood Pressure": "130/85", + "Heart Rate": 68, + "Daily Steps": 8000, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "007", + "features": { + "Person ID": 306, + "Gender": "Female", + "Age": 51, + "Occupation": "Nurse", + "Sleep Duration": 6.1, + "Quality of Sleep": 6, + "Physical Activity Level": 90, + "Stress Level": 8, + "BMI Category": "Overweight", + "Blood Pressure": "140/95", + "Heart Rate": 75, + "Daily Steps": 10000, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "008", + "features": { + "Person ID": 237, + "Gender": "Male", + "Age": 44, + "Occupation": "Salesperson", + "Sleep Duration": 6.4, + "Quality of Sleep": 6, + "Physical Activity Level": 45, + "Stress Level": 7, + "BMI Category": "Overweight", + "Blood Pressure": "130/85", + "Heart Rate": 72, + "Daily Steps": 6000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "009", + "features": { + "Person ID": 49, + "Gender": "Male", + "Age": 31, + "Occupation": "Doctor", + "Sleep Duration": 7.7, + "Quality of Sleep": 7, + "Physical Activity Level": 75, + "Stress Level": 6, + "BMI Category": "Normal", + "Blood Pressure": "120/80", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "010", + "features": { + "Person ID": 338, + "Gender": "Female", + "Age": 54, + "Occupation": "Engineer", + "Sleep Duration": 8.4, + "Quality of Sleep": 9, + "Physical Activity Level": 30, + "Stress Level": 3, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 65, + "Daily Steps": 5000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "011", + "features": { + "Person ID": 20, + "Gender": "Male", + "Age": 30, + "Occupation": "Doctor", + "Sleep Duration": 7.6, + "Quality of Sleep": 7, + "Physical Activity Level": 75, + "Stress Level": 6, + "BMI Category": "Normal", + "Blood Pressure": "120/80", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "012", + "features": { + "Person ID": 174, + "Gender": "Male", + "Age": 41, + "Occupation": "Lawyer", + "Sleep Duration": 7.7, + "Quality of Sleep": 8, + "Physical Activity Level": 90, + "Stress Level": 5, + "BMI Category": "Normal", + "Blood Pressure": "130/85", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "013", + "features": { + "Person ID": 318, + "Gender": "Female", + "Age": 53, + "Occupation": "Engineer", + "Sleep Duration": 8.5, + "Quality of Sleep": 9, + "Physical Activity Level": 30, + "Stress Level": 3, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 65, + "Daily Steps": 5000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "014", + "features": { + "Person ID": 109, + "Gender": "Male", + "Age": 37, + "Occupation": "Engineer", + "Sleep Duration": 7.8, + "Quality of Sleep": 8, + "Physical Activity Level": 70, + "Stress Level": 4, + "BMI Category": "Normal Weight", + "Blood Pressure": "120/80", + "Heart Rate": 68, + "Daily Steps": 7000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "015", + "features": { + "Person ID": 181, + "Gender": "Male", + "Age": 42, + "Occupation": "Lawyer", + "Sleep Duration": 7.8, + "Quality of Sleep": 8, + "Physical Activity Level": 90, + "Stress Level": 5, + "BMI Category": "Normal", + "Blood Pressure": "130/85", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "016", + "features": { + "Person ID": 206, + "Gender": "Male", + "Age": 43, + "Occupation": "Engineer", + "Sleep Duration": 7.7, + "Quality of Sleep": 8, + "Physical Activity Level": 90, + "Stress Level": 5, + "BMI Category": "Normal", + "Blood Pressure": "130/85", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "017", + "features": { + "Person ID": 332, + "Gender": "Female", + "Age": 53, + "Occupation": "Engineer", + "Sleep Duration": 8.4, + "Quality of Sleep": 9, + "Physical Activity Level": 30, + "Stress Level": 3, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 65, + "Daily Steps": 5000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "018", + "features": { + "Person ID": 80, + "Gender": "Male", + "Age": 33, + "Occupation": "Doctor", + "Sleep Duration": 6.0, + "Quality of Sleep": 6, + "Physical Activity Level": 30, + "Stress Level": 8, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 72, + "Daily Steps": 5000, + "Sleep 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- }, - { - "scenario_id": "062", - "features": { - "Person ID": 19, - "Gender": "Female", - "Age": 29, - "Occupation": "Nurse", - "Sleep Duration": 6.5, - "Quality of Sleep": 5, - "Physical Activity Level": 40, - "Stress Level": 7, - "BMI Category": "Normal Weight", - "Blood Pressure": "132/87", - "Heart Rate": 80, - "Daily Steps": 4000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "063", - "features": { - "Person ID": 203, - "Gender": "Male", - "Age": 43, - "Occupation": "Engineer", - "Sleep Duration": 7.8, - "Quality of Sleep": 8, - "Physical Activity Level": 90, - "Stress Level": 5, - "BMI Category": "Normal", - "Blood Pressure": "130/85", - "Heart Rate": 70, - "Daily Steps": 8000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "064", - "features": { - "Person ID": 81, - "Gender": "Female", - "Age": 34, - "Occupation": "Scientist", - "Sleep Duration": 5.8, - "Quality of Sleep": 4, - "Physical Activity Level": 32, - "Stress Level": 8, - "BMI Category": "Overweight", - "Blood Pressure": "131/86", - "Heart Rate": 81, - "Daily Steps": 5200, - "Sleep Disorder": "Sleep Apnea" - } - }, - { - "scenario_id": "065", - "features": { - "Person ID": 177, - "Gender": "Male", - "Age": 41, - "Occupation": "Lawyer", - "Sleep Duration": 7.6, - "Quality of Sleep": 8, - "Physical Activity Level": 90, - "Stress Level": 5, - "BMI Category": "Normal", - "Blood Pressure": "130/85", - "Heart Rate": 70, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "066", - "features": { - "Person ID": 173, - "Gender": "Male", - "Age": 41, - "Occupation": "Lawyer", - "Sleep Duration": 7.7, - "Quality of Sleep": 8, - "Physical Activity Level": 90, - "Stress Level": 5, - "BMI Category": "Normal", - "Blood Pressure": "130/85", - "Heart Rate": 70, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "067", - "features": { - "Person ID": 325, - "Gender": "Female", - "Age": 53, - "Occupation": "Engineer", - "Sleep Duration": 8.3, - "Quality of Sleep": 9, - "Physical Activity Level": 30, - "Stress Level": 3, - "BMI Category": "Normal", - "Blood Pressure": "125/80", - "Heart Rate": 65, - "Daily Steps": 5000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "068", - "features": { - "Person ID": 22, - "Gender": "Male", - "Age": 30, - "Occupation": "Doctor", - "Sleep Duration": 7.7, - "Quality of Sleep": 7, - "Physical Activity Level": 75, - "Stress Level": 6, - "BMI Category": "Normal", - "Blood Pressure": "120/80", - "Heart Rate": 70, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "069", - "features": { - "Person ID": 29, - "Gender": "Male", - "Age": 30, - "Occupation": "Doctor", - "Sleep Duration": 7.9, - "Quality of Sleep": 7, - "Physical Activity Level": 75, - "Stress Level": 6, - "BMI Category": "Normal", - "Blood Pressure": "120/80", - "Heart Rate": 70, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "070", - "features": { - "Person ID": 355, - "Gender": "Female", - "Age": 58, - "Occupation": "Nurse", - "Sleep Duration": 8.0, - "Quality of Sleep": 9, - "Physical Activity Level": 75, - "Stress Level": 3, - "BMI Category": "Overweight", - "Blood Pressure": "140/95", - "Heart Rate": 68, - "Daily Steps": 7000, - "Sleep Disorder": "Sleep Apnea" - } - }, - { - "scenario_id": "071", - "features": { - "Person ID": 267, - "Gender": "Male", - "Age": 48, - "Occupation": "Doctor", - "Sleep Duration": 7.3, - "Quality of Sleep": 7, - "Physical Activity Level": 65, - "Stress Level": 5, - "BMI Category": "Obese", - "Blood Pressure": "142/92", - "Heart Rate": 83, - "Daily Steps": 3500, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "072", - "features": { - "Person ID": 76, - "Gender": "Male", - "Age": 33, - "Occupation": "Doctor", - "Sleep Duration": 6.0, - "Quality of Sleep": 6, - "Physical Activity Level": 30, - "Stress Level": 8, - "BMI Category": "Normal", - "Blood Pressure": "125/80", - "Heart Rate": 72, - "Daily Steps": 5000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "073", - "features": { - "Person ID": 213, - "Gender": "Male", - "Age": 43, - "Occupation": "Engineer", - "Sleep Duration": 7.8, - "Quality of Sleep": 8, - "Physical Activity Level": 90, - "Stress Level": 5, - "BMI Category": "Normal", - "Blood Pressure": "130/85", - "Heart Rate": 70, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "074", - "features": { - "Person ID": 313, - "Gender": "Female", - "Age": 52, - "Occupation": "Engineer", - "Sleep Duration": 8.4, - "Quality of Sleep": 9, - "Physical Activity Level": 30, - "Stress Level": 3, - "BMI Category": "Normal", - "Blood Pressure": "125/80", - "Heart Rate": 65, - "Daily Steps": 5000, - "Sleep Disorder": null - } - } - ], - "target_column": "Sleep Disorder", - "task_sub_type": "classification", - "ranking_ground_truth": { - "top_k_ids": [ - "002", - "004", - "008", - "021", - "023", - "028", - "033", - "036", - "047", - "049", - "050", - "061", - "062", - "063", - "071" - ] - }, - "active_filters": [], - "target_class_value": "insomnia" - }, - "response": "", - "evaluation_score": {} -} \ No newline at end of file + "active_filters": [], + "target_class_value": "insomnia" + }, + "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_001/history.csv b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_001/history.csv index ba0b20afcede5e53ff26b91a0b5b81ad5a674a7b..7ca0d37ff2272308e6f803dae8dbbd0788a04b39 100644 --- a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_001/history.csv +++ b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_001/history.csv @@ -1,152 +1,152 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activity Level,Stress Level,BMI Category,Blood Pressure,Heart Rate,Daily Steps,Sleep Disorder -1,Male,27,Software Engineer,6.1,6,42,6,Overweight,126/83,77,4200, -2,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000, -3,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000, +1,Male,27,Software Engineer,6.1,6,42,6,Overweight,126/83,77,4200,No Disorder +2,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000,No Disorder +3,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000,No Disorder 5,Male,28,Sales Representative,5.9,4,30,8,Obese,140/90,85,3000,Sleep Apnea 7,Male,29,Teacher,6.3,6,40,7,Obese,140/90,82,3500,Insomnia -8,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -10,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -11,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000, -12,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -13,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000, -14,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000, -15,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000, -16,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000, +8,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +10,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +11,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000,No Disorder +12,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +13,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000,No Disorder +14,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000,No Disorder +15,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000,No Disorder +16,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000,No Disorder 17,Female,29,Nurse,6.5,5,40,7,Normal Weight,132/87,80,4000,Sleep Apnea 18,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000,Sleep Apnea -21,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -23,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -24,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -25,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -26,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000, -28,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000, -30,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000, +21,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +23,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +24,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +25,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +26,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,No Disorder +28,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,No Disorder +30,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,No Disorder 31,Female,30,Nurse,6.4,5,35,7,Normal Weight,130/86,78,4100,Sleep Apnea 32,Female,30,Nurse,6.4,5,35,7,Normal Weight,130/86,78,4100,Insomnia -33,Female,31,Nurse,7.9,8,75,4,Normal Weight,117/76,69,6800, -34,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -38,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -39,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -40,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -41,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -42,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -43,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -44,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -45,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -46,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -47,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -48,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000, +33,Female,31,Nurse,7.9,8,75,4,Normal Weight,117/76,69,6800,No Disorder +34,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +38,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +39,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +40,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +41,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +42,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +43,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +44,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +45,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +46,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +47,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +48,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder 50,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,Sleep Apnea -51,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000, -52,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000, -53,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -54,Male,32,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -55,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -56,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -57,Male,32,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -58,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -59,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -60,Male,32,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -61,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -62,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -63,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000, -64,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000, -65,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000, -67,Male,32,Accountant,7.2,8,50,6,Normal Weight,118/76,68,7000, +51,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000,No Disorder +52,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000,No Disorder +53,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +54,Male,32,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +55,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +56,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +57,Male,32,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +58,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +59,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +60,Male,32,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +61,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +62,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +63,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,No Disorder +64,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,No Disorder +65,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,No Disorder +67,Male,32,Accountant,7.2,8,50,6,Normal Weight,118/76,68,7000,No Disorder 68,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,Insomnia -69,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500, -70,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500, -71,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -72,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -73,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -74,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -75,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -77,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -78,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -79,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, +69,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500,No Disorder +70,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500,No Disorder +71,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +72,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +73,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +74,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +75,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +77,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +78,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +79,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder 82,Female,34,Scientist,5.8,4,32,8,Overweight,131/86,81,5200,Sleep Apnea -83,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600, -84,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600, -85,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000, -86,Female,35,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -87,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000, -89,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000, -90,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000, -91,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000, -92,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000, -93,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000, +83,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600,No Disorder +84,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600,No Disorder +85,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000,No Disorder +86,Female,35,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +87,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000,No Disorder +89,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,No Disorder +90,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,No Disorder +91,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,No Disorder +92,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,No Disorder +93,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000,No Disorder 94,Male,35,Lawyer,7.4,7,60,5,Obese,135/88,84,3300,Sleep Apnea 95,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000,Insomnia -96,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -98,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -99,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000, -100,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000, -102,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000, -103,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000, +96,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +98,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +99,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +100,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +102,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +103,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,No Disorder 104,Male,36,Teacher,6.6,5,35,7,Overweight,129/84,74,4800,Sleep Apnea 105,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,Sleep Apnea 106,Male,36,Teacher,6.6,5,35,7,Overweight,129/84,74,4800,Insomnia -107,Female,37,Nurse,6.1,6,42,6,Overweight,126/83,77,4200, -108,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000, -111,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -112,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000, -113,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -114,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000, -116,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -117,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -119,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -120,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -121,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -122,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -125,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -126,Female,37,Nurse,7.5,8,60,4,Normal Weight,120/80,70,8000, -127,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -128,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -131,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -133,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -134,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -135,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -136,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -137,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -138,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000, -139,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -140,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000, -141,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -142,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000, -143,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -144,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, +107,Female,37,Nurse,6.1,6,42,6,Overweight,126/83,77,4200,No Disorder +108,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000,No Disorder +111,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +112,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,No Disorder +113,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +114,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,No Disorder +116,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +117,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +119,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +120,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +121,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +122,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +125,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +126,Female,37,Nurse,7.5,8,60,4,Normal Weight,120/80,70,8000,No Disorder +127,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +128,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +131,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +133,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +134,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +135,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +136,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +137,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +138,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,No Disorder +139,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +140,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,No Disorder +141,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +142,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,No Disorder +143,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +144,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder 146,Female,38,Lawyer,7.4,7,60,5,Obese,135/88,84,3300,Sleep Apnea 147,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,Insomnia 148,Male,39,Engineer,6.5,5,40,7,Overweight,132/87,80,4000,Insomnia -149,Female,39,Lawyer,6.9,7,50,6,Normal Weight,128/85,75,5500, -150,Female,39,Accountant,8.0,9,80,3,Normal Weight,115/78,67,7500, -151,Female,39,Accountant,8.0,9,80,3,Normal Weight,115/78,67,7500, -152,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -153,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -154,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -155,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -156,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -157,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -158,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -159,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -160,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -161,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -162,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300, -163,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300, -164,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000, +149,Female,39,Lawyer,6.9,7,50,6,Normal Weight,128/85,75,5500,No Disorder +150,Female,39,Accountant,8.0,9,80,3,Normal Weight,115/78,67,7500,No Disorder +151,Female,39,Accountant,8.0,9,80,3,Normal Weight,115/78,67,7500,No Disorder +152,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +153,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +154,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +155,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +156,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +157,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +158,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +159,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +160,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +161,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +162,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300,No Disorder +163,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300,No Disorder +164,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000,No Disorder 166,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,Insomnia -167,Male,41,Engineer,7.3,8,70,6,Normal Weight,121/79,72,6200, -169,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000, -170,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -171,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -172,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -175,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000, -176,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000, +167,Male,41,Engineer,7.3,8,70,6,Normal Weight,121/79,72,6200,No Disorder +169,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000,No Disorder +170,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +171,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +172,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +175,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,No Disorder +176,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,No Disorder 178,Male,42,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia -180,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, -182,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, -183,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, -184,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, +180,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +182,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +183,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +184,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder 186,Female,42,Teacher,6.8,6,45,7,Overweight,130/85,78,5000,Sleep Apnea 187,Female,43,Teacher,6.7,7,45,4,Overweight,135/90,65,6000,Insomnia 189,Female,43,Teacher,6.7,7,45,4,Overweight,135/90,65,6000,Insomnia @@ -161,18 +161,18 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 200,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia 201,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia 202,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Insomnia -205,Male,43,Engineer,7.6,8,75,4,Overweight,122/80,68,6800, -207,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -208,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -209,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -210,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -211,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -212,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -214,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -215,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -216,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -217,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -218,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, +205,Male,43,Engineer,7.6,8,75,4,Overweight,122/80,68,6800,No Disorder +207,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +208,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +209,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +210,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +211,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +212,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +214,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +215,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +216,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +217,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +218,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder 219,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Sleep Apnea 220,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Sleep Apnea 222,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia @@ -198,8 +198,8 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 246,Female,44,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia 247,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia 248,Male,44,Engineer,6.8,7,45,7,Overweight,130/85,78,5000,Insomnia -249,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000, -250,Male,44,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000, +249,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,No Disorder +250,Male,44,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,No Disorder 251,Female,45,Teacher,6.8,7,30,6,Overweight,135/90,65,6000,Insomnia 252,Female,45,Teacher,6.8,7,30,6,Overweight,135/90,65,6000,Insomnia 253,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia @@ -209,12 +209,12 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 258,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia 260,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia 261,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia -262,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000, -263,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000, -264,Female,45,Manager,6.9,7,55,5,Overweight,125/82,75,5500, +262,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,No Disorder +263,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,No Disorder +264,Female,45,Manager,6.9,7,55,5,Overweight,125/82,75,5500,No Disorder 265,Male,48,Doctor,7.3,7,65,5,Obese,142/92,83,3500,Insomnia 266,Female,48,Nurse,5.9,6,90,8,Overweight,140/95,75,10000,Sleep Apnea -268,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000, +268,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,No Disorder 269,Female,49,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 270,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 271,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea @@ -225,7 +225,7 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 277,Male,49,Doctor,8.1,9,85,3,Obese,139/91,86,3700,Sleep Apnea 278,Male,49,Doctor,8.1,9,85,3,Obese,139/91,86,3700,Sleep Apnea 279,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Insomnia -280,Female,50,Engineer,8.3,9,30,3,Normal,125/80,65,5000, +280,Female,50,Engineer,8.3,9,30,3,Normal,125/80,65,5000,No Disorder 282,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 283,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 284,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea @@ -242,11 +242,11 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 296,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 297,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 298,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea -299,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -300,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -301,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -302,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -303,Female,51,Nurse,7.1,7,55,6,Normal Weight,125/82,72,6000, +299,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +300,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +301,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +302,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +303,Female,51,Nurse,7.1,7,55,6,Normal Weight,125/82,72,6000,No Disorder 304,Female,51,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 305,Female,51,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 307,Female,52,Accountant,6.5,7,45,7,Overweight,130/85,72,6000,Insomnia @@ -255,30 +255,30 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 310,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia 311,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia 312,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia -314,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -315,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -317,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -319,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -320,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -321,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -322,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -323,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -324,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -327,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000, -328,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -329,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000, -330,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -331,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -333,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -335,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -336,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -337,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -339,Female,54,Engineer,8.5,9,30,3,Normal,125/80,65,5000, +314,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +315,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +317,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +319,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +320,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +321,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +322,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +323,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +324,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +327,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,No Disorder +328,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +329,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,No Disorder +330,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +331,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +333,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +335,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +336,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +337,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +339,Female,54,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder 340,Female,55,Nurse,8.1,9,75,4,Overweight,140/95,72,5000,Sleep Apnea 341,Female,55,Nurse,8.1,9,75,4,Overweight,140/95,72,5000,Sleep Apnea -342,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000, -343,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000, -344,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000, +342,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000,No Disorder +343,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000,No Disorder +344,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,No Disorder 345,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 347,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 348,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea @@ -287,7 +287,7 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 352,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 353,Female,58,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 357,Female,58,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,Sleep Apnea -359,Female,59,Nurse,8.0,9,75,3,Overweight,140/95,68,7000, +359,Female,59,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,No Disorder 361,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 362,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 364,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea diff --git a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_001/info.json b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_001/info.json index ac7f1ad59f059be3648f2769a10128e59277c544..c68a30219902530a9154e48d4957e5bb70f65c0f 100644 --- a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_001/info.json +++ b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_001/info.json @@ -1,121 +1,122 @@ -{ - "name": "Sleep Health and Lifestyle Dataset", - "source": "https://www.kaggle.com/datasets/uom190346a/sleep-health-and-lifestyle-dataset/data", - "data_intro": "The Sleep Health and Lifestyle Dataset comprises 400 rows and 13 columns, covering a wide range of variables related to sleep and daily habits. It includes details such as gender, age, occupation, sleep duration, quality of sleep, physical activity level, stress levels, BMI category, blood pressure, heart rate, daily steps, and the presence or absence of sleep disorders.", - "is_splited": false, - "overall_size": 374, - "train_size": 0, - "test_size": 0, - "c_classes": 5, - "n_classes": 8, - "cat_feature_intro": { - "Gender": "Gender: The gender of the person (Male/Female).
", - "Occupation": "Occupation: The occupation or profession of the person.
", - "BMI Category": "BMI Category: The BMI category of the person (e.g., Underweight, Normal, Overweight).
", - "Blood Pressure": "Blood Pressure (systolic/diastolic): The blood pressure measurement of the person, indicated as systolic pressure over diastolic pressure.
", - "Sleep Disorder": "- None: The individual does not exhibit any specific sleep disorder." - }, - "num_feature_intro": { - "Person ID": "Person ID: An identifier for each individual.
", - "Age": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", - "Sleep Duration": "Sleep Duration (hours): The number of hours the person sleeps per day.
", - "Quality of Sleep": "Quality of Sleep (scale: 1-10): A subjective rating of the quality of sleep, ranging from 1 to 10.
", - "Physical Activity Level": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", - "Stress Level": "Stress Level (scale: 1-10): A subjective rating of the stress level experienced by the person, ranging from 1 to 10.
", - "Heart Rate": "Heart Rate (bpm): The resting heart rate of the person in beats per minute.
", - "Daily Steps": "Daily Steps: The number of steps the person takes per day.
" - }, - "evaluation_metric": null, - "task_type": "classification", - "target": "Sleep Disorder", - "num_feature_value": { - "Age": [ - 27.0, - 59.0 - ], - "Daily Steps": [ - 3000.0, - 10000.0 - ], - "Heart Rate": [ - 65.0, - 86.0 - ], - "Person ID": [ - 1.0, - 374.0 - ], - "Physical Activity Level": [ - 30.0, - 90.0 - ], - "Quality of Sleep": [ - 4.0, - 9.0 - ], - "Sleep Duration": [ - 5.8, - 8.5 - ], - "Stress Level": [ - 3.0, - 8.0 - ] - }, - "cat_feature_value": { - "BMI Category": [ - "Normal", - "Normal Weight", - "Obese", - "Overweight" - ], - "Blood Pressure": [ - "115/75", - "115/78", - "117/76", - "118/75", - "118/76", - "119/77", - "120/80", - "121/79", - "122/80", - "125/80", - "125/82", - "126/83", - "128/84", - "128/85", - "129/84", - "130/85", - "130/86", - "131/86", - "132/87", - "135/88", - "135/90", - "139/91", - "140/90", - "140/95", - "142/92" - ], - "Gender": [ - "Female", - "Male" - ], - "Occupation": [ - "Accountant", - "Doctor", - "Engineer", - "Lawyer", - "Manager", - "Nurse", - "Sales Representative", - "Salesperson", - "Scientist", - "Software Engineer", - "Teacher" - ], - "Sleep Disorder": [ - "Insomnia", - "Sleep Apnea" - ] - } -} \ No newline at end of file +{ + "name": "Sleep Health and Lifestyle Dataset", + "source": "https://www.kaggle.com/datasets/uom190346a/sleep-health-and-lifestyle-dataset/data", + "data_intro": "The Sleep Health and Lifestyle Dataset comprises 400 rows and 13 columns, covering a wide range of variables related to sleep and daily habits. It includes details such as gender, age, occupation, sleep duration, quality of sleep, physical activity level, stress levels, BMI category, blood pressure, heart rate, daily steps, and the presence or absence of sleep disorders.", + "is_splited": false, + "overall_size": 374, + "train_size": 0, + "test_size": 0, + "c_classes": 5, + "n_classes": 8, + "cat_feature_intro": { + "Gender": "Gender: The gender of the person (Male/Female).
", + "Occupation": "Occupation: The occupation or profession of the person.
", + "BMI Category": "BMI Category: The BMI category of the person (e.g., Underweight, Normal, Overweight).
", + "Blood Pressure": "Blood Pressure (systolic/diastolic): The blood pressure measurement of the person, indicated as systolic pressure over diastolic pressure.
", + "Sleep Disorder": "Sleep disorder category: No Disorder, Insomnia, or Sleep Apnea." + }, + "num_feature_intro": { + "Person ID": "Person ID: An identifier for each individual.
", + "Age": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", + "Sleep Duration": "Sleep Duration (hours): The number of hours the person sleeps per day.
", + "Quality of Sleep": "Quality of Sleep (scale: 1-10): A subjective rating of the quality of sleep, ranging from 1 to 10.
", + "Physical Activity Level": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", + "Stress Level": "Stress Level (scale: 1-10): A subjective rating of the stress level experienced by the person, ranging from 1 to 10.
", + "Heart Rate": "Heart Rate (bpm): The resting heart rate of the person in beats per minute.
", + "Daily Steps": "Daily Steps: The number of steps the person takes per day.
" + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "Sleep Disorder", + "num_feature_value": { + "Age": [ + 27.0, + 59.0 + ], + "Daily Steps": [ + 3000.0, + 10000.0 + ], + "Heart Rate": [ + 65.0, + 86.0 + ], + "Person ID": [ + 1.0, + 374.0 + ], + "Physical Activity Level": [ + 30.0, + 90.0 + ], + "Quality of Sleep": [ + 4.0, + 9.0 + ], + "Sleep Duration": [ + 5.8, + 8.5 + ], + "Stress Level": [ + 3.0, + 8.0 + ] + }, + "cat_feature_value": { + "BMI Category": [ + "Normal", + "Normal Weight", + "Obese", + "Overweight" + ], + "Blood Pressure": [ + "115/75", + "115/78", + "117/76", + "118/75", + "118/76", + "119/77", + "120/80", + "121/79", + "122/80", + "125/80", + "125/82", + "126/83", + "128/84", + "128/85", + "129/84", + "130/85", + "130/86", + "131/86", + "132/87", + "135/88", + "135/90", + "139/91", + "140/90", + "140/95", + "142/92" + ], + "Gender": [ + "Female", + "Male" + ], + "Occupation": [ + "Accountant", + "Doctor", + "Engineer", + "Lawyer", + "Manager", + "Nurse", + "Sales Representative", + "Salesperson", + "Scientist", + "Software Engineer", + "Teacher" + ], + "Sleep Disorder": [ + "Insomnia", + "No Disorder", + "Sleep Apnea" + ] + } +} diff --git a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_002/Sleep_Health_and_Lifestyle_Dataset_B4_002_current.json b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_002/Sleep_Health_and_Lifestyle_Dataset_B4_002_current.json index e225b76a80c29d9c431fec4765fafe827d038ed5..72eb9aa70d17ef38e8bc46935dec8718d6bc9740 100644 --- a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_002/Sleep_Health_and_Lifestyle_Dataset_B4_002_current.json +++ b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_002/Sleep_Health_and_Lifestyle_Dataset_B4_002_current.json @@ -1,1375 +1,1375 @@ { - "id": "002", - "task_type": "B4", - "subtask_type": "list", - "perspective": "data_holder", - "dataset_name": "Sleep Health and Lifestyle Dataset", - "table_path": "output/Sleep Health and Lifestyle Dataset_B4/Sleep Health and Lifestyle Dataset_B4_002/test.csv", - "query": "Our clinic’s archive of past sleep assessments is available for your review. Please go through the incoming intake records and list every person whose profile suggests they may have sleep apnea, similar to the cases in our history.", - "meta_info": { - "domain": "medical" + "id": "002", + "task_type": "B4", + "subtask_type": "list", + "perspective": "data_holder", + "dataset_name": "Sleep Health and Lifestyle Dataset", + "table_path": "output/Sleep Health and Lifestyle Dataset_B4/Sleep Health and Lifestyle Dataset_B4_002/test.csv", + "query": "Our clinic’s archive of past sleep assessments is available for your review. Please go through the incoming intake records and list every person whose profile suggests they may have sleep apnea, similar to the cases in our history.", + "meta_info": { + "domain": "medical" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "Person ID": 256, + "Gender": "Female", + "Age": 45, + "Occupation": "Teacher", + "Sleep Duration": 6.5, + "Quality of Sleep": 7, + "Physical Activity Level": 45, + "Stress Level": 4, + "BMI Category": "Overweight", + "Blood Pressure": "135/90", + "Heart Rate": 65, + "Daily Steps": 6000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "002", + "features": { + "Person ID": 98, + "Gender": "Female", + "Age": 36, + "Occupation": "Accountant", + "Sleep Duration": 7.1, + "Quality of Sleep": 8, + "Physical Activity Level": 60, + "Stress Level": 4, + "BMI Category": "Normal", + "Blood Pressure": "115/75", + "Heart Rate": 68, + "Daily Steps": 7000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "003", + "features": { + "Person ID": 49, + "Gender": "Male", + "Age": 31, + "Occupation": "Doctor", + "Sleep Duration": 7.7, + "Quality of Sleep": 7, + "Physical Activity Level": 75, + "Stress Level": 6, + "BMI Category": "Normal", + "Blood Pressure": "120/80", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "004", + "features": { + "Person ID": 217, + "Gender": "Male", + "Age": 43, + "Occupation": "Engineer", + "Sleep Duration": 7.8, + "Quality of Sleep": 8, + "Physical Activity Level": 90, + "Stress Level": 5, + "BMI Category": "Normal", + "Blood Pressure": "130/85", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "005", + "features": { + "Person ID": 325, + "Gender": "Female", + "Age": 53, + "Occupation": "Engineer", + "Sleep Duration": 8.3, + "Quality of Sleep": 9, + "Physical Activity Level": 30, + "Stress Level": 3, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 65, + "Daily Steps": 5000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "006", + "features": { + "Person ID": 16, + "Gender": "Male", + "Age": 29, + "Occupation": "Doctor", + "Sleep Duration": 6.0, + "Quality of Sleep": 6, + "Physical Activity Level": 30, + "Stress Level": 8, + "BMI Category": "Normal", + "Blood Pressure": "120/80", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "007", + "features": { + "Person ID": 313, + "Gender": "Female", + "Age": 52, + "Occupation": "Engineer", + "Sleep Duration": 8.4, + "Quality of Sleep": 9, + "Physical Activity Level": 30, + "Stress Level": 3, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 65, + "Daily Steps": 5000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "008", + "features": { + "Person ID": 308, + "Gender": "Female", + "Age": 52, + "Occupation": "Accountant", + "Sleep Duration": 6.5, + "Quality of Sleep": 7, + "Physical Activity Level": 45, + "Stress Level": 7, + "BMI Category": "Overweight", + "Blood Pressure": "130/85", + "Heart Rate": 72, + "Daily Steps": 6000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "009", + "features": { + "Person ID": 60, + "Gender": "Male", + "Age": 32, + "Occupation": "Doctor", + "Sleep Duration": 7.7, + "Quality of Sleep": 7, + "Physical Activity Level": 75, + "Stress Level": 6, + "BMI Category": "Normal", + "Blood Pressure": "120/80", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "010", + "features": { + "Person ID": 143, + "Gender": "Female", + "Age": 38, + "Occupation": "Accountant", + "Sleep Duration": 7.1, + "Quality of Sleep": 8, + "Physical Activity Level": 60, + "Stress Level": 4, + "BMI Category": "Normal", + "Blood Pressure": "115/75", + "Heart Rate": 68, + "Daily Steps": 7000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "011", + "features": { + "Person ID": 182, + "Gender": "Male", + "Age": 42, + "Occupation": "Lawyer", + "Sleep Duration": 7.8, + "Quality of Sleep": 8, + "Physical Activity Level": 90, + "Stress Level": 5, + "BMI Category": "Normal", + "Blood Pressure": "130/85", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "012", + "features": { + "Person ID": 92, + "Gender": "Male", + "Age": 35, + "Occupation": "Engineer", + "Sleep Duration": 7.3, + "Quality of Sleep": 8, + "Physical Activity Level": 60, + "Stress Level": 4, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 65, + "Daily Steps": 5000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "013", + "features": { + "Person ID": 369, + "Gender": "Female", + "Age": 59, + "Occupation": "Nurse", + "Sleep Duration": 8.1, + "Quality of Sleep": 9, + "Physical Activity Level": 75, + "Stress Level": 3, + "BMI Category": "Overweight", + "Blood Pressure": "140/95", + "Heart Rate": 68, + "Daily Steps": 7000, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "014", + "features": { + "Person ID": 347, + "Gender": "Female", + "Age": 57, + "Occupation": "Nurse", + "Sleep Duration": 8.2, + "Quality of Sleep": 9, + "Physical Activity Level": 75, + "Stress Level": 3, + "BMI Category": "Overweight", + "Blood Pressure": "140/95", + "Heart Rate": 68, + "Daily Steps": 7000, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "015", + "features": { + "Person ID": 9, + "Gender": "Male", + "Age": 29, + "Occupation": "Doctor", + "Sleep Duration": 7.8, + "Quality of Sleep": 7, + "Physical Activity Level": 75, + "Stress Level": 6, + "BMI Category": "Normal", + "Blood Pressure": "120/80", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "016", + "features": { + "Person ID": 63, + "Gender": "Male", + "Age": 32, + "Occupation": "Doctor", + "Sleep Duration": 6.2, + "Quality of Sleep": 6, + "Physical Activity Level": 30, + "Stress Level": 8, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 72, + "Daily Steps": 5000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "017", + "features": { + "Person ID": 41, + "Gender": "Male", + "Age": 31, + "Occupation": "Doctor", + "Sleep Duration": 7.7, + "Quality of Sleep": 7, + "Physical Activity Level": 75, + "Stress Level": 6, + "BMI Category": "Normal", + "Blood Pressure": "120/80", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "018", + "features": { + "Person ID": 315, + "Gender": "Female", + "Age": 52, + "Occupation": "Engineer", + "Sleep Duration": 8.4, + "Quality of Sleep": 9, + "Physical Activity Level": 30, + "Stress Level": 3, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 65, + "Daily Steps": 5000, + "Sleep Disorder": "No 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Pressure": "130/85", - "Heart Rate": 72, - "Daily Steps": 6000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "068", - "features": { - "Person ID": 62, - "Gender": "Male", - "Age": 32, - "Occupation": "Doctor", - "Sleep Duration": 6.0, - "Quality of Sleep": 6, - "Physical Activity Level": 30, - "Stress Level": 8, - "BMI Category": "Normal", - "Blood Pressure": "125/80", - "Heart Rate": 72, - "Daily Steps": 5000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "069", - "features": { - "Person ID": 341, - "Gender": "Female", - "Age": 55, - "Occupation": "Nurse", - "Sleep Duration": 8.1, - "Quality of Sleep": 9, - "Physical Activity Level": 75, - "Stress Level": 4, - "BMI Category": "Overweight", - "Blood Pressure": "140/95", - "Heart Rate": 72, - "Daily Steps": 5000, - "Sleep Disorder": "Sleep Apnea" - } - }, - { - "scenario_id": "070", - "features": { - "Person ID": 146, - "Gender": "Female", - "Age": 38, - "Occupation": "Lawyer", - "Sleep Duration": 7.4, - "Quality of Sleep": 7, - "Physical Activity Level": 60, - "Stress Level": 5, - "BMI Category": "Obese", - "Blood Pressure": "135/88", - "Heart Rate": 84, - "Daily Steps": 3300, - "Sleep Disorder": "Sleep Apnea" - } - }, - { - "scenario_id": "071", - "features": { - "Person ID": 116, - "Gender": "Female", - "Age": 37, - "Occupation": "Accountant", - "Sleep Duration": 7.2, - "Quality of Sleep": 8, - "Physical Activity Level": 60, - "Stress Level": 4, - "BMI Category": "Normal", - "Blood Pressure": "115/75", - "Heart Rate": 68, - "Daily Steps": 7000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "072", - "features": { - "Person ID": 112, - "Gender": "Male", - "Age": 37, - "Occupation": "Lawyer", - "Sleep Duration": 7.4, - "Quality of Sleep": 8, - "Physical Activity Level": 60, - "Stress Level": 5, - "BMI Category": "Normal", - "Blood Pressure": "130/85", - "Heart Rate": 68, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "073", - "features": { - "Person ID": 274, - "Gender": "Female", - "Age": 49, - "Occupation": "Nurse", - "Sleep Duration": 6.2, - "Quality of Sleep": 6, - "Physical Activity Level": 90, - "Stress Level": 8, - "BMI Category": "Overweight", - "Blood Pressure": "140/95", - "Heart Rate": 75, - "Daily Steps": 10000, - "Sleep Disorder": "Sleep Apnea" - } - }, - { - "scenario_id": "074", - "features": { - "Person ID": 86, - "Gender": "Female", - "Age": 35, - "Occupation": "Accountant", - "Sleep Duration": 7.2, - "Quality of Sleep": 8, - "Physical Activity Level": 60, - "Stress Level": 4, - "BMI Category": "Normal", - "Blood Pressure": "115/75", - "Heart Rate": 68, - "Daily Steps": 7000, - "Sleep Disorder": null - } - } - ], - "target_column": "Sleep Disorder", - "task_sub_type": "classification", - "ranking_ground_truth": { - "top_k_ids": [ - "013", - "014", - "025", - "042", - "044", - "045", - "048", - "050", - "054", - "059", - "061", - "063", - "069", - "070", - "073" - ] - }, - "active_filters": [], - "target_class_value": "sleep apnea" - }, - "response": "", - "evaluation_score": {} -} \ No newline at end of file + "active_filters": [], + "target_class_value": "sleep apnea" + }, + "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_002/history.csv b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_002/history.csv index bc020ace3a436c6e6be1bd63d941279d133b6204..52bf42750070bb0c0620707fe9735b11d97733d1 100644 --- a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_002/history.csv +++ b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_002/history.csv @@ -1,149 +1,149 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activity Level,Stress Level,BMI Category,Blood Pressure,Heart Rate,Daily Steps,Sleep Disorder -1,Male,27,Software Engineer,6.1,6,42,6,Overweight,126/83,77,4200, -2,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000, -3,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000, +1,Male,27,Software Engineer,6.1,6,42,6,Overweight,126/83,77,4200,No Disorder +2,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000,No Disorder +3,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000,No Disorder 4,Male,28,Sales Representative,5.9,4,30,8,Obese,140/90,85,3000,Sleep Apnea 5,Male,28,Sales Representative,5.9,4,30,8,Obese,140/90,85,3000,Sleep Apnea 6,Male,28,Software Engineer,5.9,4,30,8,Obese,140/90,85,3000,Insomnia 7,Male,29,Teacher,6.3,6,40,7,Obese,140/90,82,3500,Insomnia -10,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -11,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000, -13,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000, -14,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000, +10,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +11,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000,No Disorder +13,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000,No Disorder +14,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000,No Disorder 17,Female,29,Nurse,6.5,5,40,7,Normal Weight,132/87,80,4000,Sleep Apnea 18,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000,Sleep Apnea 19,Female,29,Nurse,6.5,5,40,7,Normal Weight,132/87,80,4000,Insomnia -20,Male,30,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -21,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -22,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -23,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -24,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -25,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -26,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000, -27,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -28,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000, -29,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000, -30,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000, +20,Male,30,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +21,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +22,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +23,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +24,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +25,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +26,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,No Disorder +27,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +28,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,No Disorder +29,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,No Disorder +30,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,No Disorder 31,Female,30,Nurse,6.4,5,35,7,Normal Weight,130/86,78,4100,Sleep Apnea 32,Female,30,Nurse,6.4,5,35,7,Normal Weight,130/86,78,4100,Insomnia -33,Female,31,Nurse,7.9,8,75,4,Normal Weight,117/76,69,6800, -34,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -36,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -38,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -39,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -40,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -42,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -43,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -44,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -47,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -48,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000, +33,Female,31,Nurse,7.9,8,75,4,Normal Weight,117/76,69,6800,No Disorder +34,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +36,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +38,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +39,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +40,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +42,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +43,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +44,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +47,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +48,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder 50,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,Sleep Apnea -51,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000, -53,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -54,Male,32,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -55,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -57,Male,32,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -58,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -61,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -64,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000, -65,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000, -66,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000, -67,Male,32,Accountant,7.2,8,50,6,Normal Weight,118/76,68,7000, +51,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000,No Disorder +53,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +54,Male,32,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +55,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +57,Male,32,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +58,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +61,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +64,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,No Disorder +65,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,No Disorder +66,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,No Disorder +67,Male,32,Accountant,7.2,8,50,6,Normal Weight,118/76,68,7000,No Disorder 68,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,Insomnia -69,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500, -71,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -72,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -73,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -74,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -76,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -77,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -78,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -79,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -80,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, +69,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500,No Disorder +71,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +72,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +73,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +74,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +76,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +77,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +78,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +79,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +80,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder 81,Female,34,Scientist,5.8,4,32,8,Overweight,131/86,81,5200,Sleep Apnea -83,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600, -84,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600, -85,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000, -88,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000, -89,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000, -90,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000, -91,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000, -93,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000, +83,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600,No Disorder +84,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600,No Disorder +85,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000,No Disorder +88,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000,No Disorder +89,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,No Disorder +90,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,No Disorder +91,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,No Disorder +93,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000,No Disorder 94,Male,35,Lawyer,7.4,7,60,5,Obese,135/88,84,3300,Sleep Apnea 95,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000,Insomnia -96,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -99,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000, -100,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000, -101,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000, -102,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000, -103,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000, +96,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +99,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +100,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +101,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +102,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +103,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,No Disorder 104,Male,36,Teacher,6.6,5,35,7,Overweight,129/84,74,4800,Sleep Apnea 105,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,Sleep Apnea 106,Male,36,Teacher,6.6,5,35,7,Overweight,129/84,74,4800,Insomnia -107,Female,37,Nurse,6.1,6,42,6,Overweight,126/83,77,4200, -108,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000, -109,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000, -110,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000, -111,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -113,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -114,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000, -115,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -117,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -118,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -119,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -120,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -121,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -122,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -123,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -125,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -126,Female,37,Nurse,7.5,8,60,4,Normal Weight,120/80,70,8000, -127,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -128,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -129,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -130,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -131,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -132,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -133,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -134,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -135,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -137,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -138,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000, -139,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -140,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000, -141,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -142,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000, -144,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, +107,Female,37,Nurse,6.1,6,42,6,Overweight,126/83,77,4200,No Disorder +108,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000,No Disorder +109,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000,No Disorder +110,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,No Disorder +111,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +113,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +114,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,No Disorder +115,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +117,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +118,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +119,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +120,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +121,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +122,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +123,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +125,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +126,Female,37,Nurse,7.5,8,60,4,Normal Weight,120/80,70,8000,No Disorder +127,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +128,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +129,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +130,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +131,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +132,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +133,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +134,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +135,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +137,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +138,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,No Disorder +139,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +140,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,No Disorder +141,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +142,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,No Disorder +144,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder 145,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,Sleep Apnea 147,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,Insomnia 148,Male,39,Engineer,6.5,5,40,7,Overweight,132/87,80,4000,Insomnia -149,Female,39,Lawyer,6.9,7,50,6,Normal Weight,128/85,75,5500, -151,Female,39,Accountant,8.0,9,80,3,Normal Weight,115/78,67,7500, -152,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -154,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -155,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -157,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -158,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -159,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -160,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -161,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -162,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300, -163,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300, -164,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000, -165,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000, +149,Female,39,Lawyer,6.9,7,50,6,Normal Weight,128/85,75,5500,No Disorder +151,Female,39,Accountant,8.0,9,80,3,Normal Weight,115/78,67,7500,No Disorder +152,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +154,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +155,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +157,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +158,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +159,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +160,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +161,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +162,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300,No Disorder +163,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300,No Disorder +164,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000,No Disorder +165,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000,No Disorder 166,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,Insomnia -167,Male,41,Engineer,7.3,8,70,6,Normal Weight,121/79,72,6200, -168,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000, -169,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000, -170,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -171,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -172,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -173,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -174,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -175,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000, -176,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000, +167,Male,41,Engineer,7.3,8,70,6,Normal Weight,121/79,72,6200,No Disorder +168,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000,No Disorder +169,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000,No Disorder +170,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +171,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +172,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +173,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +174,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +175,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,No Disorder +176,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,No Disorder 178,Male,42,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia -180,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, -183,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, +180,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +183,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder 185,Female,42,Teacher,6.8,6,45,7,Overweight,130/85,78,5000,Sleep Apnea 186,Female,42,Teacher,6.8,6,45,7,Overweight,130/85,78,5000,Sleep Apnea 187,Female,43,Teacher,6.7,7,45,4,Overweight,135/90,65,6000,Insomnia @@ -161,18 +161,18 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 201,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia 202,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Insomnia 203,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Insomnia -204,Male,43,Engineer,6.9,6,47,7,Normal Weight,117/76,69,6800, -205,Male,43,Engineer,7.6,8,75,4,Overweight,122/80,68,6800, -206,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -207,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -208,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -209,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -211,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -212,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -214,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -215,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -216,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -218,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, +204,Male,43,Engineer,6.9,6,47,7,Normal Weight,117/76,69,6800,No Disorder +205,Male,43,Engineer,7.6,8,75,4,Overweight,122/80,68,6800,No Disorder +206,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +207,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +208,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +209,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +211,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +212,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +214,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +215,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +216,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +218,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder 219,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Sleep Apnea 220,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Sleep Apnea 222,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia @@ -198,8 +198,8 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 245,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia 247,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia 248,Male,44,Engineer,6.8,7,45,7,Overweight,130/85,78,5000,Insomnia -249,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000, -250,Male,44,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000, +249,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,No Disorder +250,Male,44,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,No Disorder 251,Female,45,Teacher,6.8,7,30,6,Overweight,135/90,65,6000,Insomnia 252,Female,45,Teacher,6.8,7,30,6,Overweight,135/90,65,6000,Insomnia 254,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia @@ -209,13 +209,13 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 259,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia 260,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia 261,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia -262,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000, -263,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000, -264,Female,45,Manager,6.9,7,55,5,Overweight,125/82,75,5500, +262,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,No Disorder +263,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,No Disorder +264,Female,45,Manager,6.9,7,55,5,Overweight,125/82,75,5500,No Disorder 265,Male,48,Doctor,7.3,7,65,5,Obese,142/92,83,3500,Insomnia 266,Female,48,Nurse,5.9,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 267,Male,48,Doctor,7.3,7,65,5,Obese,142/92,83,3500,Insomnia -268,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000, +268,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,No Disorder 269,Female,49,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 271,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 272,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea @@ -223,8 +223,8 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 276,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 277,Male,49,Doctor,8.1,9,85,3,Obese,139/91,86,3700,Sleep Apnea 278,Male,49,Doctor,8.1,9,85,3,Obese,139/91,86,3700,Sleep Apnea -280,Female,50,Engineer,8.3,9,30,3,Normal,125/80,65,5000, -281,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000, +280,Female,50,Engineer,8.3,9,30,3,Normal,125/80,65,5000,No Disorder +281,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,No Disorder 282,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 283,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 284,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea @@ -240,10 +240,10 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 296,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 297,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 298,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea -299,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -300,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -301,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -302,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, +299,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +300,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +301,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +302,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder 304,Female,51,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 305,Female,51,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 306,Female,51,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea @@ -252,30 +252,30 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 310,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia 311,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia 312,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia -314,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000, +314,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder 316,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,Insomnia -317,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -318,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -319,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -320,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -321,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -322,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -323,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -324,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -326,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -327,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000, -329,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000, -330,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -332,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -333,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -334,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -335,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -336,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -338,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -339,Female,54,Engineer,8.5,9,30,3,Normal,125/80,65,5000, +317,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +318,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +319,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +320,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +321,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +322,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +323,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +324,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +326,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +327,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,No Disorder +329,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,No Disorder +330,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +332,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +333,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +334,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +335,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +336,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +338,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +339,Female,54,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder 340,Female,55,Nurse,8.1,9,75,4,Overweight,140/95,72,5000,Sleep Apnea -343,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000, -344,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000, +343,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000,No Disorder +344,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,No Disorder 345,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 346,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 349,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea @@ -285,8 +285,8 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 355,Female,58,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 356,Female,58,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 357,Female,58,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,Sleep Apnea -359,Female,59,Nurse,8.0,9,75,3,Overweight,140/95,68,7000, -360,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000, +359,Female,59,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,No Disorder +360,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,No Disorder 361,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 362,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 363,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea diff --git a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_002/info.json b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_002/info.json index ac7f1ad59f059be3648f2769a10128e59277c544..c68a30219902530a9154e48d4957e5bb70f65c0f 100644 --- a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_002/info.json +++ b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_002/info.json @@ -1,121 +1,122 @@ -{ - "name": "Sleep Health and Lifestyle Dataset", - "source": "https://www.kaggle.com/datasets/uom190346a/sleep-health-and-lifestyle-dataset/data", - "data_intro": "The Sleep Health and Lifestyle Dataset comprises 400 rows and 13 columns, covering a wide range of variables related to sleep and daily habits. It includes details such as gender, age, occupation, sleep duration, quality of sleep, physical activity level, stress levels, BMI category, blood pressure, heart rate, daily steps, and the presence or absence of sleep disorders.", - "is_splited": false, - "overall_size": 374, - "train_size": 0, - "test_size": 0, - "c_classes": 5, - "n_classes": 8, - "cat_feature_intro": { - "Gender": "Gender: The gender of the person (Male/Female).
", - "Occupation": "Occupation: The occupation or profession of the person.
", - "BMI Category": "BMI Category: The BMI category of the person (e.g., Underweight, Normal, Overweight).
", - "Blood Pressure": "Blood Pressure (systolic/diastolic): The blood pressure measurement of the person, indicated as systolic pressure over diastolic pressure.
", - "Sleep Disorder": "- None: The individual does not exhibit any specific sleep disorder." - }, - "num_feature_intro": { - "Person ID": "Person ID: An identifier for each individual.
", - "Age": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", - "Sleep Duration": "Sleep Duration (hours): The number of hours the person sleeps per day.
", - "Quality of Sleep": "Quality of Sleep (scale: 1-10): A subjective rating of the quality of sleep, ranging from 1 to 10.
", - "Physical Activity Level": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", - "Stress Level": "Stress Level (scale: 1-10): A subjective rating of the stress level experienced by the person, ranging from 1 to 10.
", - "Heart Rate": "Heart Rate (bpm): The resting heart rate of the person in beats per minute.
", - "Daily Steps": "Daily Steps: The number of steps the person takes per day.
" - }, - "evaluation_metric": null, - "task_type": "classification", - "target": "Sleep Disorder", - "num_feature_value": { - "Age": [ - 27.0, - 59.0 - ], - "Daily Steps": [ - 3000.0, - 10000.0 - ], - "Heart Rate": [ - 65.0, - 86.0 - ], - "Person ID": [ - 1.0, - 374.0 - ], - "Physical Activity Level": [ - 30.0, - 90.0 - ], - "Quality of Sleep": [ - 4.0, - 9.0 - ], - "Sleep Duration": [ - 5.8, - 8.5 - ], - "Stress Level": [ - 3.0, - 8.0 - ] - }, - "cat_feature_value": { - "BMI Category": [ - "Normal", - "Normal Weight", - "Obese", - "Overweight" - ], - "Blood Pressure": [ - "115/75", - "115/78", - "117/76", - "118/75", - "118/76", - "119/77", - "120/80", - "121/79", - "122/80", - "125/80", - "125/82", - "126/83", - "128/84", - "128/85", - "129/84", - "130/85", - "130/86", - "131/86", - "132/87", - "135/88", - "135/90", - "139/91", - "140/90", - "140/95", - "142/92" - ], - "Gender": [ - "Female", - "Male" - ], - "Occupation": [ - "Accountant", - "Doctor", - "Engineer", - "Lawyer", - "Manager", - "Nurse", - "Sales Representative", - "Salesperson", - "Scientist", - "Software Engineer", - "Teacher" - ], - "Sleep Disorder": [ - "Insomnia", - "Sleep Apnea" - ] - } -} \ No newline at end of file +{ + "name": "Sleep Health and Lifestyle Dataset", + "source": "https://www.kaggle.com/datasets/uom190346a/sleep-health-and-lifestyle-dataset/data", + "data_intro": "The Sleep Health and Lifestyle Dataset comprises 400 rows and 13 columns, covering a wide range of variables related to sleep and daily habits. It includes details such as gender, age, occupation, sleep duration, quality of sleep, physical activity level, stress levels, BMI category, blood pressure, heart rate, daily steps, and the presence or absence of sleep disorders.", + "is_splited": false, + "overall_size": 374, + "train_size": 0, + "test_size": 0, + "c_classes": 5, + "n_classes": 8, + "cat_feature_intro": { + "Gender": "Gender: The gender of the person (Male/Female).
", + "Occupation": "Occupation: The occupation or profession of the person.
", + "BMI Category": "BMI Category: The BMI category of the person (e.g., Underweight, Normal, Overweight).
", + "Blood Pressure": "Blood Pressure (systolic/diastolic): The blood pressure measurement of the person, indicated as systolic pressure over diastolic pressure.
", + "Sleep Disorder": "Sleep disorder category: No Disorder, Insomnia, or Sleep Apnea." + }, + "num_feature_intro": { + "Person ID": "Person ID: An identifier for each individual.
", + "Age": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", + "Sleep Duration": "Sleep Duration (hours): The number of hours the person sleeps per day.
", + "Quality of Sleep": "Quality of Sleep (scale: 1-10): A subjective rating of the quality of sleep, ranging from 1 to 10.
", + "Physical Activity Level": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", + "Stress Level": "Stress Level (scale: 1-10): A subjective rating of the stress level experienced by the person, ranging from 1 to 10.
", + "Heart Rate": "Heart Rate (bpm): The resting heart rate of the person in beats per minute.
", + "Daily Steps": "Daily Steps: The number of steps the person takes per day.
" + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "Sleep Disorder", + "num_feature_value": { + "Age": [ + 27.0, + 59.0 + ], + "Daily Steps": [ + 3000.0, + 10000.0 + ], + "Heart Rate": [ + 65.0, + 86.0 + ], + "Person ID": [ + 1.0, + 374.0 + ], + "Physical Activity Level": [ + 30.0, + 90.0 + ], + "Quality of Sleep": [ + 4.0, + 9.0 + ], + "Sleep Duration": [ + 5.8, + 8.5 + ], + "Stress Level": [ + 3.0, + 8.0 + ] + }, + "cat_feature_value": { + "BMI Category": [ + "Normal", + "Normal Weight", + "Obese", + "Overweight" + ], + "Blood Pressure": [ + "115/75", + "115/78", + "117/76", + "118/75", + "118/76", + "119/77", + "120/80", + "121/79", + "122/80", + "125/80", + "125/82", + "126/83", + "128/84", + "128/85", + "129/84", + "130/85", + "130/86", + "131/86", + "132/87", + "135/88", + "135/90", + "139/91", + "140/90", + "140/95", + "142/92" + ], + "Gender": [ + "Female", + "Male" + ], + "Occupation": [ + "Accountant", + "Doctor", + "Engineer", + "Lawyer", + "Manager", + "Nurse", + "Sales Representative", + "Salesperson", + "Scientist", + "Software Engineer", + "Teacher" + ], + "Sleep Disorder": [ + "Insomnia", + "No Disorder", + "Sleep Apnea" + ] + } +} diff --git a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_003/Sleep_Health_and_Lifestyle_Dataset_B4_003_current.json b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_003/Sleep_Health_and_Lifestyle_Dataset_B4_003_current.json index b068f61eacb16cd3e6f63401e3459ffeb882ad4e..61a9a3495ed6f3a0db866c8b5931c0cbd8d5c4a3 100644 --- a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_003/Sleep_Health_and_Lifestyle_Dataset_B4_003_current.json +++ b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_003/Sleep_Health_and_Lifestyle_Dataset_B4_003_current.json @@ -1,1375 +1,1375 @@ { - "id": "003", - "task_type": "B4", - "subtask_type": "list", - "perspective": "data_holder", - "dataset_name": "Sleep Health and Lifestyle Dataset", - "table_path": "output/Sleep Health and Lifestyle Dataset_B4/Sleep Health and Lifestyle Dataset_B4_003/test.csv", - "query": "Using the previous case histories I’ve shared, scan the latest group of employee health screenings. Identify all staff members who appear to be suffering from insomnia, as indicated by the patterns in our past logs.", - "meta_info": { - "domain": "medical" + "id": "003", + "task_type": "B4", + "subtask_type": "list", + "perspective": "data_holder", + "dataset_name": "Sleep Health and Lifestyle Dataset", + "table_path": "output/Sleep Health and Lifestyle Dataset_B4/Sleep Health and Lifestyle Dataset_B4_003/test.csv", + "query": "Using the previous case histories I’ve shared, scan the latest group of employee health screenings. Identify all staff members who appear to be suffering from insomnia, as indicated by the patterns in our past logs.", + "meta_info": { + "domain": "medical" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "Person ID": 312, + "Gender": "Female", + "Age": 52, + "Occupation": "Accountant", + "Sleep Duration": 6.6, + "Quality of Sleep": 7, + "Physical Activity Level": 45, + "Stress Level": 7, + "BMI Category": "Overweight", + "Blood Pressure": "130/85", + "Heart Rate": 72, + "Daily Steps": 6000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "002", + "features": { + "Person ID": 349, + "Gender": "Female", + "Age": 57, + "Occupation": "Nurse", + "Sleep Duration": 8.2, + "Quality of Sleep": 9, + "Physical Activity Level": 75, + "Stress Level": 3, + "BMI Category": "Overweight", + "Blood Pressure": "140/95", + "Heart Rate": 68, + "Daily Steps": 7000, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "003", + "features": { + "Person ID": 74, + "Gender": "Male", + "Age": 33, + "Occupation": "Doctor", + "Sleep Duration": 6.1, + "Quality of Sleep": 6, + "Physical Activity Level": 30, + "Stress Level": 8, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 72, + "Daily Steps": 5000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "004", + "features": { + "Person ID": 12, + "Gender": "Male", + "Age": 29, + "Occupation": "Doctor", + "Sleep Duration": 7.8, + "Quality of Sleep": 7, + "Physical Activity Level": 75, + "Stress Level": 6, + "BMI Category": "Normal", + "Blood Pressure": "120/80", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "005", + "features": { + "Person ID": 171, + "Gender": "Male", + "Age": 41, + "Occupation": "Lawyer", + "Sleep Duration": 7.7, + "Quality of Sleep": 8, + "Physical Activity Level": 90, + "Stress Level": 5, + "BMI Category": "Normal", + "Blood Pressure": "130/85", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "006", + "features": { + "Person ID": 299, + "Gender": "Female", + "Age": 51, + "Occupation": "Engineer", + "Sleep Duration": 8.5, + "Quality of Sleep": 9, + "Physical Activity Level": 30, + "Stress Level": 3, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 65, + "Daily Steps": 5000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "007", + "features": { + "Person ID": 359, + "Gender": "Female", + "Age": 59, + "Occupation": "Nurse", + "Sleep Duration": 8.0, + "Quality of Sleep": 9, + "Physical Activity Level": 75, + "Stress Level": 3, + "BMI Category": "Overweight", + "Blood Pressure": "140/95", + "Heart Rate": 68, + "Daily Steps": 7000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "008", + "features": { + "Person ID": 292, + "Gender": "Female", + "Age": 50, + "Occupation": "Nurse", + "Sleep Duration": 6.1, + "Quality of Sleep": 6, + "Physical Activity Level": 90, + "Stress Level": 8, + "BMI Category": "Overweight", + "Blood Pressure": "140/95", + "Heart Rate": 75, + "Daily Steps": 10000, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "009", + "features": { + "Person ID": 257, + "Gender": "Female", + "Age": 45, + "Occupation": "Teacher", + "Sleep Duration": 6.6, + "Quality of Sleep": 7, + "Physical Activity Level": 45, + "Stress Level": 4, + "BMI Category": "Overweight", + "Blood Pressure": "135/90", + "Heart Rate": 65, + "Daily Steps": 6000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "010", + "features": { + "Person ID": 90, + "Gender": "Male", + "Age": 35, + "Occupation": "Engineer", + "Sleep Duration": 7.3, + "Quality of Sleep": 8, + "Physical Activity Level": 60, + "Stress Level": 4, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 65, + "Daily Steps": 5000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "011", + "features": { + "Person ID": 276, + "Gender": "Female", + "Age": 49, + "Occupation": "Nurse", + "Sleep Duration": 6.2, + "Quality of Sleep": 6, + "Physical Activity Level": 90, + "Stress Level": 8, + "BMI Category": "Overweight", + "Blood Pressure": "140/95", + "Heart Rate": 75, + "Daily Steps": 10000, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "012", + "features": { + "Person ID": 50, + "Gender": "Male", + "Age": 31, + "Occupation": "Doctor", + "Sleep Duration": 7.7, + "Quality of Sleep": 7, + "Physical Activity Level": 75, + "Stress Level": 6, + "BMI Category": "Normal", + "Blood Pressure": "120/80", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "013", + "features": { + "Person ID": 211, + "Gender": "Male", + "Age": 43, + "Occupation": "Engineer", + "Sleep Duration": 7.7, + "Quality of Sleep": 8, + "Physical Activity Level": 90, + "Stress Level": 5, + "BMI Category": "Normal", + "Blood Pressure": "130/85", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "014", + "features": { + "Person ID": 364, + "Gender": "Female", + "Age": 59, + "Occupation": "Nurse", + "Sleep Duration": 8.2, + "Quality of Sleep": 9, + "Physical Activity Level": 75, + "Stress Level": 3, + "BMI Category": "Overweight", + "Blood Pressure": "140/95", + "Heart Rate": 68, + "Daily Steps": 7000, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "015", + "features": { + "Person ID": 278, + "Gender": "Male", + "Age": 49, + "Occupation": "Doctor", + "Sleep Duration": 8.1, + "Quality of Sleep": 9, + "Physical Activity Level": 85, + "Stress Level": 3, + "BMI Category": "Obese", + "Blood Pressure": "139/91", + "Heart Rate": 86, + "Daily Steps": 3700, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "016", + "features": { + "Person ID": 128, + "Gender": "Female", + "Age": 38, + "Occupation": "Accountant", + "Sleep Duration": 7.1, + "Quality of Sleep": 8, + "Physical Activity Level": 60, + "Stress Level": 4, + "BMI Category": "Normal", + "Blood Pressure": "115/75", + "Heart Rate": 68, + "Daily Steps": 7000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "017", + "features": { + "Person ID": 91, + "Gender": "Male", + "Age": 35, + "Occupation": "Engineer", + "Sleep Duration": 7.3, + "Quality of Sleep": 8, + "Physical Activity Level": 60, + "Stress Level": 4, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 65, + "Daily Steps": 5000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "018", + "features": { + "Person ID": 315, + "Gender": "Female", + "Age": 52, + "Occupation": "Engineer", + "Sleep Duration": 8.4, + "Quality of Sleep": 9, + "Physical Activity Level": 30, + "Stress Level": 3, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 65, + "Daily Steps": 5000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "019", + "features": { + "Person ID": 254, + "Gender": "Female", + "Age": 45, + "Occupation": "Teacher", + "Sleep Duration": 6.5, + "Quality of Sleep": 7, + "Physical Activity Level": 45, + "Stress Level": 4, + "BMI Category": "Overweight", + "Blood Pressure": "135/90", + "Heart Rate": 65, + "Daily Steps": 6000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "020", + "features": { + "Person ID": 98, + "Gender": "Female", + "Age": 36, + "Occupation": "Accountant", + "Sleep Duration": 7.1, + "Quality of Sleep": 8, + "Physical Activity Level": 60, + "Stress Level": 4, + "BMI Category": "Normal", + "Blood Pressure": "115/75", + "Heart Rate": 68, + "Daily Steps": 7000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "021", + "features": { + "Person ID": 31, + "Gender": "Female", + "Age": 30, + "Occupation": "Nurse", + "Sleep Duration": 6.4, + "Quality of Sleep": 5, + "Physical Activity Level": 35, + "Stress Level": 7, + "BMI Category": "Normal Weight", + "Blood Pressure": "130/86", + "Heart Rate": 78, + "Daily Steps": 4100, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "022", + "features": { + "Person ID": 133, + "Gender": "Male", + "Age": 38, + "Occupation": "Lawyer", + "Sleep Duration": 7.3, + "Quality of Sleep": 8, + "Physical Activity Level": 60, + "Stress Level": 5, + "BMI Category": "Normal", + "Blood Pressure": "130/85", + "Heart Rate": 68, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "023", + "features": { + "Person ID": 141, + "Gender": "Female", + "Age": 38, + "Occupation": "Accountant", + "Sleep Duration": 7.1, + "Quality of Sleep": 8, + "Physical Activity Level": 60, + "Stress Level": 4, + "BMI Category": "Normal", + "Blood Pressure": "115/75", + "Heart Rate": 68, + "Daily Steps": 7000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "024", + "features": { + "Person ID": 46, + "Gender": "Male", + "Age": 31, + "Occupation": "Doctor", + 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- } - }, - { - "scenario_id": "073", - "features": { - "Person ID": 67, - "Gender": "Male", - "Age": 32, - "Occupation": "Accountant", - "Sleep Duration": 7.2, - "Quality of Sleep": 8, - "Physical Activity Level": 50, - "Stress Level": 6, - "BMI Category": "Normal Weight", - "Blood Pressure": "118/76", - "Heart Rate": 68, - "Daily Steps": 7000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "074", - "features": { - "Person ID": 182, - "Gender": "Male", - "Age": 42, - "Occupation": "Lawyer", - "Sleep Duration": 7.8, - "Quality of Sleep": 8, - "Physical Activity Level": 90, - "Stress Level": 5, - "BMI Category": "Normal", - "Blood Pressure": "130/85", - "Heart Rate": 70, - "Daily Steps": 8000, - "Sleep Disorder": null - } - } - ], - "target_column": "Sleep Disorder", - "task_sub_type": "classification", - "ranking_ground_truth": { - "top_k_ids": [ - "001", - "009", - "019", - "028", - "032", - "034", - "038", - "039", - "042", - "053", - "054", - "064", - "065", - "066", - "071" - ] - }, - "active_filters": [], - "target_class_value": "insomnia" - }, - "response": "", - "evaluation_score": {} -} \ No newline at end of file + "active_filters": [], + "target_class_value": "insomnia" + }, + "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_003/history.csv b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_003/history.csv index 285abc0f8d0130e23bdae9c5583dd0141a26e78b..633bc16d112bc82ae9e0c1b32b2346966b941d61 100644 --- a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_003/history.csv +++ b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_003/history.csv @@ -1,152 +1,152 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activity Level,Stress Level,BMI Category,Blood Pressure,Heart Rate,Daily Steps,Sleep Disorder -1,Male,27,Software Engineer,6.1,6,42,6,Overweight,126/83,77,4200, -2,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000, -3,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000, +1,Male,27,Software Engineer,6.1,6,42,6,Overweight,126/83,77,4200,No Disorder +2,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000,No Disorder +3,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000,No Disorder 4,Male,28,Sales Representative,5.9,4,30,8,Obese,140/90,85,3000,Sleep Apnea 5,Male,28,Sales Representative,5.9,4,30,8,Obese,140/90,85,3000,Sleep Apnea 6,Male,28,Software Engineer,5.9,4,30,8,Obese,140/90,85,3000,Insomnia 7,Male,29,Teacher,6.3,6,40,7,Obese,140/90,82,3500,Insomnia -8,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -9,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -10,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -11,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000, -13,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000, -14,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000, -15,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000, +8,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +9,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +10,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +11,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000,No Disorder +13,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000,No Disorder +14,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000,No Disorder +15,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000,No Disorder 17,Female,29,Nurse,6.5,5,40,7,Normal Weight,132/87,80,4000,Sleep Apnea 18,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000,Sleep Apnea 19,Female,29,Nurse,6.5,5,40,7,Normal Weight,132/87,80,4000,Insomnia -21,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -22,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -23,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -24,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -26,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000, -28,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000, -29,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000, -30,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000, +21,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +22,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +23,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +24,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +26,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,No Disorder +28,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,No Disorder +29,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,No Disorder +30,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,No Disorder 32,Female,30,Nurse,6.4,5,35,7,Normal Weight,130/86,78,4100,Insomnia -33,Female,31,Nurse,7.9,8,75,4,Normal Weight,117/76,69,6800, -34,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -36,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -38,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -39,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -40,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -41,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -42,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -43,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -44,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -45,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -47,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -48,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -49,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -51,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000, -52,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000, -54,Male,32,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -55,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -56,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -57,Male,32,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -58,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -59,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -60,Male,32,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -61,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -63,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000, -64,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000, -65,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000, -69,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500, -70,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500, -71,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -72,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -73,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -75,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -76,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -77,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -78,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -79,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -80,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, +33,Female,31,Nurse,7.9,8,75,4,Normal Weight,117/76,69,6800,No Disorder +34,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +36,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +38,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +39,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +40,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +41,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +42,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +43,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +44,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +45,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +47,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +48,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +49,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +51,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000,No Disorder +52,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000,No Disorder +54,Male,32,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +55,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +56,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +57,Male,32,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +58,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +59,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +60,Male,32,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +61,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +63,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,No Disorder +64,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,No Disorder +65,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,No Disorder +69,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500,No Disorder +70,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500,No Disorder +71,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +72,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +73,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +75,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +76,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +77,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +78,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +79,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +80,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder 81,Female,34,Scientist,5.8,4,32,8,Overweight,131/86,81,5200,Sleep Apnea 82,Female,34,Scientist,5.8,4,32,8,Overweight,131/86,81,5200,Sleep Apnea -83,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600, -84,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600, -85,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000, -86,Female,35,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -87,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000, -88,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000, -89,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000, -92,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000, -93,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000, +83,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600,No Disorder +84,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600,No Disorder +85,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000,No Disorder +86,Female,35,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +87,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000,No Disorder +88,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000,No Disorder +89,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,No Disorder +92,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,No Disorder +93,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000,No Disorder 94,Male,35,Lawyer,7.4,7,60,5,Obese,135/88,84,3300,Sleep Apnea 95,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000,Insomnia -96,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -99,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000, -100,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000, -101,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000, -102,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000, -103,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000, +96,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +99,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +100,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +101,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +102,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +103,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,No Disorder 104,Male,36,Teacher,6.6,5,35,7,Overweight,129/84,74,4800,Sleep Apnea 106,Male,36,Teacher,6.6,5,35,7,Overweight,129/84,74,4800,Insomnia -107,Female,37,Nurse,6.1,6,42,6,Overweight,126/83,77,4200, -108,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000, -109,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000, -110,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000, -111,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -112,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000, -113,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -114,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000, -115,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -116,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -117,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -118,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -119,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -120,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -121,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -122,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -123,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -124,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -125,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -127,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -129,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -130,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -131,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -132,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -134,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -135,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -136,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -137,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -138,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000, -139,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -140,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000, -144,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, +107,Female,37,Nurse,6.1,6,42,6,Overweight,126/83,77,4200,No Disorder +108,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000,No Disorder +109,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000,No Disorder +110,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,No Disorder +111,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +112,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,No Disorder +113,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +114,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,No Disorder +115,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +116,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +117,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +118,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +119,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +120,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +121,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +122,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +123,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +124,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +125,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +127,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +129,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +130,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +131,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +132,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +134,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +135,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +136,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +137,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +138,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,No Disorder +139,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +140,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,No Disorder +144,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder 145,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,Sleep Apnea 147,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,Insomnia 148,Male,39,Engineer,6.5,5,40,7,Overweight,132/87,80,4000,Insomnia -149,Female,39,Lawyer,6.9,7,50,6,Normal Weight,128/85,75,5500, -151,Female,39,Accountant,8.0,9,80,3,Normal Weight,115/78,67,7500, -152,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -153,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -154,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -155,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -156,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -157,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -158,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -159,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -160,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -161,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -163,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300, -164,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000, -165,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000, +149,Female,39,Lawyer,6.9,7,50,6,Normal Weight,128/85,75,5500,No Disorder +151,Female,39,Accountant,8.0,9,80,3,Normal Weight,115/78,67,7500,No Disorder +152,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +153,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +154,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +155,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +156,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +157,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +158,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +159,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +160,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +161,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +163,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300,No Disorder +164,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000,No Disorder +165,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000,No Disorder 166,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,Insomnia -168,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000, -169,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000, -172,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -175,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000, -176,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000, -177,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000, +168,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000,No Disorder +169,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000,No Disorder +172,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +175,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,No Disorder +176,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,No Disorder +177,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,No Disorder 178,Male,42,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia -179,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, -180,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, -181,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, -183,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, -184,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, +179,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +180,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +181,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +183,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +184,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder 185,Female,42,Teacher,6.8,6,45,7,Overweight,130/85,78,5000,Sleep Apnea 188,Male,43,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia 189,Female,43,Teacher,6.7,7,45,4,Overweight,135/90,65,6000,Insomnia @@ -160,18 +160,18 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 200,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia 201,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia 202,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Insomnia -204,Male,43,Engineer,6.9,6,47,7,Normal Weight,117/76,69,6800, -205,Male,43,Engineer,7.6,8,75,4,Overweight,122/80,68,6800, -206,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -207,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -208,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -210,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -212,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -213,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -214,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -215,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -216,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -217,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, +204,Male,43,Engineer,6.9,6,47,7,Normal Weight,117/76,69,6800,No Disorder +205,Male,43,Engineer,7.6,8,75,4,Overweight,122/80,68,6800,No Disorder +206,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +207,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +208,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +210,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +212,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +213,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +214,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +215,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +216,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +217,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder 220,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Sleep Apnea 221,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia 222,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia @@ -198,7 +198,7 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 246,Female,44,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia 247,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia 248,Male,44,Engineer,6.8,7,45,7,Overweight,130/85,78,5000,Insomnia -249,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000, +249,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,No Disorder 251,Female,45,Teacher,6.8,7,30,6,Overweight,135/90,65,6000,Insomnia 252,Female,45,Teacher,6.8,7,30,6,Overweight,135/90,65,6000,Insomnia 253,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia @@ -207,13 +207,13 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 258,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia 259,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia 260,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia -262,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000, -263,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000, -264,Female,45,Manager,6.9,7,55,5,Overweight,125/82,75,5500, +262,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,No Disorder +263,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,No Disorder +264,Female,45,Manager,6.9,7,55,5,Overweight,125/82,75,5500,No Disorder 265,Male,48,Doctor,7.3,7,65,5,Obese,142/92,83,3500,Insomnia 266,Female,48,Nurse,5.9,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 267,Male,48,Doctor,7.3,7,65,5,Obese,142/92,83,3500,Insomnia -268,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000, +268,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,No Disorder 269,Female,49,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 270,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 271,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea @@ -222,8 +222,8 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 275,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 277,Male,49,Doctor,8.1,9,85,3,Obese,139/91,86,3700,Sleep Apnea 279,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Insomnia -280,Female,50,Engineer,8.3,9,30,3,Normal,125/80,65,5000, -281,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000, +280,Female,50,Engineer,8.3,9,30,3,Normal,125/80,65,5000,No Disorder +281,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,No Disorder 282,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 283,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 284,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea @@ -240,41 +240,41 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 296,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 297,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 298,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea -300,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -301,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -302,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -303,Female,51,Nurse,7.1,7,55,6,Normal Weight,125/82,72,6000, +300,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +301,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +302,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +303,Female,51,Nurse,7.1,7,55,6,Normal Weight,125/82,72,6000,No Disorder 304,Female,51,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 305,Female,51,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 306,Female,51,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 308,Female,52,Accountant,6.5,7,45,7,Overweight,130/85,72,6000,Insomnia 310,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia 311,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia -313,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000, +313,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder 316,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,Insomnia -319,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -320,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -321,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -322,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -323,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -324,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -327,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000, -328,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -329,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000, -330,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -332,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -333,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -334,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -335,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -336,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -337,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -338,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -339,Female,54,Engineer,8.5,9,30,3,Normal,125/80,65,5000, +319,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +320,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +321,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +322,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +323,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +324,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +327,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,No Disorder +328,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +329,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,No Disorder +330,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +332,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +333,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +334,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +335,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +336,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +337,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +338,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +339,Female,54,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder 340,Female,55,Nurse,8.1,9,75,4,Overweight,140/95,72,5000,Sleep Apnea 341,Female,55,Nurse,8.1,9,75,4,Overweight,140/95,72,5000,Sleep Apnea -342,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000, -343,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000, -344,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000, +342,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000,No Disorder +343,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000,No Disorder +344,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,No Disorder 345,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 346,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 347,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea @@ -288,7 +288,7 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 356,Female,58,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 357,Female,58,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 358,Female,58,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,Sleep Apnea -360,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000, +360,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,No Disorder 361,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 362,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 363,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea diff --git a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_003/info.json b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_003/info.json index ac7f1ad59f059be3648f2769a10128e59277c544..c68a30219902530a9154e48d4957e5bb70f65c0f 100644 --- a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_003/info.json +++ b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_003/info.json @@ -1,121 +1,122 @@ -{ - "name": "Sleep Health and Lifestyle Dataset", - "source": "https://www.kaggle.com/datasets/uom190346a/sleep-health-and-lifestyle-dataset/data", - "data_intro": "The Sleep Health and Lifestyle Dataset comprises 400 rows and 13 columns, covering a wide range of variables related to sleep and daily habits. It includes details such as gender, age, occupation, sleep duration, quality of sleep, physical activity level, stress levels, BMI category, blood pressure, heart rate, daily steps, and the presence or absence of sleep disorders.", - "is_splited": false, - "overall_size": 374, - "train_size": 0, - "test_size": 0, - "c_classes": 5, - "n_classes": 8, - "cat_feature_intro": { - "Gender": "Gender: The gender of the person (Male/Female).
", - "Occupation": "Occupation: The occupation or profession of the person.
", - "BMI Category": "BMI Category: The BMI category of the person (e.g., Underweight, Normal, Overweight).
", - "Blood Pressure": "Blood Pressure (systolic/diastolic): The blood pressure measurement of the person, indicated as systolic pressure over diastolic pressure.
", - "Sleep Disorder": "- None: The individual does not exhibit any specific sleep disorder." - }, - "num_feature_intro": { - "Person ID": "Person ID: An identifier for each individual.
", - "Age": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", - "Sleep Duration": "Sleep Duration (hours): The number of hours the person sleeps per day.
", - "Quality of Sleep": "Quality of Sleep (scale: 1-10): A subjective rating of the quality of sleep, ranging from 1 to 10.
", - "Physical Activity Level": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", - "Stress Level": "Stress Level (scale: 1-10): A subjective rating of the stress level experienced by the person, ranging from 1 to 10.
", - "Heart Rate": "Heart Rate (bpm): The resting heart rate of the person in beats per minute.
", - "Daily Steps": "Daily Steps: The number of steps the person takes per day.
" - }, - "evaluation_metric": null, - "task_type": "classification", - "target": "Sleep Disorder", - "num_feature_value": { - "Age": [ - 27.0, - 59.0 - ], - "Daily Steps": [ - 3000.0, - 10000.0 - ], - "Heart Rate": [ - 65.0, - 86.0 - ], - "Person ID": [ - 1.0, - 374.0 - ], - "Physical Activity Level": [ - 30.0, - 90.0 - ], - "Quality of Sleep": [ - 4.0, - 9.0 - ], - "Sleep Duration": [ - 5.8, - 8.5 - ], - "Stress Level": [ - 3.0, - 8.0 - ] - }, - "cat_feature_value": { - "BMI Category": [ - "Normal", - "Normal Weight", - "Obese", - "Overweight" - ], - "Blood Pressure": [ - "115/75", - "115/78", - "117/76", - "118/75", - "118/76", - "119/77", - "120/80", - "121/79", - "122/80", - "125/80", - "125/82", - "126/83", - "128/84", - "128/85", - "129/84", - "130/85", - "130/86", - "131/86", - "132/87", - "135/88", - "135/90", - "139/91", - "140/90", - "140/95", - "142/92" - ], - "Gender": [ - "Female", - "Male" - ], - "Occupation": [ - "Accountant", - "Doctor", - "Engineer", - "Lawyer", - "Manager", - "Nurse", - "Sales Representative", - "Salesperson", - "Scientist", - "Software Engineer", - "Teacher" - ], - "Sleep Disorder": [ - "Insomnia", - "Sleep Apnea" - ] - } -} \ No newline at end of file +{ + "name": "Sleep Health and Lifestyle Dataset", + "source": "https://www.kaggle.com/datasets/uom190346a/sleep-health-and-lifestyle-dataset/data", + "data_intro": "The Sleep Health and Lifestyle Dataset comprises 400 rows and 13 columns, covering a wide range of variables related to sleep and daily habits. It includes details such as gender, age, occupation, sleep duration, quality of sleep, physical activity level, stress levels, BMI category, blood pressure, heart rate, daily steps, and the presence or absence of sleep disorders.", + "is_splited": false, + "overall_size": 374, + "train_size": 0, + "test_size": 0, + "c_classes": 5, + "n_classes": 8, + "cat_feature_intro": { + "Gender": "Gender: The gender of the person (Male/Female).
", + "Occupation": "Occupation: The occupation or profession of the person.
", + "BMI Category": "BMI Category: The BMI category of the person (e.g., Underweight, Normal, Overweight).
", + "Blood Pressure": "Blood Pressure (systolic/diastolic): The blood pressure measurement of the person, indicated as systolic pressure over diastolic pressure.
", + "Sleep Disorder": "Sleep disorder category: No Disorder, Insomnia, or Sleep Apnea." + }, + "num_feature_intro": { + "Person ID": "Person ID: An identifier for each individual.
", + "Age": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", + "Sleep Duration": "Sleep Duration (hours): The number of hours the person sleeps per day.
", + "Quality of Sleep": "Quality of Sleep (scale: 1-10): A subjective rating of the quality of sleep, ranging from 1 to 10.
", + "Physical Activity Level": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", + "Stress Level": "Stress Level (scale: 1-10): A subjective rating of the stress level experienced by the person, ranging from 1 to 10.
", + "Heart Rate": "Heart Rate (bpm): The resting heart rate of the person in beats per minute.
", + "Daily Steps": "Daily Steps: The number of steps the person takes per day.
" + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "Sleep Disorder", + "num_feature_value": { + "Age": [ + 27.0, + 59.0 + ], + "Daily Steps": [ + 3000.0, + 10000.0 + ], + "Heart Rate": [ + 65.0, + 86.0 + ], + "Person ID": [ + 1.0, + 374.0 + ], + "Physical Activity Level": [ + 30.0, + 90.0 + ], + "Quality of Sleep": [ + 4.0, + 9.0 + ], + "Sleep Duration": [ + 5.8, + 8.5 + ], + "Stress Level": [ + 3.0, + 8.0 + ] + }, + "cat_feature_value": { + "BMI Category": [ + "Normal", + "Normal Weight", + "Obese", + "Overweight" + ], + "Blood Pressure": [ + "115/75", + "115/78", + "117/76", + "118/75", + "118/76", + "119/77", + "120/80", + "121/79", + "122/80", + "125/80", + "125/82", + "126/83", + "128/84", + "128/85", + "129/84", + "130/85", + "130/86", + "131/86", + "132/87", + "135/88", + "135/90", + "139/91", + "140/90", + "140/95", + "142/92" + ], + "Gender": [ + "Female", + "Male" + ], + "Occupation": [ + "Accountant", + "Doctor", + "Engineer", + "Lawyer", + "Manager", + "Nurse", + "Sales Representative", + "Salesperson", + "Scientist", + "Software Engineer", + "Teacher" + ], + "Sleep Disorder": [ + "Insomnia", + "No Disorder", + "Sleep Apnea" + ] + } +} diff --git a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_004/Sleep_Health_and_Lifestyle_Dataset_B4_004_current.json b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_004/Sleep_Health_and_Lifestyle_Dataset_B4_004_current.json index 25129df4189ce8b17ee315a8561a28da38f79127..1a7218e9aaeb2b3ca2590bb711eb7db41c66b00a 100644 --- a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_004/Sleep_Health_and_Lifestyle_Dataset_B4_004_current.json +++ b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_004/Sleep_Health_and_Lifestyle_Dataset_B4_004_current.json @@ -1,1375 +1,1375 @@ { - "id": "004", - "task_type": "B4", - "subtask_type": "list", - "perspective": "data_holder", - "dataset_name": "Sleep Health and Lifestyle Dataset", - "table_path": "output/Sleep Health and Lifestyle Dataset_B4/Sleep Health and Lifestyle Dataset_B4_004/test.csv", - "query": "I’m providing our historical database of sleep disorder diagnoses. Compare it against these new patient entries and filter out those who likely have sleep apnea, matching the profiles we’ve seen before.", - "meta_info": { - "domain": "medical" + "id": "004", + "task_type": "B4", + "subtask_type": "list", + "perspective": "data_holder", + "dataset_name": "Sleep Health and Lifestyle Dataset", + "table_path": "output/Sleep Health and Lifestyle Dataset_B4/Sleep Health and Lifestyle Dataset_B4_004/test.csv", + "query": "I’m providing our historical database of sleep disorder diagnoses. Compare it against these new patient entries and filter out those who likely have sleep apnea, matching the profiles we’ve seen before.", + "meta_info": { + "domain": "medical" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "Person ID": 31, + "Gender": "Female", + "Age": 30, + "Occupation": "Nurse", + "Sleep Duration": 6.4, + "Quality of Sleep": 5, + "Physical Activity Level": 35, + "Stress Level": 7, + "BMI Category": "Normal Weight", + "Blood Pressure": "130/86", + "Heart Rate": 78, + "Daily Steps": 4100, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "002", + "features": { + "Person ID": 11, + "Gender": "Male", + "Age": 29, + "Occupation": "Doctor", + "Sleep Duration": 6.1, + "Quality of Sleep": 6, + "Physical Activity Level": 30, + "Stress Level": 8, + "BMI Category": "Normal", + "Blood Pressure": "120/80", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "003", + "features": { + "Person ID": 7, + "Gender": "Male", + "Age": 29, + "Occupation": "Teacher", + "Sleep Duration": 6.3, + "Quality of Sleep": 6, + "Physical Activity Level": 40, + "Stress Level": 7, + "BMI Category": "Obese", + "Blood Pressure": "140/90", + "Heart Rate": 82, + "Daily Steps": 3500, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "004", + "features": { + "Person ID": 30, + "Gender": "Male", + "Age": 30, + "Occupation": "Doctor", + "Sleep Duration": 7.9, + "Quality of Sleep": 7, + "Physical Activity Level": 75, + "Stress Level": 6, + "BMI Category": "Normal", + "Blood Pressure": "120/80", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "005", + "features": { + "Person ID": 234, + "Gender": "Male", + "Age": 44, + "Occupation": "Salesperson", + "Sleep Duration": 6.3, + "Quality of Sleep": 6, + "Physical Activity Level": 45, + "Stress Level": 7, + "BMI Category": "Overweight", + "Blood Pressure": "130/85", + "Heart Rate": 72, + "Daily Steps": 6000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "006", + "features": { + "Person ID": 45, + "Gender": "Male", + "Age": 31, + "Occupation": "Doctor", + "Sleep Duration": 7.7, + "Quality of Sleep": 7, + "Physical Activity Level": 75, + "Stress Level": 6, + "BMI Category": "Normal", + "Blood Pressure": "120/80", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "007", + "features": { + "Person ID": 68, + "Gender": "Male", + "Age": 33, + "Occupation": "Doctor", + "Sleep Duration": 6.0, + "Quality of Sleep": 6, + "Physical Activity Level": 30, + "Stress Level": 8, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 72, + "Daily Steps": 5000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "008", + "features": { + "Person ID": 51, + "Gender": "Male", + "Age": 32, + "Occupation": "Engineer", + "Sleep Duration": 7.5, + "Quality of Sleep": 8, + "Physical Activity Level": 45, + "Stress Level": 3, + "BMI Category": "Normal", + "Blood Pressure": "120/80", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "009", + "features": { + "Person ID": 181, + "Gender": "Male", + "Age": 42, + "Occupation": "Lawyer", + "Sleep Duration": 7.8, + "Quality of Sleep": 8, + "Physical Activity Level": 90, + "Stress Level": 5, + "BMI Category": "Normal", + "Blood Pressure": "130/85", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "010", + "features": { + "Person ID": 5, + "Gender": "Male", + "Age": 28, + "Occupation": "Sales Representative", + "Sleep Duration": 5.9, + "Quality of Sleep": 4, + "Physical Activity Level": 30, + "Stress Level": 8, + "BMI Category": "Obese", + "Blood Pressure": "140/90", + "Heart Rate": 85, + "Daily Steps": 3000, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "011", + "features": { + "Person ID": 237, + "Gender": "Male", + "Age": 44, + "Occupation": "Salesperson", + "Sleep Duration": 6.4, + "Quality of Sleep": 6, + "Physical Activity Level": 45, + "Stress Level": 7, + "BMI Category": "Overweight", + "Blood Pressure": "130/85", + "Heart Rate": 72, + "Daily Steps": 6000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "012", + "features": { + "Person ID": 190, + "Gender": "Male", + "Age": 43, + "Occupation": "Salesperson", + "Sleep Duration": 6.5, + "Quality of Sleep": 6, + "Physical Activity Level": 45, + "Stress Level": 7, + "BMI Category": "Overweight", + "Blood Pressure": "130/85", + "Heart Rate": 72, + "Daily Steps": 6000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "013", + "features": { + "Person ID": 275, + "Gender": "Female", + "Age": 49, + "Occupation": "Nurse", + "Sleep Duration": 6.2, + "Quality of Sleep": 6, + "Physical Activity Level": 90, + "Stress Level": 8, + "BMI Category": "Overweight", + "Blood Pressure": "140/95", + "Heart Rate": 75, + "Daily Steps": 10000, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "014", + "features": { + "Person ID": 172, + "Gender": "Male", + "Age": 41, + "Occupation": "Lawyer", + "Sleep Duration": 7.7, + "Quality of Sleep": 8, + "Physical Activity Level": 90, + "Stress Level": 5, + "BMI Category": "Normal", + "Blood Pressure": "130/85", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "015", + "features": { + "Person ID": 189, + "Gender": "Female", + "Age": 43, + "Occupation": "Teacher", + "Sleep Duration": 6.7, + "Quality of Sleep": 7, + "Physical Activity Level": 45, + "Stress Level": 4, + "BMI Category": "Overweight", + "Blood Pressure": "135/90", + "Heart Rate": 65, + "Daily Steps": 6000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "016", + "features": { + "Person ID": 369, + "Gender": "Female", + "Age": 59, + "Occupation": "Nurse", + "Sleep Duration": 8.1, + "Quality of Sleep": 9, + "Physical Activity Level": 75, + "Stress Level": 3, + "BMI Category": "Overweight", + "Blood Pressure": "140/95", + "Heart Rate": 68, + "Daily Steps": 7000, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "017", + "features": { + "Person ID": 277, + "Gender": "Male", + "Age": 49, + "Occupation": "Doctor", + "Sleep Duration": 8.1, + "Quality of Sleep": 9, + "Physical Activity Level": 85, + "Stress Level": 3, + "BMI Category": "Obese", + "Blood Pressure": "139/91", + "Heart Rate": 86, + "Daily Steps": 3700, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "018", + "features": { + "Person ID": 22, + "Gender": "Male", + "Age": 30, + "Occupation": "Doctor", + "Sleep Duration": 7.7, + "Quality of Sleep": 7, + "Physical Activity Level": 75, + "Stress Level": 6, + "BMI Category": "Normal", + "Blood Pressure": "120/80", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "019", + "features": { + "Person ID": 154, + "Gender": "Male", + "Age": 39, + "Occupation": "Lawyer", + "Sleep Duration": 7.2, + "Quality of Sleep": 8, + "Physical Activity Level": 60, + "Stress Level": 5, + "BMI Category": "Normal", + "Blood Pressure": "130/85", + "Heart Rate": 68, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "020", + "features": { + "Person ID": 104, + "Gender": "Male", + "Age": 36, + "Occupation": "Teacher", + "Sleep Duration": 6.6, + "Quality of Sleep": 5, + "Physical Activity Level": 35, + "Stress Level": 7, + "BMI Category": "Overweight", + "Blood Pressure": "129/84", + "Heart Rate": 74, + "Daily Steps": 4800, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "021", + "features": { + "Person ID": 343, + "Gender": "Female", + "Age": 56, + "Occupation": "Doctor", + "Sleep Duration": 8.2, + "Quality of Sleep": 9, + "Physical Activity Level": 90, + "Stress Level": 3, + "BMI Category": "Normal Weight", + "Blood Pressure": "118/75", + "Heart Rate": 65, + "Daily Steps": 10000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "022", + "features": { + "Person ID": 62, + "Gender": "Male", + "Age": 32, + "Occupation": "Doctor", + "Sleep Duration": 6.0, + "Quality of Sleep": 6, + "Physical Activity Level": 30, + "Stress Level": 8, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 72, + "Daily Steps": 5000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "023", + "features": { + "Person ID": 364, + "Gender": "Female", + "Age": 59, + "Occupation": "Nurse", + "Sleep Duration": 8.2, + "Quality of Sleep": 9, + "Physical Activity Level": 75, + "Stress Level": 3, + "BMI Category": "Overweight", + "Blood Pressure": "140/95", + "Heart Rate": 68, + "Daily Steps": 7000, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "024", + "features": { + "Person ID": 242, + "Gender": "Male", + "Age": 44, + "Occupation": "Salesperson", + "Sleep Duration": 6.3, + "Quality of Sleep": 6, + "Physical Activity Level": 45, + "Stress Level": 7, + "BMI Category": "Overweight", + "Blood Pressure": "130/85", + "Heart Rate": 72, + "Daily Steps": 6000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "025", + "features": { + "Person ID": 328, + "Gender": "Female", + "Age": 53, + "Occupation": "Engineer", + "Sleep Duration": 8.5, + "Quality of Sleep": 9, + "Physical Activity Level": 30, + "Stress Level": 3, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 65, + "Daily Steps": 5000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "026", + "features": { + "Person ID": 241, + "Gender": "Female", + "Age": 44, + "Occupation": "Teacher", + "Sleep Duration": 6.5, + "Quality of Sleep": 7, + "Physical Activity Level": 45, + "Stress Level": 4, + "BMI Category": "Overweight", + "Blood Pressure": "135/90", + "Heart Rate": 65, + "Daily Steps": 6000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "027", + "features": { + "Person ID": 299, + "Gender": "Female", + "Age": 51, + "Occupation": "Engineer", + "Sleep Duration": 8.5, + "Quality of Sleep": 9, + "Physical Activity Level": 30, + "Stress Level": 3, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 65, + "Daily Steps": 5000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "028", + "features": { + "Person ID": 372, + "Gender": "Female", + "Age": 59, + "Occupation": "Nurse", + "Sleep Duration": 8.1, + "Quality of Sleep": 9, + "Physical Activity Level": 75, + "Stress Level": 3, + "BMI Category": "Overweight", + "Blood Pressure": "140/95", + "Heart Rate": 68, + "Daily Steps": 7000, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "029", + "features": { + "Person ID": 56, + "Gender": "Male", + "Age": 32, + "Occupation": "Doctor", + "Sleep Duration": 6.0, + "Quality of Sleep": 6, + "Physical Activity Level": 30, + "Stress Level": 8, + "BMI Category": 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"Stress Level": 4, - "BMI Category": "Normal", - "Blood Pressure": "115/75", - "Heart Rate": 68, - "Daily Steps": 7000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "073", - "features": { - "Person ID": 49, - "Gender": "Male", - "Age": 31, - "Occupation": "Doctor", - "Sleep Duration": 7.7, - "Quality of Sleep": 7, - "Physical Activity Level": 75, - "Stress Level": 6, - "BMI Category": "Normal", - "Blood Pressure": "120/80", - "Heart Rate": 70, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "074", - "features": { - "Person ID": 33, - "Gender": "Female", - "Age": 31, - "Occupation": "Nurse", - "Sleep Duration": 7.9, - "Quality of Sleep": 8, - "Physical Activity Level": 75, - "Stress Level": 4, - "BMI Category": "Normal Weight", - "Blood Pressure": "117/76", - "Heart Rate": 69, - "Daily Steps": 6800, - "Sleep Disorder": null - } - } - ], - "target_column": "Sleep Disorder", - "task_sub_type": "classification", - "ranking_ground_truth": { - "top_k_ids": [ - "001", - "010", - "013", - "016", - "017", - "020", - "023", - "028", - "036", - "037", - "045", - "056", - "059", - "064", - "066" - ] - }, - "active_filters": [], - "target_class_value": "sleep apnea" - }, - "response": "", - "evaluation_score": {} -} \ No newline at end of file + "active_filters": [], + "target_class_value": "sleep apnea" + }, + "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_004/history.csv b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_004/history.csv index 0b6c45f106984cd82905894e0f7cd31ad56507f4..a9ed9c9507a0b437fe865761931093c47aff7704 100644 --- a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_004/history.csv +++ b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_004/history.csv @@ -1,153 +1,153 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activity Level,Stress Level,BMI Category,Blood Pressure,Heart Rate,Daily Steps,Sleep Disorder -1,Male,27,Software Engineer,6.1,6,42,6,Overweight,126/83,77,4200, -2,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000, -3,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000, +1,Male,27,Software Engineer,6.1,6,42,6,Overweight,126/83,77,4200,No Disorder +2,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000,No Disorder +3,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000,No Disorder 4,Male,28,Sales Representative,5.9,4,30,8,Obese,140/90,85,3000,Sleep Apnea 6,Male,28,Software Engineer,5.9,4,30,8,Obese,140/90,85,3000,Insomnia -8,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -9,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -10,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -12,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -13,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000, -15,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000, -16,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000, +8,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +9,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +10,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +12,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +13,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000,No Disorder +15,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000,No Disorder +16,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000,No Disorder 17,Female,29,Nurse,6.5,5,40,7,Normal Weight,132/87,80,4000,Sleep Apnea 18,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000,Sleep Apnea -20,Male,30,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -21,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -23,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -24,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -25,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -26,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000, -27,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -28,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000, -29,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000, +20,Male,30,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +21,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +23,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +24,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +25,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +26,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,No Disorder +27,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +28,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,No Disorder +29,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,No Disorder 32,Female,30,Nurse,6.4,5,35,7,Normal Weight,130/86,78,4100,Insomnia -34,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -35,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -36,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -37,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -38,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -39,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -41,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -42,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -43,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -44,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -46,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -47,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -48,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000, +34,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +35,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +36,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +37,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +38,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +39,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +41,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +42,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +43,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +44,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +46,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +47,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +48,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder 50,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,Sleep Apnea -52,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000, -53,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -54,Male,32,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -55,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -57,Male,32,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -58,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -59,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -61,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -63,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000, -64,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000, -65,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000, -66,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000, -69,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500, -70,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500, -71,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -72,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -73,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -74,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -75,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -76,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -77,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -78,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -79,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -80,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, +52,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000,No Disorder +53,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +54,Male,32,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +55,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +57,Male,32,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +58,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +59,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +61,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +63,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,No Disorder +64,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,No Disorder +65,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,No Disorder +66,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,No Disorder +69,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500,No Disorder +70,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500,No Disorder +71,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +72,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +73,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +74,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +75,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +76,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +77,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +78,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +79,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +80,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder 81,Female,34,Scientist,5.8,4,32,8,Overweight,131/86,81,5200,Sleep Apnea 82,Female,34,Scientist,5.8,4,32,8,Overweight,131/86,81,5200,Sleep Apnea -83,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600, -84,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600, -86,Female,35,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -87,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000, -89,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000, -91,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000, -92,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000, -93,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000, +83,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600,No Disorder +84,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600,No Disorder +86,Female,35,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +87,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000,No Disorder +89,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,No Disorder +91,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,No Disorder +92,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,No Disorder +93,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000,No Disorder 94,Male,35,Lawyer,7.4,7,60,5,Obese,135/88,84,3300,Sleep Apnea 95,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000,Insomnia -96,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -97,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -98,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -99,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000, -100,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000, -101,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000, -102,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000, -103,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000, +96,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +97,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +98,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +99,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +100,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +101,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +102,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +103,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,No Disorder 105,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,Sleep Apnea 106,Male,36,Teacher,6.6,5,35,7,Overweight,129/84,74,4800,Insomnia -107,Female,37,Nurse,6.1,6,42,6,Overweight,126/83,77,4200, -108,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000, -110,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000, -112,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000, -113,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -114,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000, -115,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -116,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -117,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -118,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -119,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -120,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -123,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -125,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -126,Female,37,Nurse,7.5,8,60,4,Normal Weight,120/80,70,8000, -127,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -128,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -129,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -130,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -131,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -133,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -134,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -135,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -136,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -137,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -138,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000, -139,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -140,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000, -141,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -142,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000, -143,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -144,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, +107,Female,37,Nurse,6.1,6,42,6,Overweight,126/83,77,4200,No Disorder +108,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000,No Disorder +110,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,No Disorder +112,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,No Disorder +113,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +114,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,No Disorder +115,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +116,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +117,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +118,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +119,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +120,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +123,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +125,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +126,Female,37,Nurse,7.5,8,60,4,Normal Weight,120/80,70,8000,No Disorder +127,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +128,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +129,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +130,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +131,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +133,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +134,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +135,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +136,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +137,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +138,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,No Disorder +139,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +140,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,No Disorder +141,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +142,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,No Disorder +143,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +144,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder 145,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,Sleep Apnea 146,Female,38,Lawyer,7.4,7,60,5,Obese,135/88,84,3300,Sleep Apnea 147,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,Insomnia 148,Male,39,Engineer,6.5,5,40,7,Overweight,132/87,80,4000,Insomnia -149,Female,39,Lawyer,6.9,7,50,6,Normal Weight,128/85,75,5500, -151,Female,39,Accountant,8.0,9,80,3,Normal Weight,115/78,67,7500, -152,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -153,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -155,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -156,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -157,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -158,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -159,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -160,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -161,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -162,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300, -163,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300, -164,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000, -165,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000, -168,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000, -169,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000, -170,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -171,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -173,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -174,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -175,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000, -176,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000, -177,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000, +149,Female,39,Lawyer,6.9,7,50,6,Normal Weight,128/85,75,5500,No Disorder +151,Female,39,Accountant,8.0,9,80,3,Normal Weight,115/78,67,7500,No Disorder +152,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +153,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +155,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +156,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +157,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +158,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +159,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +160,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +161,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +162,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300,No Disorder +163,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300,No Disorder +164,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000,No Disorder +165,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000,No Disorder +168,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000,No Disorder +169,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000,No Disorder +170,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +171,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +173,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +174,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +175,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,No Disorder +176,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,No Disorder +177,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,No Disorder 178,Male,42,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia -179,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, -180,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, -183,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, -184,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, +179,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +180,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +183,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +184,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder 185,Female,42,Teacher,6.8,6,45,7,Overweight,130/85,78,5000,Sleep Apnea 186,Female,42,Teacher,6.8,6,45,7,Overweight,130/85,78,5000,Sleep Apnea 187,Female,43,Teacher,6.7,7,45,4,Overweight,135/90,65,6000,Insomnia @@ -162,21 +162,21 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 200,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia 201,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia 203,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Insomnia -204,Male,43,Engineer,6.9,6,47,7,Normal Weight,117/76,69,6800, -205,Male,43,Engineer,7.6,8,75,4,Overweight,122/80,68,6800, -206,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -207,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -208,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -209,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -210,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -211,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -212,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -213,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -214,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -215,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -216,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -217,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -218,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, +204,Male,43,Engineer,6.9,6,47,7,Normal Weight,117/76,69,6800,No Disorder +205,Male,43,Engineer,7.6,8,75,4,Overweight,122/80,68,6800,No Disorder +206,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +207,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +208,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +209,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +210,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +211,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +212,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +213,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +214,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +215,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +216,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +217,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +218,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder 219,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Sleep Apnea 220,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Sleep Apnea 221,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia @@ -199,8 +199,8 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 245,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia 246,Female,44,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia 247,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia -249,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000, -250,Male,44,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000, +249,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,No Disorder +250,Male,44,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,No Disorder 251,Female,45,Teacher,6.8,7,30,6,Overweight,135/90,65,6000,Insomnia 252,Female,45,Teacher,6.8,7,30,6,Overweight,135/90,65,6000,Insomnia 253,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia @@ -210,12 +210,12 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 259,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia 260,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia 261,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia -263,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000, -264,Female,45,Manager,6.9,7,55,5,Overweight,125/82,75,5500, +263,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,No Disorder +264,Female,45,Manager,6.9,7,55,5,Overweight,125/82,75,5500,No Disorder 265,Male,48,Doctor,7.3,7,65,5,Obese,142/92,83,3500,Insomnia 266,Female,48,Nurse,5.9,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 267,Male,48,Doctor,7.3,7,65,5,Obese,142/92,83,3500,Insomnia -268,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000, +268,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,No Disorder 269,Female,49,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 270,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 271,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea @@ -225,8 +225,8 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 276,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 278,Male,49,Doctor,8.1,9,85,3,Obese,139/91,86,3700,Sleep Apnea 279,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Insomnia -280,Female,50,Engineer,8.3,9,30,3,Normal,125/80,65,5000, -281,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000, +280,Female,50,Engineer,8.3,9,30,3,Normal,125/80,65,5000,No Disorder +281,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,No Disorder 282,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 283,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 284,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea @@ -241,9 +241,9 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 296,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 297,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 298,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea -300,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -301,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -302,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, +300,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +301,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +302,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder 304,Female,51,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 305,Female,51,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 306,Female,51,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea @@ -252,31 +252,31 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 310,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia 311,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia 312,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia -313,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -315,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000, +313,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +315,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder 316,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,Insomnia -317,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -318,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -319,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -320,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -321,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -323,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -326,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -327,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000, -329,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000, -330,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -331,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -332,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -333,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -335,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -336,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -337,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -338,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -339,Female,54,Engineer,8.5,9,30,3,Normal,125/80,65,5000, +317,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +318,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +319,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +320,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +321,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +323,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +326,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +327,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,No Disorder +329,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,No Disorder +330,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +331,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +332,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +333,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +335,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +336,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +337,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +338,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +339,Female,54,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder 340,Female,55,Nurse,8.1,9,75,4,Overweight,140/95,72,5000,Sleep Apnea 341,Female,55,Nurse,8.1,9,75,4,Overweight,140/95,72,5000,Sleep Apnea -342,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000, -344,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000, +342,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000,No Disorder +344,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,No Disorder 345,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 346,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 347,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea @@ -288,7 +288,7 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 354,Female,58,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 355,Female,58,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 356,Female,58,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,Sleep Apnea -359,Female,59,Nurse,8.0,9,75,3,Overweight,140/95,68,7000, +359,Female,59,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,No Disorder 361,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 362,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 365,Female,59,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,Sleep Apnea diff --git a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_004/info.json b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_004/info.json index ac7f1ad59f059be3648f2769a10128e59277c544..c68a30219902530a9154e48d4957e5bb70f65c0f 100644 --- a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_004/info.json +++ b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_004/info.json @@ -1,121 +1,122 @@ -{ - "name": "Sleep Health and Lifestyle Dataset", - "source": "https://www.kaggle.com/datasets/uom190346a/sleep-health-and-lifestyle-dataset/data", - "data_intro": "The Sleep Health and Lifestyle Dataset comprises 400 rows and 13 columns, covering a wide range of variables related to sleep and daily habits. It includes details such as gender, age, occupation, sleep duration, quality of sleep, physical activity level, stress levels, BMI category, blood pressure, heart rate, daily steps, and the presence or absence of sleep disorders.", - "is_splited": false, - "overall_size": 374, - "train_size": 0, - "test_size": 0, - "c_classes": 5, - "n_classes": 8, - "cat_feature_intro": { - "Gender": "Gender: The gender of the person (Male/Female).
", - "Occupation": "Occupation: The occupation or profession of the person.
", - "BMI Category": "BMI Category: The BMI category of the person (e.g., Underweight, Normal, Overweight).
", - "Blood Pressure": "Blood Pressure (systolic/diastolic): The blood pressure measurement of the person, indicated as systolic pressure over diastolic pressure.
", - "Sleep Disorder": "- None: The individual does not exhibit any specific sleep disorder." - }, - "num_feature_intro": { - "Person ID": "Person ID: An identifier for each individual.
", - "Age": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", - "Sleep Duration": "Sleep Duration (hours): The number of hours the person sleeps per day.
", - "Quality of Sleep": "Quality of Sleep (scale: 1-10): A subjective rating of the quality of sleep, ranging from 1 to 10.
", - "Physical Activity Level": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", - "Stress Level": "Stress Level (scale: 1-10): A subjective rating of the stress level experienced by the person, ranging from 1 to 10.
", - "Heart Rate": "Heart Rate (bpm): The resting heart rate of the person in beats per minute.
", - "Daily Steps": "Daily Steps: The number of steps the person takes per day.
" - }, - "evaluation_metric": null, - "task_type": "classification", - "target": "Sleep Disorder", - "num_feature_value": { - "Age": [ - 27.0, - 59.0 - ], - "Daily Steps": [ - 3000.0, - 10000.0 - ], - "Heart Rate": [ - 65.0, - 86.0 - ], - "Person ID": [ - 1.0, - 374.0 - ], - "Physical Activity Level": [ - 30.0, - 90.0 - ], - "Quality of Sleep": [ - 4.0, - 9.0 - ], - "Sleep Duration": [ - 5.8, - 8.5 - ], - "Stress Level": [ - 3.0, - 8.0 - ] - }, - "cat_feature_value": { - "BMI Category": [ - "Normal", - "Normal Weight", - "Obese", - "Overweight" - ], - "Blood Pressure": [ - "115/75", - "115/78", - "117/76", - "118/75", - "118/76", - "119/77", - "120/80", - "121/79", - "122/80", - "125/80", - "125/82", - "126/83", - "128/84", - "128/85", - "129/84", - "130/85", - "130/86", - "131/86", - "132/87", - "135/88", - "135/90", - "139/91", - "140/90", - "140/95", - "142/92" - ], - "Gender": [ - "Female", - "Male" - ], - "Occupation": [ - "Accountant", - "Doctor", - "Engineer", - "Lawyer", - "Manager", - "Nurse", - "Sales Representative", - "Salesperson", - "Scientist", - "Software Engineer", - "Teacher" - ], - "Sleep Disorder": [ - "Insomnia", - "Sleep Apnea" - ] - } -} \ No newline at end of file +{ + "name": "Sleep Health and Lifestyle Dataset", + "source": "https://www.kaggle.com/datasets/uom190346a/sleep-health-and-lifestyle-dataset/data", + "data_intro": "The Sleep Health and Lifestyle Dataset comprises 400 rows and 13 columns, covering a wide range of variables related to sleep and daily habits. It includes details such as gender, age, occupation, sleep duration, quality of sleep, physical activity level, stress levels, BMI category, blood pressure, heart rate, daily steps, and the presence or absence of sleep disorders.", + "is_splited": false, + "overall_size": 374, + "train_size": 0, + "test_size": 0, + "c_classes": 5, + "n_classes": 8, + "cat_feature_intro": { + "Gender": "Gender: The gender of the person (Male/Female).
", + "Occupation": "Occupation: The occupation or profession of the person.
", + "BMI Category": "BMI Category: The BMI category of the person (e.g., Underweight, Normal, Overweight).
", + "Blood Pressure": "Blood Pressure (systolic/diastolic): The blood pressure measurement of the person, indicated as systolic pressure over diastolic pressure.
", + "Sleep Disorder": "Sleep disorder category: No Disorder, Insomnia, or Sleep Apnea." + }, + "num_feature_intro": { + "Person ID": "Person ID: An identifier for each individual.
", + "Age": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", + "Sleep Duration": "Sleep Duration (hours): The number of hours the person sleeps per day.
", + "Quality of Sleep": "Quality of Sleep (scale: 1-10): A subjective rating of the quality of sleep, ranging from 1 to 10.
", + "Physical Activity Level": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", + "Stress Level": "Stress Level (scale: 1-10): A subjective rating of the stress level experienced by the person, ranging from 1 to 10.
", + "Heart Rate": "Heart Rate (bpm): The resting heart rate of the person in beats per minute.
", + "Daily Steps": "Daily Steps: The number of steps the person takes per day.
" + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "Sleep Disorder", + "num_feature_value": { + "Age": [ + 27.0, + 59.0 + ], + "Daily Steps": [ + 3000.0, + 10000.0 + ], + "Heart Rate": [ + 65.0, + 86.0 + ], + "Person ID": [ + 1.0, + 374.0 + ], + "Physical Activity Level": [ + 30.0, + 90.0 + ], + "Quality of Sleep": [ + 4.0, + 9.0 + ], + "Sleep Duration": [ + 5.8, + 8.5 + ], + "Stress Level": [ + 3.0, + 8.0 + ] + }, + "cat_feature_value": { + "BMI Category": [ + "Normal", + "Normal Weight", + "Obese", + "Overweight" + ], + "Blood Pressure": [ + "115/75", + "115/78", + "117/76", + "118/75", + "118/76", + "119/77", + "120/80", + "121/79", + "122/80", + "125/80", + "125/82", + "126/83", + "128/84", + "128/85", + "129/84", + "130/85", + "130/86", + "131/86", + "132/87", + "135/88", + "135/90", + "139/91", + "140/90", + "140/95", + "142/92" + ], + "Gender": [ + "Female", + "Male" + ], + "Occupation": [ + "Accountant", + "Doctor", + "Engineer", + "Lawyer", + "Manager", + "Nurse", + "Sales Representative", + "Salesperson", + "Scientist", + "Software Engineer", + "Teacher" + ], + "Sleep Disorder": [ + "Insomnia", + "No Disorder", + "Sleep Apnea" + ] + } +} diff --git a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_005/Sleep_Health_and_Lifestyle_Dataset_B4_005_current.json b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_005/Sleep_Health_and_Lifestyle_Dataset_B4_005_current.json index 5d08e9f7121f79dc357b415b519dfd5b00fa7222..99857d4845b80d77bc6287a3fbb497bff6af7f99 100644 --- a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_005/Sleep_Health_and_Lifestyle_Dataset_B4_005_current.json +++ b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_005/Sleep_Health_and_Lifestyle_Dataset_B4_005_current.json @@ -1,1377 +1,1377 @@ { - "id": "005", - "task_type": "B4", - "subtask_type": "filter", - "perspective": "data_holder", - "dataset_name": "Sleep Health and Lifestyle Dataset", - "table_path": "output/Sleep Health and Lifestyle Dataset_B4/Sleep Health and Lifestyle Dataset_B4_005/test.csv", - "query": "I've shared our historical patient wellness logs with you. From the new batch of assessments, I need you to flag all the male candidates who are most likely suffering from Sleep Apnea. This will help our clinic prioritize follow-up consultations.", - "meta_info": { - "domain": "medical" + "id": "005", + "task_type": "B4", + "subtask_type": "filter", + "perspective": "data_holder", + "dataset_name": "Sleep Health and Lifestyle Dataset", + "table_path": "output/Sleep Health and Lifestyle Dataset_B4/Sleep Health and Lifestyle Dataset_B4_005/test.csv", + "query": "I've shared our historical patient wellness logs with you. From the new batch of assessments, I need you to flag all the male candidates who are most likely suffering from Sleep Apnea. This will help our clinic prioritize follow-up consultations.", + "meta_info": { + "domain": "medical" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "Person ID": 101, + "Gender": "Female", + "Age": 36, + "Occupation": "Teacher", + "Sleep Duration": 7.2, + "Quality of Sleep": 8, + "Physical Activity Level": 60, + "Stress Level": 4, + "BMI Category": "Normal", + "Blood Pressure": "115/75", + "Heart Rate": 68, + "Daily Steps": 7000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "002", + "features": { + "Person ID": 257, + "Gender": "Female", + "Age": 45, + "Occupation": "Teacher", + "Sleep Duration": 6.6, + "Quality of Sleep": 7, + "Physical Activity Level": 45, + "Stress Level": 4, + "BMI Category": "Overweight", + "Blood Pressure": "135/90", + "Heart Rate": 65, + "Daily Steps": 6000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "003", + "features": { + "Person ID": 153, + "Gender": "Male", + "Age": 39, + "Occupation": "Lawyer", + "Sleep Duration": 7.2, + "Quality of Sleep": 8, + "Physical Activity Level": 60, + "Stress Level": 5, + "BMI Category": "Normal", + "Blood Pressure": "130/85", + "Heart Rate": 68, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "004", + "features": { + "Person ID": 225, + "Gender": "Female", + "Age": 44, + "Occupation": "Teacher", + "Sleep Duration": 6.6, + "Quality of Sleep": 7, + "Physical Activity Level": 45, + "Stress Level": 4, + "BMI Category": "Overweight", + "Blood Pressure": "135/90", + "Heart Rate": 65, + "Daily Steps": 6000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "005", + "features": { + "Person ID": 180, + "Gender": "Male", + "Age": 42, + "Occupation": "Lawyer", + "Sleep Duration": 7.8, + "Quality of Sleep": 8, + "Physical Activity Level": 90, + "Stress Level": 5, + "BMI Category": "Normal", + "Blood Pressure": "130/85", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "006", + "features": { + "Person ID": 57, + "Gender": "Male", + "Age": 32, + "Occupation": "Doctor", + "Sleep Duration": 7.7, + "Quality of Sleep": 7, + "Physical Activity Level": 75, + "Stress Level": 6, + "BMI Category": "Normal", + "Blood Pressure": "120/80", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "007", + "features": { + "Person ID": 2, + "Gender": "Male", + "Age": 28, + "Occupation": "Doctor", + "Sleep Duration": 6.2, + "Quality of Sleep": 6, + "Physical Activity Level": 60, + "Stress Level": 8, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 75, + "Daily Steps": 10000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "008", + "features": { + "Person ID": 321, + "Gender": "Female", + "Age": 53, + "Occupation": "Engineer", + "Sleep Duration": 8.5, + "Quality of Sleep": 9, + "Physical Activity Level": 30, + "Stress Level": 3, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 65, + "Daily Steps": 5000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "009", + "features": { + "Person ID": 209, + "Gender": "Male", + "Age": 43, + "Occupation": "Engineer", + "Sleep Duration": 7.7, + "Quality of Sleep": 8, + "Physical Activity Level": 90, + "Stress Level": 5, + "BMI Category": "Normal", + "Blood Pressure": "130/85", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "010", + "features": { + "Person ID": 146, + "Gender": "Female", + "Age": 38, + "Occupation": "Lawyer", + "Sleep Duration": 7.4, + "Quality of Sleep": 7, + "Physical Activity Level": 60, + "Stress Level": 5, + "BMI Category": "Obese", + "Blood Pressure": "135/88", + "Heart Rate": 84, + "Daily Steps": 3300, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "011", + "features": { + "Person ID": 278, + "Gender": "Male", + "Age": 49, + "Occupation": "Doctor", + "Sleep Duration": 8.1, + "Quality of Sleep": 9, + "Physical Activity Level": 85, + "Stress Level": 3, + "BMI Category": "Obese", + "Blood Pressure": "139/91", + "Heart Rate": 86, + "Daily Steps": 3700, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "012", + "features": { + "Person ID": 238, + "Gender": "Female", + "Age": 44, + "Occupation": "Teacher", + "Sleep Duration": 6.5, + "Quality of Sleep": 7, + "Physical Activity Level": 45, + "Stress Level": 4, + "BMI Category": "Overweight", + "Blood Pressure": "135/90", + "Heart Rate": 65, + "Daily Steps": 6000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "013", + "features": { + "Person ID": 50, + "Gender": "Male", + "Age": 31, + "Occupation": "Doctor", + "Sleep Duration": 7.7, + "Quality of Sleep": 7, + "Physical Activity Level": 75, + "Stress Level": 6, + "BMI Category": "Normal", + "Blood Pressure": "120/80", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "014", + "features": { + "Person ID": 172, + "Gender": "Male", + "Age": 41, + "Occupation": "Lawyer", + "Sleep Duration": 7.7, + "Quality of Sleep": 8, + "Physical Activity Level": 90, + "Stress Level": 5, + "BMI Category": "Normal", + "Blood Pressure": "130/85", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "015", + "features": { + "Person ID": 31, + "Gender": "Female", + "Age": 30, + "Occupation": "Nurse", + "Sleep Duration": 6.4, + "Quality of Sleep": 5, + "Physical Activity Level": 35, + "Stress Level": 7, + "BMI Category": "Normal Weight", + "Blood Pressure": "130/86", + "Heart Rate": 78, + "Daily Steps": 4100, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "016", + "features": { + "Person ID": 179, + "Gender": "Male", + "Age": 42, + "Occupation": "Lawyer", + "Sleep Duration": 7.8, + "Quality of Sleep": 8, + "Physical Activity Level": 90, + "Stress Level": 5, + "BMI Category": "Normal", + "Blood Pressure": "130/85", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "017", + "features": { + "Person ID": 58, + "Gender": "Male", + "Age": 32, + "Occupation": "Doctor", + "Sleep Duration": 6.0, + "Quality of Sleep": 6, + "Physical Activity Level": 30, + "Stress Level": 8, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 72, + "Daily Steps": 5000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "018", + "features": { + "Person ID": 6, + "Gender": "Male", + "Age": 28, + "Occupation": "Software Engineer", + "Sleep Duration": 5.9, + "Quality of Sleep": 4, + "Physical Activity Level": 30, + "Stress Level": 8, + "BMI Category": "Obese", + "Blood Pressure": "140/90", + "Heart Rate": 85, + "Daily Steps": 3000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "019", + "features": { + "Person ID": 217, + "Gender": "Male", + 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Disorder": "No Disorder" + } + }, + { + "scenario_id": "022", + "features": { + "Person ID": 212, + "Gender": "Male", + "Age": 43, + "Occupation": "Engineer", + "Sleep Duration": 7.8, + "Quality of Sleep": 8, + "Physical Activity Level": 90, + "Stress Level": 5, + "BMI Category": "Normal", + "Blood Pressure": "130/85", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "023", + "features": { + "Person ID": 54, + "Gender": "Male", + "Age": 32, + "Occupation": "Doctor", + "Sleep Duration": 7.6, + "Quality of Sleep": 7, + "Physical Activity Level": 75, + "Stress Level": 6, + "BMI Category": "Normal", + "Blood Pressure": "120/80", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "024", + "features": { + "Person ID": 206, + "Gender": "Male", + "Age": 43, + "Occupation": "Engineer", + "Sleep Duration": 7.7, + "Quality of Sleep": 8, + "Physical Activity Level": 90, + "Stress 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"Insomnia" + } + }, + { + "scenario_id": "030", + "features": { + "Person ID": 111, + "Gender": "Female", + "Age": 37, + "Occupation": "Accountant", + "Sleep Duration": 7.2, + "Quality of Sleep": 8, + "Physical Activity Level": 60, + "Stress Level": 4, + "BMI Category": "Normal", + "Blood Pressure": "115/75", + "Heart Rate": 68, + "Daily Steps": 7000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "031", + "features": { + "Person ID": 130, + "Gender": "Male", + "Age": 38, + "Occupation": "Lawyer", + "Sleep Duration": 7.3, + "Quality of Sleep": 8, + "Physical Activity Level": 60, + "Stress Level": 5, + "BMI Category": "Normal", + "Blood Pressure": "130/85", + "Heart Rate": 68, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "032", + "features": { + "Person ID": 34, + "Gender": "Male", + "Age": 31, + "Occupation": "Doctor", + "Sleep Duration": 6.1, + "Quality of Sleep": 6, + "Physical Activity Level": 30, + "Stress Level": 8, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 72, + "Daily Steps": 5000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "033", + "features": { + "Person ID": 8, + "Gender": "Male", + "Age": 29, + "Occupation": "Doctor", + "Sleep Duration": 7.8, + "Quality of Sleep": 7, + "Physical Activity Level": 75, + "Stress Level": 6, + "BMI Category": "Normal", + "Blood Pressure": "120/80", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "034", + "features": { + "Person ID": 328, + "Gender": "Female", + "Age": 53, + "Occupation": "Engineer", + "Sleep Duration": 8.5, + "Quality of Sleep": 9, + "Physical Activity Level": 30, + "Stress Level": 3, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 65, + "Daily Steps": 5000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "035", + "features": { + "Person ID": 220, + "Gender": "Male", + "Age": 43, + "Occupation": 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Disorder" + } + }, + { + "scenario_id": "038", + "features": { + "Person ID": 362, + "Gender": "Female", + "Age": 59, + "Occupation": "Nurse", + "Sleep Duration": 8.2, + "Quality of Sleep": 9, + "Physical Activity Level": 75, + "Stress Level": 3, + "BMI Category": "Overweight", + "Blood Pressure": "140/95", + "Heart Rate": 68, + "Daily Steps": 7000, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "039", + "features": { + "Person ID": 106, + "Gender": "Male", + "Age": 36, + "Occupation": "Teacher", + "Sleep Duration": 6.6, + "Quality of Sleep": 5, + "Physical Activity Level": 35, + "Stress Level": 7, + "BMI Category": "Overweight", + "Blood Pressure": "129/84", + "Heart Rate": 74, + "Daily Steps": 4800, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "040", + "features": { + "Person ID": 373, + "Gender": "Female", + "Age": 59, + "Occupation": "Nurse", + "Sleep Duration": 8.1, + "Quality of Sleep": 9, + "Physical Activity Level": 75, + "Stress Level": 3, + 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"Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "044", - "features": { - "Person ID": 208, - "Gender": "Male", - "Age": 43, - "Occupation": "Engineer", - "Sleep Duration": 7.7, - "Quality of Sleep": 8, - "Physical Activity Level": 90, - "Stress Level": 5, - "BMI Category": "Normal", - "Blood Pressure": "130/85", - "Heart Rate": 70, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "045", - "features": { - "Person ID": 277, - "Gender": "Male", - "Age": 49, - "Occupation": "Doctor", - "Sleep Duration": 8.1, - "Quality of Sleep": 9, - "Physical Activity Level": 85, - "Stress Level": 3, - "BMI Category": "Obese", - "Blood Pressure": "139/91", - "Heart Rate": 86, - "Daily Steps": 3700, - "Sleep Disorder": "Sleep Apnea" - } - }, - { - "scenario_id": "046", - "features": { - "Person ID": 37, - "Gender": "Male", - "Age": 31, - "Occupation": "Doctor", - "Sleep Duration": 6.1, - "Quality of Sleep": 6, - "Physical Activity Level": 30, - "Stress Level": 8, - "BMI Category": "Normal", - "Blood Pressure": "125/80", - "Heart Rate": 72, - "Daily Steps": 5000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "047", - "features": { - "Person ID": 363, - "Gender": "Female", - "Age": 59, - "Occupation": "Nurse", - "Sleep Duration": 8.2, - "Quality of Sleep": 9, - "Physical Activity Level": 75, - "Stress Level": 3, - "BMI Category": "Overweight", - "Blood Pressure": "140/95", - "Heart Rate": 68, - "Daily Steps": 7000, - "Sleep Disorder": "Sleep Apnea" - } - }, - { - "scenario_id": "048", - "features": { - "Person ID": 105, - "Gender": "Female", - "Age": 36, - "Occupation": "Teacher", - "Sleep Duration": 7.2, - "Quality of Sleep": 8, - "Physical Activity Level": 60, - "Stress Level": 4, - "BMI Category": "Normal", - "Blood Pressure": "115/75", - "Heart Rate": 68, - "Daily Steps": 7000, - "Sleep Disorder": "Sleep Apnea" - } - }, - { - "scenario_id": "049", - "features": { - "Person ID": 331, - "Gender": "Female", - "Age": 53, - "Occupation": 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"Gender": "Female", - "Age": 49, - "Occupation": "Nurse", - "Sleep Duration": 6.1, - "Quality of Sleep": 6, - "Physical Activity Level": 90, - "Stress Level": 8, - "BMI Category": "Overweight", - "Blood Pressure": "140/95", - "Heart Rate": 75, - "Daily Steps": 10000, - "Sleep Disorder": "Sleep Apnea" - } - }, - { - "scenario_id": "061", - "features": { - "Person ID": 68, - "Gender": "Male", - "Age": 33, - "Occupation": "Doctor", - "Sleep Duration": 6.0, - "Quality of Sleep": 6, - "Physical Activity Level": 30, - "Stress Level": 8, - "BMI Category": "Normal", - "Blood Pressure": "125/80", - "Heart Rate": 72, - "Daily Steps": 5000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "062", - "features": { - "Person ID": 18, - "Gender": "Male", - "Age": 29, - "Occupation": "Doctor", - "Sleep Duration": 6.0, - "Quality of Sleep": 6, - "Physical Activity Level": 30, - "Stress Level": 8, - "BMI Category": "Normal", - "Blood Pressure": "120/80", - "Heart Rate": 70, - "Daily Steps": 8000, - "Sleep Disorder": "Sleep Apnea" - } - }, - { - "scenario_id": "063", - "features": { - "Person ID": 184, - "Gender": "Male", - "Age": 42, - "Occupation": "Lawyer", - "Sleep Duration": 7.8, - "Quality of Sleep": 8, - "Physical Activity Level": 90, - "Stress Level": 5, - "BMI Category": "Normal", - "Blood Pressure": "130/85", - "Heart Rate": 70, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "064", - "features": { - "Person ID": 13, - "Gender": "Male", - "Age": 29, - "Occupation": "Doctor", - "Sleep Duration": 6.1, - "Quality of Sleep": 6, - "Physical Activity Level": 30, - "Stress Level": 8, - "BMI Category": "Normal", - "Blood Pressure": "120/80", - "Heart Rate": 70, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "065", - "features": { - "Person ID": 86, - "Gender": "Female", - "Age": 35, - "Occupation": "Accountant", - "Sleep Duration": 7.2, - "Quality of Sleep": 8, - "Physical Activity Level": 60, - "Stress Level": 4, - "BMI Category": "Normal", - "Blood Pressure": "115/75", - "Heart Rate": 68, - "Daily Steps": 7000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "066", - "features": { - "Person ID": 90, - "Gender": "Male", - "Age": 35, - "Occupation": "Engineer", - "Sleep Duration": 7.3, - "Quality of Sleep": 8, - "Physical Activity Level": 60, - "Stress Level": 4, - "BMI Category": "Normal", - "Blood Pressure": "125/80", - "Heart Rate": 65, - "Daily Steps": 5000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "067", - "features": { - "Person ID": 344, - "Gender": "Female", - "Age": 57, - "Occupation": "Nurse", - "Sleep Duration": 8.1, - "Quality of Sleep": 9, - "Physical Activity Level": 75, - "Stress Level": 3, - "BMI Category": "Overweight", - "Blood Pressure": "140/95", - "Heart Rate": 68, - "Daily Steps": 7000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "068", - "features": { - "Person ID": 139, - "Gender": "Female", - "Age": 38, - "Occupation": "Accountant", - 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"071", - "features": { - "Person ID": 347, - "Gender": "Female", - "Age": 57, - "Occupation": "Nurse", - "Sleep Duration": 8.2, - "Quality of Sleep": 9, - "Physical Activity Level": 75, - "Stress Level": 3, - "BMI Category": "Overweight", - "Blood Pressure": "140/95", - "Heart Rate": 68, - "Daily Steps": 7000, - "Sleep Disorder": "Sleep Apnea" - } - }, - { - "scenario_id": "072", - "features": { - "Person ID": 104, - "Gender": "Male", - "Age": 36, - "Occupation": "Teacher", - "Sleep Duration": 6.6, - "Quality of Sleep": 5, - "Physical Activity Level": 35, - "Stress Level": 7, - "BMI Category": "Overweight", - "Blood Pressure": "129/84", - "Heart Rate": 74, - "Daily Steps": 4800, - "Sleep Disorder": "Sleep Apnea" - } - }, - { - "scenario_id": "073", - "features": { - "Person ID": 219, - "Gender": "Male", - "Age": 43, - "Occupation": "Engineer", - "Sleep Duration": 7.8, - "Quality of Sleep": 8, - "Physical Activity Level": 90, - "Stress Level": 5, - "BMI Category": "Normal", - "Blood Pressure": "130/85", - "Heart Rate": 70, - "Daily Steps": 8000, - "Sleep Disorder": "Sleep Apnea" - } - }, - { - "scenario_id": "074", - "features": { - "Person ID": 183, - "Gender": "Male", - "Age": 42, - "Occupation": "Lawyer", - "Sleep Duration": 7.8, - "Quality of Sleep": 8, - "Physical Activity Level": 90, - "Stress Level": 5, - "BMI Category": "Normal", - "Blood Pressure": "130/85", - "Heart Rate": 70, - "Daily Steps": 8000, - "Sleep Disorder": null - } - } - ], - "target_column": "Sleep Disorder", - "task_sub_type": "classification", - "ranking_ground_truth": { - "top_k_ids": [ - "011", - "013", - "027", - "035", - "042", - "045", - "052", - "058", - "062", - "072", - "073" - ] - }, - "active_filters": [ - { - "col": "Gender", - "op": "==", - "val": "Male" - } - ], - "target_class_value": "sleep apnea" - }, - "response": "", - "evaluation_score": {} -} \ No newline at end of file + "active_filters": [ + { + "col": "Gender", + "op": "==", + "val": "Male" + } + ], + "target_class_value": "sleep apnea" + }, + "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_005/history.csv b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_005/history.csv index bad09e0310dd9113f8d4e8d174c792272ebebbdf..d2bc37881ec4f8bcece4ead3217701e43cd06bc3 100644 --- a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_005/history.csv +++ b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_005/history.csv @@ -1,142 +1,142 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activity Level,Stress Level,BMI Category,Blood Pressure,Heart Rate,Daily Steps,Sleep Disorder -1,Male,27,Software Engineer,6.1,6,42,6,Overweight,126/83,77,4200, -3,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000, +1,Male,27,Software Engineer,6.1,6,42,6,Overweight,126/83,77,4200,No Disorder +3,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000,No Disorder 7,Male,29,Teacher,6.3,6,40,7,Obese,140/90,82,3500,Insomnia -9,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -10,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -11,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000, -12,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -14,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000, -15,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000, -16,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000, +9,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +10,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +11,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000,No Disorder +12,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +14,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000,No Disorder +15,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000,No Disorder +16,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000,No Disorder 17,Female,29,Nurse,6.5,5,40,7,Normal Weight,132/87,80,4000,Sleep Apnea 19,Female,29,Nurse,6.5,5,40,7,Normal Weight,132/87,80,4000,Insomnia -22,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -23,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -24,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -25,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -26,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000, -27,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -29,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000, -30,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000, +22,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +23,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +24,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +25,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +26,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,No Disorder +27,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +29,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,No Disorder +30,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,No Disorder 32,Female,30,Nurse,6.4,5,35,7,Normal Weight,130/86,78,4100,Insomnia -33,Female,31,Nurse,7.9,8,75,4,Normal Weight,117/76,69,6800, -35,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -38,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -39,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -43,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -45,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -46,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -47,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -48,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -49,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -52,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000, -53,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -55,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -56,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -59,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -60,Male,32,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -61,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -62,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -63,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000, -64,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000, -65,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000, -66,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000, -67,Male,32,Accountant,7.2,8,50,6,Normal Weight,118/76,68,7000, -69,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500, -70,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500, -71,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -72,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -73,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -74,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -75,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -76,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -77,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -78,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -79,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -80,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, +33,Female,31,Nurse,7.9,8,75,4,Normal Weight,117/76,69,6800,No Disorder +35,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +38,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +39,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +43,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +45,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +46,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +47,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +48,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +49,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +52,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000,No Disorder +53,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +55,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +56,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +59,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +60,Male,32,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +61,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +62,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +63,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,No Disorder +64,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,No Disorder +65,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,No Disorder +66,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,No Disorder +67,Male,32,Accountant,7.2,8,50,6,Normal Weight,118/76,68,7000,No Disorder +69,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500,No Disorder +70,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500,No Disorder +71,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +72,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +73,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +74,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +75,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +76,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +77,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +78,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +79,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +80,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder 81,Female,34,Scientist,5.8,4,32,8,Overweight,131/86,81,5200,Sleep Apnea 82,Female,34,Scientist,5.8,4,32,8,Overweight,131/86,81,5200,Sleep Apnea -83,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600, -84,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600, -85,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000, -87,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000, -88,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000, -89,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000, -91,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000, -92,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000, -93,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000, +83,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600,No Disorder +84,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600,No Disorder +85,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000,No Disorder +87,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000,No Disorder +88,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000,No Disorder +89,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,No Disorder +91,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,No Disorder +92,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,No Disorder +93,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000,No Disorder 95,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000,Insomnia -96,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -97,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -98,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -99,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000, -100,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000, -102,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000, -103,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000, -107,Female,37,Nurse,6.1,6,42,6,Overweight,126/83,77,4200, -108,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000, -109,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000, -110,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000, -112,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000, -113,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -114,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000, -115,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -116,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -117,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -118,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -119,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -120,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -121,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -122,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -123,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -124,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -125,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -126,Female,37,Nurse,7.5,8,60,4,Normal Weight,120/80,70,8000, -127,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -128,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -129,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -132,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -133,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -134,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -135,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -136,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -137,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -138,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000, -140,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000, -141,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -142,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000, -143,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, +96,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +97,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +98,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +99,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +100,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +102,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +103,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +107,Female,37,Nurse,6.1,6,42,6,Overweight,126/83,77,4200,No Disorder +108,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000,No Disorder +109,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000,No Disorder +110,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,No Disorder +112,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,No Disorder +113,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +114,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,No Disorder +115,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +116,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +117,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +118,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +119,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +120,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +121,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +122,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +123,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +124,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +125,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +126,Female,37,Nurse,7.5,8,60,4,Normal Weight,120/80,70,8000,No Disorder +127,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +128,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +129,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +132,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +133,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +134,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +135,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +136,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +137,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +138,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,No Disorder +140,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,No Disorder +141,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +142,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,No Disorder +143,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder 147,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,Insomnia 148,Male,39,Engineer,6.5,5,40,7,Overweight,132/87,80,4000,Insomnia -149,Female,39,Lawyer,6.9,7,50,6,Normal Weight,128/85,75,5500, -150,Female,39,Accountant,8.0,9,80,3,Normal Weight,115/78,67,7500, -151,Female,39,Accountant,8.0,9,80,3,Normal Weight,115/78,67,7500, -152,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -154,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -155,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -157,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -158,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -159,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -160,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -161,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -162,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300, -163,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300, -165,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000, +149,Female,39,Lawyer,6.9,7,50,6,Normal Weight,128/85,75,5500,No Disorder +150,Female,39,Accountant,8.0,9,80,3,Normal Weight,115/78,67,7500,No Disorder +151,Female,39,Accountant,8.0,9,80,3,Normal Weight,115/78,67,7500,No Disorder +152,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +154,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +155,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +157,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +158,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +159,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +160,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +161,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +162,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300,No Disorder +163,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300,No Disorder +165,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000,No Disorder 166,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,Insomnia -167,Male,41,Engineer,7.3,8,70,6,Normal Weight,121/79,72,6200, -168,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000, -169,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000, -170,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -171,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -173,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -174,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -175,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000, -176,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000, -177,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000, +167,Male,41,Engineer,7.3,8,70,6,Normal Weight,121/79,72,6200,No Disorder +168,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000,No Disorder +169,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000,No Disorder +170,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +171,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +173,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +174,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +175,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,No Disorder +176,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,No Disorder +177,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,No Disorder 178,Male,42,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia -181,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, -182,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, +181,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +182,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder 185,Female,42,Teacher,6.8,6,45,7,Overweight,130/85,78,5000,Sleep Apnea 186,Female,42,Teacher,6.8,6,45,7,Overweight,130/85,78,5000,Sleep Apnea 187,Female,43,Teacher,6.7,7,45,4,Overweight,135/90,65,6000,Insomnia @@ -153,15 +153,15 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 200,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia 201,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia 202,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Insomnia -204,Male,43,Engineer,6.9,6,47,7,Normal Weight,117/76,69,6800, -205,Male,43,Engineer,7.6,8,75,4,Overweight,122/80,68,6800, -210,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -211,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -213,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -214,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -215,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -216,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -218,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, +204,Male,43,Engineer,6.9,6,47,7,Normal Weight,117/76,69,6800,No Disorder +205,Male,43,Engineer,7.6,8,75,4,Overweight,122/80,68,6800,No Disorder +210,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +211,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +213,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +214,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +215,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +216,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +218,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder 221,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia 222,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia 223,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia @@ -187,8 +187,8 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 246,Female,44,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia 247,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia 248,Male,44,Engineer,6.8,7,45,7,Overweight,130/85,78,5000,Insomnia -249,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000, -250,Male,44,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000, +249,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,No Disorder +250,Male,44,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,No Disorder 251,Female,45,Teacher,6.8,7,30,6,Overweight,135/90,65,6000,Insomnia 252,Female,45,Teacher,6.8,7,30,6,Overweight,135/90,65,6000,Insomnia 253,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia @@ -199,13 +199,13 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 259,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia 260,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia 261,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia -262,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000, -263,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000, -264,Female,45,Manager,6.9,7,55,5,Overweight,125/82,75,5500, +262,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,No Disorder +263,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,No Disorder +264,Female,45,Manager,6.9,7,55,5,Overweight,125/82,75,5500,No Disorder 265,Male,48,Doctor,7.3,7,65,5,Obese,142/92,83,3500,Insomnia 266,Female,48,Nurse,5.9,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 267,Male,48,Doctor,7.3,7,65,5,Obese,142/92,83,3500,Insomnia -268,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000, +268,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,No Disorder 269,Female,49,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 270,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 271,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea @@ -214,8 +214,8 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 275,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 276,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 279,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Insomnia -280,Female,50,Engineer,8.3,9,30,3,Normal,125/80,65,5000, -281,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000, +280,Female,50,Engineer,8.3,9,30,3,Normal,125/80,65,5000,No Disorder +281,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,No Disorder 282,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 283,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 284,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea @@ -233,10 +233,10 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 296,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 297,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 298,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea -299,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -300,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -301,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -302,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, +299,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +300,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +301,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +302,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder 304,Female,51,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 305,Female,51,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 306,Female,51,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea @@ -246,34 +246,34 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 310,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia 311,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia 312,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia -313,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -314,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -315,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000, +313,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +314,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +315,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder 316,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,Insomnia -317,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -318,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -319,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -320,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -322,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -323,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -324,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -325,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000, -326,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -327,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000, -329,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000, -330,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -332,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -333,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -334,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -335,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -336,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -337,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -338,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -339,Female,54,Engineer,8.5,9,30,3,Normal,125/80,65,5000, +317,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +318,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +319,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +320,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +322,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +323,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +324,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +325,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,No Disorder +326,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +327,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,No Disorder +329,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,No Disorder +330,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +332,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +333,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +334,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +335,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +336,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +337,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +338,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +339,Female,54,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder 340,Female,55,Nurse,8.1,9,75,4,Overweight,140/95,72,5000,Sleep Apnea 341,Female,55,Nurse,8.1,9,75,4,Overweight,140/95,72,5000,Sleep Apnea -342,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000, -343,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000, +342,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000,No Disorder +343,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000,No Disorder 345,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 346,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 348,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea @@ -287,8 +287,8 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 356,Female,58,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 357,Female,58,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 358,Female,58,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,Sleep Apnea -359,Female,59,Nurse,8.0,9,75,3,Overweight,140/95,68,7000, -360,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000, +359,Female,59,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,No Disorder +360,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,No Disorder 361,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 365,Female,59,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 366,Female,59,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,Sleep Apnea diff --git a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_005/info.json b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_005/info.json index ac7f1ad59f059be3648f2769a10128e59277c544..c68a30219902530a9154e48d4957e5bb70f65c0f 100644 --- a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_005/info.json +++ b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_005/info.json @@ -1,121 +1,122 @@ -{ - "name": "Sleep Health and Lifestyle Dataset", - "source": "https://www.kaggle.com/datasets/uom190346a/sleep-health-and-lifestyle-dataset/data", - "data_intro": "The Sleep Health and Lifestyle Dataset comprises 400 rows and 13 columns, covering a wide range of variables related to sleep and daily habits. It includes details such as gender, age, occupation, sleep duration, quality of sleep, physical activity level, stress levels, BMI category, blood pressure, heart rate, daily steps, and the presence or absence of sleep disorders.", - "is_splited": false, - "overall_size": 374, - "train_size": 0, - "test_size": 0, - "c_classes": 5, - "n_classes": 8, - "cat_feature_intro": { - "Gender": "Gender: The gender of the person (Male/Female).
", - "Occupation": "Occupation: The occupation or profession of the person.
", - "BMI Category": "BMI Category: The BMI category of the person (e.g., Underweight, Normal, Overweight).
", - "Blood Pressure": "Blood Pressure (systolic/diastolic): The blood pressure measurement of the person, indicated as systolic pressure over diastolic pressure.
", - "Sleep Disorder": "- None: The individual does not exhibit any specific sleep disorder." - }, - "num_feature_intro": { - "Person ID": "Person ID: An identifier for each individual.
", - "Age": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", - "Sleep Duration": "Sleep Duration (hours): The number of hours the person sleeps per day.
", - "Quality of Sleep": "Quality of Sleep (scale: 1-10): A subjective rating of the quality of sleep, ranging from 1 to 10.
", - "Physical Activity Level": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", - "Stress Level": "Stress Level (scale: 1-10): A subjective rating of the stress level experienced by the person, ranging from 1 to 10.
", - "Heart Rate": "Heart Rate (bpm): The resting heart rate of the person in beats per minute.
", - "Daily Steps": "Daily Steps: The number of steps the person takes per day.
" - }, - "evaluation_metric": null, - "task_type": "classification", - "target": "Sleep Disorder", - "num_feature_value": { - "Age": [ - 27.0, - 59.0 - ], - "Daily Steps": [ - 3000.0, - 10000.0 - ], - "Heart Rate": [ - 65.0, - 86.0 - ], - "Person ID": [ - 1.0, - 374.0 - ], - "Physical Activity Level": [ - 30.0, - 90.0 - ], - "Quality of Sleep": [ - 4.0, - 9.0 - ], - "Sleep Duration": [ - 5.8, - 8.5 - ], - "Stress Level": [ - 3.0, - 8.0 - ] - }, - "cat_feature_value": { - "BMI Category": [ - "Normal", - "Normal Weight", - "Obese", - "Overweight" - ], - "Blood Pressure": [ - "115/75", - "115/78", - "117/76", - "118/75", - "118/76", - "119/77", - "120/80", - "121/79", - "122/80", - "125/80", - "125/82", - "126/83", - "128/84", - "128/85", - "129/84", - "130/85", - "130/86", - "131/86", - "132/87", - "135/88", - "135/90", - "139/91", - "140/90", - "140/95", - "142/92" - ], - "Gender": [ - "Female", - "Male" - ], - "Occupation": [ - "Accountant", - "Doctor", - "Engineer", - "Lawyer", - "Manager", - "Nurse", - "Sales Representative", - "Salesperson", - "Scientist", - "Software Engineer", - "Teacher" - ], - "Sleep Disorder": [ - "Insomnia", - "Sleep Apnea" - ] - } -} \ No newline at end of file +{ + "name": "Sleep Health and Lifestyle Dataset", + "source": "https://www.kaggle.com/datasets/uom190346a/sleep-health-and-lifestyle-dataset/data", + "data_intro": "The Sleep Health and Lifestyle Dataset comprises 400 rows and 13 columns, covering a wide range of variables related to sleep and daily habits. It includes details such as gender, age, occupation, sleep duration, quality of sleep, physical activity level, stress levels, BMI category, blood pressure, heart rate, daily steps, and the presence or absence of sleep disorders.", + "is_splited": false, + "overall_size": 374, + "train_size": 0, + "test_size": 0, + "c_classes": 5, + "n_classes": 8, + "cat_feature_intro": { + "Gender": "Gender: The gender of the person (Male/Female).
", + "Occupation": "Occupation: The occupation or profession of the person.
", + "BMI Category": "BMI Category: The BMI category of the person (e.g., Underweight, Normal, Overweight).
", + "Blood Pressure": "Blood Pressure (systolic/diastolic): The blood pressure measurement of the person, indicated as systolic pressure over diastolic pressure.
", + "Sleep Disorder": "Sleep disorder category: No Disorder, Insomnia, or Sleep Apnea." + }, + "num_feature_intro": { + "Person ID": "Person ID: An identifier for each individual.
", + "Age": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", + "Sleep Duration": "Sleep Duration (hours): The number of hours the person sleeps per day.
", + "Quality of Sleep": "Quality of Sleep (scale: 1-10): A subjective rating of the quality of sleep, ranging from 1 to 10.
", + "Physical Activity Level": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", + "Stress Level": "Stress Level (scale: 1-10): A subjective rating of the stress level experienced by the person, ranging from 1 to 10.
", + "Heart Rate": "Heart Rate (bpm): The resting heart rate of the person in beats per minute.
", + "Daily Steps": "Daily Steps: The number of steps the person takes per day.
" + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "Sleep Disorder", + "num_feature_value": { + "Age": [ + 27.0, + 59.0 + ], + "Daily Steps": [ + 3000.0, + 10000.0 + ], + "Heart Rate": [ + 65.0, + 86.0 + ], + "Person ID": [ + 1.0, + 374.0 + ], + "Physical Activity Level": [ + 30.0, + 90.0 + ], + "Quality of Sleep": [ + 4.0, + 9.0 + ], + "Sleep Duration": [ + 5.8, + 8.5 + ], + "Stress Level": [ + 3.0, + 8.0 + ] + }, + "cat_feature_value": { + "BMI Category": [ + "Normal", + "Normal Weight", + "Obese", + "Overweight" + ], + "Blood Pressure": [ + "115/75", + "115/78", + "117/76", + "118/75", + "118/76", + "119/77", + "120/80", + "121/79", + "122/80", + "125/80", + "125/82", + "126/83", + "128/84", + "128/85", + "129/84", + "130/85", + "130/86", + "131/86", + "132/87", + "135/88", + "135/90", + "139/91", + "140/90", + "140/95", + "142/92" + ], + "Gender": [ + "Female", + "Male" + ], + "Occupation": [ + "Accountant", + "Doctor", + "Engineer", + "Lawyer", + "Manager", + "Nurse", + "Sales Representative", + "Salesperson", + "Scientist", + "Software Engineer", + "Teacher" + ], + "Sleep Disorder": [ + "Insomnia", + "No Disorder", + "Sleep Apnea" + ] + } +} diff --git a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_006/Sleep_Health_and_Lifestyle_Dataset_B4_006_current.json b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_006/Sleep_Health_and_Lifestyle_Dataset_B4_006_current.json index 966d08309f6289ad2852e455b02fd0153cd63ee1..020b6756d61d68d659cdd7559c4d58a428b67a6c 100644 --- a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_006/Sleep_Health_and_Lifestyle_Dataset_B4_006_current.json +++ b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_006/Sleep_Health_and_Lifestyle_Dataset_B4_006_current.json @@ -1,1381 +1,1381 @@ { - "id": "006", - "task_type": "B4", - "subtask_type": "filter", - "perspective": "data_holder", - "dataset_name": "Sleep Health and Lifestyle Dataset", - "table_path": "output/Sleep Health and Lifestyle Dataset_B4/Sleep Health and Lifestyle Dataset_B4_006/test.csv", - "query": "Our sales team is launching a campaign for our new sleep aid product. Using the lifestyle data archive I've provided, can you isolate the high-stress individuals (those with a stress level of 7 or higher) from this new group who are most likely to be diagnosed with Insomnia? They are our primary target audience.", - "meta_info": { - "domain": "medical" + "id": "006", + "task_type": "B4", + "subtask_type": "filter", + "perspective": "data_holder", + "dataset_name": "Sleep Health and Lifestyle Dataset", + "table_path": "output/Sleep Health and Lifestyle Dataset_B4/Sleep Health and Lifestyle Dataset_B4_006/test.csv", + "query": "Our sales team is launching a campaign for our new sleep aid product. Using the lifestyle data archive I've provided, can you isolate the high-stress individuals (those with a stress level of 7 or higher) from this new group who are most likely to be diagnosed with Insomnia? They are our primary target audience.", + "meta_info": { + "domain": "medical" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "Person ID": 82, + "Gender": "Female", + "Age": 34, + "Occupation": "Scientist", + "Sleep Duration": 5.8, + "Quality of Sleep": 4, + "Physical Activity Level": 32, + "Stress Level": 8, + "BMI Category": "Overweight", + "Blood Pressure": "131/86", + "Heart Rate": 81, + "Daily Steps": 5200, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "002", + "features": { + "Person ID": 73, + "Gender": "Male", + "Age": 33, + "Occupation": "Doctor", + "Sleep Duration": 6.1, + "Quality of Sleep": 6, + "Physical Activity Level": 30, + "Stress Level": 8, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 72, + "Daily Steps": 5000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "003", + "features": { + "Person ID": 110, + "Gender": "Male", + "Age": 37, + "Occupation": "Lawyer", + "Sleep Duration": 7.4, + "Quality of Sleep": 8, + "Physical Activity Level": 60, + "Stress Level": 5, + "BMI Category": "Normal", + "Blood Pressure": "130/85", + "Heart Rate": 68, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "004", + "features": { + "Person ID": 19, + "Gender": "Female", + "Age": 29, + "Occupation": "Nurse", + "Sleep Duration": 6.5, + "Quality of Sleep": 5, + "Physical Activity Level": 40, + "Stress Level": 7, + "BMI Category": "Normal Weight", + "Blood Pressure": "132/87", + "Heart Rate": 80, + "Daily Steps": 4000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "005", + "features": { + "Person ID": 139, + "Gender": "Female", + "Age": 38, + "Occupation": "Accountant", + "Sleep Duration": 7.1, + "Quality of Sleep": 8, + "Physical Activity Level": 60, + "Stress Level": 4, + "BMI Category": "Normal", + "Blood Pressure": "115/75", + "Heart Rate": 68, + "Daily Steps": 7000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "006", + "features": { + "Person ID": 350, + "Gender": "Female", + "Age": 57, + "Occupation": "Nurse", + "Sleep Duration": 8.1, + "Quality of Sleep": 9, + "Physical Activity Level": 75, + "Stress Level": 3, + "BMI Category": "Overweight", + "Blood Pressure": "140/95", + "Heart Rate": 68, + "Daily Steps": 7000, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "007", + "features": { + "Person ID": 186, + "Gender": "Female", + "Age": 42, + "Occupation": "Teacher", + "Sleep Duration": 6.8, + "Quality of Sleep": 6, + "Physical Activity Level": 45, + "Stress Level": 7, + "BMI Category": "Overweight", + "Blood Pressure": "130/85", + "Heart Rate": 78, + "Daily Steps": 5000, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "008", + "features": { + "Person ID": 64, + "Gender": "Male", + "Age": 32, + "Occupation": "Doctor", + "Sleep Duration": 6.2, + "Quality of Sleep": 6, + "Physical Activity Level": 30, + "Stress Level": 8, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 72, + "Daily Steps": 5000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "009", + "features": { + "Person ID": 310, + "Gender": "Female", + "Age": 52, + "Occupation": "Accountant", + "Sleep Duration": 6.6, + "Quality of Sleep": 7, + "Physical Activity Level": 45, + "Stress Level": 7, + "BMI Category": "Overweight", + "Blood Pressure": "130/85", + "Heart Rate": 72, + "Daily Steps": 6000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "010", + "features": { + "Person ID": 89, + "Gender": "Male", + "Age": 35, + "Occupation": "Engineer", + "Sleep Duration": 7.3, + "Quality of Sleep": 8, + "Physical Activity Level": 60, + "Stress Level": 4, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 65, + "Daily Steps": 5000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "011", + "features": { + "Person ID": 278, + "Gender": "Male", + "Age": 49, + "Occupation": "Doctor", + "Sleep Duration": 8.1, + "Quality of Sleep": 9, + "Physical Activity Level": 85, + "Stress Level": 3, + "BMI Category": "Obese", + "Blood Pressure": "139/91", + "Heart Rate": 86, + "Daily Steps": 3700, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "012", + "features": { + "Person ID": 216, + "Gender": "Male", + "Age": 43, + "Occupation": "Engineer", + "Sleep Duration": 7.8, + "Quality of Sleep": 8, + "Physical Activity Level": 90, + "Stress Level": 5, + "BMI Category": "Normal", + "Blood Pressure": "130/85", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "013", + "features": { + "Person ID": 242, + "Gender": "Male", + "Age": 44, + "Occupation": "Salesperson", + "Sleep Duration": 6.3, + "Quality of Sleep": 6, + "Physical Activity Level": 45, + "Stress Level": 7, + "BMI Category": "Overweight", + "Blood Pressure": "130/85", + "Heart Rate": 72, + "Daily Steps": 6000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "014", + "features": { + "Person ID": 147, + "Gender": "Male", + "Age": 39, + "Occupation": "Lawyer", + "Sleep Duration": 7.2, + "Quality of Sleep": 8, + "Physical Activity Level": 60, + "Stress Level": 5, + "BMI Category": "Normal", + "Blood Pressure": "130/85", + "Heart Rate": 68, + "Daily Steps": 8000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "015", + "features": { + "Person ID": 266, + "Gender": "Female", + "Age": 48, + "Occupation": "Nurse", + "Sleep Duration": 5.9, + "Quality of Sleep": 6, + "Physical Activity Level": 90, + "Stress Level": 8, + "BMI Category": "Overweight", + "Blood Pressure": "140/95", + "Heart Rate": 75, + "Daily Steps": 10000, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "016", + "features": { + "Person ID": 98, + "Gender": "Female", + "Age": 36, + "Occupation": "Accountant", + "Sleep Duration": 7.1, + "Quality of Sleep": 8, + "Physical Activity Level": 60, + "Stress Level": 4, + "BMI Category": "Normal", + "Blood Pressure": "115/75", + "Heart Rate": 68, + "Daily Steps": 7000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "017", + "features": { + "Person ID": 41, + "Gender": "Male", + "Age": 31, + "Occupation": "Doctor", + "Sleep Duration": 7.7, + "Quality of Sleep": 7, + "Physical Activity Level": 75, + "Stress Level": 6, + "BMI Category": "Normal", + "Blood Pressure": "120/80", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "018", + "features": { + "Person ID": 11, + "Gender": "Male", + "Age": 29, + "Occupation": "Doctor", + "Sleep Duration": 6.1, + "Quality of Sleep": 6, + "Physical Activity Level": 30, + "Stress Level": 8, + "BMI Category": "Normal", + "Blood Pressure": "120/80", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "019", + "features": { + "Person ID": 265, + "Gender": "Male", + "Age": 48, + "Occupation": "Doctor", + "Sleep Duration": 7.3, + "Quality of Sleep": 7, + "Physical Activity Level": 65, + "Stress Level": 5, + "BMI Category": "Obese", + "Blood Pressure": "142/92", + "Heart Rate": 83, + "Daily Steps": 3500, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "020", + "features": { + "Person ID": 133, + "Gender": "Male", + "Age": 38, + "Occupation": "Lawyer", + "Sleep Duration": 7.3, + "Quality of Sleep": 8, + "Physical Activity Level": 60, + "Stress Level": 5, + "BMI Category": "Normal", + "Blood Pressure": "130/85", + "Heart Rate": 68, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "021", + "features": { + "Person ID": 339, + "Gender": "Female", + "Age": 54, + "Occupation": "Engineer", + "Sleep Duration": 8.5, + "Quality of Sleep": 9, + "Physical Activity Level": 30, + "Stress Level": 3, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 65, + "Daily Steps": 5000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "022", + "features": { + "Person ID": 204, + "Gender": "Male", + "Age": 43, + "Occupation": "Engineer", + "Sleep Duration": 6.9, + "Quality of Sleep": 6, + "Physical Activity Level": 47, + "Stress Level": 7, + "BMI Category": "Normal Weight", + "Blood Pressure": "117/76", + "Heart Rate": 69, + "Daily Steps": 6800, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "023", + "features": { + "Person ID": 248, + "Gender": "Male", + "Age": 44, + "Occupation": "Engineer", + "Sleep Duration": 6.8, + "Quality of Sleep": 7, + "Physical Activity Level": 45, + "Stress Level": 7, + "BMI Category": "Overweight", + "Blood Pressure": "130/85", + "Heart Rate": 78, + "Daily Steps": 5000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "024", + "features": { + "Person ID": 4, + "Gender": "Male", + "Age": 28, + "Occupation": "Sales Representative", + "Sleep Duration": 5.9, + "Quality of Sleep": 4, + "Physical Activity Level": 30, + "Stress Level": 8, + "BMI Category": "Obese", + "Blood Pressure": "140/90", + "Heart Rate": 85, + "Daily Steps": 3000, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "025", + "features": { + "Person ID": 360, + "Gender": "Female", + "Age": 59, + "Occupation": "Nurse", + "Sleep Duration": 8.1, + "Quality of Sleep": 9, + "Physical Activity Level": 75, + "Stress Level": 3, + "BMI Category": "Overweight", + "Blood Pressure": "140/95", + "Heart Rate": 68, + "Daily Steps": 7000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "026", + "features": { + "Person ID": 210, + "Gender": "Male", + "Age": 43, + "Occupation": "Engineer", + "Sleep Duration": 7.8, + "Quality of Sleep": 8, + "Physical Activity Level": 90, + "Stress Level": 5, + "BMI Category": "Normal", + "Blood Pressure": "130/85", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "027", + "features": { + "Person ID": 273, + "Gender": 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+ "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "030", + "features": { + "Person ID": 258, + "Gender": "Female", + "Age": 45, + "Occupation": "Teacher", + "Sleep Duration": 6.6, + "Quality of Sleep": 7, + "Physical Activity Level": 45, + "Stress Level": 4, + "BMI Category": "Overweight", + "Blood Pressure": "135/90", + "Heart Rate": 65, + "Daily Steps": 6000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "031", + "features": { + "Person ID": 23, + "Gender": "Male", + "Age": 30, + "Occupation": "Doctor", + "Sleep Duration": 7.7, + "Quality of Sleep": 7, + "Physical Activity Level": 75, + "Stress Level": 6, + "BMI Category": "Normal", + "Blood Pressure": "120/80", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "032", + "features": { + "Person ID": 15, + "Gender": "Male", + "Age": 29, + "Occupation": "Doctor", + "Sleep Duration": 6.0, + "Quality of Sleep": 6, + "Physical Activity Level": 30, + "Stress Level": 8, + "BMI Category": "Normal", + "Blood Pressure": "120/80", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "033", + "features": { + "Person ID": 51, + "Gender": "Male", + "Age": 32, + "Occupation": "Engineer", + "Sleep Duration": 7.5, + "Quality of Sleep": 8, + "Physical Activity Level": 45, + "Stress Level": 3, + "BMI Category": "Normal", + "Blood Pressure": "120/80", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "034", + "features": { + "Person ID": 33, + "Gender": "Female", + "Age": 31, + "Occupation": "Nurse", + "Sleep Duration": 7.9, + "Quality of Sleep": 8, + "Physical Activity Level": 75, + "Stress Level": 4, + "BMI Category": "Normal Weight", + "Blood Pressure": "117/76", + "Heart Rate": 69, + "Daily Steps": 6800, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "035", + "features": { + "Person ID": 178, + "Gender": "Male", + "Age": 42, + "Occupation": "Salesperson", + "Sleep Duration": 6.5, + "Quality of Sleep": 6, + "Physical Activity Level": 45, + "Stress Level": 7, + "BMI Category": "Overweight", + "Blood Pressure": "130/85", + "Heart Rate": 72, + "Daily Steps": 6000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "036", + "features": { + "Person ID": 325, + "Gender": "Female", + "Age": 53, + "Occupation": "Engineer", + "Sleep Duration": 8.3, + "Quality of Sleep": 9, + "Physical Activity Level": 30, + "Stress Level": 3, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 65, + "Daily Steps": 5000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "037", + "features": { + "Person ID": 309, + "Gender": "Female", + "Age": 52, + "Occupation": "Accountant", + "Sleep Duration": 6.6, + "Quality of Sleep": 7, + "Physical Activity Level": 45, + "Stress Level": 7, + "BMI Category": "Overweight", + "Blood Pressure": "130/85", + "Heart Rate": 72, + "Daily Steps": 6000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "038", + "features": { + "Person ID": 157, + "Gender": "Male", + "Age": 39, + "Occupation": "Lawyer", + "Sleep Duration": 7.2, + "Quality of Sleep": 8, + "Physical Activity Level": 60, + "Stress Level": 5, + "BMI Category": "Normal", + "Blood Pressure": "130/85", + "Heart Rate": 68, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "039", + "features": { + "Person ID": 337, + "Gender": "Female", + "Age": 54, + "Occupation": "Engineer", + "Sleep Duration": 8.4, + "Quality of Sleep": 9, + "Physical Activity Level": 30, + "Stress Level": 3, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 65, + "Daily Steps": 5000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "040", + "features": { + "Person ID": 158, + "Gender": "Male", + "Age": 39, + "Occupation": "Lawyer", + "Sleep Duration": 7.2, + "Quality of Sleep": 8, + "Physical Activity Level": 60, + "Stress Level": 5, + "BMI Category": "Normal", + "Blood Pressure": "130/85", + "Heart Rate": 68, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "041", + "features": { + "Person ID": 311, + "Gender": "Female", + "Age": 52, + "Occupation": "Accountant", + "Sleep Duration": 6.6, + "Quality of Sleep": 7, + "Physical Activity Level": 45, + "Stress Level": 7, + "BMI Category": "Overweight", + "Blood Pressure": "130/85", + "Heart Rate": 72, + "Daily Steps": 6000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "042", + "features": { + "Person ID": 201, + "Gender": "Male", + "Age": 43, + "Occupation": "Salesperson", + "Sleep Duration": 6.5, + "Quality of Sleep": 6, + "Physical Activity Level": 45, + "Stress Level": 7, + "BMI Category": "Overweight", + "Blood Pressure": "130/85", + "Heart Rate": 72, + "Daily Steps": 6000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "043", + "features": { + "Person ID": 223, + "Gender": "Male", + "Age": 44, + "Occupation": "Salesperson", + "Sleep Duration": 6.3, + "Quality of Sleep": 6, + "Physical Activity Level": 45, + "Stress Level": 7, + "BMI Category": "Overweight", + "Blood Pressure": "130/85", + "Heart Rate": 72, + "Daily Steps": 6000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "044", + "features": { + "Person ID": 172, + "Gender": "Male", + "Age": 41, + "Occupation": "Lawyer", + "Sleep Duration": 7.7, + "Quality of Sleep": 8, + "Physical Activity Level": 90, + "Stress Level": 5, + "BMI Category": "Normal", + "Blood Pressure": "130/85", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "045", + "features": { + "Person ID": 209, + "Gender": "Male", + "Age": 43, + "Occupation": "Engineer", + "Sleep Duration": 7.7, + "Quality of Sleep": 8, + "Physical Activity Level": 90, + "Stress Level": 5, + "BMI Category": "Normal", + "Blood Pressure": "130/85", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "046", + "features": { + "Person ID": 289, + "Gender": "Female", + "Age": 50, + "Occupation": "Nurse", + "Sleep Duration": 6.0, + "Quality of Sleep": 6, + "Physical Activity Level": 90, + "Stress Level": 8, + "BMI Category": "Overweight", + "Blood Pressure": "140/95", + "Heart Rate": 75, + "Daily Steps": 10000, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "047", + "features": { + "Person ID": 268, + "Gender": "Female", + "Age": 49, + "Occupation": "Nurse", + "Sleep Duration": 6.2, + "Quality of Sleep": 6, + "Physical Activity Level": 90, + "Stress Level": 8, + "BMI Category": "Overweight", + "Blood Pressure": "140/95", + "Heart Rate": 75, + "Daily Steps": 10000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "048", + "features": { + "Person ID": 180, + "Gender": "Male", + "Age": 42, + "Occupation": "Lawyer", + "Sleep Duration": 7.8, + "Quality of Sleep": 8, + "Physical Activity 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"scenario_id": "038", - "features": { - "Person ID": 157, - "Gender": "Male", - "Age": 39, - "Occupation": "Lawyer", - "Sleep Duration": 7.2, - "Quality of Sleep": 8, - "Physical Activity Level": 60, - "Stress Level": 5, - "BMI Category": "Normal", - "Blood Pressure": "130/85", - "Heart Rate": 68, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "039", - "features": { - "Person ID": 337, - "Gender": "Female", - "Age": 54, - "Occupation": "Engineer", - "Sleep Duration": 8.4, - "Quality of Sleep": 9, - "Physical Activity Level": 30, - "Stress Level": 3, - "BMI Category": "Normal", - "Blood Pressure": "125/80", - "Heart Rate": 65, - "Daily Steps": 5000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "040", - "features": { - "Person ID": 158, - "Gender": "Male", - "Age": 39, - "Occupation": "Lawyer", - "Sleep Duration": 7.2, - "Quality of Sleep": 8, - "Physical Activity Level": 60, - "Stress Level": 5, - "BMI Category": "Normal", - "Blood Pressure": "130/85", - "Heart Rate": 68, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "041", - "features": { - "Person ID": 311, - "Gender": "Female", - "Age": 52, - "Occupation": "Accountant", - "Sleep Duration": 6.6, - "Quality of Sleep": 7, - "Physical Activity Level": 45, - "Stress Level": 7, - "BMI Category": "Overweight", - "Blood Pressure": "130/85", - "Heart Rate": 72, - "Daily Steps": 6000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "042", - "features": { - "Person ID": 201, - "Gender": "Male", - "Age": 43, - "Occupation": "Salesperson", - "Sleep Duration": 6.5, - "Quality of Sleep": 6, - "Physical Activity Level": 45, - "Stress Level": 7, - "BMI Category": "Overweight", - "Blood Pressure": "130/85", - "Heart Rate": 72, - "Daily Steps": 6000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "043", - "features": { - "Person ID": 223, - "Gender": "Male", - "Age": 44, - "Occupation": "Salesperson", - "Sleep Duration": 6.3, - "Quality of Sleep": 6, - "Physical Activity Level": 45, - "Stress Level": 7, - "BMI Category": "Overweight", - "Blood Pressure": "130/85", - "Heart Rate": 72, - "Daily Steps": 6000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "044", - "features": { - "Person ID": 172, - "Gender": "Male", - "Age": 41, - "Occupation": "Lawyer", - "Sleep Duration": 7.7, - "Quality of Sleep": 8, - "Physical Activity Level": 90, - "Stress Level": 5, - "BMI Category": "Normal", - "Blood Pressure": "130/85", - "Heart Rate": 70, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "045", - "features": { - "Person ID": 209, - "Gender": "Male", - "Age": 43, - "Occupation": "Engineer", - "Sleep Duration": 7.7, - "Quality of Sleep": 8, - "Physical Activity Level": 90, - "Stress Level": 5, - "BMI Category": "Normal", - "Blood Pressure": "130/85", - "Heart Rate": 70, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "046", - "features": { - "Person ID": 289, - "Gender": "Female", - "Age": 50, - "Occupation": "Nurse", - "Sleep Duration": 6.0, - "Quality of Sleep": 6, - "Physical Activity Level": 90, - "Stress Level": 8, - "BMI Category": "Overweight", - "Blood Pressure": "140/95", - "Heart Rate": 75, - "Daily Steps": 10000, - "Sleep Disorder": "Sleep Apnea" - } - }, - { - "scenario_id": "047", - "features": { - "Person ID": 268, - "Gender": "Female", - "Age": 49, - "Occupation": "Nurse", - "Sleep Duration": 6.2, - "Quality of Sleep": 6, - "Physical Activity Level": 90, - "Stress Level": 8, - "BMI Category": "Overweight", - "Blood Pressure": "140/95", - "Heart Rate": 75, - "Daily Steps": 10000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "048", - "features": { - "Person ID": 180, - "Gender": "Male", - "Age": 42, - "Occupation": "Lawyer", - "Sleep Duration": 7.8, - "Quality of Sleep": 8, - "Physical Activity Level": 90, - "Stress Level": 5, - "BMI Category": "Normal", - "Blood Pressure": "130/85", - "Heart Rate": 70, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "049", - "features": { - "Person ID": 124, - "Gender": "Female", - "Age": 37, - "Occupation": "Accountant", - "Sleep Duration": 7.2, - "Quality of Sleep": 8, - "Physical Activity Level": 60, - "Stress Level": 4, - "BMI Category": "Normal", - "Blood Pressure": "115/75", - "Heart Rate": 68, - "Daily Steps": 7000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "050", - "features": { - "Person ID": 74, - "Gender": "Male", - "Age": 33, - "Occupation": "Doctor", - "Sleep Duration": 6.1, - "Quality of Sleep": 6, - "Physical Activity Level": 30, - "Stress Level": 8, - "BMI Category": "Normal", - "Blood Pressure": "125/80", - "Heart Rate": 72, - "Daily Steps": 5000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "051", - "features": { - "Person ID": 334, - "Gender": "Female", - "Age": 54, - "Occupation": "Engineer", - "Sleep Duration": 8.4, - "Quality of Sleep": 9, - "Physical Activity Level": 30, - "Stress Level": 3, - "BMI Category": "Normal", - "Blood Pressure": "125/80", - "Heart Rate": 65, - "Daily Steps": 5000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "052", - "features": { - "Person ID": 44, - "Gender": "Male", - "Age": 31, - "Occupation": "Doctor", - "Sleep Duration": 7.8, - "Quality of Sleep": 7, - "Physical Activity Level": 75, - "Stress Level": 6, - "BMI Category": "Normal", - "Blood Pressure": "120/80", - "Heart Rate": 70, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "053", - "features": { - "Person ID": 196, - "Gender": "Male", - "Age": 43, - "Occupation": "Salesperson", - "Sleep Duration": 6.5, - "Quality of Sleep": 6, - "Physical Activity Level": 45, - "Stress Level": 7, - "BMI Category": "Overweight", - "Blood Pressure": "130/85", - "Heart Rate": 72, - "Daily Steps": 6000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "054", - "features": { - "Person ID": 131, - "Gender": "Female", - "Age": 38, - "Occupation": "Accountant", - "Sleep Duration": 7.1, - "Quality of Sleep": 8, - "Physical Activity Level": 60, - "Stress Level": 4, - "BMI Category": "Normal", - "Blood Pressure": "115/75", - "Heart Rate": 68, - "Daily Steps": 7000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "055", - "features": { - "Person ID": 238, - "Gender": "Female", - "Age": 44, - "Occupation": "Teacher", - "Sleep Duration": 6.5, - "Quality of Sleep": 7, - "Physical Activity Level": 45, - "Stress Level": 4, - "BMI Category": "Overweight", - "Blood Pressure": "135/90", - "Heart Rate": 65, - "Daily Steps": 6000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "056", - "features": { - "Person ID": 327, - "Gender": "Female", - "Age": 53, - "Occupation": "Engineer", - "Sleep Duration": 8.3, - "Quality of Sleep": 9, - "Physical Activity Level": 30, - "Stress Level": 3, - "BMI Category": "Normal", - "Blood Pressure": "125/80", - "Heart Rate": 65, - "Daily Steps": 5000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "057", - "features": { - "Person ID": 317, - "Gender": "Female", - "Age": 53, - "Occupation": "Engineer", - "Sleep Duration": 8.5, - "Quality of Sleep": 9, - "Physical Activity Level": 30, - "Stress Level": 3, - "BMI Category": "Normal", - "Blood Pressure": "125/80", - "Heart Rate": 65, - "Daily Steps": 5000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "058", - "features": { - "Person ID": 129, - "Gender": "Male", - "Age": 38, - "Occupation": "Lawyer", - "Sleep Duration": 7.3, - "Quality of Sleep": 8, - "Physical Activity Level": 60, - "Stress Level": 5, - "BMI Category": "Normal", - "Blood Pressure": "130/85", - "Heart Rate": 68, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "059", - "features": { - "Person ID": 345, - "Gender": "Female", - "Age": 57, - "Occupation": "Nurse", - "Sleep Duration": 8.2, - "Quality of Sleep": 9, - "Physical Activity Level": 75, - "Stress Level": 3, - "BMI Category": "Overweight", - "Blood Pressure": "140/95", - "Heart Rate": 68, - "Daily Steps": 7000, - "Sleep Disorder": "Sleep Apnea" - } - }, - { - "scenario_id": "060", - "features": { - "Person ID": 371, - "Gender": "Female", - "Age": 59, - "Occupation": "Nurse", - "Sleep Duration": 8.0, - "Quality of Sleep": 9, - "Physical Activity Level": 75, - "Stress Level": 3, - "BMI Category": "Overweight", - "Blood Pressure": "140/95", - "Heart Rate": 68, - "Daily Steps": 7000, - "Sleep Disorder": "Sleep Apnea" - } - }, - { - "scenario_id": "061", - "features": { - "Person ID": 232, - "Gender": "Male", - "Age": 44, - "Occupation": "Salesperson", - "Sleep Duration": 6.3, - "Quality of Sleep": 6, - "Physical Activity Level": 45, - "Stress Level": 7, - "BMI Category": "Overweight", - "Blood Pressure": "130/85", - "Heart Rate": 72, - "Daily Steps": 6000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "062", - "features": { - "Person ID": 78, - "Gender": "Male", - "Age": 33, - "Occupation": "Doctor", - "Sleep Duration": 6.0, - "Quality of Sleep": 6, - "Physical Activity Level": 30, - "Stress Level": 8, - "BMI Category": "Normal", - "Blood Pressure": "125/80", - "Heart Rate": 72, - "Daily Steps": 5000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "063", - "features": { - "Person ID": 335, - "Gender": "Female", - "Age": 54, - "Occupation": "Engineer", - "Sleep Duration": 8.4, - "Quality of Sleep": 9, - "Physical Activity Level": 30, - "Stress Level": 3, - "BMI Category": "Normal", - "Blood Pressure": "125/80", - "Heart Rate": 65, - "Daily Steps": 5000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "064", - "features": { - "Person ID": 256, - "Gender": "Female", - "Age": 45, - "Occupation": "Teacher", - "Sleep Duration": 6.5, - "Quality of Sleep": 7, - "Physical Activity Level": 45, - "Stress Level": 4, - "BMI Category": "Overweight", - "Blood Pressure": "135/90", - "Heart Rate": 65, - "Daily Steps": 6000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "065", - "features": { - "Person ID": 155, - "Gender": "Male", - "Age": 39, - "Occupation": "Lawyer", - "Sleep Duration": 7.2, - "Quality of Sleep": 8, - "Physical Activity Level": 60, - "Stress Level": 5, - "BMI Category": "Normal", - "Blood Pressure": "130/85", - "Heart Rate": 68, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "066", - "features": { - "Person ID": 240, - "Gender": "Male", - "Age": 44, - "Occupation": "Salesperson", - "Sleep Duration": 6.4, - "Quality of Sleep": 6, - "Physical Activity Level": 45, - "Stress Level": 7, - "BMI Category": "Overweight", - "Blood Pressure": "130/85", - "Heart Rate": 72, - "Daily Steps": 6000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "067", - "features": { - "Person ID": 214, - "Gender": "Male", - "Age": 43, - "Occupation": "Engineer", - "Sleep Duration": 7.8, - "Quality of Sleep": 8, - "Physical Activity Level": 90, - "Stress Level": 5, - "BMI Category": "Normal", - "Blood Pressure": "130/85", - "Heart Rate": 70, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "068", - "features": { - "Person ID": 224, - "Gender": "Male", - "Age": 44, - "Occupation": "Salesperson", - "Sleep Duration": 6.4, - "Quality of Sleep": 6, - "Physical Activity Level": 45, - "Stress Level": 7, - "BMI Category": "Overweight", - "Blood Pressure": "130/85", - "Heart Rate": 72, - "Daily Steps": 6000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "069", - "features": { - "Person ID": 218, - "Gender": "Male", - "Age": 43, - "Occupation": "Engineer", - "Sleep Duration": 7.8, - "Quality of Sleep": 8, - "Physical Activity Level": 90, - "Stress Level": 5, - "BMI Category": "Normal", - "Blood Pressure": "130/85", - "Heart Rate": 70, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "070", - "features": { - "Person ID": 295, - "Gender": "Female", - "Age": 50, - "Occupation": "Nurse", - "Sleep Duration": 6.1, - "Quality of Sleep": 6, - "Physical Activity Level": 90, - "Stress Level": 8, - "BMI Category": "Overweight", - "Blood Pressure": "140/95", - "Heart Rate": 75, - "Daily Steps": 10000, - "Sleep Disorder": "Sleep Apnea" - } - }, - { - "scenario_id": "071", - "features": { - "Person ID": 241, - "Gender": "Female", - "Age": 44, - "Occupation": "Teacher", - "Sleep Duration": 6.5, - "Quality of Sleep": 7, - "Physical Activity Level": 45, - "Stress Level": 4, - "BMI Category": "Overweight", - "Blood Pressure": "135/90", - "Heart Rate": 65, - "Daily Steps": 6000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "072", - "features": { - "Person ID": 312, - "Gender": "Female", - "Age": 52, - "Occupation": "Accountant", - "Sleep Duration": 6.6, - "Quality of Sleep": 7, - "Physical Activity Level": 45, - "Stress Level": 7, - "BMI Category": "Overweight", - "Blood Pressure": "130/85", - "Heart Rate": 72, - "Daily Steps": 6000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "073", - "features": { - "Person ID": 160, - "Gender": "Male", - "Age": 39, - "Occupation": "Lawyer", - "Sleep Duration": 7.2, - "Quality of Sleep": 8, - "Physical Activity Level": 60, - "Stress Level": 5, - "BMI Category": "Normal", - "Blood Pressure": "130/85", - "Heart Rate": 68, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "074", - "features": { - "Person ID": 12, - "Gender": "Male", - "Age": 29, - "Occupation": "Doctor", - "Sleep Duration": 7.8, - "Quality of Sleep": 7, - "Physical Activity Level": 75, - "Stress Level": 6, - "BMI Category": "Normal", - "Blood Pressure": "120/80", - "Heart Rate": 70, - "Daily Steps": 8000, - "Sleep Disorder": null - } - } - ], - "target_column": "Sleep Disorder", - "task_sub_type": "classification", - "ranking_ground_truth": { - "top_k_ids": [ - "004", - "009", - "013", - "023", - "028", - "035", - "037", - "041", - "042", - "043", - "053", - "061", - "066", - "068", - "072" - ] - }, - "active_filters": [ - { - "col": "Stress Level", - "op": ">=", - "val": 7 - } - ], - "target_class_value": "insomnia" - }, - "response": "", - "evaluation_score": {} -} \ No newline at end of file + "active_filters": [ + { + "col": "Stress Level", + "op": ">=", + "val": 7 + } + ], + "target_class_value": "insomnia" + }, + "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_006/history.csv b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_006/history.csv index 648c6e923f0372eef04e8d79888d0d32073df445..4747906692328831703b0caf5b7fd2d3848de766 100644 --- a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_006/history.csv +++ b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_006/history.csv @@ -1,156 +1,156 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activity Level,Stress Level,BMI Category,Blood Pressure,Heart Rate,Daily Steps,Sleep Disorder -1,Male,27,Software Engineer,6.1,6,42,6,Overweight,126/83,77,4200, -2,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000, -3,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000, +1,Male,27,Software Engineer,6.1,6,42,6,Overweight,126/83,77,4200,No Disorder +2,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000,No Disorder +3,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000,No Disorder 5,Male,28,Sales Representative,5.9,4,30,8,Obese,140/90,85,3000,Sleep Apnea 6,Male,28,Software Engineer,5.9,4,30,8,Obese,140/90,85,3000,Insomnia 7,Male,29,Teacher,6.3,6,40,7,Obese,140/90,82,3500,Insomnia -8,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -9,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -10,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -13,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000, -14,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000, -16,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000, +8,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +9,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +10,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +13,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000,No Disorder +14,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000,No Disorder +16,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000,No Disorder 17,Female,29,Nurse,6.5,5,40,7,Normal Weight,132/87,80,4000,Sleep Apnea 18,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000,Sleep Apnea -20,Male,30,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -21,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -22,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -24,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -25,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -26,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000, -27,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -28,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000, -29,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000, -30,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000, +20,Male,30,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +21,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +22,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +24,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +25,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +26,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,No Disorder +27,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +28,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,No Disorder +29,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,No Disorder +30,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,No Disorder 31,Female,30,Nurse,6.4,5,35,7,Normal Weight,130/86,78,4100,Sleep Apnea 32,Female,30,Nurse,6.4,5,35,7,Normal Weight,130/86,78,4100,Insomnia -34,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -35,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -36,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -37,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -38,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -39,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -40,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -42,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -43,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -45,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -46,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -47,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -48,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -49,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, +34,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +35,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +36,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +37,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +38,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +39,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +40,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +42,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +43,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +45,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +46,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +47,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +48,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +49,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder 50,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,Sleep Apnea -52,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000, -53,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -54,Male,32,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -55,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -56,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -57,Male,32,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -58,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -59,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -60,Male,32,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -61,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -62,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -63,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000, -65,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000, -66,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000, -67,Male,32,Accountant,7.2,8,50,6,Normal Weight,118/76,68,7000, -69,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500, -70,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500, -71,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -72,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -75,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -76,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -77,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -79,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -80,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, +52,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000,No Disorder +53,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +54,Male,32,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +55,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +56,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +57,Male,32,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +58,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +59,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +60,Male,32,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +61,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +62,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +63,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,No Disorder +65,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,No Disorder +66,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,No Disorder +67,Male,32,Accountant,7.2,8,50,6,Normal Weight,118/76,68,7000,No Disorder +69,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500,No Disorder +70,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500,No Disorder +71,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +72,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +75,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +76,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +77,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +79,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +80,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder 81,Female,34,Scientist,5.8,4,32,8,Overweight,131/86,81,5200,Sleep Apnea -83,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600, -84,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600, -85,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000, -86,Female,35,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -87,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000, -88,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000, -90,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000, -91,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000, -92,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000, -93,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000, +83,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600,No Disorder +84,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600,No Disorder +85,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000,No Disorder +86,Female,35,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +87,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000,No Disorder +88,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000,No Disorder +90,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,No Disorder +91,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,No Disorder +92,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,No Disorder +93,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000,No Disorder 94,Male,35,Lawyer,7.4,7,60,5,Obese,135/88,84,3300,Sleep Apnea 95,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000,Insomnia -96,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -97,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -99,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000, -100,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000, -101,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000, -102,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000, -103,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000, +96,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +97,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +99,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +100,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +101,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +102,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +103,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,No Disorder 104,Male,36,Teacher,6.6,5,35,7,Overweight,129/84,74,4800,Sleep Apnea 105,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,Sleep Apnea 106,Male,36,Teacher,6.6,5,35,7,Overweight,129/84,74,4800,Insomnia -107,Female,37,Nurse,6.1,6,42,6,Overweight,126/83,77,4200, -108,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000, -109,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000, -111,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -112,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000, -113,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -114,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000, -115,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -116,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -117,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -118,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -119,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -120,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -121,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -122,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -123,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -125,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -126,Female,37,Nurse,7.5,8,60,4,Normal Weight,120/80,70,8000, -127,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -128,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -130,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -132,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -134,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -135,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -136,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -137,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -138,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000, -140,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000, -141,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -142,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000, -143,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -144,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, +107,Female,37,Nurse,6.1,6,42,6,Overweight,126/83,77,4200,No Disorder +108,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000,No Disorder +109,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000,No Disorder +111,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +112,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,No Disorder +113,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +114,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,No Disorder +115,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +116,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +117,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +118,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +119,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +120,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +121,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +122,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +123,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +125,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +126,Female,37,Nurse,7.5,8,60,4,Normal Weight,120/80,70,8000,No Disorder +127,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +128,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +130,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +132,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +134,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +135,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +136,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +137,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +138,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,No Disorder +140,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,No Disorder +141,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +142,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,No Disorder +143,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +144,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder 145,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,Sleep Apnea 146,Female,38,Lawyer,7.4,7,60,5,Obese,135/88,84,3300,Sleep Apnea 148,Male,39,Engineer,6.5,5,40,7,Overweight,132/87,80,4000,Insomnia -149,Female,39,Lawyer,6.9,7,50,6,Normal Weight,128/85,75,5500, -150,Female,39,Accountant,8.0,9,80,3,Normal Weight,115/78,67,7500, -151,Female,39,Accountant,8.0,9,80,3,Normal Weight,115/78,67,7500, -152,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -153,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -154,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -156,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -159,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -161,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -162,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300, -163,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300, -164,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000, -165,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000, +149,Female,39,Lawyer,6.9,7,50,6,Normal Weight,128/85,75,5500,No Disorder +150,Female,39,Accountant,8.0,9,80,3,Normal Weight,115/78,67,7500,No Disorder +151,Female,39,Accountant,8.0,9,80,3,Normal Weight,115/78,67,7500,No Disorder +152,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +153,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +154,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +156,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +159,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +161,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +162,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300,No Disorder +163,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300,No Disorder +164,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000,No Disorder +165,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000,No Disorder 166,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,Insomnia -167,Male,41,Engineer,7.3,8,70,6,Normal Weight,121/79,72,6200, -168,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000, -169,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000, -170,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -171,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -173,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -174,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -175,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000, -176,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000, -177,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000, -179,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, -181,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, -182,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, -183,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, -184,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, +167,Male,41,Engineer,7.3,8,70,6,Normal Weight,121/79,72,6200,No Disorder +168,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000,No Disorder +169,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000,No Disorder +170,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +171,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +173,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +174,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +175,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,No Disorder +176,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,No Disorder +177,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,No Disorder +179,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +181,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +182,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +183,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +184,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder 185,Female,42,Teacher,6.8,6,45,7,Overweight,130/85,78,5000,Sleep Apnea 187,Female,43,Teacher,6.7,7,45,4,Overweight,135/90,65,6000,Insomnia 188,Male,43,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia @@ -167,15 +167,15 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 200,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia 202,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Insomnia 203,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Insomnia -205,Male,43,Engineer,7.6,8,75,4,Overweight,122/80,68,6800, -206,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -207,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -208,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -211,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -212,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -213,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -215,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -217,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, +205,Male,43,Engineer,7.6,8,75,4,Overweight,122/80,68,6800,No Disorder +206,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +207,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +208,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +211,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +212,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +213,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +215,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +217,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder 219,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Sleep Apnea 220,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Sleep Apnea 221,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia @@ -198,8 +198,8 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 245,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia 246,Female,44,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia 247,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia -249,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000, -250,Male,44,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000, +249,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,No Disorder +250,Male,44,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,No Disorder 251,Female,45,Teacher,6.8,7,30,6,Overweight,135/90,65,6000,Insomnia 252,Female,45,Teacher,6.8,7,30,6,Overweight,135/90,65,6000,Insomnia 253,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia @@ -209,9 +209,9 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 259,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia 260,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia 261,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia -262,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000, -263,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000, -264,Female,45,Manager,6.9,7,55,5,Overweight,125/82,75,5500, +262,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,No Disorder +263,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,No Disorder +264,Female,45,Manager,6.9,7,55,5,Overweight,125/82,75,5500,No Disorder 267,Male,48,Doctor,7.3,7,65,5,Obese,142/92,83,3500,Insomnia 269,Female,49,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 270,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea @@ -222,8 +222,8 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 276,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 277,Male,49,Doctor,8.1,9,85,3,Obese,139/91,86,3700,Sleep Apnea 279,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Insomnia -280,Female,50,Engineer,8.3,9,30,3,Normal,125/80,65,5000, -281,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000, +280,Female,50,Engineer,8.3,9,30,3,Normal,125/80,65,5000,No Disorder +281,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,No Disorder 282,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 283,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 284,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea @@ -239,41 +239,41 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 296,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 297,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 298,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea -299,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -300,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -301,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -302,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -303,Female,51,Nurse,7.1,7,55,6,Normal Weight,125/82,72,6000, +299,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +300,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +301,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +302,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +303,Female,51,Nurse,7.1,7,55,6,Normal Weight,125/82,72,6000,No Disorder 304,Female,51,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 305,Female,51,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 306,Female,51,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 307,Female,52,Accountant,6.5,7,45,7,Overweight,130/85,72,6000,Insomnia 308,Female,52,Accountant,6.5,7,45,7,Overweight,130/85,72,6000,Insomnia -313,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -314,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -315,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000, +313,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +314,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +315,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder 316,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,Insomnia -318,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -319,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -320,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -321,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -322,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -323,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -324,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -326,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -328,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -329,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000, -330,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -331,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -332,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -333,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -336,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -338,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, +318,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +319,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +320,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +321,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +322,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +323,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +324,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +326,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +328,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +329,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,No Disorder +330,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +331,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +332,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +333,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +336,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +338,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder 340,Female,55,Nurse,8.1,9,75,4,Overweight,140/95,72,5000,Sleep Apnea 341,Female,55,Nurse,8.1,9,75,4,Overweight,140/95,72,5000,Sleep Apnea -342,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000, -343,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000, -344,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000, +342,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000,No Disorder +343,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000,No Disorder +344,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,No Disorder 346,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 347,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 348,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea @@ -286,7 +286,7 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 356,Female,58,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 357,Female,58,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 358,Female,58,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,Sleep Apnea -359,Female,59,Nurse,8.0,9,75,3,Overweight,140/95,68,7000, +359,Female,59,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,No Disorder 361,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 362,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 364,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea diff --git a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_006/info.json b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_006/info.json index ac7f1ad59f059be3648f2769a10128e59277c544..c68a30219902530a9154e48d4957e5bb70f65c0f 100644 --- a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_006/info.json +++ b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_006/info.json @@ -1,121 +1,122 @@ -{ - "name": "Sleep Health and Lifestyle Dataset", - "source": "https://www.kaggle.com/datasets/uom190346a/sleep-health-and-lifestyle-dataset/data", - "data_intro": "The Sleep Health and Lifestyle Dataset comprises 400 rows and 13 columns, covering a wide range of variables related to sleep and daily habits. It includes details such as gender, age, occupation, sleep duration, quality of sleep, physical activity level, stress levels, BMI category, blood pressure, heart rate, daily steps, and the presence or absence of sleep disorders.", - "is_splited": false, - "overall_size": 374, - "train_size": 0, - "test_size": 0, - "c_classes": 5, - "n_classes": 8, - "cat_feature_intro": { - "Gender": "Gender: The gender of the person (Male/Female).
", - "Occupation": "Occupation: The occupation or profession of the person.
", - "BMI Category": "BMI Category: The BMI category of the person (e.g., Underweight, Normal, Overweight).
", - "Blood Pressure": "Blood Pressure (systolic/diastolic): The blood pressure measurement of the person, indicated as systolic pressure over diastolic pressure.
", - "Sleep Disorder": "- None: The individual does not exhibit any specific sleep disorder." - }, - "num_feature_intro": { - "Person ID": "Person ID: An identifier for each individual.
", - "Age": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", - "Sleep Duration": "Sleep Duration (hours): The number of hours the person sleeps per day.
", - "Quality of Sleep": "Quality of Sleep (scale: 1-10): A subjective rating of the quality of sleep, ranging from 1 to 10.
", - "Physical Activity Level": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", - "Stress Level": "Stress Level (scale: 1-10): A subjective rating of the stress level experienced by the person, ranging from 1 to 10.
", - "Heart Rate": "Heart Rate (bpm): The resting heart rate of the person in beats per minute.
", - "Daily Steps": "Daily Steps: The number of steps the person takes per day.
" - }, - "evaluation_metric": null, - "task_type": "classification", - "target": "Sleep Disorder", - "num_feature_value": { - "Age": [ - 27.0, - 59.0 - ], - "Daily Steps": [ - 3000.0, - 10000.0 - ], - "Heart Rate": [ - 65.0, - 86.0 - ], - "Person ID": [ - 1.0, - 374.0 - ], - "Physical Activity Level": [ - 30.0, - 90.0 - ], - "Quality of Sleep": [ - 4.0, - 9.0 - ], - "Sleep Duration": [ - 5.8, - 8.5 - ], - "Stress Level": [ - 3.0, - 8.0 - ] - }, - "cat_feature_value": { - "BMI Category": [ - "Normal", - "Normal Weight", - "Obese", - "Overweight" - ], - "Blood Pressure": [ - "115/75", - "115/78", - "117/76", - "118/75", - "118/76", - "119/77", - "120/80", - "121/79", - "122/80", - "125/80", - "125/82", - "126/83", - "128/84", - "128/85", - "129/84", - "130/85", - "130/86", - "131/86", - "132/87", - "135/88", - "135/90", - "139/91", - "140/90", - "140/95", - "142/92" - ], - "Gender": [ - "Female", - "Male" - ], - "Occupation": [ - "Accountant", - "Doctor", - "Engineer", - "Lawyer", - "Manager", - "Nurse", - "Sales Representative", - "Salesperson", - "Scientist", - "Software Engineer", - "Teacher" - ], - "Sleep Disorder": [ - "Insomnia", - "Sleep Apnea" - ] - } -} \ No newline at end of file +{ + "name": "Sleep Health and Lifestyle Dataset", + "source": "https://www.kaggle.com/datasets/uom190346a/sleep-health-and-lifestyle-dataset/data", + "data_intro": "The Sleep Health and Lifestyle Dataset comprises 400 rows and 13 columns, covering a wide range of variables related to sleep and daily habits. It includes details such as gender, age, occupation, sleep duration, quality of sleep, physical activity level, stress levels, BMI category, blood pressure, heart rate, daily steps, and the presence or absence of sleep disorders.", + "is_splited": false, + "overall_size": 374, + "train_size": 0, + "test_size": 0, + "c_classes": 5, + "n_classes": 8, + "cat_feature_intro": { + "Gender": "Gender: The gender of the person (Male/Female).
", + "Occupation": "Occupation: The occupation or profession of the person.
", + "BMI Category": "BMI Category: The BMI category of the person (e.g., Underweight, Normal, Overweight).
", + "Blood Pressure": "Blood Pressure (systolic/diastolic): The blood pressure measurement of the person, indicated as systolic pressure over diastolic pressure.
", + "Sleep Disorder": "Sleep disorder category: No Disorder, Insomnia, or Sleep Apnea." + }, + "num_feature_intro": { + "Person ID": "Person ID: An identifier for each individual.
", + "Age": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", + "Sleep Duration": "Sleep Duration (hours): The number of hours the person sleeps per day.
", + "Quality of Sleep": "Quality of Sleep (scale: 1-10): A subjective rating of the quality of sleep, ranging from 1 to 10.
", + "Physical Activity Level": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", + "Stress Level": "Stress Level (scale: 1-10): A subjective rating of the stress level experienced by the person, ranging from 1 to 10.
", + "Heart Rate": "Heart Rate (bpm): The resting heart rate of the person in beats per minute.
", + "Daily Steps": "Daily Steps: The number of steps the person takes per day.
" + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "Sleep Disorder", + "num_feature_value": { + "Age": [ + 27.0, + 59.0 + ], + "Daily Steps": [ + 3000.0, + 10000.0 + ], + "Heart Rate": [ + 65.0, + 86.0 + ], + "Person ID": [ + 1.0, + 374.0 + ], + "Physical Activity Level": [ + 30.0, + 90.0 + ], + "Quality of Sleep": [ + 4.0, + 9.0 + ], + "Sleep Duration": [ + 5.8, + 8.5 + ], + "Stress Level": [ + 3.0, + 8.0 + ] + }, + "cat_feature_value": { + "BMI Category": [ + "Normal", + "Normal Weight", + "Obese", + "Overweight" + ], + "Blood Pressure": [ + "115/75", + "115/78", + "117/76", + "118/75", + "118/76", + "119/77", + "120/80", + "121/79", + "122/80", + "125/80", + "125/82", + "126/83", + "128/84", + "128/85", + "129/84", + "130/85", + "130/86", + "131/86", + "132/87", + "135/88", + "135/90", + "139/91", + "140/90", + "140/95", + "142/92" + ], + "Gender": [ + "Female", + "Male" + ], + "Occupation": [ + "Accountant", + "Doctor", + "Engineer", + "Lawyer", + "Manager", + "Nurse", + "Sales Representative", + "Salesperson", + "Scientist", + "Software Engineer", + "Teacher" + ], + "Sleep Disorder": [ + "Insomnia", + "No Disorder", + "Sleep Apnea" + ] + } +} diff --git a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_007/Sleep_Health_and_Lifestyle_Dataset_B4_007_current.json b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_007/Sleep_Health_and_Lifestyle_Dataset_B4_007_current.json index 0514a340ff2a6731cd8b40cb41611c13eb795f0d..d0ca98da49f73f75eb206c2bd0d16583a934000b 100644 --- a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_007/Sleep_Health_and_Lifestyle_Dataset_B4_007_current.json +++ b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_007/Sleep_Health_and_Lifestyle_Dataset_B4_007_current.json @@ -1,1381 +1,1381 @@ { - "id": "007", - "task_type": "B4", - "subtask_type": "filter", - "perspective": "data_holder", - "dataset_name": "Sleep Health and Lifestyle Dataset", - "table_path": "output/Sleep Health and Lifestyle Dataset_B4/Sleep Health and Lifestyle Dataset_B4_007/test.csv", - "query": "We're analyzing our archive to refine pre-screening for a clinical study. Please review the new candidate data and group all individuals aged 50 and above who are most probable to have Sleep Apnea. This helps us streamline participant selection.", - "meta_info": { - "domain": "medical" + "id": "007", + "task_type": "B4", + "subtask_type": "filter", + "perspective": "data_holder", + "dataset_name": "Sleep Health and Lifestyle Dataset", + "table_path": "output/Sleep Health and Lifestyle Dataset_B4/Sleep Health and Lifestyle Dataset_B4_007/test.csv", + "query": "We're analyzing our archive to refine pre-screening for a clinical study. Please review the new candidate data and group all individuals aged 50 and above who are most probable to have Sleep Apnea. This helps us streamline participant selection.", + "meta_info": { + "domain": "medical" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "Person ID": 284, + "Gender": "Female", + "Age": 50, + "Occupation": "Nurse", + "Sleep Duration": 6.0, + "Quality of Sleep": 6, + "Physical Activity Level": 90, + "Stress Level": 8, + "BMI Category": "Overweight", + "Blood Pressure": "140/95", + "Heart Rate": 75, + "Daily Steps": 10000, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "002", + "features": { + "Person ID": 265, + "Gender": "Male", + "Age": 48, + "Occupation": "Doctor", + "Sleep Duration": 7.3, + "Quality of Sleep": 7, + "Physical Activity Level": 65, + "Stress Level": 5, + "BMI Category": "Obese", + "Blood Pressure": "142/92", + "Heart Rate": 83, + "Daily Steps": 3500, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "003", + "features": { + "Person ID": 267, + "Gender": "Male", + "Age": 48, + "Occupation": "Doctor", + "Sleep Duration": 7.3, + "Quality of Sleep": 7, + "Physical Activity Level": 65, + "Stress Level": 5, + "BMI Category": "Obese", + "Blood Pressure": "142/92", + "Heart Rate": 83, + "Daily Steps": 3500, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "004", + "features": { + "Person ID": 308, + "Gender": "Female", + "Age": 52, + "Occupation": "Accountant", + "Sleep Duration": 6.5, + "Quality of Sleep": 7, + "Physical Activity Level": 45, + "Stress Level": 7, + "BMI Category": "Overweight", + "Blood Pressure": "130/85", + "Heart Rate": 72, + "Daily Steps": 6000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "005", + "features": { + "Person ID": 340, + "Gender": "Female", + "Age": 55, + "Occupation": "Nurse", + "Sleep Duration": 8.1, + "Quality of Sleep": 9, + "Physical Activity Level": 75, + "Stress Level": 4, + "BMI Category": "Overweight", + "Blood Pressure": "140/95", + "Heart Rate": 72, + "Daily Steps": 5000, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "006", + "features": { + "Person ID": 259, + "Gender": "Female", + "Age": 45, + "Occupation": "Teacher", + "Sleep Duration": 6.6, + "Quality of Sleep": 7, + "Physical Activity Level": 45, + "Stress Level": 4, + "BMI Category": "Overweight", + "Blood Pressure": "135/90", + "Heart Rate": 65, + "Daily Steps": 6000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "007", + "features": { + "Person ID": 373, + "Gender": "Female", + "Age": 59, + "Occupation": "Nurse", + "Sleep Duration": 8.1, + "Quality of Sleep": 9, + "Physical Activity Level": 75, + "Stress Level": 3, + "BMI Category": "Overweight", + "Blood Pressure": "140/95", + "Heart Rate": 68, + "Daily Steps": 7000, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "008", + "features": { + "Person ID": 344, + "Gender": "Female", + "Age": 57, + "Occupation": "Nurse", + "Sleep Duration": 8.1, + "Quality of Sleep": 9, + "Physical Activity Level": 75, + "Stress Level": 3, + "BMI Category": "Overweight", + "Blood Pressure": "140/95", + "Heart Rate": 68, + "Daily Steps": 7000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "009", + "features": { + "Person ID": 262, + "Gender": "Female", + "Age": 45, + "Occupation": "Teacher", + "Sleep Duration": 6.6, + "Quality of Sleep": 7, + "Physical Activity Level": 45, + "Stress Level": 4, + "BMI Category": "Overweight", + "Blood Pressure": "135/90", + "Heart Rate": 65, + "Daily Steps": 6000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "010", + "features": { + "Person ID": 243, + "Gender": "Male", + "Age": 44, + "Occupation": "Salesperson", + "Sleep Duration": 6.4, + "Quality of Sleep": 6, + "Physical Activity Level": 45, + "Stress Level": 7, + "BMI Category": "Overweight", + "Blood Pressure": "130/85", + "Heart Rate": 72, + "Daily Steps": 6000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "011", + "features": { + "Person ID": 88, + "Gender": "Male", + "Age": 35, + "Occupation": "Engineer", + "Sleep Duration": 7.2, + "Quality of Sleep": 8, + "Physical Activity Level": 60, + "Stress Level": 4, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 65, + "Daily Steps": 5000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "012", + "features": { + "Person ID": 107, + "Gender": "Female", + "Age": 37, + "Occupation": "Nurse", + "Sleep Duration": 6.1, + "Quality of Sleep": 6, + "Physical Activity Level": 42, + "Stress Level": 6, + "BMI Category": "Overweight", + "Blood Pressure": "126/83", + "Heart Rate": 77, + "Daily Steps": 4200, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "013", + "features": { + "Person ID": 281, + "Gender": "Female", + "Age": 50, + "Occupation": "Nurse", + "Sleep Duration": 6.0, + "Quality of Sleep": 6, + "Physical Activity Level": 90, + "Stress Level": 8, + "BMI Category": "Overweight", + "Blood Pressure": "140/95", + "Heart Rate": 75, + "Daily Steps": 10000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "014", + "features": { + "Person ID": 334, + "Gender": "Female", + "Age": 54, + "Occupation": "Engineer", + "Sleep Duration": 8.4, + "Quality of Sleep": 9, + "Physical Activity Level": 30, + "Stress Level": 3, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 65, + "Daily Steps": 5000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "015", + "features": { + "Person ID": 356, + "Gender": "Female", + "Age": 58, + "Occupation": "Nurse", + "Sleep Duration": 8.0, + "Quality of Sleep": 9, + "Physical Activity Level": 75, + "Stress Level": 3, + "BMI Category": "Overweight", + "Blood Pressure": "140/95", + "Heart Rate": 68, + "Daily Steps": 7000, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "016", + "features": { + "Person ID": 138, + "Gender": "Male", + "Age": 38, + "Occupation": "Lawyer", + "Sleep Duration": 7.1, + "Quality of Sleep": 8, + "Physical 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"Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "022", + "features": { + "Person ID": 304, + "Gender": "Female", + "Age": 51, + "Occupation": "Nurse", + "Sleep Duration": 6.0, + "Quality of Sleep": 6, + "Physical Activity Level": 90, + "Stress Level": 8, + "BMI Category": "Overweight", + "Blood Pressure": "140/95", + "Heart Rate": 75, + "Daily Steps": 10000, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "023", + "features": { + "Person ID": 320, + "Gender": "Female", + "Age": 53, + "Occupation": "Engineer", + "Sleep Duration": 8.4, + "Quality of Sleep": 9, + "Physical Activity Level": 30, + "Stress Level": 3, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 65, + "Daily Steps": 5000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "024", + "features": { + "Person ID": 140, + "Gender": "Male", + "Age": 38, + "Occupation": "Lawyer", + "Sleep Duration": 7.1, + "Quality of Sleep": 8, + "Physical 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352, + "Gender": "Female", + "Age": 57, + "Occupation": "Nurse", + "Sleep Duration": 8.1, + "Quality of Sleep": 9, + "Physical Activity Level": 75, + "Stress Level": 3, + "BMI Category": "Overweight", + "Blood Pressure": "140/95", + "Heart Rate": 68, + "Daily Steps": 7000, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "044", + "features": { + "Person ID": 133, + "Gender": "Male", + "Age": 38, + "Occupation": "Lawyer", + "Sleep Duration": 7.3, + "Quality of Sleep": 8, + "Physical Activity Level": 60, + "Stress Level": 5, + "BMI Category": "Normal", + "Blood Pressure": "130/85", + "Heart Rate": 68, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "045", + "features": { + "Person ID": 134, + "Gender": "Female", + "Age": 38, + "Occupation": "Accountant", + "Sleep Duration": 7.1, + "Quality of Sleep": 8, + "Physical Activity Level": 60, + "Stress Level": 4, + "BMI Category": "Normal", + "Blood Pressure": "115/75", + "Heart Rate": 68, + "Daily Steps": 7000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "046", + "features": { + "Person ID": 246, + "Gender": "Female", + "Age": 44, + "Occupation": "Teacher", + "Sleep Duration": 6.5, + "Quality of Sleep": 7, + "Physical Activity Level": 45, + "Stress Level": 4, + "BMI Category": "Overweight", + "Blood Pressure": "135/90", + "Heart Rate": 65, + "Daily Steps": 6000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "047", + "features": { + "Person ID": 277, + "Gender": "Male", + "Age": 49, + "Occupation": "Doctor", + "Sleep Duration": 8.1, + "Quality of Sleep": 9, + "Physical Activity Level": 85, + "Stress Level": 3, + "BMI Category": "Obese", + "Blood Pressure": "139/91", + "Heart Rate": 86, + "Daily Steps": 3700, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "048", + "features": { + "Person ID": 349, + "Gender": "Female", + "Age": 57, + "Occupation": "Nurse", + "Sleep Duration": 8.2, + "Quality of Sleep": 9, + "Physical 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"Sleep Disorder": "Sleep Apnea" - } - }, - { - "scenario_id": "038", - "features": { - "Person ID": 331, - "Gender": "Female", - "Age": 53, - "Occupation": "Engineer", - "Sleep Duration": 8.5, - "Quality of Sleep": 9, - "Physical Activity Level": 30, - "Stress Level": 3, - "BMI Category": "Normal", - "Blood Pressure": "125/80", - "Heart Rate": 65, - "Daily Steps": 5000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "039", - "features": { - "Person ID": 173, - "Gender": "Male", - "Age": 41, - "Occupation": "Lawyer", - "Sleep Duration": 7.7, - "Quality of Sleep": 8, - "Physical Activity Level": 90, - "Stress Level": 5, - "BMI Category": "Normal", - "Blood Pressure": "130/85", - "Heart Rate": 70, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "040", - "features": { - "Person ID": 151, - "Gender": "Female", - "Age": 39, - "Occupation": "Accountant", - "Sleep Duration": 8.0, - "Quality of Sleep": 9, - "Physical Activity Level": 80, - "Stress Level": 3, - "BMI Category": "Normal Weight", - "Blood Pressure": "115/78", - "Heart Rate": 67, - "Daily Steps": 7500, - "Sleep Disorder": null - } - }, - { - "scenario_id": "041", - "features": { - "Person ID": 63, - "Gender": "Male", - "Age": 32, - "Occupation": "Doctor", - "Sleep Duration": 6.2, - "Quality of Sleep": 6, - "Physical Activity Level": 30, - "Stress Level": 8, - "BMI Category": "Normal", - "Blood Pressure": "125/80", - "Heart Rate": 72, - "Daily Steps": 5000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "042", - "features": { - "Person ID": 310, - "Gender": "Female", - "Age": 52, - "Occupation": "Accountant", - "Sleep Duration": 6.6, - "Quality of Sleep": 7, - "Physical Activity Level": 45, - "Stress Level": 7, - "BMI Category": "Overweight", - "Blood Pressure": "130/85", - "Heart Rate": 72, - "Daily Steps": 6000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "043", - "features": { - "Person ID": 352, - "Gender": "Female", - "Age": 57, - "Occupation": "Nurse", - "Sleep Duration": 8.1, - "Quality of Sleep": 9, - "Physical Activity Level": 75, - "Stress Level": 3, - "BMI Category": "Overweight", - "Blood Pressure": "140/95", - "Heart Rate": 68, - "Daily Steps": 7000, - "Sleep Disorder": "Sleep Apnea" - } - }, - { - "scenario_id": "044", - "features": { - "Person ID": 133, - "Gender": "Male", - "Age": 38, - "Occupation": "Lawyer", - "Sleep Duration": 7.3, - "Quality of Sleep": 8, - "Physical Activity Level": 60, - "Stress Level": 5, - "BMI Category": "Normal", - "Blood Pressure": "130/85", - "Heart Rate": 68, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "045", - "features": { - "Person ID": 134, - "Gender": "Female", - "Age": 38, - "Occupation": "Accountant", - "Sleep Duration": 7.1, - "Quality of Sleep": 8, - "Physical Activity Level": 60, - "Stress Level": 4, - "BMI Category": "Normal", - "Blood Pressure": "115/75", - "Heart Rate": 68, - "Daily Steps": 7000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "046", - "features": { - "Person ID": 246, - "Gender": "Female", - "Age": 44, - "Occupation": "Teacher", - "Sleep Duration": 6.5, - "Quality of Sleep": 7, - "Physical Activity Level": 45, - "Stress Level": 4, - "BMI Category": "Overweight", - "Blood Pressure": "135/90", - "Heart Rate": 65, - "Daily Steps": 6000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "047", - "features": { - "Person ID": 277, - "Gender": "Male", - "Age": 49, - "Occupation": "Doctor", - "Sleep Duration": 8.1, - "Quality of Sleep": 9, - "Physical Activity Level": 85, - "Stress Level": 3, - "BMI Category": "Obese", - "Blood Pressure": "139/91", - "Heart Rate": 86, - "Daily Steps": 3700, - "Sleep Disorder": "Sleep Apnea" - } - }, - { - "scenario_id": "048", - "features": { - "Person ID": 349, - "Gender": "Female", - "Age": 57, - "Occupation": "Nurse", - "Sleep Duration": 8.2, - "Quality of Sleep": 9, - "Physical Activity Level": 75, - "Stress Level": 3, - "BMI Category": "Overweight", - "Blood Pressure": "140/95", - "Heart Rate": 68, - "Daily Steps": 7000, - "Sleep Disorder": "Sleep Apnea" - } - }, - { - "scenario_id": "049", - "features": { - "Person ID": 157, - "Gender": "Male", - "Age": 39, - "Occupation": "Lawyer", - "Sleep Duration": 7.2, - "Quality of Sleep": 8, - "Physical Activity Level": 60, - "Stress Level": 5, - "BMI Category": "Normal", - "Blood Pressure": "130/85", - "Heart Rate": 68, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "050", - "features": { - "Person ID": 185, - "Gender": "Female", - "Age": 42, - "Occupation": "Teacher", - "Sleep Duration": 6.8, - "Quality of Sleep": 6, - "Physical Activity Level": 45, - "Stress Level": 7, - "BMI Category": "Overweight", - "Blood Pressure": "130/85", - "Heart Rate": 78, - "Daily Steps": 5000, - "Sleep Disorder": "Sleep Apnea" - } - }, - { - "scenario_id": "051", - "features": { - "Person ID": 283, - "Gender": "Female", - "Age": 50, - "Occupation": "Nurse", - "Sleep Duration": 6.0, - "Quality of Sleep": 6, - "Physical Activity Level": 90, - "Stress Level": 8, - "BMI Category": "Overweight", - "Blood Pressure": "140/95", - "Heart Rate": 75, - "Daily Steps": 10000, - "Sleep Disorder": "Sleep Apnea" - } - }, - { - "scenario_id": "052", - "features": { - "Person ID": 43, - "Gender": "Male", - "Age": 31, - "Occupation": "Doctor", - "Sleep Duration": 7.7, - "Quality of Sleep": 7, - "Physical Activity Level": 75, - "Stress Level": 6, - "BMI Category": "Normal", - "Blood Pressure": "120/80", - "Heart Rate": 70, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "053", - "features": { - "Person ID": 78, - "Gender": "Male", - "Age": 33, - "Occupation": "Doctor", - "Sleep Duration": 6.0, - "Quality of Sleep": 6, - "Physical Activity Level": 30, - "Stress Level": 8, - "BMI Category": "Normal", - "Blood Pressure": "125/80", - "Heart Rate": 72, - "Daily Steps": 5000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "054", - "features": { - "Person ID": 94, - "Gender": "Male", - "Age": 35, - "Occupation": "Lawyer", - "Sleep Duration": 7.4, - "Quality of Sleep": 7, - "Physical Activity Level": 60, - "Stress Level": 5, - "BMI Category": "Obese", - "Blood Pressure": "135/88", - "Heart Rate": 84, - "Daily Steps": 3300, - "Sleep Disorder": "Sleep Apnea" - } - }, - { - "scenario_id": "055", - "features": { - "Person ID": 209, - "Gender": "Male", - "Age": 43, - "Occupation": "Engineer", - "Sleep Duration": 7.7, - "Quality of Sleep": 8, - "Physical Activity Level": 90, - "Stress Level": 5, - "BMI Category": "Normal", - "Blood Pressure": "130/85", - "Heart Rate": 70, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "056", - "features": { - "Person ID": 372, - "Gender": "Female", - "Age": 59, - "Occupation": "Nurse", - "Sleep Duration": 8.1, - "Quality of Sleep": 9, - "Physical Activity Level": 75, - "Stress Level": 3, - "BMI Category": "Overweight", - "Blood Pressure": "140/95", - "Heart Rate": 68, - "Daily Steps": 7000, - "Sleep Disorder": "Sleep Apnea" - } - }, - { - "scenario_id": "057", - "features": { - "Person ID": 47, - "Gender": "Male", - "Age": 31, - "Occupation": "Doctor", - "Sleep Duration": 7.7, - "Quality of Sleep": 7, - "Physical Activity Level": 75, - "Stress Level": 6, - "BMI Category": "Normal", - "Blood Pressure": "120/80", - "Heart Rate": 70, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "058", - "features": { - "Person ID": 206, - "Gender": "Male", - "Age": 43, - "Occupation": "Engineer", - "Sleep Duration": 7.7, - "Quality of Sleep": 8, - "Physical Activity Level": 90, - "Stress Level": 5, - "BMI Category": "Normal", - "Blood Pressure": "130/85", - "Heart Rate": 70, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "059", - "features": { - "Person ID": 303, - "Gender": "Female", - "Age": 51, - "Occupation": "Nurse", - "Sleep Duration": 7.1, - "Quality of Sleep": 7, - "Physical Activity Level": 55, - "Stress Level": 6, - "BMI Category": "Normal Weight", - "Blood Pressure": "125/82", - "Heart Rate": 72, - "Daily Steps": 6000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "060", - "features": { - "Person ID": 111, - "Gender": "Female", - "Age": 37, - "Occupation": "Accountant", - "Sleep Duration": 7.2, - "Quality of Sleep": 8, - "Physical Activity Level": 60, - "Stress Level": 4, - "BMI Category": "Normal", - "Blood Pressure": "115/75", - "Heart Rate": 68, - "Daily Steps": 7000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "061", - "features": { - "Person ID": 54, - "Gender": "Male", - "Age": 32, - "Occupation": "Doctor", - "Sleep Duration": 7.6, - "Quality of Sleep": 7, - "Physical Activity Level": 75, - "Stress Level": 6, - "BMI Category": "Normal", - "Blood Pressure": "120/80", - "Heart Rate": 70, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "062", - "features": { - "Person ID": 39, - "Gender": "Male", - "Age": 31, - "Occupation": "Doctor", - "Sleep Duration": 7.6, - "Quality of Sleep": 7, - "Physical Activity Level": 75, - "Stress Level": 6, - "BMI Category": "Normal", - "Blood Pressure": "120/80", - "Heart Rate": 70, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "063", - "features": { - "Person ID": 291, - "Gender": "Female", - "Age": 50, - "Occupation": "Nurse", - "Sleep Duration": 6.0, - "Quality of Sleep": 6, - "Physical Activity Level": 90, - "Stress Level": 8, - "BMI Category": "Overweight", - "Blood Pressure": "140/95", - "Heart Rate": 75, - "Daily Steps": 10000, - "Sleep Disorder": "Sleep Apnea" - } - }, - { - "scenario_id": "064", - "features": { - "Person ID": 228, - "Gender": "Male", - "Age": 44, - "Occupation": "Salesperson", - "Sleep Duration": 6.3, - "Quality of Sleep": 6, - "Physical Activity Level": 45, - "Stress Level": 7, - "BMI Category": "Overweight", - "Blood Pressure": "130/85", - "Heart Rate": 72, - "Daily Steps": 6000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "065", - "features": { - "Person ID": 204, - "Gender": "Male", - "Age": 43, - "Occupation": "Engineer", - "Sleep Duration": 6.9, - "Quality of Sleep": 6, - "Physical Activity Level": 47, - "Stress Level": 7, - "BMI Category": "Normal Weight", - "Blood Pressure": "117/76", - "Heart Rate": 69, - "Daily Steps": 6800, - "Sleep Disorder": null - } - }, - { - "scenario_id": "066", - "features": { - "Person ID": 271, - "Gender": "Female", - "Age": 49, - "Occupation": "Nurse", - "Sleep Duration": 6.1, - "Quality of Sleep": 6, - "Physical Activity Level": 90, - "Stress Level": 8, - "BMI Category": "Overweight", - "Blood Pressure": "140/95", - "Heart Rate": 75, - "Daily Steps": 10000, - "Sleep Disorder": "Sleep Apnea" - } - }, - { - "scenario_id": "067", - "features": { - "Person ID": 176, - "Gender": "Male", - "Age": 41, - "Occupation": "Lawyer", - "Sleep Duration": 7.6, - "Quality of Sleep": 8, - "Physical Activity Level": 90, - "Stress Level": 5, - "BMI Category": "Normal", - "Blood Pressure": "130/85", - "Heart Rate": 70, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "068", - "features": { - "Person ID": 235, - "Gender": "Female", - "Age": 44, - "Occupation": "Teacher", - "Sleep Duration": 6.6, - "Quality of Sleep": 7, - "Physical Activity Level": 45, - "Stress Level": 4, - "BMI Category": "Overweight", - "Blood Pressure": "135/90", - "Heart Rate": 65, - "Daily Steps": 6000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "069", - "features": { - "Person ID": 237, - "Gender": "Male", - "Age": 44, - "Occupation": "Salesperson", - "Sleep Duration": 6.4, - "Quality of Sleep": 6, - "Physical Activity Level": 45, - "Stress Level": 7, - "BMI Category": "Overweight", - "Blood Pressure": "130/85", - "Heart Rate": 72, - "Daily Steps": 6000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "070", - "features": { - "Person ID": 110, - "Gender": "Male", - "Age": 37, - "Occupation": "Lawyer", - "Sleep Duration": 7.4, - "Quality of Sleep": 8, - "Physical Activity Level": 60, - "Stress Level": 5, - "BMI Category": "Normal", - "Blood Pressure": "130/85", - "Heart Rate": 68, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "071", - "features": { - "Person ID": 367, - "Gender": "Female", - "Age": 59, - "Occupation": "Nurse", - "Sleep Duration": 8.1, - "Quality of Sleep": 9, - "Physical Activity Level": 75, - "Stress Level": 3, - "BMI Category": "Overweight", - "Blood Pressure": "140/95", - "Heart Rate": 68, - "Daily Steps": 7000, - "Sleep Disorder": "Sleep Apnea" - } - }, - { - "scenario_id": "072", - "features": { - "Person ID": 231, - "Gender": "Female", - "Age": 44, - "Occupation": "Teacher", - "Sleep Duration": 6.6, - "Quality of Sleep": 7, - "Physical Activity Level": 45, - "Stress Level": 4, - "BMI Category": "Overweight", - "Blood Pressure": "135/90", - "Heart Rate": 65, - "Daily Steps": 6000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "073", - "features": { - "Person ID": 1, - "Gender": "Male", - "Age": 27, - "Occupation": "Software Engineer", - "Sleep Duration": 6.1, - "Quality of Sleep": 6, - "Physical Activity Level": 42, - "Stress Level": 6, - "BMI Category": "Overweight", - "Blood Pressure": "126/83", - "Heart Rate": 77, - "Daily Steps": 4200, - "Sleep Disorder": null - } - }, - { - "scenario_id": "074", - "features": { - "Person ID": 282, - "Gender": "Female", - "Age": 50, - "Occupation": "Nurse", - "Sleep Duration": 6.1, - "Quality of Sleep": 6, - "Physical Activity Level": 90, - "Stress Level": 8, - "BMI Category": "Overweight", - "Blood Pressure": "140/95", - "Heart Rate": 75, - "Daily Steps": 10000, - "Sleep Disorder": "Sleep Apnea" - } - } - ], - "target_column": "Sleep Disorder", - "task_sub_type": "classification", - "ranking_ground_truth": { - "top_k_ids": [ - "001", - "005", - "007", - "015", - "020", - "022", - "030", - "037", - "043", - "048", - "051", - "056", - "063", - "071", - "074" - ] - }, - "active_filters": [ - { - "col": "Age", - "op": ">=", - "val": 50 - } - ], - "target_class_value": "sleep apnea" - }, - "response": "", - "evaluation_score": {} -} \ No newline at end of file + "active_filters": [ + { + "col": "Age", + "op": ">=", + "val": 50 + } + ], + "target_class_value": "sleep apnea" + }, + "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_007/history.csv b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_007/history.csv index 4c392c725af1146884515eae0255785309440825..8233ee87ab756690a156254a948fba9f3337de1b 100644 --- a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_007/history.csv +++ b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_007/history.csv @@ -1,160 +1,160 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activity Level,Stress Level,BMI Category,Blood Pressure,Heart Rate,Daily Steps,Sleep Disorder -2,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000, -3,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000, +2,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000,No Disorder +3,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000,No Disorder 4,Male,28,Sales Representative,5.9,4,30,8,Obese,140/90,85,3000,Sleep Apnea 5,Male,28,Sales Representative,5.9,4,30,8,Obese,140/90,85,3000,Sleep Apnea 6,Male,28,Software Engineer,5.9,4,30,8,Obese,140/90,85,3000,Insomnia 7,Male,29,Teacher,6.3,6,40,7,Obese,140/90,82,3500,Insomnia -8,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -9,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -10,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -11,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000, -12,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -13,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000, -14,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000, -15,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000, -16,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000, +8,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +9,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +10,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +11,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000,No Disorder +12,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +13,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000,No Disorder +14,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000,No Disorder +15,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000,No Disorder +16,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000,No Disorder 17,Female,29,Nurse,6.5,5,40,7,Normal Weight,132/87,80,4000,Sleep Apnea 18,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000,Sleep Apnea 19,Female,29,Nurse,6.5,5,40,7,Normal Weight,132/87,80,4000,Insomnia -20,Male,30,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -21,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -22,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -23,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -24,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -25,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -26,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000, -27,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -28,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000, -29,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000, -30,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000, +20,Male,30,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +21,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +22,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +23,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +24,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +25,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +26,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,No Disorder +27,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +28,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,No Disorder +29,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,No Disorder +30,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,No Disorder 31,Female,30,Nurse,6.4,5,35,7,Normal Weight,130/86,78,4100,Sleep Apnea 32,Female,30,Nurse,6.4,5,35,7,Normal Weight,130/86,78,4100,Insomnia -33,Female,31,Nurse,7.9,8,75,4,Normal Weight,117/76,69,6800, -34,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -35,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -36,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -37,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -38,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -40,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -41,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -42,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -44,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -45,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -46,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -48,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -49,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, +33,Female,31,Nurse,7.9,8,75,4,Normal Weight,117/76,69,6800,No Disorder +34,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +35,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +36,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +37,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +38,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +40,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +41,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +42,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder 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Disorder +67,Male,32,Accountant,7.2,8,50,6,Normal Weight,118/76,68,7000,No Disorder 68,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,Insomnia -69,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500, -70,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500, -72,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -73,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -74,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -75,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -76,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -77,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -79,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -80,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, +69,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500,No Disorder +70,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500,No Disorder +72,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +73,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +74,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +75,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +76,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +77,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +79,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +80,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder 81,Female,34,Scientist,5.8,4,32,8,Overweight,131/86,81,5200,Sleep Apnea 82,Female,34,Scientist,5.8,4,32,8,Overweight,131/86,81,5200,Sleep Apnea -83,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600, -84,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600, -85,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000, -86,Female,35,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -87,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000, -89,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000, -90,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000, -91,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000, -92,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000, -93,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000, +83,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600,No Disorder +84,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600,No Disorder +85,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000,No Disorder +86,Female,35,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +87,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000,No Disorder +89,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,No Disorder +90,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,No Disorder +91,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,No Disorder +92,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,No Disorder +93,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000,No Disorder 95,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000,Insomnia -96,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -97,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -98,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -99,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000, -100,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000, -101,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000, -102,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000, -103,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000, +96,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +97,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +98,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +99,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +100,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +101,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +102,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +103,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,No Disorder 104,Male,36,Teacher,6.6,5,35,7,Overweight,129/84,74,4800,Sleep Apnea 105,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,Sleep Apnea 106,Male,36,Teacher,6.6,5,35,7,Overweight,129/84,74,4800,Insomnia -108,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000, -109,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000, -112,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000, -113,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -114,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000, -116,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -117,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -118,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -119,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -120,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -121,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -122,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -123,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -124,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -125,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -126,Female,37,Nurse,7.5,8,60,4,Normal Weight,120/80,70,8000, -127,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -128,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -129,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -130,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -131,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -132,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -135,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -136,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -137,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -139,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -141,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -143,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -144,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, +108,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000,No Disorder +109,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000,No Disorder +112,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,No Disorder +113,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +114,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,No Disorder +116,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +117,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +118,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +119,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +120,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +121,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +122,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +123,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +124,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +125,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +126,Female,37,Nurse,7.5,8,60,4,Normal Weight,120/80,70,8000,No Disorder +127,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +128,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +129,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +130,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +131,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +132,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +135,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +136,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +137,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +139,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +141,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +143,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +144,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder 145,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,Sleep Apnea 146,Female,38,Lawyer,7.4,7,60,5,Obese,135/88,84,3300,Sleep Apnea 148,Male,39,Engineer,6.5,5,40,7,Overweight,132/87,80,4000,Insomnia -149,Female,39,Lawyer,6.9,7,50,6,Normal Weight,128/85,75,5500, -150,Female,39,Accountant,8.0,9,80,3,Normal Weight,115/78,67,7500, -152,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -153,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -154,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -155,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -156,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -158,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -159,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -160,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -161,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -162,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300, -164,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000, -165,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000, +149,Female,39,Lawyer,6.9,7,50,6,Normal Weight,128/85,75,5500,No Disorder +150,Female,39,Accountant,8.0,9,80,3,Normal Weight,115/78,67,7500,No Disorder +152,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +153,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +154,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +155,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +156,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +158,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +159,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +160,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +161,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +162,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300,No Disorder +164,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000,No Disorder +165,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000,No Disorder 166,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,Insomnia -167,Male,41,Engineer,7.3,8,70,6,Normal Weight,121/79,72,6200, -168,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000, -169,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000, -170,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -171,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -172,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -174,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -175,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000, -177,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000, +167,Male,41,Engineer,7.3,8,70,6,Normal Weight,121/79,72,6200,No Disorder +168,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000,No Disorder +169,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000,No Disorder +170,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +171,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +172,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +174,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +175,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,No Disorder +177,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,No Disorder 178,Male,42,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia -179,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, -180,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, -181,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, -182,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, +179,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +180,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +181,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +182,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder 186,Female,42,Teacher,6.8,6,45,7,Overweight,130/85,78,5000,Sleep Apnea 187,Female,43,Teacher,6.7,7,45,4,Overweight,135/90,65,6000,Insomnia 188,Male,43,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia @@ -172,18 +172,18 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 201,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia 202,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Insomnia 203,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Insomnia -205,Male,43,Engineer,7.6,8,75,4,Overweight,122/80,68,6800, -207,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -208,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -210,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -211,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -212,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -213,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -214,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -215,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -216,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -217,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -218,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, +205,Male,43,Engineer,7.6,8,75,4,Overweight,122/80,68,6800,No Disorder +207,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +208,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +210,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +211,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +212,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +213,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +214,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +215,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +216,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +217,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +218,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder 219,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Sleep Apnea 220,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Sleep Apnea 221,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia @@ -204,8 +204,8 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 244,Female,44,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia 245,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia 248,Male,44,Engineer,6.8,7,45,7,Overweight,130/85,78,5000,Insomnia -249,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000, -250,Male,44,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000, +249,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,No Disorder +250,Male,44,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,No Disorder 251,Female,45,Teacher,6.8,7,30,6,Overweight,135/90,65,6000,Insomnia 252,Female,45,Teacher,6.8,7,30,6,Overweight,135/90,65,6000,Insomnia 253,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia @@ -216,10 +216,10 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 258,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia 260,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia 261,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia -263,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000, -264,Female,45,Manager,6.9,7,55,5,Overweight,125/82,75,5500, +263,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,No Disorder +264,Female,45,Manager,6.9,7,55,5,Overweight,125/82,75,5500,No Disorder 266,Female,48,Nurse,5.9,6,90,8,Overweight,140/95,75,10000,Sleep Apnea -268,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000, +268,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,No Disorder 269,Female,49,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 270,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 272,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea @@ -229,7 +229,7 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 276,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 278,Male,49,Doctor,8.1,9,85,3,Obese,139/91,86,3700,Sleep Apnea 279,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Insomnia -280,Female,50,Engineer,8.3,9,30,3,Normal,125/80,65,5000, +280,Female,50,Engineer,8.3,9,30,3,Normal,125/80,65,5000,No Disorder 285,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 286,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 287,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea @@ -242,41 +242,41 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 296,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 297,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 298,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea -299,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -300,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -301,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -302,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, +299,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +300,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +301,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +302,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder 305,Female,51,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 306,Female,51,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 307,Female,52,Accountant,6.5,7,45,7,Overweight,130/85,72,6000,Insomnia 309,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia 311,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia 312,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia -313,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -314,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -315,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000, +313,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +314,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +315,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder 316,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,Insomnia -317,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -318,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -321,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -322,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -323,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -324,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -325,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000, -326,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -327,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000, -329,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000, -330,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -332,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -333,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -335,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -336,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -337,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -338,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -339,Female,54,Engineer,8.5,9,30,3,Normal,125/80,65,5000, +317,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +318,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +321,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +322,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +323,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +324,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +325,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,No Disorder +326,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +327,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,No Disorder +329,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,No Disorder +330,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +332,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +333,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +335,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +336,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +337,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +338,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +339,Female,54,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder 341,Female,55,Nurse,8.1,9,75,4,Overweight,140/95,72,5000,Sleep Apnea -342,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000, -343,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000, +342,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000,No Disorder +343,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000,No Disorder 345,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 346,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 347,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea @@ -288,8 +288,8 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 355,Female,58,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 357,Female,58,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 358,Female,58,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,Sleep Apnea -359,Female,59,Nurse,8.0,9,75,3,Overweight,140/95,68,7000, -360,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000, +359,Female,59,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,No Disorder +360,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,No Disorder 361,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 362,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 363,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea diff --git a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_007/info.json b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_007/info.json index ac7f1ad59f059be3648f2769a10128e59277c544..c68a30219902530a9154e48d4957e5bb70f65c0f 100644 --- a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_007/info.json +++ b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_007/info.json @@ -1,121 +1,122 @@ -{ - "name": "Sleep Health and Lifestyle Dataset", - "source": "https://www.kaggle.com/datasets/uom190346a/sleep-health-and-lifestyle-dataset/data", - "data_intro": "The Sleep Health and Lifestyle Dataset comprises 400 rows and 13 columns, covering a wide range of variables related to sleep and daily habits. It includes details such as gender, age, occupation, sleep duration, quality of sleep, physical activity level, stress levels, BMI category, blood pressure, heart rate, daily steps, and the presence or absence of sleep disorders.", - "is_splited": false, - "overall_size": 374, - "train_size": 0, - "test_size": 0, - "c_classes": 5, - "n_classes": 8, - "cat_feature_intro": { - "Gender": "Gender: The gender of the person (Male/Female).
", - "Occupation": "Occupation: The occupation or profession of the person.
", - "BMI Category": "BMI Category: The BMI category of the person (e.g., Underweight, Normal, Overweight).
", - "Blood Pressure": "Blood Pressure (systolic/diastolic): The blood pressure measurement of the person, indicated as systolic pressure over diastolic pressure.
", - "Sleep Disorder": "- None: The individual does not exhibit any specific sleep disorder." - }, - "num_feature_intro": { - "Person ID": "Person ID: An identifier for each individual.
", - "Age": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", - "Sleep Duration": "Sleep Duration (hours): The number of hours the person sleeps per day.
", - "Quality of Sleep": "Quality of Sleep (scale: 1-10): A subjective rating of the quality of sleep, ranging from 1 to 10.
", - "Physical Activity Level": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", - "Stress Level": "Stress Level (scale: 1-10): A subjective rating of the stress level experienced by the person, ranging from 1 to 10.
", - "Heart Rate": "Heart Rate (bpm): The resting heart rate of the person in beats per minute.
", - "Daily Steps": "Daily Steps: The number of steps the person takes per day.
" - }, - "evaluation_metric": null, - "task_type": "classification", - "target": "Sleep Disorder", - "num_feature_value": { - "Age": [ - 27.0, - 59.0 - ], - "Daily Steps": [ - 3000.0, - 10000.0 - ], - "Heart Rate": [ - 65.0, - 86.0 - ], - "Person ID": [ - 1.0, - 374.0 - ], - "Physical Activity Level": [ - 30.0, - 90.0 - ], - "Quality of Sleep": [ - 4.0, - 9.0 - ], - "Sleep Duration": [ - 5.8, - 8.5 - ], - "Stress Level": [ - 3.0, - 8.0 - ] - }, - "cat_feature_value": { - "BMI Category": [ - "Normal", - "Normal Weight", - "Obese", - "Overweight" - ], - "Blood Pressure": [ - "115/75", - "115/78", - "117/76", - "118/75", - "118/76", - "119/77", - "120/80", - "121/79", - "122/80", - "125/80", - "125/82", - "126/83", - "128/84", - "128/85", - "129/84", - "130/85", - "130/86", - "131/86", - "132/87", - "135/88", - "135/90", - "139/91", - "140/90", - "140/95", - "142/92" - ], - "Gender": [ - "Female", - "Male" - ], - "Occupation": [ - "Accountant", - "Doctor", - "Engineer", - "Lawyer", - "Manager", - "Nurse", - "Sales Representative", - "Salesperson", - "Scientist", - "Software Engineer", - "Teacher" - ], - "Sleep Disorder": [ - "Insomnia", - "Sleep Apnea" - ] - } -} \ No newline at end of file +{ + "name": "Sleep Health and Lifestyle Dataset", + "source": "https://www.kaggle.com/datasets/uom190346a/sleep-health-and-lifestyle-dataset/data", + "data_intro": "The Sleep Health and Lifestyle Dataset comprises 400 rows and 13 columns, covering a wide range of variables related to sleep and daily habits. It includes details such as gender, age, occupation, sleep duration, quality of sleep, physical activity level, stress levels, BMI category, blood pressure, heart rate, daily steps, and the presence or absence of sleep disorders.", + "is_splited": false, + "overall_size": 374, + "train_size": 0, + "test_size": 0, + "c_classes": 5, + "n_classes": 8, + "cat_feature_intro": { + "Gender": "Gender: The gender of the person (Male/Female).
", + "Occupation": "Occupation: The occupation or profession of the person.
", + "BMI Category": "BMI Category: The BMI category of the person (e.g., Underweight, Normal, Overweight).
", + "Blood Pressure": "Blood Pressure (systolic/diastolic): The blood pressure measurement of the person, indicated as systolic pressure over diastolic pressure.
", + "Sleep Disorder": "Sleep disorder category: No Disorder, Insomnia, or Sleep Apnea." + }, + "num_feature_intro": { + "Person ID": "Person ID: An identifier for each individual.
", + "Age": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", + "Sleep Duration": "Sleep Duration (hours): The number of hours the person sleeps per day.
", + "Quality of Sleep": "Quality of Sleep (scale: 1-10): A subjective rating of the quality of sleep, ranging from 1 to 10.
", + "Physical Activity Level": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", + "Stress Level": "Stress Level (scale: 1-10): A subjective rating of the stress level experienced by the person, ranging from 1 to 10.
", + "Heart Rate": "Heart Rate (bpm): The resting heart rate of the person in beats per minute.
", + "Daily Steps": "Daily Steps: The number of steps the person takes per day.
" + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "Sleep Disorder", + "num_feature_value": { + "Age": [ + 27.0, + 59.0 + ], + "Daily Steps": [ + 3000.0, + 10000.0 + ], + "Heart Rate": [ + 65.0, + 86.0 + ], + "Person ID": [ + 1.0, + 374.0 + ], + "Physical Activity Level": [ + 30.0, + 90.0 + ], + "Quality of Sleep": [ + 4.0, + 9.0 + ], + "Sleep Duration": [ + 5.8, + 8.5 + ], + "Stress Level": [ + 3.0, + 8.0 + ] + }, + "cat_feature_value": { + "BMI Category": [ + "Normal", + "Normal Weight", + "Obese", + "Overweight" + ], + "Blood Pressure": [ + "115/75", + "115/78", + "117/76", + "118/75", + "118/76", + "119/77", + "120/80", + "121/79", + "122/80", + "125/80", + "125/82", + "126/83", + "128/84", + "128/85", + "129/84", + "130/85", + "130/86", + "131/86", + "132/87", + "135/88", + "135/90", + "139/91", + "140/90", + "140/95", + "142/92" + ], + "Gender": [ + "Female", + "Male" + ], + "Occupation": [ + "Accountant", + "Doctor", + "Engineer", + "Lawyer", + "Manager", + "Nurse", + "Sales Representative", + "Salesperson", + "Scientist", + "Software Engineer", + "Teacher" + ], + "Sleep Disorder": [ + "Insomnia", + "No Disorder", + "Sleep Apnea" + ] + } +} diff --git a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_008/Sleep_Health_and_Lifestyle_Dataset_B4_008_current.json b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_008/Sleep_Health_and_Lifestyle_Dataset_B4_008_current.json index 9aad6ce8e434b0c14f373d7b37348866bbc8fd8d..8b0dff7693dff6e9865354c8afda05742848e685 100644 --- a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_008/Sleep_Health_and_Lifestyle_Dataset_B4_008_current.json +++ b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_008/Sleep_Health_and_Lifestyle_Dataset_B4_008_current.json @@ -1,1381 +1,1381 @@ { - "id": "008", - "task_type": "B4", - "subtask_type": "filter", - "perspective": "data_holder", - "dataset_name": "Sleep Health and Lifestyle Dataset", - "table_path": "output/Sleep Health and Lifestyle Dataset_B4/Sleep Health and Lifestyle Dataset_B4_008/test.csv", - "query": "For a new meditation app partnership, we need to find potential beta testers. Using our wellness database as a reference, identify candidates in the \"Teacher\" occupation from the new data who are likely struggling with Insomnia, as they could benefit most from the stress-management features.", - "meta_info": { - "domain": "medical" + "id": "008", + "task_type": "B4", + "subtask_type": "filter", + "perspective": "data_holder", + "dataset_name": "Sleep Health and Lifestyle Dataset", + "table_path": "output/Sleep Health and Lifestyle Dataset_B4/Sleep Health and Lifestyle Dataset_B4_008/test.csv", + "query": "For a new meditation app partnership, we need to find potential beta testers. Using our wellness database as a reference, identify candidates in the \"Teacher\" occupation from the new data who are likely struggling with Insomnia, as they could benefit most from the stress-management features.", + "meta_info": { + "domain": "medical" + }, + "final_decision": {}, + "what_if": {}, + "ground_truth": { + "extracted_features": [ + { + "scenario_id": "001", + "features": { + "Person ID": 373, + "Gender": "Female", + "Age": 59, + "Occupation": "Nurse", + "Sleep Duration": 8.1, + "Quality of Sleep": 9, + "Physical Activity Level": 75, + "Stress Level": 3, + "BMI Category": "Overweight", + "Blood Pressure": "140/95", + "Heart Rate": 68, + "Daily Steps": 7000, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "002", + "features": { + "Person ID": 314, + "Gender": "Female", + "Age": 52, + "Occupation": "Engineer", + "Sleep Duration": 8.4, + "Quality of Sleep": 9, + "Physical Activity Level": 30, + "Stress Level": 3, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 65, + "Daily Steps": 5000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "003", + "features": { + "Person ID": 208, + "Gender": "Male", + "Age": 43, + "Occupation": "Engineer", + "Sleep Duration": 7.7, + "Quality of Sleep": 8, + "Physical Activity Level": 90, + "Stress Level": 5, + "BMI Category": "Normal", + "Blood Pressure": "130/85", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "004", + "features": { + "Person ID": 106, + "Gender": "Male", + "Age": 36, + "Occupation": "Teacher", + "Sleep Duration": 6.6, + "Quality of Sleep": 5, + "Physical Activity Level": 35, + "Stress Level": 7, + "BMI Category": "Overweight", + "Blood Pressure": "129/84", + "Heart Rate": 74, + "Daily Steps": 4800, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "005", + "features": { + "Person ID": 78, + "Gender": "Male", + "Age": 33, + "Occupation": "Doctor", + "Sleep Duration": 6.0, + "Quality of Sleep": 6, + "Physical Activity Level": 30, + "Stress Level": 8, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 72, + "Daily Steps": 5000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "006", + "features": { + "Person ID": 227, + "Gender": "Female", + "Age": 44, + "Occupation": "Teacher", + "Sleep Duration": 6.6, + "Quality of Sleep": 7, + "Physical Activity Level": 45, + "Stress Level": 4, + "BMI Category": "Overweight", + "Blood Pressure": "135/90", + "Heart Rate": 65, + "Daily Steps": 6000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "007", + "features": { + "Person ID": 86, + "Gender": "Female", + "Age": 35, + "Occupation": "Accountant", + "Sleep Duration": 7.2, + "Quality of Sleep": 8, + "Physical Activity Level": 60, + "Stress Level": 4, + "BMI Category": "Normal", + "Blood Pressure": "115/75", + "Heart Rate": 68, + "Daily Steps": 7000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "008", + "features": { + "Person ID": 183, + "Gender": "Male", + "Age": 42, + "Occupation": "Lawyer", + "Sleep Duration": 7.8, + "Quality of Sleep": 8, + "Physical Activity Level": 90, + "Stress Level": 5, + "BMI Category": "Normal", + "Blood Pressure": "130/85", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "009", + "features": { + "Person ID": 294, + "Gender": "Female", + "Age": 50, + "Occupation": "Nurse", + "Sleep Duration": 6.0, + "Quality of Sleep": 6, + "Physical Activity Level": 90, + "Stress Level": 8, + "BMI Category": "Overweight", + "Blood Pressure": "140/95", + "Heart Rate": 75, + "Daily Steps": 10000, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "010", + "features": { + "Person ID": 252, + "Gender": "Female", + "Age": 45, + "Occupation": "Teacher", + "Sleep Duration": 6.8, + "Quality of Sleep": 7, + "Physical Activity Level": 30, + "Stress Level": 6, + "BMI Category": "Overweight", + "Blood Pressure": "135/90", + "Heart Rate": 65, + "Daily Steps": 6000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "011", + "features": { + "Person ID": 302, + "Gender": "Female", + "Age": 51, + "Occupation": "Engineer", + "Sleep Duration": 8.5, + "Quality of Sleep": 9, + "Physical Activity Level": 30, + "Stress Level": 3, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 65, + "Daily Steps": 5000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "012", + "features": { + "Person ID": 60, + "Gender": "Male", + "Age": 32, + "Occupation": "Doctor", + "Sleep Duration": 7.7, + "Quality of Sleep": 7, + "Physical Activity Level": 75, + "Stress Level": 6, + "BMI Category": "Normal", + "Blood Pressure": "120/80", + "Heart Rate": 70, + "Daily Steps": 8000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "013", + "features": { + "Person ID": 144, + "Gender": "Female", + "Age": 38, + "Occupation": "Accountant", + "Sleep Duration": 7.1, + "Quality of Sleep": 8, + "Physical Activity Level": 60, + "Stress Level": 4, + "BMI Category": "Normal", + "Blood Pressure": "115/75", + "Heart Rate": 68, + "Daily Steps": 7000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "014", + "features": { + "Person ID": 192, + "Gender": "Male", + "Age": 43, + "Occupation": "Salesperson", + "Sleep Duration": 6.4, + "Quality of Sleep": 6, + "Physical Activity Level": 45, + "Stress Level": 7, + "BMI Category": "Overweight", + "Blood Pressure": "130/85", + "Heart Rate": 72, + "Daily Steps": 6000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "015", + "features": { + "Person ID": 89, + "Gender": "Male", + "Age": 35, + "Occupation": "Engineer", + "Sleep Duration": 7.3, + "Quality of Sleep": 8, + "Physical Activity Level": 60, + "Stress Level": 4, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 65, + "Daily Steps": 5000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "016", + "features": { + "Person ID": 104, + "Gender": "Male", + "Age": 36, + "Occupation": "Teacher", + "Sleep Duration": 6.6, + "Quality of Sleep": 5, + "Physical Activity Level": 35, + "Stress Level": 7, + "BMI Category": "Overweight", + "Blood Pressure": "129/84", + "Heart Rate": 74, + "Daily Steps": 4800, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "017", + "features": { + "Person ID": 297, + "Gender": "Female", + "Age": 50, + "Occupation": "Nurse", + "Sleep Duration": 6.1, + "Quality of Sleep": 6, + "Physical Activity Level": 90, + "Stress Level": 8, + "BMI Category": "Overweight", + "Blood Pressure": "140/95", + "Heart Rate": 75, + "Daily Steps": 10000, + "Sleep Disorder": "Sleep Apnea" + } + }, + { + "scenario_id": "018", + "features": { + "Person ID": 90, + "Gender": "Male", + "Age": 35, + "Occupation": "Engineer", + "Sleep Duration": 7.3, + "Quality of Sleep": 8, + "Physical Activity Level": 60, + "Stress Level": 4, + "BMI Category": "Normal", + "Blood 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+ "Blood Pressure": "115/75", + "Heart Rate": 68, + "Daily Steps": 7000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "043", + "features": { + "Person ID": 241, + "Gender": "Female", + "Age": 44, + "Occupation": "Teacher", + "Sleep Duration": 6.5, + "Quality of Sleep": 7, + "Physical Activity Level": 45, + "Stress Level": 4, + "BMI Category": "Overweight", + "Blood Pressure": "135/90", + "Heart Rate": 65, + "Daily Steps": 6000, + "Sleep Disorder": "Insomnia" + } + }, + { + "scenario_id": "044", + "features": { + "Person ID": 55, + "Gender": "Male", + "Age": 32, + "Occupation": "Doctor", + "Sleep Duration": 6.0, + "Quality of Sleep": 6, + "Physical Activity Level": 30, + "Stress Level": 8, + "BMI Category": "Normal", + "Blood Pressure": "125/80", + "Heart Rate": 72, + "Daily Steps": 5000, + "Sleep Disorder": "No Disorder" + } + }, + { + "scenario_id": "045", + "features": { + "Person ID": 229, + "Gender": "Female", + "Age": 44, + "Occupation": "Teacher", + "Sleep 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Level": 45, - "Stress Level": 4, - "BMI Category": "Overweight", - "Blood Pressure": "135/90", - "Heart Rate": 65, - "Daily Steps": 6000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "038", - "features": { - "Person ID": 147, - "Gender": "Male", - "Age": 39, - "Occupation": "Lawyer", - "Sleep Duration": 7.2, - "Quality of Sleep": 8, - "Physical Activity Level": 60, - "Stress Level": 5, - "BMI Category": "Normal", - "Blood Pressure": "130/85", - "Heart Rate": 68, - "Daily Steps": 8000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "039", - "features": { - "Person ID": 309, - "Gender": "Female", - "Age": 52, - "Occupation": "Accountant", - "Sleep Duration": 6.6, - "Quality of Sleep": 7, - "Physical Activity Level": 45, - "Stress Level": 7, - "BMI Category": "Overweight", - "Blood Pressure": "130/85", - "Heart Rate": 72, - "Daily Steps": 6000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "040", - "features": { - "Person ID": 17, - "Gender": "Female", - "Age": 29, - "Occupation": "Nurse", - "Sleep Duration": 6.5, - "Quality of Sleep": 5, - "Physical Activity Level": 40, - "Stress Level": 7, - "BMI Category": "Normal Weight", - "Blood Pressure": "132/87", - "Heart Rate": 80, - "Daily Steps": 4000, - "Sleep Disorder": "Sleep Apnea" - } - }, - { - "scenario_id": "041", - "features": { - "Person ID": 68, - "Gender": "Male", - "Age": 33, - "Occupation": "Doctor", - "Sleep Duration": 6.0, - "Quality of Sleep": 6, - "Physical Activity Level": 30, - "Stress Level": 8, - "BMI Category": "Normal", - "Blood Pressure": "125/80", - "Heart Rate": 72, - "Daily Steps": 5000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "042", - "features": { - "Person ID": 118, - "Gender": "Female", - "Age": 37, - "Occupation": "Accountant", - "Sleep Duration": 7.2, - "Quality of Sleep": 8, - "Physical Activity Level": 60, - "Stress Level": 4, - "BMI Category": "Normal", - "Blood Pressure": "115/75", - "Heart Rate": 68, - "Daily Steps": 7000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "043", - "features": { - "Person ID": 241, - "Gender": "Female", - "Age": 44, - "Occupation": "Teacher", - "Sleep Duration": 6.5, - "Quality of Sleep": 7, - "Physical Activity Level": 45, - "Stress Level": 4, - "BMI Category": "Overweight", - "Blood Pressure": "135/90", - "Heart Rate": 65, - "Daily Steps": 6000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "044", - "features": { - "Person ID": 55, - "Gender": "Male", - "Age": 32, - "Occupation": "Doctor", - "Sleep Duration": 6.0, - "Quality of Sleep": 6, - "Physical Activity Level": 30, - "Stress Level": 8, - "BMI Category": "Normal", - "Blood Pressure": "125/80", - "Heart Rate": 72, - "Daily Steps": 5000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "045", - "features": { - "Person ID": 229, - "Gender": "Female", - "Age": 44, - "Occupation": "Teacher", - "Sleep Duration": 6.6, - "Quality of Sleep": 7, - "Physical Activity Level": 45, - "Stress Level": 4, - "BMI Category": "Overweight", - "Blood Pressure": "135/90", - "Heart Rate": 65, - "Daily Steps": 6000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "046", - "features": { - "Person ID": 365, - "Gender": "Female", - "Age": 59, - "Occupation": "Nurse", - "Sleep Duration": 8.0, - "Quality of Sleep": 9, - "Physical Activity Level": 75, - "Stress Level": 3, - "BMI Category": "Overweight", - "Blood Pressure": "140/95", - "Heart Rate": 68, - "Daily Steps": 7000, - "Sleep Disorder": "Sleep Apnea" - } - }, - { - "scenario_id": "047", - "features": { - "Person ID": 138, - "Gender": "Male", - "Age": 38, - "Occupation": "Lawyer", - "Sleep Duration": 7.1, - "Quality of Sleep": 8, - "Physical Activity Level": 60, - "Stress Level": 5, - "BMI Category": "Normal", - "Blood Pressure": "130/85", - "Heart Rate": 68, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "048", - "features": { - "Person ID": 220, - "Gender": "Male", - "Age": 43, - "Occupation": "Salesperson", - "Sleep Duration": 6.5, - "Quality of Sleep": 6, - "Physical Activity Level": 45, - "Stress Level": 7, - "BMI Category": "Overweight", - "Blood Pressure": "130/85", - "Heart Rate": 72, - "Daily Steps": 6000, - "Sleep Disorder": "Sleep Apnea" - } - }, - { - "scenario_id": "049", - "features": { - "Person ID": 19, - "Gender": "Female", - "Age": 29, - "Occupation": "Nurse", - "Sleep Duration": 6.5, - "Quality of Sleep": 5, - "Physical Activity Level": 40, - "Stress Level": 7, - "BMI Category": "Normal Weight", - "Blood Pressure": "132/87", - "Heart Rate": 80, - "Daily Steps": 4000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "050", - "features": { - "Person ID": 98, - "Gender": "Female", - "Age": 36, - "Occupation": "Accountant", - "Sleep Duration": 7.1, - "Quality of Sleep": 8, - "Physical Activity Level": 60, - "Stress Level": 4, - "BMI Category": "Normal", - "Blood Pressure": "115/75", - "Heart Rate": 68, - "Daily Steps": 7000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "051", - "features": { - "Person ID": 231, - "Gender": "Female", - "Age": 44, - "Occupation": "Teacher", - "Sleep Duration": 6.6, - "Quality of Sleep": 7, - "Physical Activity Level": 45, - "Stress Level": 4, - "BMI Category": "Overweight", - "Blood Pressure": "135/90", - "Heart Rate": 65, - "Daily Steps": 6000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "052", - "features": { - "Person ID": 7, - "Gender": "Male", - "Age": 29, - "Occupation": "Teacher", - "Sleep Duration": 6.3, - "Quality of Sleep": 6, - "Physical Activity Level": 40, - "Stress Level": 7, - "BMI Category": "Obese", - "Blood Pressure": "140/90", - "Heart Rate": 82, - "Daily Steps": 3500, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "053", - "features": { - "Person ID": 225, - "Gender": "Female", - "Age": 44, - "Occupation": "Teacher", - "Sleep Duration": 6.6, - "Quality of Sleep": 7, - "Physical Activity Level": 45, - "Stress Level": 4, - "BMI Category": "Overweight", - "Blood Pressure": "135/90", - "Heart Rate": 65, - "Daily Steps": 6000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "054", - "features": { - "Person ID": 272, - "Gender": "Female", - "Age": 49, - "Occupation": "Nurse", - "Sleep Duration": 6.1, - "Quality of Sleep": 6, - "Physical Activity Level": 90, - "Stress Level": 8, - "BMI Category": "Overweight", - "Blood Pressure": "140/95", - "Heart Rate": 75, - "Daily Steps": 10000, - "Sleep Disorder": "Sleep Apnea" - } - }, - { - "scenario_id": "055", - "features": { - "Person ID": 129, - "Gender": "Male", - "Age": 38, - "Occupation": "Lawyer", - "Sleep Duration": 7.3, - "Quality of Sleep": 8, - "Physical Activity Level": 60, - "Stress Level": 5, - "BMI Category": "Normal", - "Blood Pressure": "130/85", - "Heart Rate": 68, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "056", - "features": { - "Person ID": 261, - "Gender": "Female", - "Age": 45, - "Occupation": "Teacher", - "Sleep Duration": 6.6, - "Quality of Sleep": 7, - "Physical Activity Level": 45, - "Stress Level": 4, - "BMI Category": "Overweight", - "Blood Pressure": "135/90", - "Heart Rate": 65, - "Daily Steps": 6000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "057", - "features": { - "Person ID": 342, - "Gender": "Female", - "Age": 56, - "Occupation": "Doctor", - "Sleep Duration": 8.2, - "Quality of Sleep": 9, - "Physical Activity Level": 90, - "Stress Level": 3, - "BMI Category": "Normal Weight", - "Blood Pressure": "118/75", - "Heart Rate": 65, - "Daily Steps": 10000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "058", - "features": { - "Person ID": 182, - "Gender": "Male", - "Age": 42, - "Occupation": "Lawyer", - "Sleep Duration": 7.8, - "Quality of Sleep": 8, - "Physical Activity Level": 90, - "Stress Level": 5, - "BMI Category": "Normal", - "Blood Pressure": "130/85", - "Heart Rate": 70, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "059", - "features": { - "Person ID": 209, - "Gender": "Male", - "Age": 43, - "Occupation": "Engineer", - "Sleep Duration": 7.7, - "Quality of Sleep": 8, - "Physical Activity Level": 90, - "Stress Level": 5, - "BMI Category": "Normal", - "Blood Pressure": "130/85", - "Heart Rate": 70, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "060", - "features": { - "Person ID": 193, - "Gender": "Male", - "Age": 43, - "Occupation": "Salesperson", - "Sleep Duration": 6.5, - "Quality of Sleep": 6, - "Physical Activity Level": 45, - "Stress Level": 7, - "BMI Category": "Overweight", - "Blood Pressure": "130/85", - "Heart Rate": 72, - "Daily Steps": 6000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "061", - "features": { - "Person ID": 62, - "Gender": "Male", - "Age": 32, - "Occupation": "Doctor", - "Sleep Duration": 6.0, - "Quality of Sleep": 6, - "Physical Activity Level": 30, - "Stress Level": 8, - "BMI Category": "Normal", - "Blood Pressure": "125/80", - "Heart Rate": 72, - "Daily Steps": 5000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "062", - "features": { - "Person ID": 151, - "Gender": "Female", - "Age": 39, - "Occupation": "Accountant", - "Sleep Duration": 8.0, - "Quality of Sleep": 9, - "Physical Activity Level": 80, - "Stress Level": 3, - "BMI Category": "Normal Weight", - "Blood Pressure": "115/78", - "Heart Rate": 67, - "Daily Steps": 7500, - "Sleep Disorder": null - } - }, - { - "scenario_id": "063", - "features": { - "Person ID": 277, - "Gender": "Male", - "Age": 49, - "Occupation": "Doctor", - "Sleep Duration": 8.1, - "Quality of Sleep": 9, - "Physical Activity Level": 85, - "Stress Level": 3, - "BMI Category": "Obese", - "Blood Pressure": "139/91", - "Heart Rate": 86, - "Daily Steps": 3700, - "Sleep Disorder": "Sleep Apnea" - } - }, - { - "scenario_id": "064", - "features": { - "Person ID": 257, - "Gender": "Female", - "Age": 45, - "Occupation": "Teacher", - "Sleep Duration": 6.6, - "Quality of Sleep": 7, - "Physical Activity Level": 45, - "Stress Level": 4, - "BMI Category": "Overweight", - "Blood Pressure": "135/90", - "Heart Rate": 65, - "Daily Steps": 6000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "065", - "features": { - "Person ID": 264, - "Gender": "Female", - "Age": 45, - "Occupation": "Manager", - "Sleep Duration": 6.9, - "Quality of Sleep": 7, - "Physical Activity Level": 55, - "Stress Level": 5, - "BMI Category": "Overweight", - "Blood Pressure": "125/82", - "Heart Rate": 75, - "Daily Steps": 5500, - "Sleep Disorder": null - } - }, - { - "scenario_id": "066", - "features": { - "Person ID": 253, - "Gender": "Female", - "Age": 45, - "Occupation": "Teacher", - "Sleep Duration": 6.5, - "Quality of Sleep": 7, - "Physical Activity Level": 45, - "Stress Level": 4, - "BMI Category": "Overweight", - "Blood Pressure": "135/90", - "Heart Rate": 65, - "Daily Steps": 6000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "067", - "features": { - "Person ID": 207, - "Gender": "Male", - "Age": 43, - "Occupation": "Engineer", - "Sleep Duration": 7.7, - "Quality of Sleep": 8, - "Physical Activity Level": 90, - "Stress Level": 5, - "BMI Category": "Normal", - "Blood Pressure": "130/85", - "Heart Rate": 70, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "068", - "features": { - "Person ID": 254, - "Gender": "Female", - "Age": 45, - "Occupation": "Teacher", - "Sleep Duration": 6.5, - "Quality of Sleep": 7, - "Physical Activity Level": 45, - "Stress Level": 4, - "BMI Category": "Overweight", - "Blood Pressure": "135/90", - "Heart Rate": 65, - "Daily Steps": 6000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "069", - "features": { - "Person ID": 224, - "Gender": "Male", - "Age": 44, - "Occupation": "Salesperson", - "Sleep Duration": 6.4, - "Quality of Sleep": 6, - "Physical Activity Level": 45, - "Stress Level": 7, - "BMI Category": "Overweight", - "Blood Pressure": "130/85", - "Heart Rate": 72, - "Daily Steps": 6000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "070", - "features": { - "Person ID": 20, - "Gender": "Male", - "Age": 30, - "Occupation": "Doctor", - "Sleep Duration": 7.6, - "Quality of Sleep": 7, - "Physical Activity Level": 75, - "Stress Level": 6, - "BMI Category": "Normal", - "Blood Pressure": "120/80", - "Heart Rate": 70, - "Daily Steps": 8000, - "Sleep Disorder": null - } - }, - { - "scenario_id": "071", - "features": { - "Person ID": 305, - "Gender": "Female", - "Age": 51, - "Occupation": "Nurse", - "Sleep Duration": 6.1, - "Quality of Sleep": 6, - "Physical Activity Level": 90, - "Stress Level": 8, - "BMI Category": "Overweight", - "Blood Pressure": "140/95", - "Heart Rate": 75, - "Daily Steps": 10000, - "Sleep Disorder": "Sleep Apnea" - } - }, - { - "scenario_id": "072", - "features": { - "Person ID": 341, - "Gender": "Female", - "Age": 55, - "Occupation": "Nurse", - "Sleep Duration": 8.1, - "Quality of Sleep": 9, - "Physical Activity Level": 75, - "Stress Level": 4, - "BMI Category": "Overweight", - "Blood Pressure": "140/95", - "Heart Rate": 72, - "Daily Steps": 5000, - "Sleep Disorder": "Sleep Apnea" - } - }, - { - "scenario_id": "073", - "features": { - "Person ID": 260, - "Gender": "Female", - "Age": 45, - "Occupation": "Teacher", - "Sleep Duration": 6.6, - "Quality of Sleep": 7, - "Physical Activity Level": 45, - "Stress Level": 4, - "BMI Category": "Overweight", - "Blood Pressure": "135/90", - "Heart Rate": 65, - "Daily Steps": 6000, - "Sleep Disorder": "Insomnia" - } - }, - { - "scenario_id": "074", - "features": { - "Person ID": 197, - "Gender": "Male", - "Age": 43, - "Occupation": "Salesperson", - "Sleep Duration": 6.5, - "Quality of Sleep": 6, - "Physical Activity Level": 45, - "Stress Level": 7, - "BMI Category": "Overweight", - "Blood Pressure": "130/85", - "Heart Rate": 72, - "Daily Steps": 6000, - "Sleep Disorder": "Insomnia" - } - } - ], - "target_column": "Sleep Disorder", - "task_sub_type": "classification", - "ranking_ground_truth": { - "top_k_ids": [ - "004", - "006", - "010", - "029", - "037", - "043", - "045", - "051", - "052", - "053", - "056", - "064", - "066", - "068", - "073" - ] - }, - "active_filters": [ - { - "col": "Occupation", - "op": "==", - "val": "Teacher" - } - ], - "target_class_value": "insomnia" - }, - "response": "", - "evaluation_score": {} -} \ No newline at end of file + "active_filters": [ + { + "col": "Occupation", + "op": "==", + "val": "Teacher" + } + ], + "target_class_value": "insomnia" + }, + "response": "", + "evaluation_score": {} +} diff --git a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_008/history.csv b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_008/history.csv index d03508d052731c48f6e6a233222ee05e75d7b02e..19f63611fabe13547946853583ff6e9f32f0a944 100644 --- a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_008/history.csv +++ b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_008/history.csv @@ -1,155 +1,155 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activity Level,Stress Level,BMI Category,Blood Pressure,Heart Rate,Daily Steps,Sleep Disorder -1,Male,27,Software Engineer,6.1,6,42,6,Overweight,126/83,77,4200, -2,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000, -3,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000, +1,Male,27,Software Engineer,6.1,6,42,6,Overweight,126/83,77,4200,No Disorder +2,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000,No Disorder +3,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000,No Disorder 4,Male,28,Sales Representative,5.9,4,30,8,Obese,140/90,85,3000,Sleep Apnea 5,Male,28,Sales Representative,5.9,4,30,8,Obese,140/90,85,3000,Sleep Apnea 6,Male,28,Software Engineer,5.9,4,30,8,Obese,140/90,85,3000,Insomnia -8,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -9,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -10,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -11,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000, -12,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -13,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000, -14,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000, -15,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000, -16,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000, +8,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +9,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +10,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +11,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000,No Disorder +12,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +13,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000,No Disorder +14,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000,No Disorder +15,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000,No Disorder +16,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000,No Disorder 18,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000,Sleep Apnea -21,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -22,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -23,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -24,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -25,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -27,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -29,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000, -30,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000, +21,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +22,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +23,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +24,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +25,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +27,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +29,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,No Disorder +30,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,No Disorder 31,Female,30,Nurse,6.4,5,35,7,Normal Weight,130/86,78,4100,Sleep Apnea 32,Female,30,Nurse,6.4,5,35,7,Normal Weight,130/86,78,4100,Insomnia -33,Female,31,Nurse,7.9,8,75,4,Normal Weight,117/76,69,6800, -34,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -35,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -36,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -37,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -38,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -39,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -40,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -41,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -42,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -43,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -44,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -45,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -46,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -47,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -48,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -49,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, +33,Female,31,Nurse,7.9,8,75,4,Normal Weight,117/76,69,6800,No Disorder +34,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +35,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +36,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +37,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +38,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +39,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +40,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +41,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +42,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +43,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +44,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +45,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +46,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +47,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +48,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +49,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder 50,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,Sleep Apnea -51,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000, -52,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000, -53,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -58,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -59,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -61,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -63,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000, -64,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000, -65,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000, -66,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000, -67,Male,32,Accountant,7.2,8,50,6,Normal Weight,118/76,68,7000, -69,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500, -70,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500, -71,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -72,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -73,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -74,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -75,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -77,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -79,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -80,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, +51,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000,No Disorder +52,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000,No Disorder +53,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +58,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +59,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +61,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +63,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,No Disorder +64,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,No Disorder +65,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,No Disorder +66,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,No Disorder +67,Male,32,Accountant,7.2,8,50,6,Normal Weight,118/76,68,7000,No Disorder +69,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500,No Disorder +70,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500,No Disorder +71,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +72,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +73,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +74,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +75,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +77,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +79,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +80,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder 81,Female,34,Scientist,5.8,4,32,8,Overweight,131/86,81,5200,Sleep Apnea 82,Female,34,Scientist,5.8,4,32,8,Overweight,131/86,81,5200,Sleep Apnea -83,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600, -84,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600, -85,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000, -87,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000, -88,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000, -91,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000, -92,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000, -93,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000, +83,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600,No Disorder +84,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600,No Disorder +85,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000,No Disorder +87,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000,No Disorder +88,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000,No Disorder +91,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,No Disorder +92,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,No Disorder +93,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000,No Disorder 94,Male,35,Lawyer,7.4,7,60,5,Obese,135/88,84,3300,Sleep Apnea 95,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000,Insomnia -96,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -97,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -99,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000, -100,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000, -101,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000, -102,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000, -103,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000, +96,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +97,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +99,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +100,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +101,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +102,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +103,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,No Disorder 105,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,Sleep Apnea -107,Female,37,Nurse,6.1,6,42,6,Overweight,126/83,77,4200, -108,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000, -109,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000, -110,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000, -112,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000, -113,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -114,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000, -115,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -116,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -117,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -119,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -120,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -121,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -122,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -123,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -124,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -125,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -126,Female,37,Nurse,7.5,8,60,4,Normal Weight,120/80,70,8000, -127,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -128,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -130,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -131,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -133,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -134,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -135,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -136,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -137,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -139,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -140,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000, -141,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -142,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000, +107,Female,37,Nurse,6.1,6,42,6,Overweight,126/83,77,4200,No Disorder +108,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000,No Disorder +109,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000,No Disorder +110,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,No Disorder +112,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,No Disorder +113,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +114,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,No Disorder +115,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +116,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +117,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +119,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +120,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +121,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +122,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +123,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +124,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +125,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +126,Female,37,Nurse,7.5,8,60,4,Normal Weight,120/80,70,8000,No Disorder +127,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +128,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +130,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +131,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +133,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +134,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +135,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +136,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +137,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +139,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +140,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,No Disorder +141,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +142,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,No Disorder 145,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,Sleep Apnea 146,Female,38,Lawyer,7.4,7,60,5,Obese,135/88,84,3300,Sleep Apnea 148,Male,39,Engineer,6.5,5,40,7,Overweight,132/87,80,4000,Insomnia -149,Female,39,Lawyer,6.9,7,50,6,Normal Weight,128/85,75,5500, -150,Female,39,Accountant,8.0,9,80,3,Normal Weight,115/78,67,7500, -152,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -153,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -154,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -155,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -156,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -157,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -158,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -159,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -160,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -161,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -162,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300, -164,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000, -165,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000, +149,Female,39,Lawyer,6.9,7,50,6,Normal Weight,128/85,75,5500,No Disorder +150,Female,39,Accountant,8.0,9,80,3,Normal Weight,115/78,67,7500,No Disorder +152,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +153,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +154,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +155,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +156,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +157,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +158,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +159,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +160,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +161,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +162,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300,No Disorder +164,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000,No Disorder +165,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000,No Disorder 166,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,Insomnia -167,Male,41,Engineer,7.3,8,70,6,Normal Weight,121/79,72,6200, -168,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000, -169,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000, -170,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -171,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -172,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -173,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -174,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -175,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000, -176,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000, -177,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000, +167,Male,41,Engineer,7.3,8,70,6,Normal Weight,121/79,72,6200,No Disorder +168,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000,No Disorder +169,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000,No Disorder +170,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +171,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +172,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +173,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +174,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +175,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,No Disorder +176,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,No Disorder +177,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,No Disorder 178,Male,42,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia -179,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, -180,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, -181,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, -184,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, +179,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +180,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +181,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +184,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder 185,Female,42,Teacher,6.8,6,45,7,Overweight,130/85,78,5000,Sleep Apnea 186,Female,42,Teacher,6.8,6,45,7,Overweight,130/85,78,5000,Sleep Apnea 187,Female,43,Teacher,6.7,7,45,4,Overweight,135/90,65,6000,Insomnia @@ -165,17 +165,17 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 201,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia 202,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Insomnia 203,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Insomnia -204,Male,43,Engineer,6.9,6,47,7,Normal Weight,117/76,69,6800, -205,Male,43,Engineer,7.6,8,75,4,Overweight,122/80,68,6800, -206,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -210,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -211,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -212,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -213,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -214,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -215,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -216,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -218,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, +204,Male,43,Engineer,6.9,6,47,7,Normal Weight,117/76,69,6800,No Disorder +205,Male,43,Engineer,7.6,8,75,4,Overweight,122/80,68,6800,No Disorder +206,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +210,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +211,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +212,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +213,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +214,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +215,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +216,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +218,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder 219,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Sleep Apnea 221,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia 222,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia @@ -197,19 +197,19 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 245,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia 247,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia 248,Male,44,Engineer,6.8,7,45,7,Overweight,130/85,78,5000,Insomnia -249,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000, -250,Male,44,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000, +249,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,No Disorder +250,Male,44,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,No Disorder 251,Female,45,Teacher,6.8,7,30,6,Overweight,135/90,65,6000,Insomnia 255,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia 256,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia 258,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia 259,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia -262,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000, -263,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000, +262,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,No Disorder +263,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,No Disorder 265,Male,48,Doctor,7.3,7,65,5,Obese,142/92,83,3500,Insomnia 266,Female,48,Nurse,5.9,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 267,Male,48,Doctor,7.3,7,65,5,Obese,142/92,83,3500,Insomnia -268,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000, +268,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,No Disorder 269,Female,49,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 270,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 273,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea @@ -218,8 +218,8 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 276,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 278,Male,49,Doctor,8.1,9,85,3,Obese,139/91,86,3700,Sleep Apnea 279,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Insomnia -280,Female,50,Engineer,8.3,9,30,3,Normal,125/80,65,5000, -281,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000, +280,Female,50,Engineer,8.3,9,30,3,Normal,125/80,65,5000,No Disorder +281,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,No Disorder 282,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 283,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 284,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea @@ -235,10 +235,10 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 295,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 296,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 298,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea -299,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -300,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -301,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -303,Female,51,Nurse,7.1,7,55,6,Normal Weight,125/82,72,6000, +299,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +300,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +301,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +303,Female,51,Nurse,7.1,7,55,6,Normal Weight,125/82,72,6000,No Disorder 304,Female,51,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 306,Female,51,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 307,Female,52,Accountant,6.5,7,45,7,Overweight,130/85,72,6000,Insomnia @@ -246,34 +246,34 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 310,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia 311,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia 312,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia -313,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -315,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000, +313,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +315,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder 316,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,Insomnia -317,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -318,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -319,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -320,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -321,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -322,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -323,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -324,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -325,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000, -326,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -327,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000, -328,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -329,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000, -330,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -331,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -332,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -333,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -334,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -335,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -336,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -338,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -339,Female,54,Engineer,8.5,9,30,3,Normal,125/80,65,5000, +317,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +318,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +319,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +320,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +321,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +322,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +323,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +324,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +325,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,No Disorder +326,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +327,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,No Disorder +328,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +329,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,No Disorder +330,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +331,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +332,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +333,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +334,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +335,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +336,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +338,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +339,Female,54,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder 340,Female,55,Nurse,8.1,9,75,4,Overweight,140/95,72,5000,Sleep Apnea -343,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000, -344,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000, +343,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000,No Disorder +344,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,No Disorder 345,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 346,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 347,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea @@ -285,8 +285,8 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 354,Female,58,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 356,Female,58,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 357,Female,58,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,Sleep Apnea -359,Female,59,Nurse,8.0,9,75,3,Overweight,140/95,68,7000, -360,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000, +359,Female,59,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,No Disorder +360,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,No Disorder 361,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 362,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 363,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea diff --git a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_008/info.json b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_008/info.json index ac7f1ad59f059be3648f2769a10128e59277c544..c68a30219902530a9154e48d4957e5bb70f65c0f 100644 --- a/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_008/info.json +++ b/ranking_and_filtering/medical/classification/Sleep_Health_and_Lifestyle_Dataset_B4/Sleep_Health_and_Lifestyle_Dataset_B4_008/info.json @@ -1,121 +1,122 @@ -{ - "name": "Sleep Health and Lifestyle Dataset", - "source": "https://www.kaggle.com/datasets/uom190346a/sleep-health-and-lifestyle-dataset/data", - "data_intro": "The Sleep Health and Lifestyle Dataset comprises 400 rows and 13 columns, covering a wide range of variables related to sleep and daily habits. It includes details such as gender, age, occupation, sleep duration, quality of sleep, physical activity level, stress levels, BMI category, blood pressure, heart rate, daily steps, and the presence or absence of sleep disorders.", - "is_splited": false, - "overall_size": 374, - "train_size": 0, - "test_size": 0, - "c_classes": 5, - "n_classes": 8, - "cat_feature_intro": { - "Gender": "Gender: The gender of the person (Male/Female).
", - "Occupation": "Occupation: The occupation or profession of the person.
", - "BMI Category": "BMI Category: The BMI category of the person (e.g., Underweight, Normal, Overweight).
", - "Blood Pressure": "Blood Pressure (systolic/diastolic): The blood pressure measurement of the person, indicated as systolic pressure over diastolic pressure.
", - "Sleep Disorder": "- None: The individual does not exhibit any specific sleep disorder." - }, - "num_feature_intro": { - "Person ID": "Person ID: An identifier for each individual.
", - "Age": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", - "Sleep Duration": "Sleep Duration (hours): The number of hours the person sleeps per day.
", - "Quality of Sleep": "Quality of Sleep (scale: 1-10): A subjective rating of the quality of sleep, ranging from 1 to 10.
", - "Physical Activity Level": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", - "Stress Level": "Stress Level (scale: 1-10): A subjective rating of the stress level experienced by the person, ranging from 1 to 10.
", - "Heart Rate": "Heart Rate (bpm): The resting heart rate of the person in beats per minute.
", - "Daily Steps": "Daily Steps: The number of steps the person takes per day.
" - }, - "evaluation_metric": null, - "task_type": "classification", - "target": "Sleep Disorder", - "num_feature_value": { - "Age": [ - 27.0, - 59.0 - ], - "Daily Steps": [ - 3000.0, - 10000.0 - ], - "Heart Rate": [ - 65.0, - 86.0 - ], - "Person ID": [ - 1.0, - 374.0 - ], - "Physical Activity Level": [ - 30.0, - 90.0 - ], - "Quality of Sleep": [ - 4.0, - 9.0 - ], - "Sleep Duration": [ - 5.8, - 8.5 - ], - "Stress Level": [ - 3.0, - 8.0 - ] - }, - "cat_feature_value": { - "BMI Category": [ - "Normal", - "Normal Weight", - "Obese", - "Overweight" - ], - "Blood Pressure": [ - "115/75", - "115/78", - "117/76", - "118/75", - "118/76", - "119/77", - "120/80", - "121/79", - "122/80", - "125/80", - "125/82", - "126/83", - "128/84", - "128/85", - "129/84", - "130/85", - "130/86", - "131/86", - "132/87", - "135/88", - "135/90", - "139/91", - "140/90", - "140/95", - "142/92" - ], - "Gender": [ - "Female", - "Male" - ], - "Occupation": [ - "Accountant", - "Doctor", - "Engineer", - "Lawyer", - "Manager", - "Nurse", - "Sales Representative", - "Salesperson", - "Scientist", - "Software Engineer", - "Teacher" - ], - "Sleep Disorder": [ - "Insomnia", - "Sleep Apnea" - ] - } -} \ No newline at end of file +{ + "name": "Sleep Health and Lifestyle Dataset", + "source": "https://www.kaggle.com/datasets/uom190346a/sleep-health-and-lifestyle-dataset/data", + "data_intro": "The Sleep Health and Lifestyle Dataset comprises 400 rows and 13 columns, covering a wide range of variables related to sleep and daily habits. It includes details such as gender, age, occupation, sleep duration, quality of sleep, physical activity level, stress levels, BMI category, blood pressure, heart rate, daily steps, and the presence or absence of sleep disorders.", + "is_splited": false, + "overall_size": 374, + "train_size": 0, + "test_size": 0, + "c_classes": 5, + "n_classes": 8, + "cat_feature_intro": { + "Gender": "Gender: The gender of the person (Male/Female).
", + "Occupation": "Occupation: The occupation or profession of the person.
", + "BMI Category": "BMI Category: The BMI category of the person (e.g., Underweight, Normal, Overweight).
", + "Blood Pressure": "Blood Pressure (systolic/diastolic): The blood pressure measurement of the person, indicated as systolic pressure over diastolic pressure.
", + "Sleep Disorder": "Sleep disorder category: No Disorder, Insomnia, or Sleep Apnea." + }, + "num_feature_intro": { + "Person ID": "Person ID: An identifier for each individual.
", + "Age": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", + "Sleep Duration": "Sleep Duration (hours): The number of hours the person sleeps per day.
", + "Quality of Sleep": "Quality of Sleep (scale: 1-10): A subjective rating of the quality of sleep, ranging from 1 to 10.
", + "Physical Activity Level": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", + "Stress Level": "Stress Level (scale: 1-10): A subjective rating of the stress level experienced by the person, ranging from 1 to 10.
", + "Heart Rate": "Heart Rate (bpm): The resting heart rate of the person in beats per minute.
", + "Daily Steps": "Daily Steps: The number of steps the person takes per day.
" + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "Sleep Disorder", + "num_feature_value": { + "Age": [ + 27.0, + 59.0 + ], + "Daily Steps": [ + 3000.0, + 10000.0 + ], + "Heart Rate": [ + 65.0, + 86.0 + ], + "Person ID": [ + 1.0, + 374.0 + ], + "Physical Activity Level": [ + 30.0, + 90.0 + ], + "Quality of Sleep": [ + 4.0, + 9.0 + ], + "Sleep Duration": [ + 5.8, + 8.5 + ], + "Stress Level": [ + 3.0, + 8.0 + ] + }, + "cat_feature_value": { + "BMI Category": [ + "Normal", + "Normal Weight", + "Obese", + "Overweight" + ], + "Blood Pressure": [ + "115/75", + "115/78", + "117/76", + "118/75", + "118/76", + "119/77", + "120/80", + "121/79", + "122/80", + "125/80", + "125/82", + "126/83", + "128/84", + "128/85", + "129/84", + "130/85", + "130/86", + "131/86", + "132/87", + "135/88", + "135/90", + "139/91", + "140/90", + "140/95", + "142/92" + ], + "Gender": [ + "Female", + "Male" + ], + "Occupation": [ + "Accountant", + "Doctor", + "Engineer", + "Lawyer", + "Manager", + "Nurse", + "Sales Representative", + "Salesperson", + "Scientist", + "Software Engineer", + "Teacher" + ], + "Sleep Disorder": [ + "Insomnia", + "No Disorder", + "Sleep Apnea" + ] + } +} diff --git a/ranking_and_filtering/medical/regression/Medical_Cost_Personal_Datasets_B4/Medical_Cost_Personal_Datasets_B4_004/current.csv b/ranking_and_filtering/medical/regression/Medical_Cost_Personal_Datasets_B4/Medical_Cost_Personal_Datasets_B4_004/current.csv index 25003c24b93190054a073065adb1acff99dbc78f..53aa852bddaf3ac5c03daf98783362c614ca6224 100644 --- a/ranking_and_filtering/medical/regression/Medical_Cost_Personal_Datasets_B4/Medical_Cost_Personal_Datasets_B4_004/current.csv +++ b/ranking_and_filtering/medical/regression/Medical_Cost_Personal_Datasets_B4/Medical_Cost_Personal_Datasets_B4_004/current.csv @@ -143,7 +143,6 @@ age,sex,bmi,children,smoker,region 52,female,37.4,0,no,southwest 64,female,33.8,1,yes,southwest 34,female,30.21,1,yes,northwest -19,male,30.59,0,no,northwest 19,male,35.53,0,no,northwest 31,female,30.495,3,no,northeast 32,female,24.6,0,yes,southwest diff --git a/single_point_prediction/daily/Apple_Quality_B1/Apple_Quality_B1_001.json b/single_point_prediction/daily/Apple_Quality_B1/Apple_Quality_B1_001.json index 0ca6cfcb0dd2b3287fffcd9cbfc66aec78728a01..a4f80c81521b54fa9dcb0cb4f85878e469fdb492 100644 --- a/single_point_prediction/daily/Apple_Quality_B1/Apple_Quality_B1_001.json +++ b/single_point_prediction/daily/Apple_Quality_B1/Apple_Quality_B1_001.json @@ -5,7 +5,7 @@ "perspective": "user", "dataset_name": "Apple Quality", "table_path": "kaggle/Apple Quality", - "query": "Picking up apple #1452, its compact size and light weight are the first things I notice. A bite reveals it's not very sweet and has a sharp, acidic kick, but it makes up for that with an incredibly crisp texture and a lot of juice. It feels like it's at a pretty standard stage of ripeness. So, honestly, is this a good one?", + "query": "Picking up apple #1452, its relatively large size and light weight are the first things I notice. A bite reveals it's not very sweet and has a sharp, acidic kick, but it makes up for that with an incredibly crisp texture and a lot of juice. It feels like it's at a pretty standard stage of ripeness. So, honestly, is this a good one?", "meta_info": { "domain": "daily" }, @@ -36,4 +36,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/single_point_prediction/daily/Apple_Quality_B1/Apple_Quality_B1_006.json b/single_point_prediction/daily/Apple_Quality_B1/Apple_Quality_B1_006.json index 8d009c6de03c80d37d40d799cec46918198bed01..943954ad5443da8c8938bac7ca78e6640bfc6654 100644 --- a/single_point_prediction/daily/Apple_Quality_B1/Apple_Quality_B1_006.json +++ b/single_point_prediction/daily/Apple_Quality_B1/Apple_Quality_B1_006.json @@ -5,7 +5,7 @@ "perspective": "data_holder", "dataset_name": "Apple Quality", "table_path": "kaggle/Apple Quality", - "query": "Found this one in the new shipment with the label 3372. It's a smaller fruit, size is about a 3.39, and it feels quite light, only 0.27 in weight. Tasted a sample and the sweetness is surprisingly low at -4.02, but it has a nice crunchiness of 0.67 and is pretty juicy at 1.50. The ripeness reads as 1.98 and its acidity level is 2.48. I've shared our whole quality log with you. With all these details in mind, what's your take—is this a good fruit or a bad one?", + "query": "Found this one in the new shipment with the label 3372. It's a larger fruit, size is about a 3.39, and it feels quite light, only 0.27 in weight. Tasted a sample and the sweetness is surprisingly low at -4.02, but it has a nice crunchiness of 0.67 and is pretty juicy at 1.50. The ripeness reads as 1.98 and its acidity level is 2.48. I've shared our whole quality log with you. With all these details in mind, what's your take—is this a good fruit or a bad one?", "meta_info": { "domain": "daily" }, @@ -36,4 +36,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/single_point_prediction/daily/Apple_Quality_B1/history.csv b/single_point_prediction/daily/Apple_Quality_B1/history.csv index c9d79df3263d20f2d59658a5049c1072a9f0fc1e..bdfdac40ab57278b0343f714a87bb58718a71757 100644 --- a/single_point_prediction/daily/Apple_Quality_B1/history.csv +++ b/single_point_prediction/daily/Apple_Quality_B1/history.csv @@ -3983,4 +3983,3 @@ A_id,Size,Weight,Sweetness,Crunchiness,Juiciness,Ripeness,Acidity,Quality 3997.0,-2.634515299,-2.13824672,-2.440461285,0.657222891,2.19970859,4.763859177,-1.334611391,bad 3998.0,-4.008003744,-1.779337107,2.366396966,-0.200329367,2.161435121,0.214488384,-2.229719806,good 3999.0,0.27853965,-1.715505028,0.121217251,-1.154074758,1.2666774,-0.77657147,1.599796456,good -,,,,,,,Created_by_Nidula_Elgiriyewithana, diff --git a/single_point_prediction/daily/Apple_Quality_B1/info.json b/single_point_prediction/daily/Apple_Quality_B1/info.json index 061594cd032cd189afdf78ebe43a94ad53166b97..8de202273508b7431d0bbe83b679ee5373c9efe5 100644 --- a/single_point_prediction/daily/Apple_Quality_B1/info.json +++ b/single_point_prediction/daily/Apple_Quality_B1/info.json @@ -1,66 +1,69 @@ -{ - "name": "Apple Quality", - "source": "https://www.kaggle.com/datasets/nelgiriyewithana/apple-quality/data", - "data_intro": "This dataset contains information about various attributes of a set of fruits, providing insights into their characteristics. The dataset includes details such as fruit ID, size, weight, sweetness, crunchiness, juiciness, ripeness, acidity, and quality.", - "is_splited": false, - "overall_size": 4000, - "train_size": 0, - "test_size": 0, - "c_classes": 2, - "n_classes": 7, - "task_type": "classification", - "target": { - "Quality": "Build a model to predict the quality rating of fruits using various attributes." - }, - "cat_feature_intro": { - "Acidity": "- Acidity: Acidity level of the fruit", - "Quality": "- Quality: Overall quality of the fruit." - }, - "num_feature_intro": { - "A_id": "- A_id: Unique identifier for each fruit", - "Size": "- Size: Size of the fruit", - "Weight": "- Weight: Weight of the fruit", - "Sweetness": "- Sweetness: Degree of sweetness of the fruit", - "Crunchiness": "- Crunchiness: Texture indicating the crunchiness of the fruit", - "Juiciness": "- Juiciness: Level of juiciness of the fruit", - "Ripeness": "- Ripeness: Stage of ripeness of the fruit" - }, - "evaluation_metric": null, - "num_feature_value": { - "A_id": [ - 0.0, - 3999.0 - ], - "Crunchiness": [ - -6.055057805, - 7.619851801 - ], - "Juiciness": [ - -5.961897048, - 7.364402864 - ], - "Ripeness": [ - -5.864598918, - 7.237836684 - ], - "Size": [ - -7.151703059, - 6.406366899 - ], - "Sweetness": [ - -6.894485494, - 6.374915513 - ], - "Weight": [ - -7.149847675, - 5.79071359 - ] - }, - "cat_feature_value": { - "Acidity": "string", - "Quality": [ - "bad", - "good" - ] - } -} \ No newline at end of file +{ + "name": "Apple Quality", + "source": "https://www.kaggle.com/datasets/nelgiriyewithana/apple-quality/data", + "data_intro": "This dataset contains information about various attributes of a set of fruits, providing insights into their characteristics. The dataset includes details such as fruit ID, size, weight, sweetness, crunchiness, juiciness, ripeness, acidity, and quality.", + "is_splited": false, + "overall_size": 4000, + "train_size": 0, + "test_size": 0, + "c_classes": 1, + "n_classes": 8, + "task_type": "classification", + "target": { + "Quality": "Build a model to predict the quality rating of fruits using various attributes." + }, + "cat_feature_intro": { + "Quality": "- Quality: Overall quality of the fruit." + }, + "num_feature_intro": { + "A_id": "- A_id: Unique identifier for each fruit", + "Size": "- Size: Size of the fruit", + "Weight": "- Weight: Weight of the fruit", + "Sweetness": "- Sweetness: Degree of sweetness of the fruit", + "Crunchiness": "- Crunchiness: Texture indicating the crunchiness of the fruit", + "Juiciness": "- Juiciness: Level of juiciness of the fruit", + "Ripeness": "- Ripeness: Stage of ripeness of the fruit", + "Acidity": "- Acidity: Acidity level of the fruit" + }, + "evaluation_metric": null, + "num_feature_value": { + "A_id": [ + 0.0, + 3999.0 + ], + "Crunchiness": [ + -6.055057805, + 7.619851801 + ], + "Juiciness": [ + -5.961897048, + 7.364402864 + ], + "Ripeness": [ + -5.864598918, + 7.237836684 + ], + "Size": [ + -7.151703059, + 6.406366899 + ], + "Sweetness": [ + -6.894485494, + 6.374915513 + ], + "Weight": [ + -7.149847675, + 5.79071359 + ], + "Acidity": [ + -7.010538475, + 7.404736238 + ] + }, + "cat_feature_value": { + "Quality": [ + "bad", + "good" + ] + } +} diff --git a/single_point_prediction/daily/Apple_Quality_B1/info_mod.json b/single_point_prediction/daily/Apple_Quality_B1/info_mod.json index ab48a49c991d52a72dd7e5764a0b37c5184b3a8c..f7777abe9021042f7030f4c3e3823bc7b06e30be 100644 --- a/single_point_prediction/daily/Apple_Quality_B1/info_mod.json +++ b/single_point_prediction/daily/Apple_Quality_B1/info_mod.json @@ -52,7 +52,7 @@ 7.237836684 ], "Acidity": [ - -6.46098926, + -7.010538475, 7.404736238 ] }, @@ -91,4 +91,4 @@ "open_text_feature_intro": {}, "open_text_features": [], "missing_from_original_info": [] -} \ No newline at end of file +} diff --git a/single_point_prediction/daily/Chocolate_Bar_Ratings_B1/Chocolate_Bar_Ratings_B1_005.json b/single_point_prediction/daily/Chocolate_Bar_Ratings_B1/Chocolate_Bar_Ratings_B1_005.json index af257722be92c871af897b395f82a95935ddce60..b93a8412cf09f67ecf0819bd0f38e62fb78ca5b4 100644 --- a/single_point_prediction/daily/Chocolate_Bar_Ratings_B1/Chocolate_Bar_Ratings_B1_005.json +++ b/single_point_prediction/daily/Chocolate_Bar_Ratings_B1/Chocolate_Bar_Ratings_B1_005.json @@ -14,7 +14,7 @@ { "scenario_id": "001", "features": { - "Company(Maker-if known)": "Na�ve", + "Company(Maker-if known)": "Naïve", "Specific Bean Originor Bar Name": "Nicaragua", "REF": 1379, "Review Date": 2014, @@ -36,4 +36,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/single_point_prediction/daily/Chocolate_Bar_Ratings_B1/history.csv b/single_point_prediction/daily/Chocolate_Bar_Ratings_B1/history.csv index abc1962853e2816a2014877a463b609f907c3874..fc1b4a4414cc96b09981b3706ace47efaa589422 100644 --- a/single_point_prediction/daily/Chocolate_Bar_Ratings_B1/history.csv +++ b/single_point_prediction/daily/Chocolate_Bar_Ratings_B1/history.csv @@ -1151,8 +1151,8 @@ Nahua,"Costa Rica, Oscuro",1049,2013,70%,Costa Rica,3.0, ,Costa Rica Naive,Trinidad & Tobago,1046,2013,70%,Lithuania,3.75, ,"Trinidad, Tobago" Naive,"Maranon Canyon, Fortunato No. 4",1133,2013,78%,Lithuania,3.75,Forastero (Nacional),Peru Naive,Grenada,867,2012,71%,Lithuania,2.5,Trinitario,Grenada -Na�ve,Chuao,1399,2014,75%,Lithuania,2.75,Trinitario,Venezuela -Na�ve,Barinas,1399,2014,70%,Lithuania,3.0, ,Venezuela +Naïve,Chuao,1399,2014,75%,Lithuania,2.75,Trinitario,Venezuela +Naïve,Barinas,1399,2014,70%,Lithuania,3.0, ,Venezuela Nanea,Criollo Blend,1007,2013,85%,U.S.A.,3.5, ,Hawaii Nathan Miller,Peru,1403,2014,72%,U.S.A.,2.25, ,Peru Nathan Miller,Hispaniola,1403,2014,70%,U.S.A.,2.5, ,Dominican Republic diff --git a/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/Forest_Fires_in_Brazil_B1_001.json b/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/Forest_Fires_in_Brazil_B1_001.json index 860faf2f26f091d95418280c741ebc062da72b09..e01f6bf05aacf224630ad12bb9e426703e63ed66 100644 --- a/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/Forest_Fires_in_Brazil_B1_001.json +++ b/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/Forest_Fires_in_Brazil_B1_001.json @@ -5,7 +5,7 @@ "perspective": "user", "dataset_name": "Forest Fires in Brazil", "table_path": "kaggle/Forest Fires in Brazil", - "query": "Ugh, just came across an old environmental report from Santa Catarina. It's specifically for the month of Dezembro, and the entry is dated 2011-01-01, summarizing the previous year of 2011. It's got me wondering, with those conditions, about how many forest fires were actually burning there back then?", + "query": "Ugh, just came across an old environmental report from Santa Catarina. It's specifically for the month of Dezembro, with 2011-01-01 stored as the year marker for 2011. It's got me wondering, with those conditions, about how many forest fires were reported there that month?", "meta_info": { "domain": "daily" }, @@ -32,4 +32,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/Forest_Fires_in_Brazil_B1_002.json b/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/Forest_Fires_in_Brazil_B1_002.json index 06ac34140a98dd6ca647502460141e735cbd0ce4..df880960c13efb80051e7fc773ce15397747f990 100644 --- a/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/Forest_Fires_in_Brazil_B1_002.json +++ b/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/Forest_Fires_in_Brazil_B1_002.json @@ -5,7 +5,7 @@ "perspective": "user", "dataset_name": "Forest Fires in Brazil", "table_path": "kaggle/Forest Fires in Brazil", - "query": "The situation down in Bahia back in 2006 has me really concerned, especially for the month of Agosto. This old report I'm holding, dated 2006-01-01, details the forest fire counts for that period. With all that in mind, can you give me an idea of how many fires were burning there at that time?", + "query": "The situation down in Bahia back in 2006 has me really concerned, especially for the month of Agosto. This old report I'm holding uses 2006-01-01 as its year marker and details the forest fire counts for that period. With all that in mind, can you give me an idea of how many fires were reported there that month?", "meta_info": { "domain": "daily" }, @@ -32,4 +32,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/Forest_Fires_in_Brazil_B1_003.json b/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/Forest_Fires_in_Brazil_B1_003.json index 56c2a4eac455bd6eed82fcef4903f11f9e06d535..e80045e1d2a21c462ff8be721cd9accf2ba84f0e 100644 --- a/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/Forest_Fires_in_Brazil_B1_003.json +++ b/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/Forest_Fires_in_Brazil_B1_003.json @@ -5,7 +5,7 @@ "perspective": "user", "dataset_name": "Forest Fires in Brazil", "table_path": "kaggle/Forest Fires in Brazil", - "query": "My friend from Rio was telling me that back in April 2015, on the first of the year no less, they were already starting to worry about the dry season. It's got me wondering, for that specific time and place, roughly how many forest fire incidents were people dealing with?", + "query": "My friend from Rio was telling me that back in April 2015, they were already starting to worry about the dry season. It's got me wondering, for that specific time and place, roughly how many forest fire incidents were people dealing with?", "meta_info": { "domain": "daily" }, @@ -32,4 +32,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/Forest_Fires_in_Brazil_B1_004.json b/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/Forest_Fires_in_Brazil_B1_004.json index da49c4af4ecee45aa893682b782e0a29810cd0a9..4ad275d48c5fe7d3aac6a4ed67f2218018247e5a 100644 --- a/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/Forest_Fires_in_Brazil_B1_004.json +++ b/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/Forest_Fires_in_Brazil_B1_004.json @@ -5,7 +5,7 @@ "perspective": "user", "dataset_name": "Forest Fires in Brazil", "table_path": "kaggle/Forest Fires in Brazil", - "query": "Back in 2005, the Distrito Federal was experiencing a particularly dry period. I've got an old report here from the 1st of July, and I'm trying to get a sense of the scale of the problem back then. Roughly how many forest fires do you think were burning on that date?", + "query": "Back in 2005, the Distrito Federal was experiencing a particularly dry period. I've got an old report here for July, with 2005-01-01 stored as the year marker, and I'm trying to get a sense of the scale of the problem back then. Roughly how many forest fires do you think were burning that month?", "meta_info": { "domain": "daily" }, @@ -32,4 +32,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/Forest_Fires_in_Brazil_B1_005.json b/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/Forest_Fires_in_Brazil_B1_005.json index 6dd711af4568456c3834dc5cce058ab66d4bb356..abc8b0425cf326df7da18ae7b8776c924504193f 100644 --- a/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/Forest_Fires_in_Brazil_B1_005.json +++ b/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/Forest_Fires_in_Brazil_B1_005.json @@ -5,7 +5,7 @@ "perspective": "data_holder", "dataset_name": "Forest Fires in Brazil", "table_path": "kaggle/Forest Fires in Brazil", - "query": "The old logs show a specific entry for Pernambuco, a state that always makes us nervous during the dry season. This particular record is for the first of the year, 2016-01-01, which fell in Janeiro. I'm having you review all our past data to see the bigger picture. For that precise date and location, how many forest fires were likely burning?", + "query": "The old logs show a specific entry for Pernambuco, a state that always makes us nervous during the dry season. This particular record is for Janeiro 2016, with 2016-01-01 stored as the year marker. I'm having you review all our past data to see the bigger picture. For that month and location, how many forest fires were likely burning?", "meta_info": { "domain": "daily" }, @@ -32,4 +32,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/Forest_Fires_in_Brazil_B1_006.json b/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/Forest_Fires_in_Brazil_B1_006.json index 7a2308de93413917ef82c1c62fdffbaa2c271c44..ebae25f9c9e418eb92604e5b3c1ad9a171d138b8 100644 --- a/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/Forest_Fires_in_Brazil_B1_006.json +++ b/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/Forest_Fires_in_Brazil_B1_006.json @@ -5,7 +5,7 @@ "perspective": "data_holder", "dataset_name": "Forest Fires in Brazil", "table_path": "kaggle/Forest Fires in Brazil", - "query": "The log entry for the first day of January in 1998 places us in the state of Acre. Flipping through the historical reports I've provided, it's striking to see how the season kicks off. Given the patterns from those past records, how many individual fire incidents do you think were reported for Acre on that New Year's Day?", + "query": "The monthly log entry for January 1998 places us in the state of Acre. Flipping through the historical reports I've provided, it's striking to see how the season kicks off. Given the patterns from those past records, how many individual fire incidents do you think were reported for Acre that January?", "meta_info": { "domain": "daily" }, @@ -32,4 +32,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/Forest_Fires_in_Brazil_B1_007.json b/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/Forest_Fires_in_Brazil_B1_007.json index 600144d6bc4ab412c5e064087fa7e521bd7759e7..45f59f386573ff95e683766c44cbf5ea0ccc453f 100644 --- a/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/Forest_Fires_in_Brazil_B1_007.json +++ b/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/Forest_Fires_in_Brazil_B1_007.json @@ -5,7 +5,7 @@ "perspective": "data_holder", "dataset_name": "Forest Fires in Brazil", "table_path": "kaggle/Forest Fires in Brazil", - "query": "While reviewing our old forest fire archives, a particular entry for the state of Sergipe caught my eye. The log states that back in Julho of 2015, specifically dated 2015-01-01, they were tracking incidents. Since you have all that decade's worth of Brazilian state data I provided, could you give me an estimate for how many fires were actually burning there on that day?", + "query": "While reviewing our old forest fire archives, a particular entry for the state of Sergipe caught my eye. The log states that back in Julho of 2015, with 2015-01-01 stored as the year marker, they were tracking incidents. Since you have all that decade's worth of Brazilian state data I provided, could you give me an estimate for how many fires were actually burning there that month?", "meta_info": { "domain": "daily" }, @@ -32,4 +32,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/Forest_Fires_in_Brazil_B1_008.json b/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/Forest_Fires_in_Brazil_B1_008.json index ae73f5d31dd67a522a6111195bd98c32d1c3906b..e7a7eb4d58f002d04df34ee4ff64124566d15b00 100644 --- a/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/Forest_Fires_in_Brazil_B1_008.json +++ b/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/Forest_Fires_in_Brazil_B1_008.json @@ -5,7 +5,7 @@ "perspective": "data_holder", "dataset_name": "Forest Fires in Brazil", "table_path": "kaggle/Forest Fires in Brazil", - "query": "Flipping through this archive, a page from Setembro 2008 in Amazonas catches my eye, with the entry for 2008-01-01. It's one piece of a much larger puzzle that I've shared with you. Comparing it to the rest of the history, what's your take on the number of fires that happened then?", + "query": "Flipping through this archive, a page from Setembro 2008 in Amazonas catches my eye, with the year marker stored as 2008-01-01. It's one piece of a much larger puzzle that I've shared with you. Comparing it to the rest of the history, what's your take on the number of fires that happened then?", "meta_info": { "domain": "daily" }, @@ -32,4 +32,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/history.csv b/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/history.csv index d5801f0ca4d57a191120e01917ea2166763dc175..1e88fe02b062c4a227c68ac52397b68d3856e02f 100644 --- a/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/history.csv +++ b/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/history.csv @@ -31,26 +31,26 @@ year,state,month,number,date 2015,Acre,Fevereiro,2.0,2015-01-01 2016,Acre,Fevereiro,5.0,2016-01-01 2017,Acre,Fevereiro,1.0,2017-01-01 -1998,Acre,Mar�o,0.0,1998-01-01 -1999,Acre,Mar�o,0.0,1999-01-01 -2000,Acre,Mar�o,11.0,2000-01-01 -2001,Acre,Mar�o,0.0,2001-01-01 -2002,Acre,Mar�o,0.0,2002-01-01 -2003,Acre,Mar�o,0.0,2003-01-01 -2004,Acre,Mar�o,0.0,2004-01-01 -2005,Acre,Mar�o,3.0,2005-01-01 -2006,Acre,Mar�o,0.0,2006-01-01 -2007,Acre,Mar�o,2.0,2007-01-01 -2008,Acre,Mar�o,0.0,2008-01-01 -2009,Acre,Mar�o,1.0,2009-01-01 -2010,Acre,Mar�o,0.0,2010-01-01 -2011,Acre,Mar�o,0.0,2011-01-01 -2012,Acre,Mar�o,1.0,2012-01-01 -2013,Acre,Mar�o,2.0,2013-01-01 -2014,Acre,Mar�o,1.0,2014-01-01 -2015,Acre,Mar�o,2.0,2015-01-01 -2016,Acre,Mar�o,0.0,2016-01-01 -2017,Acre,Mar�o,0.0,2017-01-01 +1998,Acre,Março,0.0,1998-01-01 +1999,Acre,Março,0.0,1999-01-01 +2000,Acre,Março,11.0,2000-01-01 +2001,Acre,Março,0.0,2001-01-01 +2002,Acre,Março,0.0,2002-01-01 +2003,Acre,Março,0.0,2003-01-01 +2004,Acre,Março,0.0,2004-01-01 +2005,Acre,Março,3.0,2005-01-01 +2006,Acre,Março,0.0,2006-01-01 +2007,Acre,Março,2.0,2007-01-01 +2008,Acre,Março,0.0,2008-01-01 +2009,Acre,Março,1.0,2009-01-01 +2010,Acre,Março,0.0,2010-01-01 +2011,Acre,Março,0.0,2011-01-01 +2012,Acre,Março,1.0,2012-01-01 +2013,Acre,Março,2.0,2013-01-01 +2014,Acre,Março,1.0,2014-01-01 +2015,Acre,Março,2.0,2015-01-01 +2016,Acre,Março,0.0,2016-01-01 +2017,Acre,Março,0.0,2017-01-01 1998,Acre,Abril,0.0,1998-01-01 1999,Acre,Abril,0.0,1999-01-01 2000,Acre,Abril,1.0,2000-01-01 @@ -271,26 +271,26 @@ year,state,month,number,date 2015,Alagoas,Fevereiro,46.0,2015-01-01 2016,Alagoas,Fevereiro,14.0,2016-01-01 2017,Alagoas,Fevereiro,21.0,2017-01-01 -1998,Alagoas,Mar�o,0.0,1998-01-01 -1999,Alagoas,Mar�o,52.0,1999-01-01 -2000,Alagoas,Mar�o,32.0,2000-01-01 -2001,Alagoas,Mar�o,0.0,2001-01-01 -2002,Alagoas,Mar�o,27.0,2002-01-01 -2003,Alagoas,Mar�o,15.0,2003-01-01 -2004,Alagoas,Mar�o,16.0,2004-01-01 -2005,Alagoas,Mar�o,35.0,2005-01-01 -2006,Alagoas,Mar�o,37.0,2006-01-01 -2007,Alagoas,Mar�o,7.0,2007-01-01 -2008,Alagoas,Mar�o,31.0,2008-01-01 -2009,Alagoas,Mar�o,31.0,2009-01-01 -2010,Alagoas,Mar�o,70.0,2010-01-01 -2011,Alagoas,Mar�o,34.0,2011-01-01 -2012,Alagoas,Mar�o,44.0,2012-01-01 -2013,Alagoas,Mar�o,31.0,2013-01-01 -2014,Alagoas,Mar�o,28.0,2014-01-01 -2015,Alagoas,Mar�o,32.0,2015-01-01 -2016,Alagoas,Mar�o,9.0,2016-01-01 -2017,Alagoas,Mar�o,23.0,2017-01-01 +1998,Alagoas,Março,0.0,1998-01-01 +1999,Alagoas,Março,52.0,1999-01-01 +2000,Alagoas,Março,32.0,2000-01-01 +2001,Alagoas,Março,0.0,2001-01-01 +2002,Alagoas,Março,27.0,2002-01-01 +2003,Alagoas,Março,15.0,2003-01-01 +2004,Alagoas,Março,16.0,2004-01-01 +2005,Alagoas,Março,35.0,2005-01-01 +2006,Alagoas,Março,37.0,2006-01-01 +2007,Alagoas,Março,7.0,2007-01-01 +2008,Alagoas,Março,31.0,2008-01-01 +2009,Alagoas,Março,31.0,2009-01-01 +2010,Alagoas,Março,70.0,2010-01-01 +2011,Alagoas,Março,34.0,2011-01-01 +2012,Alagoas,Março,44.0,2012-01-01 +2013,Alagoas,Março,31.0,2013-01-01 +2014,Alagoas,Março,28.0,2014-01-01 +2015,Alagoas,Março,32.0,2015-01-01 +2016,Alagoas,Março,9.0,2016-01-01 +2017,Alagoas,Março,23.0,2017-01-01 1998,Alagoas,Abril,0.0,1998-01-01 1999,Alagoas,Abril,4.0,1999-01-01 2000,Alagoas,Abril,20.0,2000-01-01 @@ -510,26 +510,26 @@ year,state,month,number,date 2015,Amapa,Fevereiro,1.0,2015-01-01 2016,Amapa,Fevereiro,1.0,2016-01-01 2017,Amapa,Fevereiro,0.0,2017-01-01 -1998,Amapa,Mar�o,0.0,1998-01-01 -1999,Amapa,Mar�o,2.0,1999-01-01 -2000,Amapa,Mar�o,0.0,2000-01-01 -2001,Amapa,Mar�o,0.0,2001-01-01 -2002,Amapa,Mar�o,0.0,2002-01-01 -2003,Amapa,Mar�o,1.0,2003-01-01 -2004,Amapa,Mar�o,0.0,2004-01-01 -2005,Amapa,Mar�o,0.0,2005-01-01 -2006,Amapa,Mar�o,0.0,2006-01-01 -2007,Amapa,Mar�o,0.0,2007-01-01 -2008,Amapa,Mar�o,1.0,2008-01-01 -2009,Amapa,Mar�o,1.0,2009-01-01 -2010,Amapa,Mar�o,2.0,2010-01-01 -2011,Amapa,Mar�o,0.0,2011-01-01 -2012,Amapa,Mar�o,1.0,2012-01-01 -2013,Amapa,Mar�o,0.0,2013-01-01 -2014,Amapa,Mar�o,1.0,2014-01-01 -2015,Amapa,Mar�o,2.0,2015-01-01 -2016,Amapa,Mar�o,5.0,2016-01-01 -2017,Amapa,Mar�o,0.0,2017-01-01 +1998,Amapa,Março,0.0,1998-01-01 +1999,Amapa,Março,2.0,1999-01-01 +2000,Amapa,Março,0.0,2000-01-01 +2001,Amapa,Março,0.0,2001-01-01 +2002,Amapa,Março,0.0,2002-01-01 +2003,Amapa,Março,1.0,2003-01-01 +2004,Amapa,Março,0.0,2004-01-01 +2005,Amapa,Março,0.0,2005-01-01 +2006,Amapa,Março,0.0,2006-01-01 +2007,Amapa,Março,0.0,2007-01-01 +2008,Amapa,Março,1.0,2008-01-01 +2009,Amapa,Março,1.0,2009-01-01 +2010,Amapa,Março,2.0,2010-01-01 +2011,Amapa,Março,0.0,2011-01-01 +2012,Amapa,Março,1.0,2012-01-01 +2013,Amapa,Março,0.0,2013-01-01 +2014,Amapa,Março,1.0,2014-01-01 +2015,Amapa,Março,2.0,2015-01-01 +2016,Amapa,Março,5.0,2016-01-01 +2017,Amapa,Março,0.0,2017-01-01 1998,Amapa,Abril,0.0,1998-01-01 1999,Amapa,Abril,0.0,1999-01-01 2000,Amapa,Abril,0.0,2000-01-01 @@ -749,26 +749,26 @@ year,state,month,number,date 2015,Amazonas,Fevereiro,107.0,2015-01-01 2016,Amazonas,Fevereiro,275.0,2016-01-01 2017,Amazonas,Fevereiro,52.0,2017-01-01 -1998,Amazonas,Mar�o,0.0,1998-01-01 -1999,Amazonas,Mar�o,7.0,1999-01-01 -2000,Amazonas,Mar�o,69.0,2000-01-01 -2001,Amazonas,Mar�o,6.0,2001-01-01 -2002,Amazonas,Mar�o,5.0,2002-01-01 -2003,Amazonas,Mar�o,64.0,2003-01-01 -2004,Amazonas,Mar�o,6.0,2004-01-01 -2005,Amazonas,Mar�o,12.0,2005-01-01 -2006,Amazonas,Mar�o,20.0,2006-01-01 -2007,Amazonas,Mar�o,6.0,2007-01-01 -2008,Amazonas,Mar�o,10.0,2008-01-01 -2009,Amazonas,Mar�o,11.0,2009-01-01 -2010,Amazonas,Mar�o,116.0,2010-01-01 -2011,Amazonas,Mar�o,49.0,2011-01-01 -2012,Amazonas,Mar�o,17.0,2012-01-01 -2013,Amazonas,Mar�o,49.0,2013-01-01 -2014,Amazonas,Mar�o,50.0,2014-01-01 -2015,Amazonas,Mar�o,46.0,2015-01-01 -2016,Amazonas,Mar�o,140.0,2016-01-01 -2017,Amazonas,Mar�o,45.0,2017-01-01 +1998,Amazonas,Março,0.0,1998-01-01 +1999,Amazonas,Março,7.0,1999-01-01 +2000,Amazonas,Março,69.0,2000-01-01 +2001,Amazonas,Março,6.0,2001-01-01 +2002,Amazonas,Março,5.0,2002-01-01 +2003,Amazonas,Março,64.0,2003-01-01 +2004,Amazonas,Março,6.0,2004-01-01 +2005,Amazonas,Março,12.0,2005-01-01 +2006,Amazonas,Março,20.0,2006-01-01 +2007,Amazonas,Março,6.0,2007-01-01 +2008,Amazonas,Março,10.0,2008-01-01 +2009,Amazonas,Março,11.0,2009-01-01 +2010,Amazonas,Março,116.0,2010-01-01 +2011,Amazonas,Março,49.0,2011-01-01 +2012,Amazonas,Março,17.0,2012-01-01 +2013,Amazonas,Março,49.0,2013-01-01 +2014,Amazonas,Março,50.0,2014-01-01 +2015,Amazonas,Março,46.0,2015-01-01 +2016,Amazonas,Março,140.0,2016-01-01 +2017,Amazonas,Março,45.0,2017-01-01 1998,Amazonas,Abril,0.0,1998-01-01 1999,Amazonas,Abril,2.0,1999-01-01 2000,Amazonas,Abril,18.0,2000-01-01 @@ -987,26 +987,26 @@ year,state,month,number,date 2015,Bahia,Fevereiro,143.0,2015-01-01 2016,Bahia,Fevereiro,418.0,2016-01-01 2017,Bahia,Fevereiro,100.0,2017-01-01 -1998,Bahia,Mar�o,0.0,1998-01-01 -1999,Bahia,Mar�o,83.0,1999-01-01 -2000,Bahia,Mar�o,65.0,2000-01-01 -2001,Bahia,Mar�o,4.0,2001-01-01 -2002,Bahia,Mar�o,146.0,2002-01-01 -2003,Bahia,Mar�o,274.0,2003-01-01 -2004,Bahia,Mar�o,115.0,2004-01-01 -2005,Bahia,Mar�o,158.0,2005-01-01 -2006,Bahia,Mar�o,199.0,2006-01-01 -2007,Bahia,Mar�o,141.0,2007-01-01 -2008,Bahia,Mar�o,135.0,2008-01-01 -2009,Bahia,Mar�o,249.0,2009-01-01 -2010,Bahia,Mar�o,156.0,2010-01-01 -2011,Bahia,Mar�o,90.0,2011-01-01 -2012,Bahia,Mar�o,404.0,2012-01-01 -2013,Bahia,Mar�o,363.0,2013-01-01 -2014,Bahia,Mar�o,148.0,2014-01-01 -2015,Bahia,Mar�o,229.0,2015-01-01 -2016,Bahia,Mar�o,281.0,2016-01-01 -2017,Bahia,Mar�o,163.0,2017-01-01 +1998,Bahia,Março,0.0,1998-01-01 +1999,Bahia,Março,83.0,1999-01-01 +2000,Bahia,Março,65.0,2000-01-01 +2001,Bahia,Março,4.0,2001-01-01 +2002,Bahia,Março,146.0,2002-01-01 +2003,Bahia,Março,274.0,2003-01-01 +2004,Bahia,Março,115.0,2004-01-01 +2005,Bahia,Março,158.0,2005-01-01 +2006,Bahia,Março,199.0,2006-01-01 +2007,Bahia,Março,141.0,2007-01-01 +2008,Bahia,Março,135.0,2008-01-01 +2009,Bahia,Março,249.0,2009-01-01 +2010,Bahia,Março,156.0,2010-01-01 +2011,Bahia,Março,90.0,2011-01-01 +2012,Bahia,Março,404.0,2012-01-01 +2013,Bahia,Março,363.0,2013-01-01 +2014,Bahia,Março,148.0,2014-01-01 +2015,Bahia,Março,229.0,2015-01-01 +2016,Bahia,Março,281.0,2016-01-01 +2017,Bahia,Março,163.0,2017-01-01 1998,Bahia,Abril,0.0,1998-01-01 1999,Bahia,Abril,55.0,1999-01-01 2000,Bahia,Abril,23.0,2000-01-01 @@ -1225,26 +1225,26 @@ year,state,month,number,date 2015,Ceara,Fevereiro,25.0,2015-01-01 2016,Ceara,Fevereiro,13.0,2016-01-01 2017,Ceara,Fevereiro,9.0,2017-01-01 -1998,Ceara,Mar�o,0.0,1998-01-01 -1999,Ceara,Mar�o,30.0,1999-01-01 -2000,Ceara,Mar�o,9.0,2000-01-01 -2001,Ceara,Mar�o,0.0,2001-01-01 -2002,Ceara,Mar�o,17.0,2002-01-01 -2003,Ceara,Mar�o,3.0,2003-01-01 -2004,Ceara,Mar�o,1.0,2004-01-01 -2005,Ceara,Mar�o,5.0,2005-01-01 -2006,Ceara,Mar�o,3.0,2006-01-01 -2007,Ceara,Mar�o,2.0,2007-01-01 -2008,Ceara,Mar�o,2.0,2008-01-01 -2009,Ceara,Mar�o,3.0,2009-01-01 -2010,Ceara,Mar�o,40.0,2010-01-01 -2011,Ceara,Mar�o,6.0,2011-01-01 -2012,Ceara,Mar�o,10.0,2012-01-01 -2013,Ceara,Mar�o,17.0,2013-01-01 -2014,Ceara,Mar�o,6.0,2014-01-01 -2015,Ceara,Mar�o,20.0,2015-01-01 -2016,Ceara,Mar�o,4.0,2016-01-01 -2017,Ceara,Mar�o,4.0,2017-01-01 +1998,Ceara,Março,0.0,1998-01-01 +1999,Ceara,Março,30.0,1999-01-01 +2000,Ceara,Março,9.0,2000-01-01 +2001,Ceara,Março,0.0,2001-01-01 +2002,Ceara,Março,17.0,2002-01-01 +2003,Ceara,Março,3.0,2003-01-01 +2004,Ceara,Março,1.0,2004-01-01 +2005,Ceara,Março,5.0,2005-01-01 +2006,Ceara,Março,3.0,2006-01-01 +2007,Ceara,Março,2.0,2007-01-01 +2008,Ceara,Março,2.0,2008-01-01 +2009,Ceara,Março,3.0,2009-01-01 +2010,Ceara,Março,40.0,2010-01-01 +2011,Ceara,Março,6.0,2011-01-01 +2012,Ceara,Março,10.0,2012-01-01 +2013,Ceara,Março,17.0,2013-01-01 +2014,Ceara,Março,6.0,2014-01-01 +2015,Ceara,Março,20.0,2015-01-01 +2016,Ceara,Março,4.0,2016-01-01 +2017,Ceara,Março,4.0,2017-01-01 1998,Ceara,Abril,0.0,1998-01-01 1999,Ceara,Abril,6.0,1999-01-01 2000,Ceara,Abril,15.0,2000-01-01 @@ -1464,26 +1464,26 @@ year,state,month,number,date 2015,Distrito Federal,Fevereiro,0.0,2015-01-01 2016,Distrito Federal,Fevereiro,2.0,2016-01-01 2017,Distrito Federal,Fevereiro,0.0,2017-01-01 -1998,Distrito Federal,Mar�o,0.0,1998-01-01 -1999,Distrito Federal,Mar�o,0.0,1999-01-01 -2000,Distrito Federal,Mar�o,0.0,2000-01-01 -2001,Distrito Federal,Mar�o,0.0,2001-01-01 -2002,Distrito Federal,Mar�o,0.0,2002-01-01 -2003,Distrito Federal,Mar�o,0.0,2003-01-01 -2004,Distrito Federal,Mar�o,0.0,2004-01-01 -2005,Distrito Federal,Mar�o,0.0,2005-01-01 -2006,Distrito Federal,Mar�o,0.0,2006-01-01 -2007,Distrito Federal,Mar�o,1.0,2007-01-01 -2008,Distrito Federal,Mar�o,2.0,2008-01-01 -2009,Distrito Federal,Mar�o,0.0,2009-01-01 -2010,Distrito Federal,Mar�o,0.0,2010-01-01 -2011,Distrito Federal,Mar�o,0.0,2011-01-01 -2012,Distrito Federal,Mar�o,0.0,2012-01-01 -2013,Distrito Federal,Mar�o,1.0,2013-01-01 -2014,Distrito Federal,Mar�o,1.0,2014-01-01 -2015,Distrito Federal,Mar�o,0.0,2015-01-01 -2016,Distrito Federal,Mar�o,1.0,2016-01-01 -2017,Distrito Federal,Mar�o,0.0,2017-01-01 +1998,Distrito Federal,Março,0.0,1998-01-01 +1999,Distrito Federal,Março,0.0,1999-01-01 +2000,Distrito Federal,Março,0.0,2000-01-01 +2001,Distrito Federal,Março,0.0,2001-01-01 +2002,Distrito Federal,Março,0.0,2002-01-01 +2003,Distrito Federal,Março,0.0,2003-01-01 +2004,Distrito Federal,Março,0.0,2004-01-01 +2005,Distrito Federal,Março,0.0,2005-01-01 +2006,Distrito Federal,Março,0.0,2006-01-01 +2007,Distrito Federal,Março,1.0,2007-01-01 +2008,Distrito Federal,Março,2.0,2008-01-01 +2009,Distrito Federal,Março,0.0,2009-01-01 +2010,Distrito Federal,Março,0.0,2010-01-01 +2011,Distrito Federal,Março,0.0,2011-01-01 +2012,Distrito Federal,Março,0.0,2012-01-01 +2013,Distrito Federal,Março,1.0,2013-01-01 +2014,Distrito Federal,Março,1.0,2014-01-01 +2015,Distrito Federal,Março,0.0,2015-01-01 +2016,Distrito Federal,Março,1.0,2016-01-01 +2017,Distrito Federal,Março,0.0,2017-01-01 1998,Distrito Federal,Abril,0.0,1998-01-01 1999,Distrito Federal,Abril,0.0,1999-01-01 2000,Distrito Federal,Abril,0.0,2000-01-01 @@ -1702,26 +1702,26 @@ year,state,month,number,date 2015,Espirito Santo,Fevereiro,60.0,2015-01-01 2016,Espirito Santo,Fevereiro,24.0,2016-01-01 2017,Espirito Santo,Fevereiro,8.0,2017-01-01 -1998,Espirito Santo,Mar�o,0.0,1998-01-01 -1999,Espirito Santo,Mar�o,8.0,1999-01-01 -2000,Espirito Santo,Mar�o,1.0,2000-01-01 -2001,Espirito Santo,Mar�o,2.0,2001-01-01 -2002,Espirito Santo,Mar�o,4.0,2002-01-01 -2003,Espirito Santo,Mar�o,28.0,2003-01-01 -2004,Espirito Santo,Mar�o,3.0,2004-01-01 -2005,Espirito Santo,Mar�o,3.0,2005-01-01 -2006,Espirito Santo,Mar�o,14.0,2006-01-01 -2007,Espirito Santo,Mar�o,9.0,2007-01-01 -2008,Espirito Santo,Mar�o,5.0,2008-01-01 -2009,Espirito Santo,Mar�o,11.0,2009-01-01 -2010,Espirito Santo,Mar�o,3.0,2010-01-01 -2011,Espirito Santo,Mar�o,5.0,2011-01-01 -2012,Espirito Santo,Mar�o,35.0,2012-01-01 -2013,Espirito Santo,Mar�o,13.0,2013-01-01 -2014,Espirito Santo,Mar�o,15.0,2014-01-01 -2015,Espirito Santo,Mar�o,25.0,2015-01-01 -2016,Espirito Santo,Mar�o,107.0,2016-01-01 -2017,Espirito Santo,Mar�o,9.0,2017-01-01 +1998,Espirito Santo,Março,0.0,1998-01-01 +1999,Espirito Santo,Março,8.0,1999-01-01 +2000,Espirito Santo,Março,1.0,2000-01-01 +2001,Espirito Santo,Março,2.0,2001-01-01 +2002,Espirito Santo,Março,4.0,2002-01-01 +2003,Espirito Santo,Março,28.0,2003-01-01 +2004,Espirito Santo,Março,3.0,2004-01-01 +2005,Espirito Santo,Março,3.0,2005-01-01 +2006,Espirito Santo,Março,14.0,2006-01-01 +2007,Espirito Santo,Março,9.0,2007-01-01 +2008,Espirito Santo,Março,5.0,2008-01-01 +2009,Espirito Santo,Março,11.0,2009-01-01 +2010,Espirito Santo,Março,3.0,2010-01-01 +2011,Espirito Santo,Março,5.0,2011-01-01 +2012,Espirito Santo,Março,35.0,2012-01-01 +2013,Espirito Santo,Março,13.0,2013-01-01 +2014,Espirito Santo,Março,15.0,2014-01-01 +2015,Espirito Santo,Março,25.0,2015-01-01 +2016,Espirito Santo,Março,107.0,2016-01-01 +2017,Espirito Santo,Março,9.0,2017-01-01 1998,Espirito Santo,Abril,0.0,1998-01-01 1999,Espirito Santo,Abril,2.0,1999-01-01 2000,Espirito Santo,Abril,3.0,2000-01-01 @@ -1941,26 +1941,26 @@ year,state,month,number,date 2015,Goias,Fevereiro,53.0,2015-01-01 2016,Goias,Fevereiro,117.0,2016-01-01 2017,Goias,Fevereiro,75.0,2017-01-01 -1998,Goias,Mar�o,0.0,1998-01-01 -1999,Goias,Mar�o,1.0,1999-01-01 -2000,Goias,Mar�o,4.0,2000-01-01 -2001,Goias,Mar�o,10.0,2001-01-01 -2002,Goias,Mar�o,48.0,2002-01-01 -2003,Goias,Mar�o,29.0,2003-01-01 -2004,Goias,Mar�o,36.0,2004-01-01 -2005,Goias,Mar�o,36.0,2005-01-01 -2006,Goias,Mar�o,29.0,2006-01-01 -2007,Goias,Mar�o,49.0,2007-01-01 -2008,Goias,Mar�o,20.0,2008-01-01 -2009,Goias,Mar�o,32.0,2009-01-01 -2010,Goias,Mar�o,65.0,2010-01-01 -2011,Goias,Mar�o,19.0,2011-01-01 -2012,Goias,Mar�o,57.0,2012-01-01 -2013,Goias,Mar�o,79.0,2013-01-01 -2014,Goias,Mar�o,80.0,2014-01-01 -2015,Goias,Mar�o,76.0,2015-01-01 -2016,Goias,Mar�o,136.0,2016-01-01 -2017,Goias,Mar�o,115.0,2017-01-01 +1998,Goias,Março,0.0,1998-01-01 +1999,Goias,Março,1.0,1999-01-01 +2000,Goias,Março,4.0,2000-01-01 +2001,Goias,Março,10.0,2001-01-01 +2002,Goias,Março,48.0,2002-01-01 +2003,Goias,Março,29.0,2003-01-01 +2004,Goias,Março,36.0,2004-01-01 +2005,Goias,Março,36.0,2005-01-01 +2006,Goias,Março,29.0,2006-01-01 +2007,Goias,Março,49.0,2007-01-01 +2008,Goias,Março,20.0,2008-01-01 +2009,Goias,Março,32.0,2009-01-01 +2010,Goias,Março,65.0,2010-01-01 +2011,Goias,Março,19.0,2011-01-01 +2012,Goias,Março,57.0,2012-01-01 +2013,Goias,Março,79.0,2013-01-01 +2014,Goias,Março,80.0,2014-01-01 +2015,Goias,Março,76.0,2015-01-01 +2016,Goias,Março,136.0,2016-01-01 +2017,Goias,Março,115.0,2017-01-01 1998,Goias,Abril,0.0,1998-01-01 1999,Goias,Abril,25.0,1999-01-01 2000,Goias,Abril,16.0,2000-01-01 @@ -2180,26 +2180,26 @@ year,state,month,number,date 2015,Maranhao,Fevereiro,95.0,2015-01-01 2016,Maranhao,Fevereiro,318.0,2016-01-01 2017,Maranhao,Fevereiro,47.0,2017-01-01 -1998,Maranhao,Mar�o,0.0,1998-01-01 -1999,Maranhao,Mar�o,17.0,1999-01-01 -2000,Maranhao,Mar�o,6.0,2000-01-01 -2001,Maranhao,Mar�o,0.0,2001-01-01 -2002,Maranhao,Mar�o,16.0,2002-01-01 -2003,Maranhao,Mar�o,16.0,2003-01-01 -2004,Maranhao,Mar�o,18.0,2004-01-01 -2005,Maranhao,Mar�o,9.0,2005-01-01 -2006,Maranhao,Mar�o,14.0,2006-01-01 -2007,Maranhao,Mar�o,52.0,2007-01-01 -2008,Maranhao,Mar�o,4.0,2008-01-01 -2009,Maranhao,Mar�o,33.0,2009-01-01 -2010,Maranhao,Mar�o,82.0,2010-01-01 -2011,Maranhao,Mar�o,22.0,2011-01-01 -2012,Maranhao,Mar�o,49.0,2012-01-01 -2013,Maranhao,Mar�o,30.0,2013-01-01 -2014,Maranhao,Mar�o,59.0,2014-01-01 -2015,Maranhao,Mar�o,53.0,2015-01-01 -2016,Maranhao,Mar�o,113.0,2016-01-01 -2017,Maranhao,Mar�o,32.0,2017-01-01 +1998,Maranhao,Março,0.0,1998-01-01 +1999,Maranhao,Março,17.0,1999-01-01 +2000,Maranhao,Março,6.0,2000-01-01 +2001,Maranhao,Março,0.0,2001-01-01 +2002,Maranhao,Março,16.0,2002-01-01 +2003,Maranhao,Março,16.0,2003-01-01 +2004,Maranhao,Março,18.0,2004-01-01 +2005,Maranhao,Março,9.0,2005-01-01 +2006,Maranhao,Março,14.0,2006-01-01 +2007,Maranhao,Março,52.0,2007-01-01 +2008,Maranhao,Março,4.0,2008-01-01 +2009,Maranhao,Março,33.0,2009-01-01 +2010,Maranhao,Março,82.0,2010-01-01 +2011,Maranhao,Março,22.0,2011-01-01 +2012,Maranhao,Março,49.0,2012-01-01 +2013,Maranhao,Março,30.0,2013-01-01 +2014,Maranhao,Março,59.0,2014-01-01 +2015,Maranhao,Março,53.0,2015-01-01 +2016,Maranhao,Março,113.0,2016-01-01 +2017,Maranhao,Março,32.0,2017-01-01 1998,Maranhao,Abril,0.0,1998-01-01 1999,Maranhao,Abril,4.0,1999-01-01 2000,Maranhao,Abril,6.0,2000-01-01 @@ -2419,26 +2419,26 @@ year,state,month,number,date 2015,Mato Grosso,Fevereiro,542.0,2015-01-01 2016,Mato Grosso,Fevereiro,1.05,2016-01-01 2017,Mato Grosso,Fevereiro,337.0,2017-01-01 -1998,Mato Grosso,Mar�o,0.0,1998-01-01 -1999,Mato Grosso,Mar�o,18.0,1999-01-01 -2000,Mato Grosso,Mar�o,60.0,2000-01-01 -2001,Mato Grosso,Mar�o,24.0,2001-01-01 -2002,Mato Grosso,Mar�o,103.0,2002-01-01 -2003,Mato Grosso,Mar�o,554.0,2003-01-01 -2004,Mato Grosso,Mar�o,743.0,2004-01-01 -2005,Mato Grosso,Mar�o,362.0,2005-01-01 -2006,Mato Grosso,Mar�o,448.0,2006-01-01 -2007,Mato Grosso,Mar�o,476.0,2007-01-01 -2008,Mato Grosso,Mar�o,180.0,2008-01-01 -2009,Mato Grosso,Mar�o,325.0,2009-01-01 -2010,Mato Grosso,Mar�o,605.0,2010-01-01 -2011,Mato Grosso,Mar�o,252.0,2011-01-01 -2012,Mato Grosso,Mar�o,423.0,2012-01-01 -2013,Mato Grosso,Mar�o,563.0,2013-01-01 -2014,Mato Grosso,Mar�o,655.0,2014-01-01 -2015,Mato Grosso,Mar�o,705.0,2015-01-01 -2016,Mato Grosso,Mar�o,1.085,2016-01-01 -2017,Mato Grosso,Mar�o,719.0,2017-01-01 +1998,Mato Grosso,Março,0.0,1998-01-01 +1999,Mato Grosso,Março,18.0,1999-01-01 +2000,Mato Grosso,Março,60.0,2000-01-01 +2001,Mato Grosso,Março,24.0,2001-01-01 +2002,Mato Grosso,Março,103.0,2002-01-01 +2003,Mato Grosso,Março,554.0,2003-01-01 +2004,Mato Grosso,Março,743.0,2004-01-01 +2005,Mato Grosso,Março,362.0,2005-01-01 +2006,Mato Grosso,Março,448.0,2006-01-01 +2007,Mato Grosso,Março,476.0,2007-01-01 +2008,Mato Grosso,Março,180.0,2008-01-01 +2009,Mato Grosso,Março,325.0,2009-01-01 +2010,Mato Grosso,Março,605.0,2010-01-01 +2011,Mato Grosso,Março,252.0,2011-01-01 +2012,Mato Grosso,Março,423.0,2012-01-01 +2013,Mato Grosso,Março,563.0,2013-01-01 +2014,Mato Grosso,Março,655.0,2014-01-01 +2015,Mato Grosso,Março,705.0,2015-01-01 +2016,Mato Grosso,Março,1.085,2016-01-01 +2017,Mato Grosso,Março,719.0,2017-01-01 1998,Mato Grosso,Abril,0.0,1998-01-01 1999,Mato Grosso,Abril,76.0,1999-01-01 2000,Mato Grosso,Abril,59.0,2000-01-01 @@ -2658,26 +2658,26 @@ year,state,month,number,date 2015,Mato Grosso,Fevereiro,148.0,2015-01-01 2016,Mato Grosso,Fevereiro,187.0,2016-01-01 2017,Mato Grosso,Fevereiro,165.0,2017-01-01 -1998,Mato Grosso,Mar�o,0.0,1998-01-01 -1999,Mato Grosso,Mar�o,100.0,1999-01-01 -2000,Mato Grosso,Mar�o,23.0,2000-01-01 -2001,Mato Grosso,Mar�o,39.0,2001-01-01 -2002,Mato Grosso,Mar�o,115.0,2002-01-01 -2003,Mato Grosso,Mar�o,137.0,2003-01-01 -2004,Mato Grosso,Mar�o,225.0,2004-01-01 -2005,Mato Grosso,Mar�o,158.0,2005-01-01 -2006,Mato Grosso,Mar�o,137.0,2006-01-01 -2007,Mato Grosso,Mar�o,137.0,2007-01-01 -2008,Mato Grosso,Mar�o,97.0,2008-01-01 -2009,Mato Grosso,Mar�o,118.0,2009-01-01 -2010,Mato Grosso,Mar�o,149.0,2010-01-01 -2011,Mato Grosso,Mar�o,37.0,2011-01-01 -2012,Mato Grosso,Mar�o,169.0,2012-01-01 -2013,Mato Grosso,Mar�o,158.0,2013-01-01 -2014,Mato Grosso,Mar�o,95.0,2014-01-01 -2015,Mato Grosso,Mar�o,190.0,2015-01-01 -2016,Mato Grosso,Mar�o,143.0,2016-01-01 -2017,Mato Grosso,Mar�o,170.0,2017-01-01 +1998,Mato Grosso,Março,0.0,1998-01-01 +1999,Mato Grosso,Março,100.0,1999-01-01 +2000,Mato Grosso,Março,23.0,2000-01-01 +2001,Mato Grosso,Março,39.0,2001-01-01 +2002,Mato Grosso,Março,115.0,2002-01-01 +2003,Mato Grosso,Março,137.0,2003-01-01 +2004,Mato Grosso,Março,225.0,2004-01-01 +2005,Mato Grosso,Março,158.0,2005-01-01 +2006,Mato Grosso,Março,137.0,2006-01-01 +2007,Mato Grosso,Março,137.0,2007-01-01 +2008,Mato Grosso,Março,97.0,2008-01-01 +2009,Mato Grosso,Março,118.0,2009-01-01 +2010,Mato Grosso,Março,149.0,2010-01-01 +2011,Mato Grosso,Março,37.0,2011-01-01 +2012,Mato Grosso,Março,169.0,2012-01-01 +2013,Mato Grosso,Março,158.0,2013-01-01 +2014,Mato Grosso,Março,95.0,2014-01-01 +2015,Mato Grosso,Março,190.0,2015-01-01 +2016,Mato Grosso,Março,143.0,2016-01-01 +2017,Mato Grosso,Março,170.0,2017-01-01 1998,Mato Grosso,Abril,0.0,1998-01-01 1999,Mato Grosso,Abril,97.0,1999-01-01 2000,Mato Grosso,Abril,90.0,2000-01-01 @@ -2897,26 +2897,26 @@ year,state,month,number,date 2015,Minas Gerais,Fevereiro,84.0,2015-01-01 2016,Minas Gerais,Fevereiro,207.0,2016-01-01 2017,Minas Gerais,Fevereiro,61.0,2017-01-01 -1998,Minas Gerais,Mar�o,0.0,1998-01-01 -1999,Minas Gerais,Mar�o,13.0,1999-01-01 -2000,Minas Gerais,Mar�o,24.0,2000-01-01 -2001,Minas Gerais,Mar�o,13.0,2001-01-01 -2002,Minas Gerais,Mar�o,62.0,2002-01-01 -2003,Minas Gerais,Mar�o,81.0,2003-01-01 -2004,Minas Gerais,Mar�o,81.0,2004-01-01 -2005,Minas Gerais,Mar�o,107.0,2005-01-01 -2006,Minas Gerais,Mar�o,69.0,2006-01-01 -2007,Minas Gerais,Mar�o,155.0,2007-01-01 -2008,Minas Gerais,Mar�o,61.0,2008-01-01 -2009,Minas Gerais,Mar�o,106.0,2009-01-01 -2010,Minas Gerais,Mar�o,109.0,2010-01-01 -2011,Minas Gerais,Mar�o,67.0,2011-01-01 -2012,Minas Gerais,Mar�o,152.0,2012-01-01 -2013,Minas Gerais,Mar�o,110.0,2013-01-01 -2014,Minas Gerais,Mar�o,77.0,2014-01-01 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1999,Minas Gerais,Abril,27.0,1999-01-01 2000,Minas Gerais,Abril,49.0,2000-01-01 @@ -3096,245 +3096,245 @@ year,state,month,number,date 2014,Minas Gerais,Dezembro,155.0,2014-01-01 2015,Minas Gerais,Dezembro,295.0,2015-01-01 2016,Minas Gerais,Dezembro,91.0,2016-01-01 -1998,Par�,Janeiro,0.0,1998-01-01 -1999,Par�,Janeiro,87.0,1999-01-01 -2000,Par�,Janeiro,30.0,2000-01-01 -2001,Par�,Janeiro,34.0,2001-01-01 -2002,Par�,Janeiro,144.0,2002-01-01 -2003,Par�,Janeiro,900.0,2003-01-01 -2004,Par�,Janeiro,579.0,2004-01-01 -2005,Par�,Janeiro,1.768,2005-01-01 -2006,Par�,Janeiro,403.0,2006-01-01 -2007,Par�,Janeiro,467.0,2007-01-01 -2008,Par�,Janeiro,131.0,2008-01-01 -2009,Par�,Janeiro,241.0,2009-01-01 -2010,Par�,Janeiro,437.0,2010-01-01 -2011,Par�,Janeiro,108.0,2011-01-01 -2012,Par�,Janeiro,392.0,2012-01-01 -2013,Par�,Janeiro,167.0,2013-01-01 -2014,Par�,Janeiro,251.0,2014-01-01 -2015,Par�,Janeiro,725.0,2015-01-01 -2016,Par�,Janeiro,1.322,2016-01-01 -2017,Par�,Janeiro,219.0,2017-01-01 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2017,Rondonia,Fevereiro,10.0,2017-01-01 -1998,Rondonia,Mar�o,0.0,1998-01-01 -1999,Rondonia,Mar�o,8.0,1999-01-01 -2000,Rondonia,Mar�o,9.0,2000-01-01 -2001,Rondonia,Mar�o,0.0,2001-01-01 -2002,Rondonia,Mar�o,1.0,2002-01-01 -2003,Rondonia,Mar�o,13.0,2003-01-01 -2004,Rondonia,Mar�o,20.0,2004-01-01 -2005,Rondonia,Mar�o,17.0,2005-01-01 -2006,Rondonia,Mar�o,15.0,2006-01-01 -2007,Rondonia,Mar�o,20.0,2007-01-01 -2008,Rondonia,Mar�o,2.0,2008-01-01 -2009,Rondonia,Mar�o,12.0,2009-01-01 -2010,Rondonia,Mar�o,11.0,2010-01-01 -2011,Rondonia,Mar�o,6.0,2011-01-01 -2012,Rondonia,Mar�o,6.0,2012-01-01 -2013,Rondonia,Mar�o,23.0,2013-01-01 -2014,Rondonia,Mar�o,13.0,2014-01-01 -2015,Rondonia,Mar�o,15.0,2015-01-01 -2016,Rondonia,Mar�o,25.0,2016-01-01 -2017,Rondonia,Mar�o,21.0,2017-01-01 +1998,Rondonia,Março,0.0,1998-01-01 +1999,Rondonia,Março,8.0,1999-01-01 +2000,Rondonia,Março,9.0,2000-01-01 +2001,Rondonia,Março,0.0,2001-01-01 +2002,Rondonia,Março,1.0,2002-01-01 +2003,Rondonia,Março,13.0,2003-01-01 +2004,Rondonia,Março,20.0,2004-01-01 +2005,Rondonia,Março,17.0,2005-01-01 +2006,Rondonia,Março,15.0,2006-01-01 +2007,Rondonia,Março,20.0,2007-01-01 +2008,Rondonia,Março,2.0,2008-01-01 +2009,Rondonia,Março,12.0,2009-01-01 +2010,Rondonia,Março,11.0,2010-01-01 +2011,Rondonia,Março,6.0,2011-01-01 +2012,Rondonia,Março,6.0,2012-01-01 +2013,Rondonia,Março,23.0,2013-01-01 +2014,Rondonia,Março,13.0,2014-01-01 +2015,Rondonia,Março,15.0,2015-01-01 +2016,Rondonia,Março,25.0,2016-01-01 +2017,Rondonia,Março,21.0,2017-01-01 1998,Rondonia,Abril,0.0,1998-01-01 1999,Rondonia,Abril,3.0,1999-01-01 2000,Rondonia,Abril,0.0,2000-01-01 @@ -5285,26 +5285,26 @@ year,state,month,number,date 2015,Roraima,Fevereiro,418.0,2015-01-01 2016,Roraima,Fevereiro,171.0,2016-01-01 2017,Roraima,Fevereiro,129.0,2017-01-01 -1998,Roraima,Mar�o,0.0,1998-01-01 -1999,Roraima,Mar�o,98.0,1999-01-01 -2000,Roraima,Mar�o,125.0,2000-01-01 -2001,Roraima,Mar�o,1.108,2001-01-01 -2002,Roraima,Mar�o,820.0,2002-01-01 -2003,Roraima,Mar�o,1.131,2003-01-01 -2004,Roraima,Mar�o,276.0,2004-01-01 -2005,Roraima,Mar�o,244.0,2005-01-01 -2006,Roraima,Mar�o,385.0,2006-01-01 -2007,Roraima,Mar�o,665.0,2007-01-01 -2008,Roraima,Mar�o,422.0,2008-01-01 -2009,Roraima,Mar�o,176.0,2009-01-01 -2010,Roraima,Mar�o,417.0,2010-01-01 -2011,Roraima,Mar�o,91.0,2011-01-01 -2012,Roraima,Mar�o,99.0,2012-01-01 -2013,Roraima,Mar�o,179.0,2013-01-01 -2014,Roraima,Mar�o,494.0,2014-01-01 -2015,Roraima,Mar�o,163.0,2015-01-01 -2016,Roraima,Mar�o,1.081,2016-01-01 -2017,Roraima,Mar�o,216.0,2017-01-01 +1998,Roraima,Março,0.0,1998-01-01 +1999,Roraima,Março,98.0,1999-01-01 +2000,Roraima,Março,125.0,2000-01-01 +2001,Roraima,Março,1.108,2001-01-01 +2002,Roraima,Março,820.0,2002-01-01 +2003,Roraima,Março,1.131,2003-01-01 +2004,Roraima,Março,276.0,2004-01-01 +2005,Roraima,Março,244.0,2005-01-01 +2006,Roraima,Março,385.0,2006-01-01 +2007,Roraima,Março,665.0,2007-01-01 +2008,Roraima,Março,422.0,2008-01-01 +2009,Roraima,Março,176.0,2009-01-01 +2010,Roraima,Março,417.0,2010-01-01 +2011,Roraima,Março,91.0,2011-01-01 +2012,Roraima,Março,99.0,2012-01-01 +2013,Roraima,Março,179.0,2013-01-01 +2014,Roraima,Março,494.0,2014-01-01 +2015,Roraima,Março,163.0,2015-01-01 +2016,Roraima,Março,1.081,2016-01-01 +2017,Roraima,Março,216.0,2017-01-01 1998,Roraima,Abril,0.0,1998-01-01 1999,Roraima,Abril,16.0,1999-01-01 2000,Roraima,Abril,0.0,2000-01-01 @@ -5524,26 +5524,26 @@ year,state,month,number,date 2015,Santa Catarina,Fevereiro,23.0,2015-01-01 2016,Santa Catarina,Fevereiro,10.0,2016-01-01 2017,Santa Catarina,Fevereiro,31.0,2017-01-01 -1998,Santa Catarina,Mar�o,0.0,1998-01-01 -1999,Santa Catarina,Mar�o,7.0,1999-01-01 -2000,Santa Catarina,Mar�o,46.0,2000-01-01 -2001,Santa Catarina,Mar�o,1.0,2001-01-01 -2002,Santa Catarina,Mar�o,12.0,2002-01-01 -2003,Santa Catarina,Mar�o,58.0,2003-01-01 -2004,Santa Catarina,Mar�o,73.0,2004-01-01 -2005,Santa Catarina,Mar�o,92.0,2005-01-01 -2006,Santa Catarina,Mar�o,48.0,2006-01-01 -2007,Santa Catarina,Mar�o,23.0,2007-01-01 -2008,Santa Catarina,Mar�o,14.0,2008-01-01 -2009,Santa Catarina,Mar�o,33.0,2009-01-01 -2010,Santa Catarina,Mar�o,9.0,2010-01-01 -2011,Santa Catarina,Mar�o,8.0,2011-01-01 -2012,Santa Catarina,Mar�o,65.0,2012-01-01 -2013,Santa Catarina,Mar�o,14.0,2013-01-01 -2014,Santa Catarina,Mar�o,15.0,2014-01-01 -2015,Santa Catarina,Mar�o,37.0,2015-01-01 -2016,Santa Catarina,Mar�o,21.0,2016-01-01 -2017,Santa Catarina,Mar�o,12.0,2017-01-01 +1998,Santa Catarina,Março,0.0,1998-01-01 +1999,Santa Catarina,Março,7.0,1999-01-01 +2000,Santa Catarina,Março,46.0,2000-01-01 +2001,Santa Catarina,Março,1.0,2001-01-01 +2002,Santa Catarina,Março,12.0,2002-01-01 +2003,Santa Catarina,Março,58.0,2003-01-01 +2004,Santa Catarina,Março,73.0,2004-01-01 +2005,Santa Catarina,Março,92.0,2005-01-01 +2006,Santa Catarina,Março,48.0,2006-01-01 +2007,Santa Catarina,Março,23.0,2007-01-01 +2008,Santa Catarina,Março,14.0,2008-01-01 +2009,Santa Catarina,Março,33.0,2009-01-01 +2010,Santa Catarina,Março,9.0,2010-01-01 +2011,Santa Catarina,Março,8.0,2011-01-01 +2012,Santa Catarina,Março,65.0,2012-01-01 +2013,Santa Catarina,Março,14.0,2013-01-01 +2014,Santa Catarina,Março,15.0,2014-01-01 +2015,Santa Catarina,Março,37.0,2015-01-01 +2016,Santa Catarina,Março,21.0,2016-01-01 +2017,Santa Catarina,Março,12.0,2017-01-01 1998,Santa Catarina,Abril,0.0,1998-01-01 1999,Santa Catarina,Abril,0.0,1999-01-01 2000,Santa Catarina,Abril,7.0,2000-01-01 @@ -5762,26 +5762,26 @@ year,state,month,number,date 2015,Sao Paulo,Fevereiro,69.0,2015-01-01 2016,Sao Paulo,Fevereiro,188.0,2016-01-01 2017,Sao Paulo,Fevereiro,107.0,2017-01-01 -1998,Sao Paulo,Mar�o,0.0,1998-01-01 -1999,Sao Paulo,Mar�o,78.0,1999-01-01 -2000,Sao Paulo,Mar�o,26.0,2000-01-01 -2001,Sao Paulo,Mar�o,35.0,2001-01-01 -2002,Sao Paulo,Mar�o,111.0,2002-01-01 -2003,Sao Paulo,Mar�o,86.0,2003-01-01 -2004,Sao Paulo,Mar�o,73.0,2004-01-01 -2005,Sao Paulo,Mar�o,73.0,2005-01-01 -2006,Sao Paulo,Mar�o,106.0,2006-01-01 -2007,Sao Paulo,Mar�o,152.0,2007-01-01 -2008,Sao Paulo,Mar�o,72.0,2008-01-01 -2009,Sao Paulo,Mar�o,55.0,2009-01-01 -2010,Sao Paulo,Mar�o,111.0,2010-01-01 -2011,Sao Paulo,Mar�o,45.0,2011-01-01 -2012,Sao Paulo,Mar�o,99.0,2012-01-01 -2013,Sao Paulo,Mar�o,57.0,2013-01-01 -2014,Sao Paulo,Mar�o,75.0,2014-01-01 -2015,Sao Paulo,Mar�o,68.0,2015-01-01 -2016,Sao Paulo,Mar�o,82.0,2016-01-01 -2017,Sao Paulo,Mar�o,103.0,2017-01-01 +1998,Sao Paulo,Março,0.0,1998-01-01 +1999,Sao Paulo,Março,78.0,1999-01-01 +2000,Sao Paulo,Março,26.0,2000-01-01 +2001,Sao Paulo,Março,35.0,2001-01-01 +2002,Sao Paulo,Março,111.0,2002-01-01 +2003,Sao Paulo,Março,86.0,2003-01-01 +2004,Sao Paulo,Março,73.0,2004-01-01 +2005,Sao Paulo,Março,73.0,2005-01-01 +2006,Sao Paulo,Março,106.0,2006-01-01 +2007,Sao Paulo,Março,152.0,2007-01-01 +2008,Sao Paulo,Março,72.0,2008-01-01 +2009,Sao Paulo,Março,55.0,2009-01-01 +2010,Sao Paulo,Março,111.0,2010-01-01 +2011,Sao Paulo,Março,45.0,2011-01-01 +2012,Sao Paulo,Março,99.0,2012-01-01 +2013,Sao Paulo,Março,57.0,2013-01-01 +2014,Sao Paulo,Março,75.0,2014-01-01 +2015,Sao Paulo,Março,68.0,2015-01-01 +2016,Sao Paulo,Março,82.0,2016-01-01 +2017,Sao Paulo,Março,103.0,2017-01-01 1998,Sao Paulo,Abril,0.0,1998-01-01 1999,Sao Paulo,Abril,291.0,1999-01-01 2000,Sao Paulo,Abril,102.0,2000-01-01 @@ -6001,26 +6001,26 @@ year,state,month,number,date 2015,Sergipe,Fevereiro,40.0,2015-01-01 2016,Sergipe,Fevereiro,22.0,2016-01-01 2017,Sergipe,Fevereiro,18.0,2017-01-01 -1998,Sergipe,Mar�o,0.0,1998-01-01 -1999,Sergipe,Mar�o,18.0,1999-01-01 -2000,Sergipe,Mar�o,6.0,2000-01-01 -2001,Sergipe,Mar�o,1.0,2001-01-01 -2002,Sergipe,Mar�o,7.0,2002-01-01 -2003,Sergipe,Mar�o,62.0,2003-01-01 -2004,Sergipe,Mar�o,36.0,2004-01-01 -2005,Sergipe,Mar�o,28.0,2005-01-01 -2006,Sergipe,Mar�o,41.0,2006-01-01 -2007,Sergipe,Mar�o,2.0,2007-01-01 -2008,Sergipe,Mar�o,43.0,2008-01-01 -2009,Sergipe,Mar�o,29.0,2009-01-01 -2010,Sergipe,Mar�o,44.0,2010-01-01 -2011,Sergipe,Mar�o,47.0,2011-01-01 -2012,Sergipe,Mar�o,71.0,2012-01-01 -2013,Sergipe,Mar�o,67.0,2013-01-01 -2014,Sergipe,Mar�o,33.0,2014-01-01 -2015,Sergipe,Mar�o,49.0,2015-01-01 -2016,Sergipe,Mar�o,26.0,2016-01-01 -2017,Sergipe,Mar�o,16.0,2017-01-01 +1998,Sergipe,Março,0.0,1998-01-01 +1999,Sergipe,Março,18.0,1999-01-01 +2000,Sergipe,Março,6.0,2000-01-01 +2001,Sergipe,Março,1.0,2001-01-01 +2002,Sergipe,Março,7.0,2002-01-01 +2003,Sergipe,Março,62.0,2003-01-01 +2004,Sergipe,Março,36.0,2004-01-01 +2005,Sergipe,Março,28.0,2005-01-01 +2006,Sergipe,Março,41.0,2006-01-01 +2007,Sergipe,Março,2.0,2007-01-01 +2008,Sergipe,Março,43.0,2008-01-01 +2009,Sergipe,Março,29.0,2009-01-01 +2010,Sergipe,Março,44.0,2010-01-01 +2011,Sergipe,Março,47.0,2011-01-01 +2012,Sergipe,Março,71.0,2012-01-01 +2013,Sergipe,Março,67.0,2013-01-01 +2014,Sergipe,Março,33.0,2014-01-01 +2015,Sergipe,Março,49.0,2015-01-01 +2016,Sergipe,Março,26.0,2016-01-01 +2017,Sergipe,Março,16.0,2017-01-01 1998,Sergipe,Abril,0.0,1998-01-01 1999,Sergipe,Abril,6.0,1999-01-01 2000,Sergipe,Abril,1.0,2000-01-01 @@ -6239,26 +6239,26 @@ year,state,month,number,date 2015,Tocantins,Fevereiro,65.0,2015-01-01 2016,Tocantins,Fevereiro,383.0,2016-01-01 2017,Tocantins,Fevereiro,25.0,2017-01-01 -1998,Tocantins,Mar�o,0.0,1998-01-01 -1999,Tocantins,Mar�o,1.0,1999-01-01 -2000,Tocantins,Mar�o,4.0,2000-01-01 -2001,Tocantins,Mar�o,0.0,2001-01-01 -2002,Tocantins,Mar�o,33.0,2002-01-01 -2003,Tocantins,Mar�o,19.0,2003-01-01 -2004,Tocantins,Mar�o,23.0,2004-01-01 -2005,Tocantins,Mar�o,13.0,2005-01-01 -2006,Tocantins,Mar�o,11.0,2006-01-01 -2007,Tocantins,Mar�o,99.0,2007-01-01 -2008,Tocantins,Mar�o,4.0,2008-01-01 -2009,Tocantins,Mar�o,23.0,2009-01-01 -2010,Tocantins,Mar�o,120.0,2010-01-01 -2011,Tocantins,Mar�o,16.0,2011-01-01 -2012,Tocantins,Mar�o,80.0,2012-01-01 -2013,Tocantins,Mar�o,60.0,2013-01-01 -2014,Tocantins,Mar�o,58.0,2014-01-01 -2015,Tocantins,Mar�o,85.0,2015-01-01 -2016,Tocantins,Mar�o,165.0,2016-01-01 -2017,Tocantins,Mar�o,98.0,2017-01-01 +1998,Tocantins,Março,0.0,1998-01-01 +1999,Tocantins,Março,1.0,1999-01-01 +2000,Tocantins,Março,4.0,2000-01-01 +2001,Tocantins,Março,0.0,2001-01-01 +2002,Tocantins,Março,33.0,2002-01-01 +2003,Tocantins,Março,19.0,2003-01-01 +2004,Tocantins,Março,23.0,2004-01-01 +2005,Tocantins,Março,13.0,2005-01-01 +2006,Tocantins,Março,11.0,2006-01-01 +2007,Tocantins,Março,99.0,2007-01-01 +2008,Tocantins,Março,4.0,2008-01-01 +2009,Tocantins,Março,23.0,2009-01-01 +2010,Tocantins,Março,120.0,2010-01-01 +2011,Tocantins,Março,16.0,2011-01-01 +2012,Tocantins,Março,80.0,2012-01-01 +2013,Tocantins,Março,60.0,2013-01-01 +2014,Tocantins,Março,58.0,2014-01-01 +2015,Tocantins,Março,85.0,2015-01-01 +2016,Tocantins,Março,165.0,2016-01-01 +2017,Tocantins,Março,98.0,2017-01-01 1998,Tocantins,Abril,0.0,1998-01-01 1999,Tocantins,Abril,9.0,1999-01-01 2000,Tocantins,Abril,0.0,2000-01-01 diff --git a/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/info.json b/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/info.json index 35eb8eddedd4f647a48469d0432201a1f53e310a..5e31ab506cc014d0a6f81ac92f5f784602790d6b 100644 --- a/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/info.json +++ b/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/info.json @@ -1,98 +1,98 @@ -{ - "name": "Forest Fires in Brazil", - "source": "https://www.kaggle.com/datasets/gustavomodelli/forest-fires-in-brazil/data", - "data_intro": "This dataset report of the number of forest fires in Brazil divided by states. The series comprises the period of approximately 10 years (1998 to 2017).", - "is_splited": false, - "overall_size": 6454, - "train_size": 0, - "test_size": 0, - "c_classes": 3, - "n_classes": 2, - "task_type": "regression", - "target": { - "number": "Given a state's name and other features, predict the number of forest fires that occurred in that state in a given year." - }, - "cat_feature_intro": { - "state": "- state:Brazilian State,Rio,Mato Grosso, other ", - "month": "- month: Month when Forest Fires happen,Janeiro,Fevereiro,other ", - "date": "- date:Date when Forest Fires where reported" - }, - "num_feature_intro": { - "year": "- year:Year when Forest Fires happen", - "number": "- number:Number of Forest Fires reported" - }, - "evaluation_metric": null, - "num_feature_value": { - "number": [ - 0.0, - 998.0 - ], - "year": [ - 1998.0, - 2017.0 - ] - }, - "cat_feature_value": { - "date": [ - "1998-01-01", - "1999-01-01", - "2000-01-01", - "2001-01-01", - "2002-01-01", - "2003-01-01", - "2004-01-01", - "2005-01-01", - "2006-01-01", - "2007-01-01", - "2008-01-01", - "2009-01-01", - "2010-01-01", - "2011-01-01", - "2012-01-01", - "2013-01-01", - "2014-01-01", - "2015-01-01", - "2016-01-01", - "2017-01-01" - ], - "month": [ - "Abril", - "Agosto", - "Dezembro", - "Fevereiro", - "Janeiro", - "Julho", - "Junho", - "Maio", - "Mar�o", - "Novembro", - "Outubro", - "Setembro" - ], - "state": [ - "Acre", - "Alagoas", - "Amapa", - "Amazonas", - "Bahia", - "Ceara", - "Distrito Federal", - "Espirito Santo", - "Goias", - "Maranhao", - "Mato Grosso", - "Minas Gerais", - "Paraiba", - "Par�", - "Pernambuco", - "Piau", - "Rio", - "Rondonia", - "Roraima", - "Santa Catarina", - "Sao Paulo", - "Sergipe", - "Tocantins" - ] - } -} \ No newline at end of file +{ + "name": "Forest Fires in Brazil", + "source": "https://www.kaggle.com/datasets/gustavomodelli/forest-fires-in-brazil/data", + "data_intro": "This dataset report of the number of forest fires in Brazil divided by states. The series comprises the period of 20 calendar years (1998 to 2017).", + "is_splited": false, + "overall_size": 6454, + "train_size": 0, + "test_size": 0, + "c_classes": 3, + "n_classes": 2, + "task_type": "regression", + "target": { + "number": "Given a state's name and other features, predict the number of forest fires reported in the specified month and year." + }, + "cat_feature_intro": { + "state": "- state:Brazilian State,Rio,Mato Grosso, other ", + "month": "- month: Month when Forest Fires happen,Janeiro,Fevereiro,other ", + "date": "- date: Year marker stored as YYYY-01-01; use the separate month field for the observation month. This is not a daily fire-count timestamp." + }, + "num_feature_intro": { + "year": "- year:Year when Forest Fires happen", + "number": "- number:Number of Forest Fires reported" + }, + "evaluation_metric": null, + "num_feature_value": { + "number": [ + 0.0, + 998.0 + ], + "year": [ + 1998.0, + 2017.0 + ] + }, + "cat_feature_value": { + "date": [ + "1998-01-01", + "1999-01-01", + "2000-01-01", + "2001-01-01", + "2002-01-01", + "2003-01-01", + "2004-01-01", + "2005-01-01", + "2006-01-01", + "2007-01-01", + "2008-01-01", + "2009-01-01", + "2010-01-01", + "2011-01-01", + "2012-01-01", + "2013-01-01", + "2014-01-01", + "2015-01-01", + "2016-01-01", + "2017-01-01" + ], + "month": [ + "Abril", + "Agosto", + "Dezembro", + "Fevereiro", + "Janeiro", + "Julho", + "Junho", + "Maio", + "Março", + "Novembro", + "Outubro", + "Setembro" + ], + "state": [ + "Acre", + "Alagoas", + "Amapa", + "Amazonas", + "Bahia", + "Ceara", + "Distrito Federal", + "Espirito Santo", + "Goias", + "Maranhao", + "Mato Grosso", + "Minas Gerais", + "Paraiba", + "Pará", + "Pernambuco", + "Piau", + "Rio", + "Rondonia", + "Roraima", + "Santa Catarina", + "Sao Paulo", + "Sergipe", + "Tocantins" + ] + } +} diff --git a/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/info_mod.json b/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/info_mod.json index 71542bb8d14504f7cf762b399238f9fe7d86d7ab..dfea5cd3a9701d1b295a129fc6a06ebb7ab8bf16 100644 --- a/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/info_mod.json +++ b/single_point_prediction/daily/Forest_Fires_in_Brazil_B1/info_mod.json @@ -1,7 +1,7 @@ { "name": "Forest Fires in Brazil", "source": "https://www.kaggle.com/datasets/gustavomodelli/forest-fires-in-brazil/data", - "data_intro": "This dataset report of the number of forest fires in Brazil divided by states. The series comprises the period of approximately 10 years (1998 to 2017).", + "data_intro": "This dataset report of the number of forest fires in Brazil divided by states. The series comprises the period of 20 calendar years (1998 to 2017).", "is_splited": false, "overall_size": 6454, "train_size": 0, @@ -13,7 +13,7 @@ "cat_feature_intro": { "state": "- state:Brazilian State,Rio,Mato Grosso, other ", "month": "- month: Month when Forest Fires happen,Janeiro,Fevereiro,other ", - "date": "- date:Date when Forest Fires where reported" + "date": "- date: Year marker stored as YYYY-01-01; use the separate month field for the observation month. This is not a daily fire-count timestamp." }, "num_feature_intro": { "year": "- year:Year when Forest Fires happen" @@ -40,7 +40,7 @@ "Mato Grosso", "Minas Gerais", "Paraiba", - "Par�", + "Pará", "Pernambuco", "Piau", "Rio", @@ -60,7 +60,7 @@ "Julho", "Junho", "Maio", - "Mar�o", + "Março", "Novembro", "Outubro", "Setembro" @@ -110,4 +110,4 @@ "open_text_feature_intro": {}, "open_text_features": [], "missing_from_original_info": [] -} \ No newline at end of file +} diff --git a/single_point_prediction/daily/Global_YouTube_Statistics_2023_B1/info.json b/single_point_prediction/daily/Global_YouTube_Statistics_2023_B1/info.json index 32b2e724985eb4b8c7e1935cfdd4e4510dbfb775..2a4d0bb2870f7f26e8f34e8121139aaccc4b4a47 100644 --- a/single_point_prediction/daily/Global_YouTube_Statistics_2023_B1/info.json +++ b/single_point_prediction/daily/Global_YouTube_Statistics_2023_B1/info.json @@ -1,288 +1,288 @@ -{ - "name": "Global YouTube Statistics 2023", - "source": "https://www.kaggle.com/datasets/nelgiriyewithana/global-youtube-statistics-2023/data", - "data_intro": "A collection of YouTube giants, this dataset offers a perfect avenue to analyze and gain valuable insights from the luminaries of the platform. With comprehensive details on top creators' subscriber counts, video views, upload frequency, country of origin, earnings, and more, this treasure trove of information is a must-explore for aspiring content creators, data enthusiasts, and anyone intrigued by the ever-evolving online content landscape. Immerse yourself in the world of YouTube success and unlock a wealth of knowledge with this extraordinary dataset.", - "is_splited": false, - "overall_size": 995, - "train_size": 0, - "test_size": 0, - "c_classes": 7, - "n_classes": 21, - "cat_feature_intro": { - "Youtuber": "Name of the YouTube channel", - "category": "Category or niche of the channel", - "Title": "Title of the YouTube channel", - "Country": "Longitude coordinate of the country's location", - "Abbreviation": "Abbreviation of the country", - "channel_type": "Ranking of the channel based on its type (individual or brand)", - "created_month": "Month when the YouTube channel was created" - }, - "num_feature_intro": { - "rank": "Ranking of the channel based on its type (individual or brand)", - "subscribers": "Number of new subscribers gained in the last 30 days", - "video views": "Total video views in the last 30 days", - "uploads": "Total number of videos uploaded on the channel", - "video_views_rank": "Ranking of the channel based on total video views", - "country_rank": "Ranking of the channel based on the number of subscribers within its country", - "channel_type_rank": "Ranking of the channel based on its type (individual or brand)", - "video_views_for_the_last_30_days": "Total video views in the last 30 days", - "lowest_monthly_earnings": "Lowest estimated monthly earnings from the channel", - "highest_monthly_earnings": "Highest estimated monthly earnings from the channel", - "lowest_yearly_earnings": "Lowest estimated yearly earnings from the channel", - "highest_yearly_earnings": "Highest estimated yearly earnings from the channel", - "subscribers_for_last_30_days": "Number of new subscribers gained in the last 30 days", - "created_year": "Year when the YouTube channel was created", - "created_date": "Exact date of the YouTube channel's creation", - "Gross tertiary education enrollment (%)": "Percentage of the population enrolled in tertiary education in the country", - "Population": "Percentage of the population living in urban areas", - "Unemployment rate": "Unemployment rate in the country", - "Urban_population": "Percentage of the population living in urban areas", - "Latitude": "Latitude coordinate of the country's location", - "Longitude": "Longitude coordinate of the country's location" - }, - "evaluation_metric": null, - "task_type": "regression", - "target": "highest_monthly_earnings", - "num_feature_value": { - "Gross tertiary education enrollment (%)": [ - 7.6, - 113.1 - ], - "Latitude": [ - -38.416097, - 61.92411 - ], - "Longitude": [ - -172.104629, - 138.252924 - ], - "Population": [ - 202506.0, - 1397715000.0 - ], - "Unemployment rate": [ - 0.75, - 14.72 - ], - "Urban_population": [ - 35588.0, - 842933962.0 - ], - "channel_type_rank": [ - 1.0, - 7741.0 - ], - "country_rank": [ - 1.0, - 7741.0 - ], - "created_date": [ - 1.0, - 31.0 - ], - "created_year": [ - 1970.0, - 2022.0 - ], - "highest_monthly_earnings": [ - 0.0, - 13600000.0 - ], - "highest_yearly_earnings": [ - 0.0, - 163400000.0 - ], - "lowest_monthly_earnings": [ - 0.0, - 850900.0 - ], - "lowest_yearly_earnings": [ - 0.0, - 10200000.0 - ], - "rank": [ - 1.0, - 995.0 - ], - "subscribers": [ - 12300000.0, - 245000000.0 - ], - "subscribers_for_last_30_days": [ - 1.0, - 8000000.0 - ], - "uploads": [ - 0.0, - 301308.0 - ], - "video views": [ - 0.0, - 228000000000.0 - ], - "video_views_for_the_last_30_days": [ - 1.0, - 6589000000.0 - ], - "video_views_rank": [ - 1.0, - 4057944.0 - ] - }, - "cat_feature_value": { - "Abbreviation": [ - "AD", - "AE", - "AF", - "AR", - "AU", - "BB", - "BD", - "BR", - "CA", - "CH", - "CL", - "CN", - "CO", - "CU", - "DE", - "EC", - "EG", - "ES", - "FI", - "FR", - "GB", - "ID", - "IN", - "IQ", - "IT", - "JO", - "JP", - "KR", - "KW", - "LV", - "MA", - "MX", - "MY", - "NL", - "PE", - "PH", - "PK", - "RU", - "SA", - "SE", - "SG", - "SV", - "TH", - "TR", - "UA", - "US", - "VE", - "VN", - "WS" - ], - "Country": [ - "Afghanistan", - "Andorra", - "Argentina", - "Australia", - "Bangladesh", - "Barbados", - "Brazil", - "Canada", - "Chile", - "China", - "Colombia", - "Cuba", - "Ecuador", - "Egypt", - "El Salvador", - "Finland", - "France", - "Germany", - "India", - "Indonesia", - "Iraq", - "Italy", - "Japan", - "Jordan", - "Kuwait", - "Latvia", - "Malaysia", - "Mexico", - "Morocco", - "Netherlands", - "Pakistan", - "Peru", - "Philippines", - "Russia", - "Samoa", - "Saudi Arabia", - "Singapore", - "South Korea", - "Spain", - "Sweden", - "Switzerland", - "Thailand", - "Turkey", - "Ukraine", - "United Arab Emirates", - "United Kingdom", - "United States", - "Venezuela", - "Vietnam" - ], - "Title": "string", - "Youtuber": "string", - "category": [ - "Autos & Vehicles", - "Comedy", - "Education", - "Entertainment", - "Film & Animation", - "Gaming", - "Howto & Style", - "Movies", - "Music", - "News & Politics", - "Nonprofits & Activism", - "People & Blogs", - "Pets & Animals", - "Science & Technology", - "Shows", - "Sports", - "Trailers", - "Travel & Events" - ], - "channel_type": [ - "Animals", - "Autos", - "Comedy", - "Education", - "Entertainment", - "Film", - "Games", - "Howto", - "Music", - "News", - "Nonprofit", - "People", - "Sports", - "Tech" - ], - "created_month": [ - "Apr", - "Aug", - "Dec", - "Feb", - "Jan", - "Jul", - "Jun", - "Mar", - "May", - "Nov", - "Oct", - "Sep" - ] - } -} \ No newline at end of file +{ + "name": "Global YouTube Statistics 2023", + "source": "https://www.kaggle.com/datasets/nelgiriyewithana/global-youtube-statistics-2023/data", + "data_intro": "A collection of YouTube giants, this dataset offers a perfect avenue to analyze and gain valuable insights from the luminaries of the platform. With comprehensive details on top creators' subscriber counts, video views, upload frequency, country of origin, earnings, and more, this treasure trove of information is a must-explore for aspiring content creators, data enthusiasts, and anyone intrigued by the ever-evolving online content landscape. Immerse yourself in the world of YouTube success and unlock a wealth of knowledge with this extraordinary dataset.", + "is_splited": false, + "overall_size": 995, + "train_size": 0, + "test_size": 0, + "c_classes": 7, + "n_classes": 21, + "cat_feature_intro": { + "Youtuber": "Name of the YouTube channel", + "category": "Category or niche of the channel", + "Title": "Title of the YouTube channel", + "Country": "Country associated with the channel.", + "Abbreviation": "Abbreviation of the country", + "channel_type": "Type or category of the channel.", + "created_month": "Month when the YouTube channel was created" + }, + "num_feature_intro": { + "rank": "Ranking of the channel based on its type (individual or brand)", + "subscribers": "Number of new subscribers gained in the last 30 days", + "video views": "Total video views in the last 30 days", + "uploads": "Total number of videos uploaded on the channel", + "video_views_rank": "Ranking of the channel based on total video views", + "country_rank": "Ranking of the channel based on the number of subscribers within its country", + "channel_type_rank": "Ranking of the channel based on its type (individual or brand)", + "video_views_for_the_last_30_days": "Total video views in the last 30 days", + "lowest_monthly_earnings": "Lowest estimated monthly earnings from the channel", + "highest_monthly_earnings": "Highest estimated monthly earnings from the channel", + "lowest_yearly_earnings": "Lowest estimated yearly earnings from the channel", + "highest_yearly_earnings": "Highest estimated yearly earnings from the channel", + "subscribers_for_last_30_days": "Number of new subscribers gained in the last 30 days", + "created_year": "Year when the YouTube channel was created", + "created_date": "Exact date of the YouTube channel's creation", + "Gross tertiary education enrollment (%)": "Percentage of the population enrolled in tertiary education in the country", + "Population": "Percentage of the population living in urban areas", + "Unemployment rate": "Unemployment rate in the country", + "Urban_population": "Percentage of the population living in urban areas", + "Latitude": "Latitude coordinate of the country's location", + "Longitude": "Longitude coordinate of the country's location" + }, + "evaluation_metric": null, + "task_type": "regression", + "target": "highest_monthly_earnings", + "num_feature_value": { + "Gross tertiary education enrollment (%)": [ + 7.6, + 113.1 + ], + "Latitude": [ + -38.416097, + 61.92411 + ], + "Longitude": [ + -172.104629, + 138.252924 + ], + "Population": [ + 202506.0, + 1397715000.0 + ], + "Unemployment rate": [ + 0.75, + 14.72 + ], + "Urban_population": [ + 35588.0, + 842933962.0 + ], + "channel_type_rank": [ + 1.0, + 7741.0 + ], + "country_rank": [ + 1.0, + 7741.0 + ], + "created_date": [ + 1.0, + 31.0 + ], + "created_year": [ + 1970.0, + 2022.0 + ], + "highest_monthly_earnings": [ + 0.0, + 13600000.0 + ], + "highest_yearly_earnings": [ + 0.0, + 163400000.0 + ], + "lowest_monthly_earnings": [ + 0.0, + 850900.0 + ], + "lowest_yearly_earnings": [ + 0.0, + 10200000.0 + ], + "rank": [ + 1.0, + 995.0 + ], + "subscribers": [ + 12300000.0, + 245000000.0 + ], + "subscribers_for_last_30_days": [ + 1.0, + 8000000.0 + ], + "uploads": [ + 0.0, + 301308.0 + ], + "video views": [ + 0.0, + 228000000000.0 + ], + "video_views_for_the_last_30_days": [ + 1.0, + 6589000000.0 + ], + "video_views_rank": [ + 1.0, + 4057944.0 + ] + }, + "cat_feature_value": { + "Abbreviation": [ + "AD", + "AE", + "AF", + "AR", + "AU", + "BB", + "BD", + "BR", + "CA", + "CH", + "CL", + "CN", + "CO", + "CU", + "DE", + "EC", + "EG", + "ES", + "FI", + "FR", + "GB", + "ID", + "IN", + "IQ", + "IT", + "JO", + "JP", + "KR", + "KW", + "LV", + "MA", + "MX", + "MY", + "NL", + "PE", + "PH", + "PK", + "RU", + "SA", + "SE", + "SG", + "SV", + "TH", + "TR", + "UA", + "US", + "VE", + "VN", + "WS" + ], + "Country": [ + "Afghanistan", + "Andorra", + "Argentina", + "Australia", + "Bangladesh", + "Barbados", + "Brazil", + "Canada", + "Chile", + "China", + "Colombia", + "Cuba", + "Ecuador", + "Egypt", + "El Salvador", + "Finland", + "France", + "Germany", + "India", + "Indonesia", + "Iraq", + "Italy", + "Japan", + "Jordan", + "Kuwait", + "Latvia", + "Malaysia", + "Mexico", + "Morocco", + "Netherlands", + "Pakistan", + "Peru", + "Philippines", + "Russia", + "Samoa", + "Saudi Arabia", + "Singapore", + "South Korea", + "Spain", + "Sweden", + "Switzerland", + "Thailand", + "Turkey", + "Ukraine", + "United Arab Emirates", + "United Kingdom", + "United States", + "Venezuela", + "Vietnam" + ], + "Title": "string", + "Youtuber": "string", + "category": [ + "Autos & Vehicles", + "Comedy", + "Education", + "Entertainment", + "Film & Animation", + "Gaming", + "Howto & Style", + "Movies", + "Music", + "News & Politics", + "Nonprofits & Activism", + "People & Blogs", + "Pets & Animals", + "Science & Technology", + "Shows", + "Sports", + "Trailers", + "Travel & Events" + ], + "channel_type": [ + "Animals", + "Autos", + "Comedy", + "Education", + "Entertainment", + "Film", + "Games", + "Howto", + "Music", + "News", + "Nonprofit", + "People", + "Sports", + "Tech" + ], + "created_month": [ + "Apr", + "Aug", + "Dec", + "Feb", + "Jan", + "Jul", + "Jun", + "Mar", + "May", + "Nov", + "Oct", + "Sep" + ] + } +} diff --git a/single_point_prediction/daily/Global_YouTube_Statistics_2023_B1/info_mod.json b/single_point_prediction/daily/Global_YouTube_Statistics_2023_B1/info_mod.json index 20e870f52ad7f9b8335b0d8a7022e913425160d1..77f6c5f0fcd2e4405d2af86487f5a64c07e6c8be 100644 --- a/single_point_prediction/daily/Global_YouTube_Statistics_2023_B1/info_mod.json +++ b/single_point_prediction/daily/Global_YouTube_Statistics_2023_B1/info_mod.json @@ -10,9 +10,9 @@ "n_classes": 21, "cat_feature_intro": { "category": "Category or niche of the channel", - "Country": "Longitude coordinate of the country's location", + "Country": "Country associated with the channel.", "Abbreviation": "Abbreviation of the country", - "channel_type": "Ranking of the channel based on its type (individual or brand)", + "channel_type": "Type or category of the channel.", "created_month": "Month when the YouTube channel was created" }, "num_feature_intro": { @@ -373,4 +373,4 @@ "Title" ], "missing_from_original_info": [] -} \ No newline at end of file +} diff --git a/single_point_prediction/daily/Hotel_booking_Demand_B1/Hotel_booking_Demand_B1_001.json b/single_point_prediction/daily/Hotel_booking_Demand_B1/Hotel_booking_Demand_B1_001.json index 5cea83dd6b22dddf20eb048dc35cebd3106972ee..247e0a3d76acee5e7bf7de2113d0264788f4b61e 100644 --- a/single_point_prediction/daily/Hotel_booking_Demand_B1/Hotel_booking_Demand_B1_001.json +++ b/single_point_prediction/daily/Hotel_booking_Demand_B1/Hotel_booking_Demand_B1_001.json @@ -5,7 +5,7 @@ "perspective": "user", "dataset_name": "Hotel booking Demand", "table_path": "kaggle/Hotel booking Demand", - "query": "The reservation details for this Resort Hotel stay show a guest from Portugal booked through a travel agent channel for a February 2016 arrival. It's a 3-night trip with a weekend night, booked 53 days out by a first-time customer who made no changes and required no special amenities. Room type A was reserved at a rate of 30.0 per night with no deposit, and the agent listed is 196. The last update was on February 29th. I'm trying to figure out if this booking was honored or if it was called off.", + "query": "The reservation details for this Resort Hotel stay show a guest from Portugal booked through a travel agent channel for a February 2016 arrival. It's a 3-night trip with a weekend night, booked 53 days out by a first-time customer who made no changes and required no special amenities. Room type A was reserved at a rate of 30.0 per night with no deposit, and the agent listed is 196. I'm trying to figure out if this booking was honored or if it was called off.", "meta_info": { "domain": "daily" }, @@ -15,7 +15,6 @@ "scenario_id": "001", "features": { "hotel": "Resort Hotel", - "is_canceled": "NULL", "lead_time": 53, "arrival_date_year": 2016, "arrival_date_month": "February", @@ -44,8 +43,7 @@ "adr": 30.0, "required_car_parking_spaces": "NULL", "total_of_special_requests": 0, - "reservation_status": "Check-Out", - "reservation_status_date": "2016-02-29" + "reservation_status": "Check-Out" } } ], @@ -59,4 +57,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/single_point_prediction/daily/Hotel_booking_Demand_B1/Hotel_booking_Demand_B1_002.json b/single_point_prediction/daily/Hotel_booking_Demand_B1/Hotel_booking_Demand_B1_002.json index bd79014260cc07eaa10651c034cfe62e24045f32..38950de0f40967465a3705310b5f590ddabbf8dc 100644 --- a/single_point_prediction/daily/Hotel_booking_Demand_B1/Hotel_booking_Demand_B1_002.json +++ b/single_point_prediction/daily/Hotel_booking_Demand_B1/Hotel_booking_Demand_B1_002.json @@ -5,7 +5,7 @@ "perspective": "user", "dataset_name": "Hotel booking Demand", "table_path": "kaggle/Hotel booking Demand", - "query": "Oh man, just got a cancellation notice for my City Hotel booking. I was all set to arrive on March 20th, 2016, for a little 3-night getaway, staying the weekend and one weeknight. The booking was made a solid 87 days ago through an online travel agent. It was for me and my partner, no kids, and we had a Bed and Breakfast plan. The final status was updated just a few days ago, on the 14th. Since I'm a first-time guest from France with no history of cancellations and I didn't even require a deposit, I'm a bit worried. What do you think the final outcome of this reservation will be?", + "query": "I'm looking at the details of my City Hotel booking. I was all set to arrive on March 20th, 2016, for a little 3-night getaway, staying the weekend and one weeknight. The booking was made a solid 87 days ago through an online travel agent. It was for me and my partner, no kids, and we had a Bed and Breakfast plan. Since I'm a first-time guest from France with no history of cancellations and I didn't even require a deposit, I'm a bit worried. What do you think the final outcome of this reservation will be?", "meta_info": { "domain": "daily" }, @@ -15,7 +15,6 @@ "scenario_id": "001", "features": { "hotel": "City Hotel", - "is_canceled": 1, "lead_time": 87, "arrival_date_year": 2016, "arrival_date_month": "March", @@ -44,8 +43,7 @@ "adr": 78.3, "required_car_parking_spaces": 0, "total_of_special_requests": 0, - "reservation_status": "Canceled", - "reservation_status_date": "2016-03-14" + "reservation_status": "Canceled" } } ], @@ -59,4 +57,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/single_point_prediction/daily/Hotel_booking_Demand_B1/Hotel_booking_Demand_B1_003.json b/single_point_prediction/daily/Hotel_booking_Demand_B1/Hotel_booking_Demand_B1_003.json index a8e968797422711a3cbd6d20aacb388ea8839c06..fbd67cd8cb0ed78caa5c62de3a35fc23d63af9c4 100644 --- a/single_point_prediction/daily/Hotel_booking_Demand_B1/Hotel_booking_Demand_B1_003.json +++ b/single_point_prediction/daily/Hotel_booking_Demand_B1/Hotel_booking_Demand_B1_003.json @@ -5,7 +5,7 @@ "perspective": "user", "dataset_name": "Hotel booking Demand", "table_path": "kaggle/Hotel booking Demand", - "query": "My heart sank when I checked my City Hotel booking - I'd planned this perfect Valentine's getaway for February 14th, 2016, arriving on a Sunday in week 8. As a solo traveler from Angola with no children or babies, I'd snagged room type A and actually got assigned the same room, all without any deposit. I booked last-minute with just 2 days' notice for 2 weekend nights plus 1 weekday night, having no prior booking history whatsoever. With zero days on a waiting list and no special requests, this transient-party reservation shows as canceled - but what actually happened with my booking status?", + "query": "My heart sank when I checked my City Hotel booking - I'd planned this perfect Valentine's getaway for February 14th, 2016, arriving on a Sunday in week 8. As a solo traveler from Angola with no children or babies, I'd snagged room type A and actually got assigned the same room, all without any deposit. I booked last-minute with just 2 days' notice for 2 weekend nights plus 1 weekday night, having no prior booking history whatsoever. With zero days on a waiting list and no special requests, what outcome would you expect for this Transient-Party booking?", "meta_info": { "domain": "daily" }, @@ -15,7 +15,6 @@ "scenario_id": "001", "features": { "hotel": "City Hotel", - "is_canceled": 1, "lead_time": 2, "arrival_date_year": 2016, "arrival_date_month": "February", @@ -44,8 +43,7 @@ "adr": "NULL", "required_car_parking_spaces": 0, "total_of_special_requests": 0, - "reservation_status": "No-Show", - "reservation_status_date": "2016-02-14" + "reservation_status": "No-Show" } } ], @@ -59,4 +57,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/single_point_prediction/daily/Hotel_booking_Demand_B1/Hotel_booking_Demand_B1_004.json b/single_point_prediction/daily/Hotel_booking_Demand_B1/Hotel_booking_Demand_B1_004.json index 843f5b2ed1e17cb240277858eb3d8f786700bad9..c78b4a7e0bce930b24c7ab4f68dde5f5c9004cf6 100644 --- a/single_point_prediction/daily/Hotel_booking_Demand_B1/Hotel_booking_Demand_B1_004.json +++ b/single_point_prediction/daily/Hotel_booking_Demand_B1/Hotel_booking_Demand_B1_004.json @@ -5,7 +5,7 @@ "perspective": "user", "dataset_name": "Hotel booking Demand", "table_path": "kaggle/Hotel booking Demand", - "query": "Booking #... let's see, it's for a City Hotel back in August 2017, with the arrival set for the 4th, which was a Friday in week 31. It was a short, one-night weekday stay for a family of two adults, one child, and even a baby. They booked a Breakfast plan directly from China, and the lead time was 61 days. The family didn't require parking, but they did have one special request. The reservation was handled by agent 14, they made one change to the booking, and paid an average daily rate of 146.0. It's noted they weren't a repeat guest and had no prior booking history, cancellations, or time on a waiting list. The final entry is the reservation status date of August 5th. So, looking at all this, what was the final outcome for this family's trip? Did they make it?", + "query": "Booking #... let's see, it's for a City Hotel back in August 2017, with the arrival set for the 4th, which was a Friday in week 31. It was a short, one-night weekday stay for a family of two adults, one child, and even a baby. They booked a Breakfast plan directly from China, and the lead time was 61 days. The family didn't require parking, but they did have one special request. The reservation was handled by agent 14, they made one change to the booking, and paid an average daily rate of 146.0. It's noted they weren't a repeat guest and had no prior booking history, cancellations, or time on a waiting list. So, looking at all this, what was the final outcome for this family's trip? Did they make it?", "meta_info": { "domain": "daily" }, @@ -15,7 +15,6 @@ "scenario_id": "001", "features": { "hotel": "City Hotel", - "is_canceled": 0, "lead_time": 61, "arrival_date_year": 2017, "arrival_date_month": "August", @@ -44,8 +43,7 @@ "adr": 146.0, "required_car_parking_spaces": 0, "total_of_special_requests": 1, - "reservation_status": "Check-Out", - "reservation_status_date": "2017-08-05" + "reservation_status": "Check-Out" } } ], @@ -59,4 +57,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/single_point_prediction/daily/Hotel_booking_Demand_B1/Hotel_booking_Demand_B1_005.json b/single_point_prediction/daily/Hotel_booking_Demand_B1/Hotel_booking_Demand_B1_005.json index e3217318c2d1500fb6b62304b5d433c446f02bbe..4935bc00d8e005c841bae80e0450d7d62fc1d958 100644 --- a/single_point_prediction/daily/Hotel_booking_Demand_B1/Hotel_booking_Demand_B1_005.json +++ b/single_point_prediction/daily/Hotel_booking_Demand_B1/Hotel_booking_Demand_B1_005.json @@ -5,7 +5,7 @@ "perspective": "data_holder", "dataset_name": "Hotel booking Demand", "table_path": "kaggle/Hotel booking Demand", - "query": "A booking just came through with a lead time of 81 days for two adults arriving in late December 2016, specifically week 51. They're booked for a long stay with 4 weekend nights and 10 weeknights, coming from Great Britain via an Online Travel Agent channel. The assigned room type is E, and they have no children or babies. Interestingly, this is their first time with us—not a repeat guest—and they've never canceled before or had any previous bookings. Their assigned agent is ID 240, they made no changes to the booking, and they had zero days on a waiting list. They are transient customers with two special requests, and the reservation status was last updated on December 30th, 2016. I'm sharing our hotel's entire booking history with you to see if this pattern looks familiar. Given all these details, do you think this reservation will be honored, or is it likely to be canceled?", + "query": "A booking just came through with a lead time of 81 days for two adults arriving in late December 2016, specifically week 51. They're booked for a long stay with 4 weekend nights and 10 weeknights, coming from Great Britain via an Online Travel Agent channel. The assigned room type is E, and they have no children or babies. Interestingly, this is their first time with us—not a repeat guest—and they've never canceled before or had any previous bookings. Their assigned agent is ID 240, they made no changes to the booking, and they had zero days on a waiting list. They are transient customers with two special requests. I'm sharing our hotel's entire booking history with you to see if this pattern looks familiar. Given all these details, do you think this reservation will be honored, or is it likely to be canceled?", "meta_info": { "domain": "daily" }, @@ -15,7 +15,6 @@ "scenario_id": "001", "features": { "hotel": "NULL", - "is_canceled": 0, "lead_time": 81, "arrival_date_year": 2016, "arrival_date_month": "December", @@ -44,8 +43,7 @@ "adr": "NULL", "required_car_parking_spaces": "NULL", "total_of_special_requests": 2, - "reservation_status": "Check-Out", - "reservation_status_date": "2016-12-30" + "reservation_status": "Check-Out" } } ], @@ -59,4 +57,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/single_point_prediction/daily/Hotel_booking_Demand_B1/Hotel_booking_Demand_B1_006.json b/single_point_prediction/daily/Hotel_booking_Demand_B1/Hotel_booking_Demand_B1_006.json index f93a3af320a8c96693a72b7c1c03a327ec69beac..47910069b235d213d802fd64a66bf7fcbafb50e2 100644 --- a/single_point_prediction/daily/Hotel_booking_Demand_B1/Hotel_booking_Demand_B1_006.json +++ b/single_point_prediction/daily/Hotel_booking_Demand_B1/Hotel_booking_Demand_B1_006.json @@ -15,7 +15,6 @@ "scenario_id": "001", "features": { "hotel": "City Hotel", - "is_canceled": 0, "lead_time": 192, "arrival_date_year": 2017, "arrival_date_month": "August", @@ -44,8 +43,7 @@ "adr": 157.5, "required_car_parking_spaces": 0, "total_of_special_requests": 2, - "reservation_status": "Check-Out", - "reservation_status_date": "2017-08-17" + "reservation_status": "Check-Out" } } ], @@ -59,4 +57,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/single_point_prediction/daily/Hotel_booking_Demand_B1/Hotel_booking_Demand_B1_007.json b/single_point_prediction/daily/Hotel_booking_Demand_B1/Hotel_booking_Demand_B1_007.json index d34f1b53062431319fc22727eb8bd33afb684722..ac8bfc68338a6d45ed69a6056d25d640c41a1616 100644 --- a/single_point_prediction/daily/Hotel_booking_Demand_B1/Hotel_booking_Demand_B1_007.json +++ b/single_point_prediction/daily/Hotel_booking_Demand_B1/Hotel_booking_Demand_B1_007.json @@ -15,7 +15,6 @@ "scenario_id": "001", "features": { "hotel": "NULL", - "is_canceled": 1, "lead_time": 93, "arrival_date_year": "NULL", "arrival_date_month": "NULL", @@ -44,8 +43,7 @@ "adr": 151.0, "required_car_parking_spaces": 0, "total_of_special_requests": 0, - "reservation_status": "Canceled", - "reservation_status_date": "2016-05-25" + "reservation_status": "Canceled" } } ], @@ -59,4 +57,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/single_point_prediction/daily/Hotel_booking_Demand_B1/Hotel_booking_Demand_B1_008.json b/single_point_prediction/daily/Hotel_booking_Demand_B1/Hotel_booking_Demand_B1_008.json index 7de889ff1961f29984c066e8baf9fcbc23f64f47..a94dadb01201d17551150aaf31f604e0448ccaad 100644 --- a/single_point_prediction/daily/Hotel_booking_Demand_B1/Hotel_booking_Demand_B1_008.json +++ b/single_point_prediction/daily/Hotel_booking_Demand_B1/Hotel_booking_Demand_B1_008.json @@ -15,7 +15,6 @@ "scenario_id": "001", "features": { "hotel": "Resort Hotel", - "is_canceled": 1, "lead_time": 198, "arrival_date_year": 2017, "arrival_date_month": "August", @@ -44,8 +43,7 @@ "adr": 231.43, "required_car_parking_spaces": 0, "total_of_special_requests": 2, - "reservation_status": "Canceled", - "reservation_status_date": "2017-02-16" + "reservation_status": "Canceled" } } ], @@ -59,4 +57,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/single_point_prediction/daily/Hotel_booking_Demand_B1/history.csv b/single_point_prediction/daily/Hotel_booking_Demand_B1/history.csv index 3612131a607b38762789df3ac8e0c54970ab661c..874c3117f7f960f07c6dba70ca567ebc0adb6b77 100644 --- a/single_point_prediction/daily/Hotel_booking_Demand_B1/history.csv +++ b/single_point_prediction/daily/Hotel_booking_Demand_B1/history.csv @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:5adde0606383239285b9995a6e26cdf7d2817a7ddc0d9a7f7495c3dfb502398c -size 16933032 +oid sha256:952c3768ae2c18bc1e2010af4e24f0540588dbc2e7221f5dd2fcf8419d95115f +size 15381212 diff --git a/single_point_prediction/daily/Hotel_booking_Demand_B1/info.json b/single_point_prediction/daily/Hotel_booking_Demand_B1/info.json index 4816c05993309461836de8622438e342f2e503ff..20956c93d07801fe3772275e08832605a78593ed 100644 --- a/single_point_prediction/daily/Hotel_booking_Demand_B1/info.json +++ b/single_point_prediction/daily/Hotel_booking_Demand_B1/info.json @@ -1,398 +1,391 @@ -{ - "name": "Hotel Booking Demand", - "source": "https://www.kaggle.com/datasets/jessemostipak/hotel-booking-demand/data", - "data_intro": "This dataset contains hotel booking data for a city hotel and a resort hotel, providing details on booking dates, stay duration, guest count, cancellation trends, and pricing. It is useful for demand forecasting, revenue management, and customer behavior analysis.", - "is_splited": false, - "overall_size": 119390, - "train_size": 0, - "test_size": 0, - "c_classes": 12, - "n_classes": 20, - "cat_feature_intro": { - "hotel": "Type of hotel (City Hotel or Resort Hotel).", - "arrival_date_month": "Month of arrival for the booking.", - "meal": "Type of meal plan booked (e.g., Breakfast included, Half board, Full board).", - "country": "Country of origin of the guest.", - "market_segment": "Booking distribution channel (e.g., direct, corporate, travel agent).", - "distribution_channel": "Channel through which the booking was made (e.g., online travel agents, direct).", - "reserved_room_type": "Type of room reserved by the guest before any changes.", - "assigned_room_type": "Final room type assigned to the guest.", - "deposit_type": "Indicates if a deposit was made and its type (e.g., No Deposit, Refundable, Non-Refundable).", - "customer_type": "Type of customer (e.g., transient, group, contract).", - "reservation_status": "Final status of the booking (e.g., Canceled, Checked-out, No-show).", - "reservation_status_date": "Date when the reservation status was last updated." - }, - "num_feature_intro": { - "is_canceled": "Indicates whether the booking was canceled (1) or not (0).", - "lead_time": "Number of days between booking and arrival.", - "arrival_date_year": "Year of arrival for the booking.", - "arrival_date_week_number": "Week number of the arrival date.", - "arrival_date_day_of_month": "Day of the month of arrival.", - "stays_in_weekend_nights": "Number of weekend nights (Saturday or Sunday) the guest stayed.", - "stays_in_week_nights": "Number of weekday nights (Monday to Friday) the guest stayed.", - "adults": "Number of adults included in the booking.", - "children": "Number of children included in the booking.", - "babies": "Number of babies included in the booking.", - "is_repeated_guest": "Indicates whether the guest is a returning customer (1) or not (0).", - "previous_cancellations": "Number of times the guest has canceled previous bookings.", - "previous_bookings_not_canceled": "Number of previous bookings not canceled by the guest.", - "booking_changes": "Number of changes made to the booking.", - "agent": "ID of the booking agent (if applicable).", - "company": "ID of the company making the booking (if applicable).", - "days_in_waiting_list": "Number of days the booking was on the waiting list before confirmation.", - "adr": "Average daily rate (price per night per room).", - "required_car_parking_spaces": "Number of car parking spaces required by the guest.", - "total_of_special_requests": "Number of special requests made by the guest (e.g., high floor, twin beds)." - }, - "evaluation_metric": null, - "task_type": "classification", - "target": "reservation_status", - "num_feature_value": { - "adr": [ - -6.38, - 5400.0 - ], - "adults": [ - 0.0, - 55.0 - ], - "agent": [ - 1.0, - 535.0 - ], - "arrival_date_day_of_month": [ - 1.0, - 31.0 - ], - "arrival_date_week_number": [ - 1.0, - 53.0 - ], - "arrival_date_year": [ - 2015.0, - 2017.0 - ], - "babies": [ - 0.0, - 10.0 - ], - "booking_changes": [ - 0.0, - 21.0 - ], - "children": [ - 0.0, - 10.0 - ], - "company": [ - 6.0, - 543.0 - ], - "days_in_waiting_list": [ - 0.0, - 391.0 - ], - "is_canceled": [ - 0.0, - 1.0 - ], - "is_repeated_guest": [ - 0.0, - 1.0 - ], - "lead_time": [ - 0.0, - 737.0 - ], - "previous_bookings_not_canceled": [ - 0.0, - 72.0 - ], - "previous_cancellations": [ - 0.0, - 26.0 - ], - "required_car_parking_spaces": [ - 0.0, - 8.0 - ], - "stays_in_week_nights": [ - 0.0, - 50.0 - ], - "stays_in_weekend_nights": [ - 0.0, - 19.0 - ], - "total_of_special_requests": [ - 0.0, - 5.0 - ] - }, - "cat_feature_value": { - "arrival_date_month": [ - "April", - "August", - "December", - "February", - "January", - "July", - "June", - "March", - "May", - "November", - "October", - "September" - ], - "assigned_room_type": [ - "A", - "B", - "C", - "D", - "E", - "F", - "G", - "H", - "I", - "K", - "L", - "P" - ], - "country": [ - "ABW", - "AGO", - "AIA", - "ALB", - "AND", - "ARE", - "ARG", - "ARM", - "ASM", - "ATA", - "ATF", - "AUS", - "AUT", - "AZE", - "BDI", - "BEL", - "BEN", - "BFA", - "BGD", - "BGR", - "BHR", - "BHS", - "BIH", - "BLR", - "BOL", - "BRA", - "BRB", - "BWA", - "CAF", - "CHE", - "CHL", - "CHN", - "CIV", - "CMR", - "CN", - "COL", - "COM", - "CPV", - "CRI", - "CUB", - "CYM", - "CYP", - "CZE", - "DEU", - "DJI", - "DMA", - "DNK", - "DOM", - "DZA", - "ECU", - "EGY", - "ESP", - "EST", - "ETH", - "FIN", - "FJI", - "FRA", - "FRO", - "GAB", - "GBR", - "GEO", - "GGY", - "GHA", - "GIB", - "GLP", - "GNB", - "GRC", - "GTM", - "GUY", - "HKG", - "HND", - "HRV", - "HUN", - "IDN", - "IMN", - "IND", - "IRL", - "IRN", - "IRQ", - "ISL", - "ISR", - "ITA", - "JAM", - "JEY", - "JOR", - "JPN", - "KAZ", - "KEN", - "KHM", - "KIR", - "KNA", - "KOR", - "KWT", - "LAO", - "LBN", - "LBY", - "LCA", - "LIE", - "LKA", - "LTU", - "LUX", - "LVA", - "MAC", - "MAR", - "MCO", - "MDG", - "MDV", - "MEX", - "MKD", - "MLI", - "MLT", - "MMR", - "MNE", - "MOZ", - "MRT", - "MUS", - "MWI", - "MYS", - "MYT", - "NAM", - "NCL", - "NGA", - "NIC", - "NLD", - "NOR", - "NPL", - "NZL", - "OMN", - "PAK", - "PAN", - "PER", - "PHL", - "PLW", - "POL", - "PRI", - "PRT", - "PRY", - "PYF", - "QAT", - "ROU", - "RUS", - "RWA", - "SAU", - "SDN", - "SEN", - "SGP", - "SLE", - "SLV", - "SMR", - "SRB", - "STP", - "SUR", - "SVK", - "SVN", - "SWE", - "SYC", - "SYR", - "TGO", - "THA", - "TJK", - "TMP", - "TUN", - "TUR", - "TWN", - "TZA", - "UGA", - "UKR", - "UMI", - "URY", - "USA", - "UZB", - "VEN", - "VGB", - "VNM", - "ZAF", - "ZMB", - "ZWE" - ], - "customer_type": [ - "Contract", - "Group", - "Transient", - "Transient-Party" - ], - "deposit_type": [ - "No Deposit", - "Non Refund", - "Refundable" - ], - "distribution_channel": [ - "Corporate", - "Direct", - "GDS", - "TA/TO", - "Undefined" - ], - "hotel": [ - "City Hotel", - "Resort Hotel" - ], - "market_segment": [ - "Aviation", - "Complementary", - "Corporate", - "Direct", - "Groups", - "Offline TA/TO", - "Online TA", - "Undefined" - ], - "meal": [ - "BB", - "FB", - "HB", - "SC", - "Undefined" - ], - "reservation_status": [ - "Canceled", - "Check-Out", - "No-Show" - ], - "reservation_status_date": "string", - "reserved_room_type": [ - "A", - "B", - "C", - "D", - "E", - "F", - "G", - "H", - "L", - "P" - ] - } -} \ No newline at end of file +{ + "name": "Hotel Booking Demand", + "source": "https://www.kaggle.com/datasets/jessemostipak/hotel-booking-demand/data", + "data_intro": "This dataset contains hotel booking data for a city hotel and a resort hotel, providing details on booking dates, stay duration, guest count, cancellation trends, and pricing. It is useful for demand forecasting, revenue management, and customer behavior analysis.", + "is_splited": false, + "overall_size": 119390, + "train_size": 0, + "test_size": 0, + "c_classes": 11, + "n_classes": 19, + "cat_feature_intro": { + "hotel": "Type of hotel (City Hotel or Resort Hotel).", + "arrival_date_month": "Month of arrival for the booking.", + "meal": "Type of meal plan booked (e.g., Breakfast included, Half board, Full board).", + "country": "Country of origin of the guest.", + "market_segment": "Booking distribution channel (e.g., direct, corporate, travel agent).", + "distribution_channel": "Channel through which the booking was made (e.g., online travel agents, direct).", + "reserved_room_type": "Type of room reserved by the guest before any changes.", + "assigned_room_type": "Final room type assigned to the guest.", + "deposit_type": "Indicates if a deposit was made and its type (e.g., No Deposit, Refundable, Non-Refundable).", + "customer_type": "Type of customer (e.g., transient, group, contract).", + "reservation_status": "Final status of the booking (e.g., Canceled, Checked-out, No-show)." + }, + "num_feature_intro": { + "lead_time": "Number of days between booking and arrival.", + "arrival_date_year": "Year of arrival for the booking.", + "arrival_date_week_number": "Week number of the arrival date.", + "arrival_date_day_of_month": "Day of the month of arrival.", + "stays_in_weekend_nights": "Number of weekend nights (Saturday or Sunday) the guest stayed.", + "stays_in_week_nights": "Number of weekday nights (Monday to Friday) the guest stayed.", + "adults": "Number of adults included in the booking.", + "children": "Number of children included in the booking.", + "babies": "Number of babies included in the booking.", + "is_repeated_guest": "Indicates whether the guest is a returning customer (1) or not (0).", + "previous_cancellations": "Number of times the guest has canceled previous bookings.", + "previous_bookings_not_canceled": "Number of previous bookings not canceled by the guest.", + "booking_changes": "Number of changes made to the booking.", + "agent": "ID of the booking agent (if applicable).", + "company": "ID of the company making the booking (if applicable).", + "days_in_waiting_list": "Number of days the booking was on the waiting list before confirmation.", + "adr": "Average daily rate (price per night per room).", + "required_car_parking_spaces": "Number of car parking spaces required by the guest.", + "total_of_special_requests": "Number of special requests made by the guest (e.g., high floor, twin beds)." + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "reservation_status", + "num_feature_value": { + "adr": [ + -6.38, + 5400.0 + ], + "adults": [ + 0.0, + 55.0 + ], + "agent": [ + 1.0, + 535.0 + ], + "arrival_date_day_of_month": [ + 1.0, + 31.0 + ], + "arrival_date_week_number": [ + 1.0, + 53.0 + ], + "arrival_date_year": [ + 2015.0, + 2017.0 + ], + "babies": [ + 0.0, + 10.0 + ], + "booking_changes": [ + 0.0, + 21.0 + ], + "children": [ + 0.0, + 10.0 + ], + "company": [ + 6.0, + 543.0 + ], + "days_in_waiting_list": [ + 0.0, + 391.0 + ], + "is_repeated_guest": [ + 0.0, + 1.0 + ], + "lead_time": [ + 0.0, + 737.0 + ], + "previous_bookings_not_canceled": [ + 0.0, + 72.0 + ], + "previous_cancellations": [ + 0.0, + 26.0 + ], + "required_car_parking_spaces": [ + 0.0, + 8.0 + ], + "stays_in_week_nights": [ + 0.0, + 50.0 + ], + "stays_in_weekend_nights": [ + 0.0, + 19.0 + ], + "total_of_special_requests": [ + 0.0, + 5.0 + ] + }, + "cat_feature_value": { + "arrival_date_month": [ + "April", + "August", + "December", + "February", + "January", + "July", + "June", + "March", + "May", + "November", + "October", + "September" + ], + "assigned_room_type": [ + "A", + "B", + "C", + "D", + "E", + "F", + "G", + "H", + "I", + "K", + "L", + "P" + ], + "country": [ + "ABW", + "AGO", + "AIA", + "ALB", + "AND", + "ARE", + "ARG", + "ARM", + "ASM", + "ATA", + "ATF", + "AUS", + "AUT", + "AZE", + "BDI", + "BEL", + "BEN", + "BFA", + "BGD", + "BGR", + "BHR", + "BHS", + "BIH", + "BLR", + "BOL", + "BRA", + "BRB", + "BWA", + "CAF", + "CHE", + "CHL", + "CHN", + "CIV", + "CMR", + "CN", + "COL", + "COM", + "CPV", + "CRI", + "CUB", + "CYM", + "CYP", + "CZE", + "DEU", + "DJI", + "DMA", + "DNK", + "DOM", + "DZA", + "ECU", + "EGY", + "ESP", + "EST", + "ETH", + "FIN", + "FJI", + "FRA", + "FRO", + "GAB", + "GBR", + "GEO", + "GGY", + "GHA", + "GIB", + "GLP", + "GNB", + "GRC", + "GTM", + "GUY", + "HKG", + "HND", + "HRV", + "HUN", + "IDN", + "IMN", + "IND", + "IRL", + "IRN", + "IRQ", + "ISL", + "ISR", + "ITA", + "JAM", + "JEY", + "JOR", + "JPN", + "KAZ", + "KEN", + "KHM", + "KIR", + "KNA", + "KOR", + "KWT", + "LAO", + "LBN", + "LBY", + "LCA", + "LIE", + "LKA", + "LTU", + "LUX", + "LVA", + "MAC", + "MAR", + "MCO", + "MDG", + "MDV", + "MEX", + "MKD", + "MLI", + "MLT", + "MMR", + "MNE", + "MOZ", + "MRT", + "MUS", + "MWI", + "MYS", + "MYT", + "NAM", + "NCL", + "NGA", + "NIC", + "NLD", + "NOR", + "NPL", + "NZL", + "OMN", + "PAK", + "PAN", + "PER", + "PHL", + "PLW", + "POL", + "PRI", + "PRT", + "PRY", + "PYF", + "QAT", + "ROU", + "RUS", + "RWA", + "SAU", + "SDN", + "SEN", + "SGP", + "SLE", + "SLV", + "SMR", + "SRB", + "STP", + "SUR", + "SVK", + "SVN", + "SWE", + "SYC", + "SYR", + "TGO", + "THA", + "TJK", + "TMP", + "TUN", + "TUR", + "TWN", + "TZA", + "UGA", + "UKR", + "UMI", + "URY", + "USA", + "UZB", + "VEN", + "VGB", + "VNM", + "ZAF", + "ZMB", + "ZWE" + ], + "customer_type": [ + "Contract", + "Group", + "Transient", + "Transient-Party" + ], + "deposit_type": [ + "No Deposit", + "Non Refund", + "Refundable" + ], + "distribution_channel": [ + "Corporate", + "Direct", + "GDS", + "TA/TO", + "Undefined" + ], + "hotel": [ + "City Hotel", + "Resort Hotel" + ], + "market_segment": [ + "Aviation", + "Complementary", + "Corporate", + "Direct", + "Groups", + "Offline TA/TO", + "Online TA", + "Undefined" + ], + "meal": [ + "BB", + "FB", + "HB", + "SC", + "Undefined" + ], + "reservation_status": [ + "Canceled", + "Check-Out", + "No-Show" + ], + "reserved_room_type": [ + "A", + "B", + "C", + "D", + "E", + "F", + "G", + "H", + "L", + "P" + ] + } +} diff --git a/single_point_prediction/daily/Hotel_booking_Demand_B1/info_mod.json b/single_point_prediction/daily/Hotel_booking_Demand_B1/info_mod.json index 9609e813606fc8bf0b15338ae4f5d5704da5f923..b10f27e7f41b7df00c38ade9c7caa9e2e7c7dc59 100644 --- a/single_point_prediction/daily/Hotel_booking_Demand_B1/info_mod.json +++ b/single_point_prediction/daily/Hotel_booking_Demand_B1/info_mod.json @@ -7,7 +7,7 @@ "train_size": 0, "test_size": 0, "c_classes": 12, - "n_classes": 20, + "n_classes": 19, "cat_feature_intro": { "hotel": "Type of hotel (City Hotel or Resort Hotel).", "arrival_date_month": "Month of arrival for the booking.", @@ -21,7 +21,6 @@ "customer_type": "Type of customer (e.g., transient, group, contract)." }, "num_feature_intro": { - "is_canceled": "Indicates whether the booking was canceled (1) or not (0).", "lead_time": "Number of days between booking and arrival.", "arrival_date_year": "Year of arrival for the booking.", "arrival_date_week_number": "Week number of the arrival date.", @@ -46,10 +45,6 @@ "task_type": "classification", "target": "reservation_status", "num_feature_value": { - "is_canceled": [ - 0.0, - 1.0 - ], "lead_time": [ 0.0, 737.0 @@ -315,4 +310,4 @@ "reservation_status_date" ], "missing_from_original_info": [] -} \ No newline at end of file +} diff --git a/single_point_prediction/daily/IBM_HR_Analytics_Employee_Attrition_&_Performance_B1/IBM_HR_Analytics_Employee_Attrition_&_Performance_B1_003.json b/single_point_prediction/daily/IBM_HR_Analytics_Employee_Attrition_&_Performance_B1/IBM_HR_Analytics_Employee_Attrition_&_Performance_B1_003.json index 40a3ea99a156c82d193c6a9de78371060d553d25..d223a33be338a7afbaf6f85a318983bfbc8074af 100644 --- a/single_point_prediction/daily/IBM_HR_Analytics_Employee_Attrition_&_Performance_B1/IBM_HR_Analytics_Employee_Attrition_&_Performance_B1_003.json +++ b/single_point_prediction/daily/IBM_HR_Analytics_Employee_Attrition_&_Performance_B1/IBM_HR_Analytics_Employee_Attrition_&_Performance_B1_003.json @@ -5,7 +5,7 @@ "perspective": "user", "dataset_name": "IBM HR Analytics Employee Attrition & Performance", "table_path": "kaggle/IBM HR Analytics Employee Attrition & Performance", - "query": "Walking out of my annual review, I can't help but feel a bit mixed. My manager, who I've only reported to for a year now, gave me a performance rating of 3, which is excellent, and a nice 19% salary hike. I'm 28, with a Bachelor's degree in the Medical field, and I've only ever worked at this one company. My daily rate is $1,172 and my monthly pay is $3,692, which breaks down to $78 an hour. I'm in a Level 1 role, and even though my job involvement is high, my satisfaction with the job and the work environment are just medium. I rarely travel for business, which is great because I live only 3 kilometers from the office. I don't work overtime, my work-life balance is better than good, and I had 3 training sessions last year. With a stock option level of 1 and having been here for exactly one year—still in my first role and with my first manager—do you think I'm a flight risk?", + "query": "Walking out of my annual review, I can't help but feel a bit mixed. My manager, who I've reported to for less than a year, gave me a performance rating of 3, which is excellent, and a nice 19% salary hike. I'm 28, with a Bachelor's degree in the Medical field, and I've only ever worked at this one company. My daily rate is $1,172 and my recorded MonthlyRate is $3,692 and my HourlyRate is $78. I'm in a Level 1 role, and even though my job involvement is high, my satisfaction with the job and the work environment are just medium. I rarely travel for business, which is great because I live only 3 kilometers from the office. I don't work overtime, my work-life balance is better than good, and I had 3 training sessions last year. With a stock option level of 1 and having been here for exactly one year—with less than a full year recorded in my current role and with my current manager—do you think I'm a flight risk?", "meta_info": { "domain": "daily" }, @@ -62,4 +62,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/single_point_prediction/finance/Default_of_Credit_Card_Clients_Dataset_B1/Default_of_Credit_Card_Clients_Dataset_B1_005.json b/single_point_prediction/finance/Default_of_Credit_Card_Clients_Dataset_B1/Default_of_Credit_Card_Clients_Dataset_B1_005.json index 295d66035500b0e41a3d1bee4f5e7d79d2b39a93..23255925cc01b3306a0d07b7b2cb13af9f47c21c 100644 --- a/single_point_prediction/finance/Default_of_Credit_Card_Clients_Dataset_B1/Default_of_Credit_Card_Clients_Dataset_B1_005.json +++ b/single_point_prediction/finance/Default_of_Credit_Card_Clients_Dataset_B1/Default_of_Credit_Card_Clients_Dataset_B1_005.json @@ -5,7 +5,7 @@ "perspective": "data_holder", "dataset_name": "Default of Credit Card Clients Dataset", "table_path": "kaggle/Default of Credit Card Clients Dataset", - "query": "The account numbered 29854 belongs to a 39-year-old university graduate with NT$50,000 in available credit. After being two months late in both April and May, this client has maintained perfect payment status from June through September. Their billing amounts have followed an interesting trajectory from NT$31,597 to NT$32,417 then down to NT$25,637, while making payments between NT$2,000 and NT$3,500. With our historical records of payment defaults now in your hands, would you flag this account as likely to miss the next payment?", + "query": "The account numbered 29854 belongs to a 39-year-old university graduate with NT$50,000 in available credit. After being two months late in both April and May, the available status entries for June, July, and September are code 0, while August's entry is missing. Their billing amounts have followed an interesting trajectory from NT$31,597 to NT$32,417 then down to NT$25,637, while making payments between NT$2,000 and NT$3,500. With our historical records of payment defaults now in your hands, would you flag this account as likely to miss the next payment?", "meta_info": { "domain": "finance" }, @@ -52,4 +52,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/single_point_prediction/medical/Indicators_of_Heart_Disease_(2022_UPDATE)_B1/Indicators_of_Heart_Disease_(2022_UPDATE)_B1_004.json b/single_point_prediction/medical/Indicators_of_Heart_Disease_(2022_UPDATE)_B1/Indicators_of_Heart_Disease_(2022_UPDATE)_B1_004.json index 1b8399cc02a729fbf435977be50c0608bbe61e38..9275605f43af739637842cb2bccb7878cb210cc6 100644 --- a/single_point_prediction/medical/Indicators_of_Heart_Disease_(2022_UPDATE)_B1/Indicators_of_Heart_Disease_(2022_UPDATE)_B1_004.json +++ b/single_point_prediction/medical/Indicators_of_Heart_Disease_(2022_UPDATE)_B1/Indicators_of_Heart_Disease_(2022_UPDATE)_B1_004.json @@ -5,7 +5,7 @@ "perspective": "user", "dataset_name": "Indicators of Heart Disease (2022 UPDATE)", "table_path": "kaggle/Indicators of Heart Disease (2022 UPDATE)", - "query": "His recent annual checkup in Utah went well, with the doctor noting this 30 to 34-year-old White, Non-Hispanic male is a former smoker who has never touched e-cigarettes. At 1.8 meters tall and 81.65 kilograms, his BMI is 25.1. He's had only one day of poor physical health in the last month and zero days of poor mental health, sleeps a solid 8 hours, and stays active. No heart attack, angina, stroke, asthma, skin cancer, COPD, depressive disorder, kidney disease, arthritis, or diabetes. His teeth are all intact, he drinks alcohol, and has no hearing, vision, concentration, walking, dressing, or errand-running difficulties. Tests for HIV and COVID were negative, he's not high-risk, got his flu and tetanus shots, but never had a pneumonia vaccine or a chest scan. With all this in mind, what would you say his overall health condition is?", + "query": "His recent annual checkup in Utah went well, with the doctor noting this 30 to 34-year-old White, Non-Hispanic male is a former smoker who has never touched e-cigarettes. At 1.8 meters tall and 81.65 kilograms, his BMI is 25.1. He's had only one day of poor physical health in the last month and zero days of poor mental health, sleeps a solid 8 hours, and stays active. No heart attack, angina, stroke, asthma, skin cancer, COPD, depressive disorder, kidney disease, arthritis, or diabetes. His teeth are all intact, he drinks alcohol, and has no hearing, vision, concentration, walking, dressing, or errand-running difficulties. He has been tested for HIV and has not reported having COVID, he's not high-risk, got his flu and tetanus shots, but never had a pneumonia vaccine or a chest scan. With all this in mind, what would you say his overall health condition is?", "meta_info": { "domain": "medical" }, @@ -67,4 +67,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/single_point_prediction/medical/Indicators_of_Heart_Disease_(2022_UPDATE)_B1/info.json b/single_point_prediction/medical/Indicators_of_Heart_Disease_(2022_UPDATE)_B1/info.json index 53272e3074451c673817a05ef4da5ee438d07863..c876b7e9aa18ec2175d72a1a6b59bf487c77e98c 100644 --- a/single_point_prediction/medical/Indicators_of_Heart_Disease_(2022_UPDATE)_B1/info.json +++ b/single_point_prediction/medical/Indicators_of_Heart_Disease_(2022_UPDATE)_B1/info.json @@ -1,401 +1,393 @@ -{ - "name": "Indicators of Heart Disease (2022 UPDATE)", - "source": "https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease/data", - "data_intro": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", - "is_splited": false, - "overall_size": 1010949, - "train_size": 0, - "test_size": 0, - "c_classes": 46, - "n_classes": 9, - "cat_feature_intro": { - "HeartDisease": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", - "Smoking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", - "AlcoholDrinking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", - "Stroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", - "DiffWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", - "Sex": "Sex is a biological factor that can influence the risk of heart disease. Men and women may experience different symptoms and risk factors for heart disease.", - "AgeCategory": "Age is a significant risk factor for heart disease. As people age, their risk of developing heart disease increases. It is important to monitor heart health as individuals get older.", - "Race": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", - "Diabetic": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", - "PhysicalActivity": "Regular physical activity is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", - "GenHealth": "General health status can influence the risk of heart disease. It is important to maintain overall health and address any underlying health issues to reduce the risk of heart-related complications.", - "Asthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", - "KidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", - "SkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", - "State": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", - "GeneralHealth": "General health status can influence the risk of heart disease. It is important to maintain overall health and address any underlying health issues to reduce the risk of heart-related complications.", - "LastCheckupTime": "The time since the last medical checkup can be an important factor in heart health. Regular checkups can help identify and address risk factors for heart disease early.", - "PhysicalActivities": "Engaging in regular physical activities is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", - "RemovedTeeth": "The number of removed teeth can be an indicator of overall health, including heart health. Poor oral health has been linked to an increased risk of heart disease.", - "HadHeartAttack": "As described above, the original dataset of nearly 300 variables was reduced to 40 variables. In addition to classical EDA, this dataset can be used to apply a number of machine learning methods, especially classifier models (logistic regression, SVM, random forest, etc.). You should treat the variable \"HadHeartAttack\" as binary (\"Yes\" - respondent had heart disease; \"No\" - respondent did not have heart disease). Note, however, that the classes are unbalanced, so the classic approach of applying a model is not advisable. Fixing the weights/undersampling should yield much better results. Based on the data set, I built a logistic regression model and embedded it in an application that might inspire you: https://share.streamlit.io/kamilpytlak/heart-condition-checker/main/app.py. Can you indicate which variables have a significant effect on the likelihood of heart disease?", - "HadAngina": "Angina is a type of chest pain caused by reduced blood flow to the heart. It is a symptom of coronary artery disease and can indicate an increased risk of heart disease.", - "HadStroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", - "HadAsthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", - "HadSkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", - "HadCOPD": "Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease that causes obstructed airflow from the lungs. It can increase the risk of heart disease.", - "HadDepressiveDisorder": "Depressive disorders can affect overall health and increase the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", - "HadKidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", - "HadArthritis": "Arthritis is a condition that causes inflammation of the joints. It can affect overall health and increase the risk of heart disease.", - "HadDiabetes": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", - "DeafOrHardOfHearing": "Hearing loss can affect overall health and quality of life. It is important to address and manage hearing issues to maintain overall health and reduce the risk of heart disease.", - "BlindOrVisionDifficulty": "Vision difficulties can affect overall health and quality of life. It is important to address and manage vision issues to maintain overall health and reduce the risk of heart disease.", - "DifficultyConcentrating": "Difficulty concentrating can be a sign of underlying health issues, including heart disease. It is important to address cognitive issues to improve overall health and reduce the risk of heart-related complications.", - "DifficultyWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", - "DifficultyDressingBathing": "Difficulty with daily activities such as dressing and bathing can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", - "DifficultyErrands": "Difficulty running errands can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", - "SmokerStatus": "Smoking is a major risk factor for heart disease. It is important to address and manage smoking habits to reduce the risk of heart-related complications.", - "ECigaretteUsage": "E-cigarette usage can affect heart health. It is important to monitor and manage e-cigarette usage to reduce the risk of heart disease.", - "ChestScan": "Chest scans can help identify underlying health issues, including heart disease. It is important to monitor and address any abnormalities found in chest scans to maintain overall health.", - "RaceEthnicityCategory": "Race and ethnicity can influence the risk of heart disease. It is important to consider these factors when assessing and managing heart health.", - "AlcoholDrinkers": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", - "HIVTesting": "HIV testing is important for overall health. It is important to address and manage HIV status to reduce the risk of heart-related complications.", - "FluVaxLast12": "Receiving a flu vaccine in the last 12 months can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", - "PneumoVaxEver": "Receiving a pneumococcal vaccine can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", - "TetanusLast10Tdap": "Receiving a tetanus vaccine in the last 10 years can help reduce the risk of infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", - "HighRiskLastYear": "Being at high risk for health issues in the last year can affect overall health and increase the risk of heart disease. It is important to address and manage high-risk factors to reduce the risk of heart-related complications.", - "CovidPos": "Testing positive for COVID-19 can affect overall health and increase the risk of heart disease. It is important to monitor and manage COVID-19 symptoms to reduce the risk of heart-related complications." - }, - "num_feature_intro": { - "BMI": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", - "PhysicalHealth": "Physical health is an important factor in overall well-being and can influence the risk of heart disease. It is important to maintain good physical health to reduce the risk of heart-related complications.", - "MentalHealth": "Mental health is an important factor in overall well-being and can influence the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", - "SleepTime": "Sleep time is an important factor in overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", - "PhysicalHealthDays": "The number of days with good physical health can be an indicator of overall well-being. It is important to maintain good physical health to reduce the risk of heart-related complications.", - "MentalHealthDays": "The number of days with good mental health can be an indicator of overall well-being. It is important to address and manage mental health to reduce the risk of heart-related complications.", - "SleepHours": "The number of hours of sleep can affect overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", - "HeightInMeters": "Height can be an indicator of overall health. It is important to monitor and maintain a healthy height-to-weight ratio to reduce the risk of heart disease.", - "WeightInKilograms": "Weight can be an indicator of overall health. It is important to maintain a healthy weight to reduce the risk of heart disease." - }, - "evaluation_metric": "The evaluation metric for this dataset can include accuracy, precision, recall, F1-score, and AUC-ROC. These metrics can help assess the performance of machine learning models in predicting heart disease.", - "task_type": "classification", - "target": "GeneralHealth", - "num_feature_value": { - "BMI": [ - 12.02, - 99.64 - ], - "HeightInMeters": [ - 0.91, - 2.41 - ], - "MentalHealth": [ - 0.0, - 30.0 - ], - "MentalHealthDays": [ - 0.0, - 30.0 - ], - "PhysicalHealth": [ - 0.0, - 30.0 - ], - "PhysicalHealthDays": [ - 0.0, - 30.0 - ], - "SleepHours": [ - 1.0, - 24.0 - ], - "SleepTime": [ - 1.0, - 24.0 - ], - "WeightInKilograms": [ - 22.68, - 292.57 - ] - }, - "cat_feature_value": { - "AgeCategory": [ - "18-24", - "25-29", - "30-34", - "35-39", - "40-44", - "45-49", - "50-54", - "55-59", - "60-64", - "65-69", - "70-74", - "75-79", - "80 or older", - "Age 18 to 24", - "Age 25 to 29", - "Age 30 to 34", - "Age 35 to 39", - "Age 40 to 44", - "Age 45 to 49", - "Age 50 to 54", - "Age 55 to 59", - "Age 60 to 64", - "Age 65 to 69", - "Age 70 to 74", - "Age 75 to 79", - "Age 80 or older" - ], - "AlcoholDrinkers": [ - "No", - "Yes" - ], - "AlcoholDrinking": [ - "No", - "Yes" - ], - "Asthma": [ - "No", - "Yes" - ], - "BlindOrVisionDifficulty": [ - "No", - "Yes" - ], - "ChestScan": [ - "No", - "Yes" - ], - "CovidPos": [ - "No", - "Tested positive using home test without a health professional", - "Yes" - ], - "DeafOrHardOfHearing": [ - "No", - "Yes" - ], - "Diabetic": [ - "No", - "No, borderline diabetes", - "Yes", - "Yes (during pregnancy)" - ], - "DiffWalking": [ - "No", - "Yes" - ], - "DifficultyConcentrating": [ - "No", - "Yes" - ], - "DifficultyDressingBathing": [ - "No", - "Yes" - ], - "DifficultyErrands": [ - "No", - "Yes" - ], - "DifficultyWalking": [ - "No", - "Yes" - ], - "ECigaretteUsage": [ - "Never used e-cigarettes in my entire life", - "Not at all (right now)", - "Use them every day", - "Use them some days" - ], - "FluVaxLast12": [ - "No", - "Yes" - ], - "GenHealth": [ - "Excellent", - "Fair", - "Good", - "Poor", - "Very good" - ], - "GeneralHealth": [ - "Excellent", - "Fair", - "Good", - "Poor", - "Very good" - ], - "HIVTesting": [ - "No", - "Yes" - ], - "HadAngina": [ - "No", - "Yes" - ], - "HadArthritis": [ - "No", - "Yes" - ], - "HadAsthma": [ - "No", - "Yes" - ], - "HadCOPD": [ - "No", - "Yes" - ], - "HadDepressiveDisorder": [ - "No", - "Yes" - ], - "HadDiabetes": [ - "No", - "No, pre-diabetes or borderline diabetes", - "Yes", - "Yes, but only during pregnancy (female)" - ], - "HadHeartAttack": [ - "No", - "Yes" - ], - "HadKidneyDisease": [ - "No", - "Yes" - ], - "HadSkinCancer": [ - "No", - "Yes" - ], - "HadStroke": [ - "No", - "Yes" - ], - "HeartDisease": [ - "No", - "Yes" - ], - "HighRiskLastYear": [ - "No", - "Yes" - ], - "KidneyDisease": [ - "No", - "Yes" - ], - "LastCheckupTime": [ - "5 or more years ago", - "Within past 2 years (1 year but less than 2 years ago)", - "Within past 5 years (2 years but less than 5 years ago)", - "Within past year (anytime less than 12 months ago)" - ], - "PhysicalActivities": [ - "No", - "Yes" - ], - "PhysicalActivity": [ - "No", - "Yes" - ], - "PneumoVaxEver": [ - "No", - "Yes" - ], - "Race": [ - "American Indian/Alaskan Native", - "Asian", - "Black", - "Hispanic", - "Other", - "White" - ], - "RaceEthnicityCategory": [ - "Black only, Non-Hispanic", - "Hispanic", - "Multiracial, Non-Hispanic", - "Other race only, Non-Hispanic", - "White only, Non-Hispanic" - ], - "RemovedTeeth": [ - "1 to 5", - "6 or more, but not all", - "All", - "None of them" - ], - "Sex": [ - "Female", - "Male" - ], - "SkinCancer": [ - "No", - "Yes" - ], - "SmokerStatus": [ - "Current smoker - now smokes every day", - "Current smoker - now smokes some days", - "Former smoker", - "Never smoked" - ], - "Smoking": [ - "No", - "Yes" - ], - "State": [ - "Alabama", - "Alaska", - "Arizona", - "Arkansas", - "California", - "Colorado", - "Connecticut", - "Delaware", - "District of Columbia", - "Florida", - "Georgia", - "Guam", - "Hawaii", - "Idaho", - "Illinois", - "Indiana", - "Iowa", - "Kansas", - "Kentucky", - "Louisiana", - "Maine", - "Maryland", - "Massachusetts", - "Michigan", - "Minnesota", - "Mississippi", - "Missouri", - "Montana", - "Nebraska", - "Nevada", - "New Hampshire", - "New Jersey", - "New Mexico", - "New York", - "North Carolina", - "North Dakota", - "Ohio", - "Oklahoma", - "Oregon", - "Pennsylvania", - "Puerto Rico", - "Rhode Island", - "South Carolina", - "South Dakota", - "Tennessee", - "Texas", - "Utah", - "Vermont", - "Virgin Islands", - "Virginia", - "Washington", - "West Virginia", - "Wisconsin", - "Wyoming" - ], - "Stroke": [ - "No", - "Yes" - ], - "TetanusLast10Tdap": [ - "No, did not receive any tetanus shot in the past 10 years", - "Yes, received Tdap", - "Yes, received tetanus shot but not sure what type", - "Yes, received tetanus shot, but not Tdap" - ] - } -} \ No newline at end of file +{ + "name": "Indicators of Heart Disease (2022 UPDATE)", + "source": "https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease/data", + "data_intro": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", + "is_splited": false, + "overall_size": 1010949, + "train_size": 0, + "test_size": 0, + "c_classes": 45, + "n_classes": 9, + "cat_feature_intro": { + "HeartDisease": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", + "Smoking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", + "AlcoholDrinking": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", + "Stroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", + "DiffWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", + "Sex": "Sex is a biological factor that can influence the risk of heart disease. Men and women may experience different symptoms and risk factors for heart disease.", + "AgeCategory": "Age is a significant risk factor for heart disease. As people age, their risk of developing heart disease increases. It is important to monitor heart health as individuals get older.", + "Race": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", + "Diabetic": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", + "PhysicalActivity": "Regular physical activity is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", + "Asthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", + "KidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", + "SkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", + "State": "The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm): \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.", + "GeneralHealth": "Self-reported general health from BRFSS GENHLTH: Excellent, Very good, Good, Fair, or Poor.", + "LastCheckupTime": "The time since the last medical checkup can be an important factor in heart health. Regular checkups can help identify and address risk factors for heart disease early.", + "PhysicalActivities": "Engaging in regular physical activities is important for maintaining heart health. It can help reduce the risk of heart disease by improving blood pressure, cholesterol levels, and overall cardiovascular fitness.", + "RemovedTeeth": "The number of removed teeth can be an indicator of overall health, including heart health. Poor oral health has been linked to an increased risk of heart disease.", + "HadHeartAttack": "As described above, the original dataset of nearly 300 variables was reduced to 40 variables. In addition to classical EDA, this dataset can be used to apply a number of machine learning methods, especially classifier models (logistic regression, SVM, random forest, etc.). You should treat the variable \"HadHeartAttack\" as binary (\"Yes\" - respondent had heart disease; \"No\" - respondent did not have heart disease). Note, however, that the classes are unbalanced, so the classic approach of applying a model is not advisable. Fixing the weights/undersampling should yield much better results. Based on the data set, I built a logistic regression model and embedded it in an application that might inspire you: https://share.streamlit.io/kamilpytlak/heart-condition-checker/main/app.py. Can you indicate which variables have a significant effect on the likelihood of heart disease?", + "HadAngina": "Angina is a type of chest pain caused by reduced blood flow to the heart. It is a symptom of coronary artery disease and can indicate an increased risk of heart disease.", + "HadStroke": "A stroke is a medical emergency that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. This can lead to serious health complications, including heart disease.", + "HadAsthma": "Asthma is a chronic respiratory condition that can affect heart health. It is important to manage asthma symptoms to reduce the risk of heart disease.", + "HadSkinCancer": "Skin cancer is a type of cancer that can affect overall health. It is important to address and treat skin cancer to maintain overall health and reduce the risk of heart disease.", + "HadCOPD": "Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease that causes obstructed airflow from the lungs. It can increase the risk of heart disease.", + "HadDepressiveDisorder": "Depressive disorders can affect overall health and increase the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", + "HadKidneyDisease": "Kidney disease can increase the risk of heart disease. It is important to monitor and manage kidney health to reduce the risk of heart-related complications.", + "HadArthritis": "Arthritis is a condition that causes inflammation of the joints. It can affect overall health and increase the risk of heart disease.", + "HadDiabetes": "Diabetes is a major risk factor for heart disease. High blood sugar levels can damage blood vessels and nerves that control the heart, leading to an increased risk of heart disease.", + "DeafOrHardOfHearing": "Hearing loss can affect overall health and quality of life. It is important to address and manage hearing issues to maintain overall health and reduce the risk of heart disease.", + "BlindOrVisionDifficulty": "Vision difficulties can affect overall health and quality of life. It is important to address and manage vision issues to maintain overall health and reduce the risk of heart disease.", + "DifficultyConcentrating": "Difficulty concentrating can be a sign of underlying health issues, including heart disease. It is important to address cognitive issues to improve overall health and reduce the risk of heart-related complications.", + "DifficultyWalking": "Difficulty walking can be a sign of underlying health issues, including heart disease. It is important to address mobility issues to improve overall health and reduce the risk of heart-related complications.", + "DifficultyDressingBathing": "Difficulty with daily activities such as dressing and bathing can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", + "DifficultyErrands": "Difficulty running errands can be a sign of underlying health issues, including heart disease. It is important to address these issues to improve overall health and reduce the risk of heart-related complications.", + "SmokerStatus": "Smoking is a major risk factor for heart disease. It is important to address and manage smoking habits to reduce the risk of heart-related complications.", + "ECigaretteUsage": "E-cigarette usage can affect heart health. It is important to monitor and manage e-cigarette usage to reduce the risk of heart disease.", + "ChestScan": "Chest scans can help identify underlying health issues, including heart disease. It is important to monitor and address any abnormalities found in chest scans to maintain overall health.", + "RaceEthnicityCategory": "Race and ethnicity can influence the risk of heart disease. It is important to consider these factors when assessing and managing heart health.", + "AlcoholDrinkers": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", + "HIVTesting": "Whether the respondent has ever been tested for HIV (Yes/No), not the result of that test.", + "FluVaxLast12": "Receiving a flu vaccine in the last 12 months can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", + "PneumoVaxEver": "Receiving a pneumococcal vaccine can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", + "TetanusLast10Tdap": "Receiving a tetanus vaccine in the last 10 years can help reduce the risk of infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", + "HighRiskLastYear": "Being at high risk for health issues in the last year can affect overall health and increase the risk of heart disease. It is important to address and manage high-risk factors to reduce the risk of heart-related complications.", + "CovidPos": "Testing positive for COVID-19 can affect overall health and increase the risk of heart disease. It is important to monitor and manage COVID-19 symptoms to reduce the risk of heart-related complications." + }, + "num_feature_intro": { + "BMI": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect \"patterns\" in the data that can predict a patient's condition.", + "PhysicalHealth": "Physical health is an important factor in overall well-being and can influence the risk of heart disease. It is important to maintain good physical health to reduce the risk of heart-related complications.", + "MentalHealth": "Mental health is an important factor in overall well-being and can influence the risk of heart disease. It is important to address and manage mental health to reduce the risk of heart-related complications.", + "SleepTime": "Sleep time is an important factor in overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", + "PhysicalHealthDays": "The number of days with good physical health can be an indicator of overall well-being. It is important to maintain good physical health to reduce the risk of heart-related complications.", + "MentalHealthDays": "The number of days with good mental health can be an indicator of overall well-being. It is important to address and manage mental health to reduce the risk of heart-related complications.", + "SleepHours": "The number of hours of sleep can affect overall health. Poor sleep can affect heart health and increase the risk of heart disease. It is important to maintain good sleep habits to reduce the risk of heart-related complications.", + "HeightInMeters": "Height can be an indicator of overall health. It is important to monitor and maintain a healthy height-to-weight ratio to reduce the risk of heart disease.", + "WeightInKilograms": "Weight can be an indicator of overall health. It is important to maintain a healthy weight to reduce the risk of heart disease." + }, + "evaluation_metric": "The evaluation metric for this dataset can include accuracy, precision, recall, F1-score, and AUC-ROC. These metrics can help assess the performance of machine learning models in predicting heart disease.", + "task_type": "classification", + "target": "GeneralHealth", + "num_feature_value": { + "BMI": [ + 12.02, + 99.64 + ], + "HeightInMeters": [ + 0.91, + 2.41 + ], + "MentalHealth": [ + 0.0, + 30.0 + ], + "MentalHealthDays": [ + 0.0, + 30.0 + ], + "PhysicalHealth": [ + 0.0, + 30.0 + ], + "PhysicalHealthDays": [ + 0.0, + 30.0 + ], + "SleepHours": [ + 1.0, + 24.0 + ], + "SleepTime": [ + 1.0, + 24.0 + ], + "WeightInKilograms": [ + 22.68, + 292.57 + ] + }, + "cat_feature_value": { + "AgeCategory": [ + "18-24", + "25-29", + "30-34", + "35-39", + "40-44", + "45-49", + "50-54", + "55-59", + "60-64", + "65-69", + "70-74", + "75-79", + "80 or older", + "Age 18 to 24", + "Age 25 to 29", + "Age 30 to 34", + "Age 35 to 39", + "Age 40 to 44", + "Age 45 to 49", + "Age 50 to 54", + "Age 55 to 59", + "Age 60 to 64", + "Age 65 to 69", + "Age 70 to 74", + "Age 75 to 79", + "Age 80 or older" + ], + "AlcoholDrinkers": [ + "No", + "Yes" + ], + "AlcoholDrinking": [ + "No", + "Yes" + ], + "Asthma": [ + "No", + "Yes" + ], + "BlindOrVisionDifficulty": [ + "No", + "Yes" + ], + "ChestScan": [ + "No", + "Yes" + ], + "CovidPos": [ + "No", + "Tested positive using home test without a health professional", + "Yes" + ], + "DeafOrHardOfHearing": [ + "No", + "Yes" + ], + "Diabetic": [ + "No", + "No, borderline diabetes", + "Yes", + "Yes (during pregnancy)" + ], + "DiffWalking": [ + "No", + "Yes" + ], + "DifficultyConcentrating": [ + "No", + "Yes" + ], + "DifficultyDressingBathing": [ + "No", + "Yes" + ], + "DifficultyErrands": [ + "No", + "Yes" + ], + "DifficultyWalking": [ + "No", + "Yes" + ], + "ECigaretteUsage": [ + "Never used e-cigarettes in my entire life", + "Not at all (right now)", + "Use them every day", + "Use them some days" + ], + "FluVaxLast12": [ + "No", + "Yes" + ], + "GeneralHealth": [ + "Excellent", + "Very good", + "Good", + "Fair", + "Poor" + ], + "HIVTesting": [ + "No", + "Yes" + ], + "HadAngina": [ + "No", + "Yes" + ], + "HadArthritis": [ + "No", + "Yes" + ], + "HadAsthma": [ + "No", + "Yes" + ], + "HadCOPD": [ + "No", + "Yes" + ], + "HadDepressiveDisorder": [ + "No", + "Yes" + ], + "HadDiabetes": [ + "No", + "No, pre-diabetes or borderline diabetes", + "Yes", + "Yes, but only during pregnancy (female)" + ], + "HadHeartAttack": [ + "No", + "Yes" + ], + "HadKidneyDisease": [ + "No", + "Yes" + ], + "HadSkinCancer": [ + "No", + "Yes" + ], + "HadStroke": [ + "No", + "Yes" + ], + "HeartDisease": [ + "No", + "Yes" + ], + "HighRiskLastYear": [ + "No", + "Yes" + ], + "KidneyDisease": [ + "No", + "Yes" + ], + "LastCheckupTime": [ + "5 or more years ago", + "Within past 2 years (1 year but less than 2 years ago)", + "Within past 5 years (2 years but less than 5 years ago)", + "Within past year (anytime less than 12 months ago)" + ], + "PhysicalActivities": [ + "No", + "Yes" + ], + "PhysicalActivity": [ + "No", + "Yes" + ], + "PneumoVaxEver": [ + "No", + "Yes" + ], + "Race": [ + "American Indian/Alaskan Native", + "Asian", + "Black", + "Hispanic", + "Other", + "White" + ], + "RaceEthnicityCategory": [ + "Black only, Non-Hispanic", + "Hispanic", + "Multiracial, Non-Hispanic", + "Other race only, Non-Hispanic", + "White only, Non-Hispanic" + ], + "RemovedTeeth": [ + "1 to 5", + "6 or more, but not all", + "All", + "None of them" + ], + "Sex": [ + "Female", + "Male" + ], + "SkinCancer": [ + "No", + "Yes" + ], + "SmokerStatus": [ + "Current smoker - now smokes every day", + "Current smoker - now smokes some days", + "Former smoker", + "Never smoked" + ], + "Smoking": [ + "No", + "Yes" + ], + "State": [ + "Alabama", + "Alaska", + "Arizona", + "Arkansas", + "California", + "Colorado", + "Connecticut", + "Delaware", + "District of Columbia", + "Florida", + "Georgia", + "Guam", + "Hawaii", + "Idaho", + "Illinois", + "Indiana", + "Iowa", + "Kansas", + "Kentucky", + "Louisiana", + "Maine", + "Maryland", + "Massachusetts", + "Michigan", + "Minnesota", + "Mississippi", + "Missouri", + "Montana", + "Nebraska", + "Nevada", + "New Hampshire", + "New Jersey", + "New Mexico", + "New York", + "North Carolina", + "North Dakota", + "Ohio", + "Oklahoma", + "Oregon", + "Pennsylvania", + "Puerto Rico", + "Rhode Island", + "South Carolina", + "South Dakota", + "Tennessee", + "Texas", + "Utah", + "Vermont", + "Virgin Islands", + "Virginia", + "Washington", + "West Virginia", + "Wisconsin", + "Wyoming" + ], + "Stroke": [ + "No", + "Yes" + ], + "TetanusLast10Tdap": [ + "No, did not receive any tetanus shot in the past 10 years", + "Yes, received Tdap", + "Yes, received tetanus shot but not sure what type", + "Yes, received tetanus shot, but not Tdap" + ] + } +} diff --git a/single_point_prediction/medical/Indicators_of_Heart_Disease_(2022_UPDATE)_B1/info_mod.json b/single_point_prediction/medical/Indicators_of_Heart_Disease_(2022_UPDATE)_B1/info_mod.json index c7adb32610f74efce39cc085901899bf4a6b65d4..f86d956d94fdc445b5cf06eb84def439cab91661 100644 --- a/single_point_prediction/medical/Indicators_of_Heart_Disease_(2022_UPDATE)_B1/info_mod.json +++ b/single_point_prediction/medical/Indicators_of_Heart_Disease_(2022_UPDATE)_B1/info_mod.json @@ -36,12 +36,13 @@ "RaceEthnicityCategory": "Race and ethnicity can influence the risk of heart disease. It is important to consider these factors when assessing and managing heart health.", "AgeCategory": "Age is a significant risk factor for heart disease. As people age, their risk of developing heart disease increases. It is important to monitor heart health as individuals get older.", "AlcoholDrinkers": "According to the [CDC](https://www.cdc.gov/heartdisease/risk_factors.htm), excessive alcohol consumption can lead to high blood pressure, heart failure, and even stroke. It is important to monitor and manage alcohol intake to reduce the risk of heart disease.", - "HIVTesting": "HIV testing is important for overall health. It is important to address and manage HIV status to reduce the risk of heart-related complications.", + "HIVTesting": "Whether the respondent has ever been tested for HIV (Yes/No), not the result of that test.", "FluVaxLast12": "Receiving a flu vaccine in the last 12 months can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", "PneumoVaxEver": "Receiving a pneumococcal vaccine can help reduce the risk of respiratory infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", "TetanusLast10Tdap": "Receiving a tetanus vaccine in the last 10 years can help reduce the risk of infections, which can affect heart health. It is important to stay up-to-date with vaccinations to maintain overall health.", "HighRiskLastYear": "Being at high risk for health issues in the last year can affect overall health and increase the risk of heart disease. It is important to address and manage high-risk factors to reduce the risk of heart-related complications.", - "CovidPos": "Testing positive for COVID-19 can affect overall health and increase the risk of heart disease. It is important to monitor and manage COVID-19 symptoms to reduce the risk of heart-related complications." + "CovidPos": "Testing positive for COVID-19 can affect overall health and increase the risk of heart disease. It is important to monitor and manage COVID-19 symptoms to reduce the risk of heart-related complications.", + "GeneralHealth": "Self-reported general health from BRFSS GENHLTH: Excellent, Very good, Good, Fair, or Poor." }, "num_feature_intro": { "PhysicalHealthDays": "The number of days with good physical health can be an indicator of overall well-being. It is important to maintain good physical health to reduce the risk of heart-related complications.", @@ -304,6 +305,13 @@ "No", "Tested positive using home test without a health professional", "Yes" + ], + "GeneralHealth": [ + "Excellent", + "Very good", + "Good", + "Fair", + "Poor" ] }, "columns": [ @@ -433,4 +441,4 @@ "open_text_feature_intro": {}, "open_text_features": [], "missing_from_original_info": [] -} \ No newline at end of file +} diff --git a/single_point_prediction/medical/Life_Expectancy_(WHO)_B1/Life_Expectancy_(WHO)_B1_004.json b/single_point_prediction/medical/Life_Expectancy_(WHO)_B1/Life_Expectancy_(WHO)_B1_004.json index ccc8de2b6ea8c894fbbe8538c40912572e16e21b..e163a482f93050dcab4508601ab55de540ecb9c0 100644 --- a/single_point_prediction/medical/Life_Expectancy_(WHO)_B1/Life_Expectancy_(WHO)_B1_004.json +++ b/single_point_prediction/medical/Life_Expectancy_(WHO)_B1/Life_Expectancy_(WHO)_B1_004.json @@ -5,7 +5,7 @@ "perspective": "user", "dataset_name": "Life Expectancy (WHO)", "table_path": "kaggle/Life Expectancy (WHO)", - "query": "Looking at this health report for Belize from 2012, a developing nation, the average life expectancy was recorded at 69.4 years. The adult mortality rate was quite high at 189 per 1000 population, though thankfully, infant deaths and under-five deaths were both at zero for that year. Alcohol consumption was measured at 6.66 liters per capita, while health expenditure was a significant 582.8 per capita. Immunization rates for Hepatitis B, Polio, and Diphtheria were all strong at 98%. The country's total health expenditure was 5.45% of its GDP, which was $4673.64 per person for a population of 33,671. The average BMI was a healthy 48.5, there were zero reported measles cases, and the HIV/AIDS prevalence was low at 0.1%. Issues with thinness in children and teens were present, with rates of 3.4 and 3.5 respectively, but the income composition of resources was a promising 0.702. With all these factors in play, what would you expect the average number of years a person spends in school to be?", + "query": "Looking at this health report for Belize from 2012, a developing nation, the average life expectancy was recorded at 69.4 years. The adult mortality rate was quite high at 189 per 1000 population, though thankfully, infant deaths and under-five deaths were both at zero for that year. Alcohol consumption was measured at 6.66 liters per capita, while the recorded health-expenditure measure was 582.8. Immunization rates for Hepatitis B, Polio, and Diphtheria were all strong at 98%. The country's total health expenditure was 5.45% of its GDP, which was $4673.64 per person for a population of 33,671. The recorded BMI value was 48.5, there were zero reported measles cases, and the HIV/AIDS prevalence was low at 0.1%. Issues with thinness in children and teens were present, with rates of 3.4 and 3.5 respectively, but the income composition of resources was a promising 0.702. With all these factors in play, what would you expect the average number of years a person spends in school to be?", "meta_info": { "domain": "medical" }, @@ -49,4 +49,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/single_point_prediction/medical/Sleep_Health_and_Lifestyle_Dataset_B1/history.csv b/single_point_prediction/medical/Sleep_Health_and_Lifestyle_Dataset_B1/history.csv index d0c59d804ef9cb0c9db59f0bc9226716daeafab1..757cb8ff3244fea26663d9a832e303868afab791 100644 --- a/single_point_prediction/medical/Sleep_Health_and_Lifestyle_Dataset_B1/history.csv +++ b/single_point_prediction/medical/Sleep_Health_and_Lifestyle_Dataset_B1/history.csv @@ -1,173 +1,173 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activity Level,Stress Level,BMI Category,Blood Pressure,Heart Rate,Daily Steps,Sleep Disorder -9,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -10,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -11,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000, -12,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -13,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000, -14,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000, -15,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000, -16,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000, +9,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +10,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +11,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000,No Disorder +12,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +13,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000,No Disorder +14,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000,No Disorder +15,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000,No Disorder +16,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000,No Disorder 17,Female,29,Nurse,6.5,5,40,7,Normal Weight,132/87,80,4000,Sleep Apnea 18,Male,29,Doctor,6.0,6,30,8,Normal,120/80,70,8000,Sleep Apnea 19,Female,29,Nurse,6.5,5,40,7,Normal Weight,132/87,80,4000,Insomnia -20,Male,30,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -21,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -22,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -23,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -24,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -25,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -26,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000, -27,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -28,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000, -29,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000, -30,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000, +20,Male,30,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +21,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +22,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +23,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +24,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +25,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +26,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,No Disorder +27,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +28,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,No Disorder +29,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,No Disorder +30,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,No Disorder 32,Female,30,Nurse,6.4,5,35,7,Normal Weight,130/86,78,4100,Insomnia -33,Female,31,Nurse,7.9,8,75,4,Normal Weight,117/76,69,6800, -34,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -35,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -36,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -37,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -38,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -39,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -40,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -41,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -42,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -43,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -44,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -45,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -46,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -47,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, -48,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000, -49,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000, +33,Female,31,Nurse,7.9,8,75,4,Normal Weight,117/76,69,6800,No Disorder +34,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +35,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +36,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +37,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +38,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +39,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +40,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +41,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +42,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +43,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +44,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +45,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +46,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +47,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder +48,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,No Disorder +49,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,No Disorder 50,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,Sleep Apnea -51,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000, -52,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000, -53,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -54,Male,32,Doctor,7.6,7,75,6,Normal,120/80,70,8000, -55,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -56,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -58,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -59,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -61,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -62,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -63,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000, -64,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000, -65,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000, -66,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000, -67,Male,32,Accountant,7.2,8,50,6,Normal Weight,118/76,68,7000, +51,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000,No Disorder +52,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000,No Disorder +53,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +54,Male,32,Doctor,7.6,7,75,6,Normal,120/80,70,8000,No Disorder +55,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +56,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +58,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +59,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +61,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +62,Male,32,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +63,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,No Disorder +64,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,No Disorder +65,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,No Disorder +66,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,No Disorder +67,Male,32,Accountant,7.2,8,50,6,Normal Weight,118/76,68,7000,No Disorder 68,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,Insomnia -69,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500, -70,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500, -71,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -72,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -73,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -74,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000, -75,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -76,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -77,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -78,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -79,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, -80,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000, +69,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500,No Disorder +70,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500,No Disorder +71,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +72,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +73,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +74,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,No Disorder +75,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +76,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +77,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +78,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +79,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder +80,Male,33,Doctor,6.0,6,30,8,Normal,125/80,72,5000,No Disorder 81,Female,34,Scientist,5.8,4,32,8,Overweight,131/86,81,5200,Sleep Apnea 82,Female,34,Scientist,5.8,4,32,8,Overweight,131/86,81,5200,Sleep Apnea -83,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600, -84,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600, -85,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000, -86,Female,35,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -87,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000, -88,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000, -89,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000, -90,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000, -91,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000, -92,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000, -93,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000, +83,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600,No Disorder +84,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600,No Disorder +85,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000,No Disorder +86,Female,35,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +87,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000,No Disorder +88,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000,No Disorder +89,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,No Disorder +90,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,No Disorder +91,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,No Disorder +92,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,No Disorder +93,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000,No Disorder 94,Male,35,Lawyer,7.4,7,60,5,Obese,135/88,84,3300,Sleep Apnea 95,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000,Insomnia -96,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -97,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -98,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -99,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000, -100,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000, -101,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000, -102,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000, -103,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000, +96,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +97,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +98,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +99,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +100,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +101,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +102,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +103,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,No Disorder 104,Male,36,Teacher,6.6,5,35,7,Overweight,129/84,74,4800,Sleep Apnea 105,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,Sleep Apnea 106,Male,36,Teacher,6.6,5,35,7,Overweight,129/84,74,4800,Insomnia -107,Female,37,Nurse,6.1,6,42,6,Overweight,126/83,77,4200, -108,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000, -109,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000, -111,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -113,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -115,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -116,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -117,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -118,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -119,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -120,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -121,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -122,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -123,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -124,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -125,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000, -126,Female,37,Nurse,7.5,8,60,4,Normal Weight,120/80,70,8000, -127,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -128,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -129,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -130,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -131,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -132,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -133,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -134,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -135,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -136,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000, -137,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -138,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000, -139,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -140,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000, -141,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -142,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000, -143,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, -144,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000, +107,Female,37,Nurse,6.1,6,42,6,Overweight,126/83,77,4200,No Disorder +108,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000,No Disorder +109,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000,No Disorder +111,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +113,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +115,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +116,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +117,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +118,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +119,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +120,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +121,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +122,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +123,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +124,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +125,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,No Disorder +126,Female,37,Nurse,7.5,8,60,4,Normal Weight,120/80,70,8000,No Disorder +127,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +128,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +129,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +130,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +131,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +132,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +133,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +134,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +135,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +136,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,No Disorder +137,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +138,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,No Disorder +139,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +140,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,No Disorder +141,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +142,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,No Disorder +143,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder +144,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,No Disorder 145,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,Sleep Apnea 146,Female,38,Lawyer,7.4,7,60,5,Obese,135/88,84,3300,Sleep Apnea 147,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,Insomnia 148,Male,39,Engineer,6.5,5,40,7,Overweight,132/87,80,4000,Insomnia -149,Female,39,Lawyer,6.9,7,50,6,Normal Weight,128/85,75,5500, -150,Female,39,Accountant,8.0,9,80,3,Normal Weight,115/78,67,7500, -151,Female,39,Accountant,8.0,9,80,3,Normal Weight,115/78,67,7500, -152,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -153,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -154,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -155,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -156,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -157,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -158,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -159,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -160,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -161,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000, -162,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300, -163,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300, -164,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000, -165,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000, -167,Male,41,Engineer,7.3,8,70,6,Normal Weight,121/79,72,6200, -168,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000, -169,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000, -170,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -171,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -172,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -173,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -174,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000, -175,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000, -176,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000, -177,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000, +149,Female,39,Lawyer,6.9,7,50,6,Normal Weight,128/85,75,5500,No Disorder +150,Female,39,Accountant,8.0,9,80,3,Normal Weight,115/78,67,7500,No Disorder +151,Female,39,Accountant,8.0,9,80,3,Normal Weight,115/78,67,7500,No Disorder +152,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +153,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +154,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +155,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +156,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +157,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +158,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +159,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +160,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +161,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,No Disorder +162,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300,No Disorder +163,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300,No Disorder +164,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000,No Disorder +165,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000,No Disorder +167,Male,41,Engineer,7.3,8,70,6,Normal Weight,121/79,72,6200,No Disorder +168,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000,No Disorder +169,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000,No Disorder +170,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +171,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +172,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +173,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +174,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +175,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,No Disorder +176,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,No Disorder +177,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,No Disorder 178,Male,42,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia -179,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, -180,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, -181,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, -182,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, -183,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, -184,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000, +179,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +180,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +181,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +182,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +183,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +184,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder 185,Female,42,Teacher,6.8,6,45,7,Overweight,130/85,78,5000,Sleep Apnea 186,Female,42,Teacher,6.8,6,45,7,Overweight,130/85,78,5000,Sleep Apnea 187,Female,43,Teacher,6.7,7,45,4,Overweight,135/90,65,6000,Insomnia @@ -187,21 +187,21 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 201,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia 202,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Insomnia 203,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Insomnia -204,Male,43,Engineer,6.9,6,47,7,Normal Weight,117/76,69,6800, -205,Male,43,Engineer,7.6,8,75,4,Overweight,122/80,68,6800, -206,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -207,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -208,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -209,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -210,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -211,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000, -212,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -213,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -214,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -215,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -216,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -217,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, -218,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000, +204,Male,43,Engineer,6.9,6,47,7,Normal Weight,117/76,69,6800,No Disorder +205,Male,43,Engineer,7.6,8,75,4,Overweight,122/80,68,6800,No Disorder +206,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +207,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +208,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +209,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +210,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +211,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,No Disorder +212,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +213,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +214,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +215,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +216,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +217,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder +218,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,No Disorder 219,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Sleep Apnea 220,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Sleep Apnea 221,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia @@ -232,8 +232,8 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 246,Female,44,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia 247,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia 248,Male,44,Engineer,6.8,7,45,7,Overweight,130/85,78,5000,Insomnia -249,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000, -250,Male,44,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000, +249,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,No Disorder +250,Male,44,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,No Disorder 251,Female,45,Teacher,6.8,7,30,6,Overweight,135/90,65,6000,Insomnia 252,Female,45,Teacher,6.8,7,30,6,Overweight,135/90,65,6000,Insomnia 253,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia @@ -245,13 +245,13 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 259,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia 260,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia 261,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia -262,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000, -263,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000, -264,Female,45,Manager,6.9,7,55,5,Overweight,125/82,75,5500, +262,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,No Disorder +263,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,No Disorder +264,Female,45,Manager,6.9,7,55,5,Overweight,125/82,75,5500,No Disorder 265,Male,48,Doctor,7.3,7,65,5,Obese,142/92,83,3500,Insomnia 266,Female,48,Nurse,5.9,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 267,Male,48,Doctor,7.3,7,65,5,Obese,142/92,83,3500,Insomnia -268,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000, +268,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,No Disorder 269,Female,49,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 270,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 271,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea @@ -263,8 +263,8 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 277,Male,49,Doctor,8.1,9,85,3,Obese,139/91,86,3700,Sleep Apnea 278,Male,49,Doctor,8.1,9,85,3,Obese,139/91,86,3700,Sleep Apnea 279,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Insomnia -280,Female,50,Engineer,8.3,9,30,3,Normal,125/80,65,5000, -281,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000, +280,Female,50,Engineer,8.3,9,30,3,Normal,125/80,65,5000,No Disorder +281,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,No Disorder 283,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 284,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 285,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea @@ -275,11 +275,11 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 291,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 294,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 296,Female,50,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea -299,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -300,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -301,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -302,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -303,Female,51,Nurse,7.1,7,55,6,Normal Weight,125/82,72,6000, +299,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +300,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +301,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +302,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +303,Female,51,Nurse,7.1,7,55,6,Normal Weight,125/82,72,6000,No Disorder 304,Female,51,Nurse,6.0,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 305,Female,51,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea 306,Female,51,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea @@ -289,38 +289,38 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 310,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia 311,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia 312,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia -313,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -314,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -315,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000, +313,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +314,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +315,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder 316,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,Insomnia -317,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -318,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -319,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -320,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -321,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -322,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -323,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -324,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -325,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000, -326,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -327,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000, -328,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -329,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000, -330,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -331,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000, -332,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -333,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -334,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -335,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -336,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -337,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -338,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000, -339,Female,54,Engineer,8.5,9,30,3,Normal,125/80,65,5000, +317,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +318,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +319,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +320,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +321,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +322,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +323,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +324,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +325,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,No Disorder +326,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +327,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,No Disorder +328,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +329,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,No Disorder +330,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +331,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder +332,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +333,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +334,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +335,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +336,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +337,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +338,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,No Disorder +339,Female,54,Engineer,8.5,9,30,3,Normal,125/80,65,5000,No Disorder 340,Female,55,Nurse,8.1,9,75,4,Overweight,140/95,72,5000,Sleep Apnea 341,Female,55,Nurse,8.1,9,75,4,Overweight,140/95,72,5000,Sleep Apnea -342,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000, -343,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000, -344,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000, +342,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000,No Disorder +343,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000,No Disorder +344,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,No Disorder 345,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 346,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 347,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea @@ -329,8 +329,8 @@ Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activit 350,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 351,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 352,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea -359,Female,59,Nurse,8.0,9,75,3,Overweight,140/95,68,7000, -360,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000, +359,Female,59,Nurse,8.0,9,75,3,Overweight,140/95,68,7000,No Disorder +360,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,No Disorder 361,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 362,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea 363,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea diff --git a/single_point_prediction/medical/Sleep_Health_and_Lifestyle_Dataset_B1/info.json b/single_point_prediction/medical/Sleep_Health_and_Lifestyle_Dataset_B1/info.json index ac7f1ad59f059be3648f2769a10128e59277c544..c68a30219902530a9154e48d4957e5bb70f65c0f 100644 --- a/single_point_prediction/medical/Sleep_Health_and_Lifestyle_Dataset_B1/info.json +++ b/single_point_prediction/medical/Sleep_Health_and_Lifestyle_Dataset_B1/info.json @@ -1,121 +1,122 @@ -{ - "name": "Sleep Health and Lifestyle Dataset", - "source": "https://www.kaggle.com/datasets/uom190346a/sleep-health-and-lifestyle-dataset/data", - "data_intro": "The Sleep Health and Lifestyle Dataset comprises 400 rows and 13 columns, covering a wide range of variables related to sleep and daily habits. It includes details such as gender, age, occupation, sleep duration, quality of sleep, physical activity level, stress levels, BMI category, blood pressure, heart rate, daily steps, and the presence or absence of sleep disorders.", - "is_splited": false, - "overall_size": 374, - "train_size": 0, - "test_size": 0, - "c_classes": 5, - "n_classes": 8, - "cat_feature_intro": { - "Gender": "Gender: The gender of the person (Male/Female).
", - "Occupation": "Occupation: The occupation or profession of the person.
", - "BMI Category": "BMI Category: The BMI category of the person (e.g., Underweight, Normal, Overweight).
", - "Blood Pressure": "Blood Pressure (systolic/diastolic): The blood pressure measurement of the person, indicated as systolic pressure over diastolic pressure.
", - "Sleep Disorder": "- None: The individual does not exhibit any specific sleep disorder." - }, - "num_feature_intro": { - "Person ID": "Person ID: An identifier for each individual.
", - "Age": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", - "Sleep Duration": "Sleep Duration (hours): The number of hours the person sleeps per day.
", - "Quality of Sleep": "Quality of Sleep (scale: 1-10): A subjective rating of the quality of sleep, ranging from 1 to 10.
", - "Physical Activity Level": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", - "Stress Level": "Stress Level (scale: 1-10): A subjective rating of the stress level experienced by the person, ranging from 1 to 10.
", - "Heart Rate": "Heart Rate (bpm): The resting heart rate of the person in beats per minute.
", - "Daily Steps": "Daily Steps: The number of steps the person takes per day.
" - }, - "evaluation_metric": null, - "task_type": "classification", - "target": "Sleep Disorder", - "num_feature_value": { - "Age": [ - 27.0, - 59.0 - ], - "Daily Steps": [ - 3000.0, - 10000.0 - ], - "Heart Rate": [ - 65.0, - 86.0 - ], - "Person ID": [ - 1.0, - 374.0 - ], - "Physical Activity Level": [ - 30.0, - 90.0 - ], - "Quality of Sleep": [ - 4.0, - 9.0 - ], - "Sleep Duration": [ - 5.8, - 8.5 - ], - "Stress Level": [ - 3.0, - 8.0 - ] - }, - "cat_feature_value": { - "BMI Category": [ - "Normal", - "Normal Weight", - "Obese", - "Overweight" - ], - "Blood Pressure": [ - "115/75", - "115/78", - "117/76", - "118/75", - "118/76", - "119/77", - "120/80", - "121/79", - "122/80", - "125/80", - "125/82", - "126/83", - "128/84", - "128/85", - "129/84", - "130/85", - "130/86", - "131/86", - "132/87", - "135/88", - "135/90", - "139/91", - "140/90", - "140/95", - "142/92" - ], - "Gender": [ - "Female", - "Male" - ], - "Occupation": [ - "Accountant", - "Doctor", - "Engineer", - "Lawyer", - "Manager", - "Nurse", - "Sales Representative", - "Salesperson", - "Scientist", - "Software Engineer", - "Teacher" - ], - "Sleep Disorder": [ - "Insomnia", - "Sleep Apnea" - ] - } -} \ No newline at end of file +{ + "name": "Sleep Health and Lifestyle Dataset", + "source": "https://www.kaggle.com/datasets/uom190346a/sleep-health-and-lifestyle-dataset/data", + "data_intro": "The Sleep Health and Lifestyle Dataset comprises 400 rows and 13 columns, covering a wide range of variables related to sleep and daily habits. It includes details such as gender, age, occupation, sleep duration, quality of sleep, physical activity level, stress levels, BMI category, blood pressure, heart rate, daily steps, and the presence or absence of sleep disorders.", + "is_splited": false, + "overall_size": 374, + "train_size": 0, + "test_size": 0, + "c_classes": 5, + "n_classes": 8, + "cat_feature_intro": { + "Gender": "Gender: The gender of the person (Male/Female).
", + "Occupation": "Occupation: The occupation or profession of the person.
", + "BMI Category": "BMI Category: The BMI category of the person (e.g., Underweight, Normal, Overweight).
", + "Blood Pressure": "Blood Pressure (systolic/diastolic): The blood pressure measurement of the person, indicated as systolic pressure over diastolic pressure.
", + "Sleep Disorder": "Sleep disorder category: No Disorder, Insomnia, or Sleep Apnea." + }, + "num_feature_intro": { + "Person ID": "Person ID: An identifier for each individual.
", + "Age": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", + "Sleep Duration": "Sleep Duration (hours): The number of hours the person sleeps per day.
", + "Quality of Sleep": "Quality of Sleep (scale: 1-10): A subjective rating of the quality of sleep, ranging from 1 to 10.
", + "Physical Activity Level": "Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
", + "Stress Level": "Stress Level (scale: 1-10): A subjective rating of the stress level experienced by the person, ranging from 1 to 10.
", + "Heart Rate": "Heart Rate (bpm): The resting heart rate of the person in beats per minute.
", + "Daily Steps": "Daily Steps: The number of steps the person takes per day.
" + }, + "evaluation_metric": null, + "task_type": "classification", + "target": "Sleep Disorder", + "num_feature_value": { + "Age": [ + 27.0, + 59.0 + ], + "Daily Steps": [ + 3000.0, + 10000.0 + ], + "Heart Rate": [ + 65.0, + 86.0 + ], + "Person ID": [ + 1.0, + 374.0 + ], + "Physical Activity Level": [ + 30.0, + 90.0 + ], + "Quality of Sleep": [ + 4.0, + 9.0 + ], + "Sleep Duration": [ + 5.8, + 8.5 + ], + "Stress Level": [ + 3.0, + 8.0 + ] + }, + "cat_feature_value": { + "BMI Category": [ + "Normal", + "Normal Weight", + "Obese", + "Overweight" + ], + "Blood Pressure": [ + "115/75", + "115/78", + "117/76", + "118/75", + "118/76", + "119/77", + "120/80", + "121/79", + "122/80", + "125/80", + "125/82", + "126/83", + "128/84", + "128/85", + "129/84", + "130/85", + "130/86", + "131/86", + "132/87", + "135/88", + "135/90", + "139/91", + "140/90", + "140/95", + "142/92" + ], + "Gender": [ + "Female", + "Male" + ], + "Occupation": [ + "Accountant", + "Doctor", + "Engineer", + "Lawyer", + "Manager", + "Nurse", + "Sales Representative", + "Salesperson", + "Scientist", + "Software Engineer", + "Teacher" + ], + "Sleep Disorder": [ + "Insomnia", + "No Disorder", + "Sleep Apnea" + ] + } +} diff --git a/single_point_prediction/medical/Sleep_Health_and_Lifestyle_Dataset_B1/info_mod.json b/single_point_prediction/medical/Sleep_Health_and_Lifestyle_Dataset_B1/info_mod.json index 61f0aae2fe42de18994a371f556dc4b0b413e67f..8f8a1336c525f3a577855f801fe66c67768f9bc7 100644 --- a/single_point_prediction/medical/Sleep_Health_and_Lifestyle_Dataset_B1/info_mod.json +++ b/single_point_prediction/medical/Sleep_Health_and_Lifestyle_Dataset_B1/info_mod.json @@ -12,7 +12,8 @@ "Gender": "Gender: The gender of the person (Male/Female).
", "Occupation": "Occupation: The occupation or profession of the person.
", "BMI Category": "BMI Category: The BMI category of the person (e.g., Underweight, Normal, Overweight).
", - "Blood Pressure": "Blood Pressure (systolic/diastolic): The blood pressure measurement of the person, indicated as systolic pressure over diastolic pressure.
" + "Blood Pressure": "Blood Pressure (systolic/diastolic): The blood pressure measurement of the person, indicated as systolic pressure over diastolic pressure.
", + "Sleep Disorder": "Sleep disorder category: No Disorder, Insomnia, or Sleep Apnea." }, "num_feature_intro": { "Person ID": "Person ID: An identifier for each individual.
", @@ -111,6 +112,11 @@ "140/90", "140/95", "142/92" + ], + "Sleep Disorder": [ + "Insomnia", + "No Disorder", + "Sleep Apnea" ] }, "columns": [ @@ -159,4 +165,4 @@ "open_text_feature_intro": {}, "open_text_features": [], "missing_from_original_info": [] -} \ No newline at end of file +} diff --git a/treatment_effect_analysis/daily/regression/Chocolate_Bar_Ratings_B3/history.csv b/treatment_effect_analysis/daily/regression/Chocolate_Bar_Ratings_B3/history.csv index b83a5a16a61392eb7f437862ea450156b7a28635..03656401bb53caac7577efc7f3a18dbd5177c406 100644 --- a/treatment_effect_analysis/daily/regression/Chocolate_Bar_Ratings_B3/history.csv +++ b/treatment_effect_analysis/daily/regression/Chocolate_Bar_Ratings_B3/history.csv @@ -1296,7 +1296,7 @@ Carlotta Chocolat,Arauca,1884,2016,65%,Colombia,3.0,Trinitario,Colombia Soma,Carenero Superior,951,2012,70%,Canada,3.75,Trinitario,Venezuela Arete,San Juan,1724,2016,70%,U.S.A.,3.5,Trinitario,Trinidad Starchild,Cacao Verapaz,1692,2015,70%,U.S.A.,3.0, ,Guatemala -Na�ve,Chuao,1399,2014,75%,Lithuania,2.75,Trinitario,Venezuela +Naïve,Chuao,1399,2014,75%,Lithuania,2.75,Trinitario,Venezuela Arete,Chuno,1598,2015,70%,U.S.A.,4.0,"Criollo, Trinitario",Nicaragua Pascha,Peru,1137,2013,70%,Peru,2.75, ,Peru A. Morin,Atsane,1676,2015,70%,France,3.0, ,Togo @@ -1391,7 +1391,7 @@ Pacari,Los Rios,224,2008,72%,Ecuador,3.0,Forastero (Arriba),Ecuador Pralus,Dominican Republic-Organic,280,2008,75%,France,2.0, ,Dominican Republic Felchlin,Maracaibo Clasificado,48,2006,65%,Switzerland,3.0,Criollo,Venezuela French Broad,"Palo Blanco, Chulucanas",883,2012,61%,U.S.A.,3.5, ,Peru -Na�ve,Barinas,1399,2014,70%,Lithuania,3.0, ,Venezuela +Naïve,Barinas,1399,2014,70%,Lithuania,3.0, ,Venezuela Pump Street Bakery,"Akesson's, batch 4411",1530,2015,74%,U.K.,3.5,Criollo,Madagascar A. Morin,Chanchamayo Province,1019,2013,70%,France,3.5, ,Peru Soma,"Alto Beni, Wild Bolivian",676,2011,70%,Canada,3.5,Nacional,Bolivia @@ -1475,7 +1475,7 @@ Mutari,"Kokoa Kamili, batch 1 SRB",1896,2016,68%,U.S.A.,2.75, ,Tanzania Bakau,"Bambamarca, 2015",1454,2015,70%,Peru,2.75, ,Peru Belyzium,Belize south,1768,2016,83%,Germany,3.5,Trinitario,Belize Szanto Tibor,"Autumn, Primary Harvest, 2012",1185,2013,70%,Hungary,3.5,Criollo,Madagascar -Na�ve,Nicaragua,1379,2014,70%,Lithuania,3.25,Criollo,Nicaragua +Naïve,Nicaragua,1379,2014,70%,Lithuania,3.25,Criollo,Nicaragua Valrhona,"Loma Sotavento, 2013",1153,2013,64%,France,3.5, ,Dominican Republic Rogue,"Camino Verde P., Balao, Guayas",979,2012,75%,U.S.A.,,Nacional (Arriba),Ecuador Cacao Atlanta,"Dominican Republic, ""Love Bar""",502,2010,75%,U.S.A.,, ,Dominican Republic diff --git a/treatment_effect_analysis/daily/regression/Gun_Violence_Data_B3/Gun_Violence_Data_B3_001.json b/treatment_effect_analysis/daily/regression/Gun_Violence_Data_B3/Gun_Violence_Data_B3_001.json index 4a0cb1a30eb7a6c5133804c78e45afbefadff7e5..2743c37bb13214eeaab0f4c9149447b5c7925c0e 100644 --- a/treatment_effect_analysis/daily/regression/Gun_Violence_Data_B3/Gun_Violence_Data_B3_001.json +++ b/treatment_effect_analysis/daily/regression/Gun_Violence_Data_B3/Gun_Violence_Data_B3_001.json @@ -5,7 +5,7 @@ "perspective": "user", "dataset_name": "Gun_Violence_Data", "table_path": "kaggle/Gun_Violence_Data", - "query": "Living in the District of Columbia, there was this incident logged under ID 330294. It happened at latitude 38.9096, longitude -77.0199, in the 1.0 congressional district. The report shows one person was injured, no fields were missing from the online record, and thankfully, nobody was killed. Now, I'm looking at the possibility of moving the entire record's location for a research comparison—shifting it to a different spot in the city, specifically to coordinates 38.8537, -76.985. Could that shift in geography alter the expected outcome?", + "query": "Living in the District of Columbia, there was this incident logged under ID 330294. It happened at latitude 38.9096, longitude -77.0199, in the 1.0 congressional district. The report shows one person was injured, no fields were missing from the online record, and thankfully, nobody was killed. Now, I'm looking at the possibility of comparing this with another case with one injury and a complete report in a different spot in the city, specifically to coordinates 38.8537, -76.985. Would you expect a different fatality count for that case?", "meta_info": { "domain": "daily" }, @@ -86,4 +86,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/treatment_effect_analysis/daily/regression/Gun_Violence_Data_B3/Gun_Violence_Data_B3_002.json b/treatment_effect_analysis/daily/regression/Gun_Violence_Data_B3/Gun_Violence_Data_B3_002.json index 4999fced94e1dcfecd9fc7dae4b56559746ad15c..85ff467c72720f354af804fb1534923d7f2e3bfd 100644 --- a/treatment_effect_analysis/daily/regression/Gun_Violence_Data_B3/Gun_Violence_Data_B3_002.json +++ b/treatment_effect_analysis/daily/regression/Gun_Violence_Data_B3/Gun_Violence_Data_B3_002.json @@ -5,7 +5,7 @@ "perspective": "data_holder", "dataset_name": "Gun_Violence_Data", "table_path": "kaggle/Gun_Violence_Data", - "query": "Living in Massachusetts, in the 1st congressional district, the last incident here at these exact coordinates, 42.1102 latitude and -72.5702 longitude, involved one gun. Thankfully, no one was injured and, most importantly, no one was killed, which is the outcome we always hope for. I’ve been offered a job in Kentucky, right near 36.8409 latitude and -87.4825 longitude. It’s a big move, and I’m worried about the change. I’ve shared our historical gun violence records with you. If I relocate to that part of Kentucky, keeping everything else the same—no injuries, one gun involved, all the report details intact—does moving there mean I should expect a different number of people killed?", + "query": "Living in Massachusetts, in the 1st congressional district, the last incident here at these exact coordinates, 42.1102 latitude and -72.5702 longitude, involved one gun. Thankfully, no one was injured and, most importantly, no one was killed, which is the outcome we always hope for. I’ve been offered a job in Kentucky, right near 36.8409 latitude and -87.4825 longitude. It’s a big move, and I’m worried about the change. I’ve shared our historical gun violence records with you. If I relocate to that part of Kentucky, considering another case with no injuries, one gun involved, and a complete report, does moving there mean I should expect a different number of people killed?", "meta_info": { "domain": "daily" }, @@ -86,4 +86,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/treatment_effect_analysis/daily/regression/Gun_Violence_Data_B3/Gun_Violence_Data_B3_003.json b/treatment_effect_analysis/daily/regression/Gun_Violence_Data_B3/Gun_Violence_Data_B3_003.json index c237fb09155c6e4f01264ee2b1c04ebb022ebda0..fe5af3e9f0326ffb7fc9b1c410a0a51e6bff4fc5 100644 --- a/treatment_effect_analysis/daily/regression/Gun_Violence_Data_B3/Gun_Violence_Data_B3_003.json +++ b/treatment_effect_analysis/daily/regression/Gun_Violence_Data_B3/Gun_Violence_Data_B3_003.json @@ -5,7 +5,7 @@ "perspective": "user", "dataset_name": "Gun_Violence_Data", "table_path": "kaggle/Gun_Violence_Data", - "query": "I'm living in California's 13th congressional district, right at latitude 37.7553 and longitude -122.189. The local report for my area, in state house district 18 and senate district 9, showed an incident with one gun involved, nobody injured, and all the report details were complete. But one person still died. It's chilling. I'm thinking of volunteering with a community watch program that would have me patrolling a block over, around latitude 37.7536 and longitude -122.186. It means I'd likely be a first responder if something happened there, which could mean reporting at least one injury from trying to help. With all that extra effort and personal risk, does this plan actually lead to predicting fewer people killed in these reports, or am I just putting myself in harm's way for no real change?", + "query": "I'm living in California's 13th congressional district, right at latitude 37.7553 and longitude -122.189. The local report for my area, in state house district 18 and senate district 9, showed an incident with one gun involved, nobody injured, and all the report details were complete. But one person still died. It's chilling. I'm thinking of volunteering with a community watch program that would have me patrolling a block over, around latitude 37.7536 and longitude -122.186. It means I'd likely be a first responder if something happened there, which could mean reporting one injury from trying to help. For a case at the new coordinates with one gun, one reported injury, and a complete report, would you predict fewer fatalities than in the original case?", "meta_info": { "domain": "daily" }, @@ -86,4 +86,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/treatment_effect_analysis/daily/regression/Gun_Violence_Data_B3/Gun_Violence_Data_B3_007.json b/treatment_effect_analysis/daily/regression/Gun_Violence_Data_B3/Gun_Violence_Data_B3_007.json index c0dac8cf95c02e68a5d9443783af636aa30269ec..fb3ebca05a650173ef3b6b7859ad772299582da4 100644 --- a/treatment_effect_analysis/daily/regression/Gun_Violence_Data_B3/Gun_Violence_Data_B3_007.json +++ b/treatment_effect_analysis/daily/regression/Gun_Violence_Data_B3/Gun_Violence_Data_B3_007.json @@ -5,7 +5,7 @@ "perspective": "user", "dataset_name": "Gun_Violence_Data", "table_path": "kaggle/Gun_Violence_Data", - "query": "There was this terrible shooting incident in Pennsylvania. It happened in the 14th congressional district, at the coordinates 40.4548 latitude and -79.8948 longitude. Only one gun was involved, and thankfully no one was injured, but one person was killed. The incident report seems complete, with no missing URL fields. I'm looking at the data and wondering... if the exact same type of incident, with one gun and no injuries, had instead been officially recorded as happening in a different place—say, in the 17th district, at coordinates 40.6927 latitude and -76.2645 longitude—would that geographical and administrative shift alone make any difference in the predicted number of fatalities?", + "query": "There was this terrible shooting incident in Pennsylvania. It happened in the 14th congressional district, at the coordinates 40.4548 latitude and -79.8948 longitude. Only one gun was involved, and thankfully no one was injured, but one person was killed. The incident report seems complete, with no missing URL fields. I'm looking at the data and wondering... if a case with one gun and no injuries were recorded in a different place—say, in the 17th district, at coordinates 40.6927 latitude and -76.2645 longitude—would you expect any difference in the predicted number of fatalities?", "meta_info": { "domain": "daily" }, @@ -86,4 +86,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/treatment_effect_analysis/daily/regression/Gun_Violence_Data_B3/Gun_Violence_Data_B3_008.json b/treatment_effect_analysis/daily/regression/Gun_Violence_Data_B3/Gun_Violence_Data_B3_008.json index 1f4020a71ef12f71945b96ece9c9f4ea8f6904eb..5b1a2f1096e3d48f7f0215faa4122ec0a0254904 100644 --- a/treatment_effect_analysis/daily/regression/Gun_Violence_Data_B3/Gun_Violence_Data_B3_008.json +++ b/treatment_effect_analysis/daily/regression/Gun_Violence_Data_B3/Gun_Violence_Data_B3_008.json @@ -5,7 +5,7 @@ "perspective": "user", "dataset_name": "Gun_Violence_Data", "table_path": "kaggle/Gun_Violence_Data", - "query": "Living in California, specifically in congressional district 34, means you hear about these incidents too often. The data point from my neighborhood—state house district 51, senate district 24—was a single gun involved, at coordinates 34.0735, -118.272. It resulted in one person killed and zero injured, and the report didn’t have any missing URL fields. I’m thinking of moving just a few blocks over, to a place at 34.0571, -118.251, which would actually shift me into state house district 53. All the other details, like the congressional district and the single gun, would stay the same. I’m wondering, if I make that move, does that change the number of people killed in such an incident for that new location?", + "query": "Living in California, specifically in congressional district 34, means you hear about these incidents too often. The data point from my neighborhood—state house district 51, senate district 24—was a single gun involved, at coordinates 34.0735, -118.272. It resulted in one person killed and zero injured, and the report didn’t have any missing URL fields. I’m thinking of moving just a few blocks over, to a place at 34.0571, -118.251, which would actually shift me into state house district 53. The congressional district, one-gun count, and zero-injury count would remain as described. I’m wondering, if I make that move, does that change the number of people killed in such an incident for that new location?", "meta_info": { "domain": "daily" }, @@ -86,4 +86,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/treatment_effect_analysis/daily/regression/Gun_Violence_Data_B3/Gun_Violence_Data_B3_009.json b/treatment_effect_analysis/daily/regression/Gun_Violence_Data_B3/Gun_Violence_Data_B3_009.json index 72ddfbaee9e739b76486d203da4da5f6ddbd245c..ec09a298b0e0f8f0ce4d2a1bcd964e7226d8be75 100644 --- a/treatment_effect_analysis/daily/regression/Gun_Violence_Data_B3/Gun_Violence_Data_B3_009.json +++ b/treatment_effect_analysis/daily/regression/Gun_Violence_Data_B3/Gun_Violence_Data_B3_009.json @@ -5,7 +5,7 @@ "perspective": "user", "dataset_name": "Gun_Violence_Data", "table_path": "kaggle/Gun_Violence_Data", - "query": "This whole situation is giving me a headache. There was an incident, ID 964743, down in Louisiana. One gun, one person hurt, but no fatalities. The record is complete, no fields missing. Now, I'm considering if maybe the location was wrong. Suppose it was actually in Kentucky all along. And in that case, let's say the injury report was a mistake—nobody was actually injured. By correcting the state to Kentucky and adjusting the injury count to zero, does that change the likelihood of anyone having been killed?", + "query": "This whole situation is giving me a headache. There was an incident, ID 964743, down in Louisiana. One gun, one person hurt, but no fatalities. The record is complete, no fields missing. Now consider another case in Kentucky with one gun, no reported injuries, and a complete record. Compared with the Louisiana case, would you expect a different fatality count?", "meta_info": { "domain": "daily" }, @@ -86,4 +86,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/treatment_effect_analysis/daily/regression/Gun_Violence_Data_B3/Gun_Violence_Data_B3_010.json b/treatment_effect_analysis/daily/regression/Gun_Violence_Data_B3/Gun_Violence_Data_B3_010.json index 3ea69c9a096641ffcc442bb911940e27341bd0fa..1f0587583cd9d8103af5c6fcd51e20e0e46880b1 100644 --- a/treatment_effect_analysis/daily/regression/Gun_Violence_Data_B3/Gun_Violence_Data_B3_010.json +++ b/treatment_effect_analysis/daily/regression/Gun_Violence_Data_B3/Gun_Violence_Data_B3_010.json @@ -5,7 +5,7 @@ "perspective": "user", "dataset_name": "Gun_Violence_Data", "table_path": "kaggle/Gun_Violence_Data", - "query": "I’ve been looking at this completed incident report from my area: one gun, located at 42.4729, -70.9499 in Massachusetts' 6th district, with no one injured and no one killed. My partner got a fantastic job offer in Pennsylvania, and we found a potential home at latitude 39.9909, longitude -75.5896. Uprooting our lives is a massive decision, and the crime statistics are a real part of the conversation. If we move, and an incident with the same other details—one gun, no injuries, complete report—occurred at that new address, would the predicted number of fatalities likely remain at zero? Tell me if it’s worth the peace of mind.", + "query": "I’ve been looking at this completed incident report from my area: one gun, located at 42.4729, -70.9499 in Massachusetts' 6th district, with no one injured and no one killed. My partner got a fantastic job offer in Pennsylvania, and we found a potential home at latitude 39.9909, longitude -75.5896. Uprooting our lives is a massive decision, and the crime statistics are a real part of the conversation. If we move, and an incident involving one gun, no injuries, and a complete report occurred at that new address, would the predicted number of fatalities likely remain at zero? Tell me if it’s worth the peace of mind.", "meta_info": { "domain": "daily" }, @@ -86,4 +86,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_001.json b/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_001.json index 6711efcf20ef69f5281dcbc08874ac285bc9451b..6a1dab0878ee0f8e33cdd7493634e03bdf5f9b6c 100644 --- a/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_001.json +++ b/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_001.json @@ -14,7 +14,6 @@ { "scenario_id": "001", "features": { - "salary": "105000", "company_location": "US", "#": "", "employment_type": "FT", @@ -31,7 +30,6 @@ { "scenario_id": "002", "features": { - "salary": "105000", "company_location": "US", "#": "", "employment_type": "FT", @@ -52,4 +50,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_002.json b/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_002.json index 4cad16d04249ece8b03bb016ae9829fe070c87f7..3d4cd95d1e4b68cdcf73ca10e025675a30ba1f98 100644 --- a/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_002.json +++ b/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_002.json @@ -14,7 +14,6 @@ { "scenario_id": "001", "features": { - "salary": "126500", "company_location": "US", "#": "", "employment_type": "FT", @@ -31,7 +30,6 @@ { "scenario_id": "002", "features": { - "salary": "155000", "company_location": "US", "#": "", "employment_type": "FT", @@ -52,4 +50,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_003.json b/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_003.json index e77366c13ebbf3273fa64b4d03886a265a2c5d84..4fd8b09bb9d1570d9c9fb95974f29e696ea41bdf 100644 --- a/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_003.json +++ b/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_003.json @@ -14,7 +14,6 @@ { "scenario_id": "001", "features": { - "salary": "115934", "company_location": "US", "#": "", "employment_type": "FT", @@ -31,7 +30,6 @@ { "scenario_id": "002", "features": { - "salary": "242000", "company_location": "US", "#": "", "employment_type": "FT", @@ -52,4 +50,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_004.json b/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_004.json index 2365336ee6434ae0a1b9a9778b275ed506724f46..bd2587aa79f10628c820295fc085ce276ba26d17 100644 --- a/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_004.json +++ b/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_004.json @@ -19,7 +19,6 @@ "company_size": "M", "salary_in_usd": "99050", "remote_ratio": "100", - "salary": "99050", "#": "", "experience_level": "SE", "company_location": "US", @@ -36,7 +35,6 @@ "company_size": "M", "salary_in_usd": "205300", "remote_ratio": "0", - "salary": "205300", "#": "", "experience_level": "SE", "company_location": "US", @@ -52,4 +50,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_005.json b/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_005.json index 1e69b593aaf3d189ecd317b44114db4db7df2651..b46940743ae21a74aca9ba5f7971832b9f368981 100644 --- a/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_005.json +++ b/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_005.json @@ -19,7 +19,6 @@ "company_size": "M", "salary_in_usd": "128875", "remote_ratio": "100", - "salary": "128875", "#": "", "experience_level": "SE", "company_location": "US", @@ -36,7 +35,6 @@ "company_size": "M", "salary_in_usd": "152500", "remote_ratio": "100", - "salary": "152500", "#": "", "experience_level": "SE", "company_location": "US", @@ -52,4 +50,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_006.json b/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_006.json index f20a6b20893d11226072b57e86cf5d4a91194f1d..c15c1e137d866ce5884b9eb6d3118102670fb749 100644 --- a/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_006.json +++ b/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_006.json @@ -19,7 +19,6 @@ "company_size": "M", "salary_in_usd": "120000", "remote_ratio": "100", - "salary": "120000", "#": "", "experience_level": "SE", "company_location": "US", @@ -36,7 +35,6 @@ "company_size": "M", "salary_in_usd": "104890", "remote_ratio": "100", - "salary": "104890", "#": "", "experience_level": "SE", "company_location": "US", @@ -52,4 +50,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_007.json b/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_007.json index dd82ef84494cbdc46b9024d0ec76d7fa753cc417..1ca2519b7cca84140a3ab760332797d894285c09 100644 --- a/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_007.json +++ b/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_007.json @@ -19,7 +19,6 @@ "company_size": "L", "salary_in_usd": "150000", "remote_ratio": "100", - "salary": "150000", "#": "", "experience_level": "MI", "company_location": "US", @@ -36,7 +35,6 @@ "company_size": "L", "salary_in_usd": "270000", "remote_ratio": "100", - "salary": "270000", "#": "", "experience_level": "MI", "company_location": "US", @@ -52,4 +50,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_008.json b/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_008.json index fa642b2bbe8b969880688583d67549275d12fcf2..c0f44cea2eb9cdb020217a2c807fe30d98661c71 100644 --- a/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_008.json +++ b/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_008.json @@ -19,7 +19,6 @@ "company_size": "M", "salary_in_usd": "116000", "remote_ratio": "0", - "salary": "116000", "#": "", "experience_level": "SE", "company_location": "US", @@ -36,7 +35,6 @@ "company_size": "M", "salary_in_usd": "145000", "remote_ratio": "100", - "salary": "145000", "#": "", "experience_level": "SE", "company_location": "US", @@ -52,4 +50,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_009.json b/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_009.json index 4d28f4d29e20a67e70647f41df3f6f29e07b091f..91e4072cfa8cf82b83df1b922e134e743f8cc05d 100644 --- a/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_009.json +++ b/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_009.json @@ -19,7 +19,6 @@ "company_size": "L", "salary_in_usd": "158200", "remote_ratio": "100", - "salary": "158200", "#": "", "experience_level": "SE", "company_location": "US", @@ -36,7 +35,6 @@ "company_size": "M", "salary_in_usd": "170000", "remote_ratio": "100", - "salary": "170000", "#": "", "experience_level": "SE", "company_location": "US", @@ -52,4 +50,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_010.json b/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_010.json index a3b0475a87fa4301d0e9fc5a24433bc9dabe8fb6..f954f042bea8c13cd04576c9d5d8e87926dc2ddb 100644 --- a/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_010.json +++ b/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/Data_Science_Job_Salaries_B3_010.json @@ -16,7 +16,6 @@ "features": { "remote_ratio": "100", "company_size": "M", - "salary": "144854", "employee_residence": "US", "work_year": "2022", "company_location": "US", @@ -33,7 +32,6 @@ "features": { "remote_ratio": "100", "company_size": "L", - "salary": "100800", "employee_residence": "US", "work_year": "2022", "company_location": "US", @@ -52,4 +50,4 @@ }, "response": "", "evaluation_score": {} -} \ No newline at end of file +} diff --git a/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/current.csv b/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/current.csv index 3b845911832d7539e84d595a1884f9ae8c5140fe..a1cbf1428f54b28046ac56c98df6f06e5d111e53 100644 --- a/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/current.csv +++ b/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/current.csv @@ -1,21 +1,21 @@ -Unnamed: 0,work_year,experience_level,employment_type,job_title,salary,salary_currency,salary_in_usd,employee_residence,remote_ratio,company_location,company_size -584,2022,SE,FT,Data Analyst,105000,USD,105000,US,100,US,M -456,2022,SE,FT,Data Engineer,105000,USD,105000,US,100,US,M -308,2022,MI,FT,Data Analyst,126500,USD,126500,US,0,US,M -290,2022,SE,FT,Data Analyst,155000,USD,155000,US,100,US,M -540,2022,SE,FT,Data Analyst,115934,USD,115934,US,100,US,M -309,2022,EX,FT,Data Engineer,242000,USD,242000,US,100,US,M -548,2022,SE,FT,Data Analyst,99050,USD,99050,US,100,US,M -558,2022,SE,FT,Data Scientist,205300,USD,205300,US,0,US,M -340,2022,SE,FT,Data Engineer,128875,USD,128875,US,100,US,M -516,2022,SE,FT,Data Science Manager,152500,USD,152500,US,100,US,M -485,2022,SE,FT,Machine Learning Engineer,120000,USD,120000,US,100,US,M -467,2022,SE,FT,Data Scientist,104890,USD,104890,US,100,US,M -169,2021,MI,FT,Data Architect,150000,USD,150000,US,100,US,L -78,2021,MI,CT,ML Engineer,270000,USD,270000,US,100,US,L -306,2022,SE,FT,Data Analyst,116000,USD,116000,US,0,US,M -556,2022,SE,FT,Data Engineer,145000,USD,145000,US,100,US,M -399,2022,SE,FT,Data Scientist,158200,USD,158200,US,100,US,L -597,2022,SE,FT,Data Analyst,170000,USD,170000,US,100,US,M -591,2022,SE,FT,Data Architect,144854,USD,144854,US,100,US,M -426,2022,SE,FT,Data Engineer,100800,USD,100800,US,100,US,L +Unnamed: 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+399,2022,SE,FT,Data Scientist,USD,158200,US,100,US,L +597,2022,SE,FT,Data Analyst,USD,170000,US,100,US,M +591,2022,SE,FT,Data Architect,USD,144854,US,100,US,M +426,2022,SE,FT,Data Engineer,USD,100800,US,100,US,L diff --git a/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/history.csv b/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/history.csv index c27e43066591b74bc8c1a746a5af2f6a0086e7a7..e3d580a54f7b796c3a26abe95647ecabaf6c46e0 100644 --- a/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/history.csv +++ b/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/history.csv @@ -1,589 +1,589 @@ -Unnamed: 0,work_year,experience_level,employment_type,job_title,salary,salary_currency,salary_in_usd,employee_residence,remote_ratio,company_location,company_size -339,2022,SE,FT,Data Analyst,109280,USD,109280.0,US,100,US,M -522,2022,MI,FT,Data Analyst,20000,USD,20000.0,GR,100,GR,S 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-284,2021,MI,FT,Research Scientist,69999,USD,69999.0,CZ,50,CZ,L -274,2021,SE,FT,Data Scientist,65720,EUR,77684.0,FR,50,FR,M -85,2021,MI,FT,Data Engineer,110000,PLN,28476.0,PL,100,PL,L -582,2022,SE,FT,Data Engineer,220110,USD,220110.0,US,100,US,M -373,2022,MI,FT,ETL Developer,50000,EUR,54957.0,GR,0,GR,M -392,2022,SE,FT,Data Analyst,112900,USD,112900.0,US,100,US,M -161,2021,EX,FT,Head of Data Science,85000,USD,85000.0,RU,0,RU,M -425,2022,MI,FT,Data Engineer,82900,USD,82900.0,US,0,US,M -442,2022,MI,FT,Data Engineer,75000,GBP,98158.0,GB,100,GB,M -268,2021,MI,FT,Data Engineer,250000,TRY,28016.0,TR,100,TR,M -100,2021,MI,FT,Data Analyst,75000,USD,75000.0,US,0,US,L -242,2021,MI,FT,Data Engineer,110000,USD,110000.0,US,100,US,L -536,2022,SE,FT,Data Analyst,112900,USD,112900.0,US,100,US,M -358,2022,SE,FT,Data Architect,192400,USD,192400.0,CA,100,CA,M -170,2021,MI,FT,Data Architect,170000,USD,170000.0,US,100,US,L -143,2021,MI,FT,Data Scientist,82500,USD,82500.0,US,100,US,S -507,2022,MI,FT,Research Scientist,59000,EUR,64849.0,AT,0,AT,L -217,2021,MI,FT,Data Scientist,76760,EUR,90734.0,DE,50,DE,L -107,2021,SE,FT,Data Engineer,115000,USD,115000.0,US,100,US,S -468,2022,SE,FT,Data Engineer,100000,USD,100000.0,US,100,US,M -282,2021,MI,PT,Data Engineer,59000,EUR,69741.0,NL,100,NL,L -424,2022,SE,FT,Data Scientist,80000,USD,80000.0,US,0,US,M -511,2022,MI,FT,Business Data Analyst,90000,CAD,70912.0,CA,50,CA,L -321,2022,SE,FT,Data Engineer,220110,USD,220110.0,US,0,US,M -533,2022,SE,FT,Machine Learning Engineer,192600,USD,192600.0,US,100,US,M -265,2021,SE,FT,Lead Data Engineer,160000,USD,160000.0,PR,50,US,S -267,2021,MI,FT,Data Engineer,111775,USD,111775.0,US,0,US,M -166,2021,EN,FT,Data Engineer,80000,USD,80000.0,US,100,US,L -575,2022,SE,FT,Data Scientist,140000,USD,140000.0,US,100,US,M -264,2021,MI,FT,Data Scientist,21600,EUR,25532.0,RS,100,DE,S -122,2021,EN,FT,Data Analyst,50000,USD,50000.0,US,100,US,M -23,2020,MI,FT,BI Data Analyst,98000,USD,98000.0,US,0,US,M -39,2020,EN,FT,Machine Learning Engineer,138000,USD,138000.0,US,100,US,S -524,2022,MI,FT,Data Scientist,135000,USD,135000.0,US,100,US,L -15,2020,MI,FT,Data Analyst,8000,USD,8000.0,PK,50,PK,L -68,2020,EN,FT,Data Scientist,105000,USD,105000.0,US,100,US,S -140,2021,MI,FT,Data Analyst,135000,USD,135000.0,US,100,US,L -233,2021,SE,FT,Data Analyst,200000,USD,200000.0,US,100,US,L -125,2021,MI,FT,Principal Data Scientist,151000,USD,151000.0,US,100,US,L -307,2022,MI,FT,Data Analyst,106260,USD,106260.0,US,0,US,M -606,2022,MI,FT,AI Scientist,200000,USD,200000.0,IN,100,US,L -440,2022,MI,FT,Data Analyst,40000,EUR,43966.0,GR,100,GR,M -153,2021,EN,FT,Data Scientist,13400,USD,13400.0,UA,100,UA,L -176,2021,MI,FT,Data Scientist,58000,MXN,2859.0,MX,0,MX,S -457,2022,SE,FT,Lead Machine Learning Engineer,80000,EUR,87932.0,DE,0,DE,M -277,2021,SE,FT,AI Scientist,55000,USD,55000.0,ES,100,ES,L -2,2020,SE,FT,Big Data Engineer,85000,GBP,109024.0,GB,50,GB,M -3,2020,MI,FT,Product Data Analyst,20000,USD,20000.0,HN,0,HN,S -92,2021,MI,FT,Lead Data Analyst,1450000,INR,19609.0,IN,100,IN,L -196,2021,EN,FT,BI Data Analyst,9272,USD,9272.0,KE,100,KE,S -6,2020,SE,FT,Lead Data Scientist,190000,USD,190000.0,US,100,US,S -79,2021,EN,FT,Data Analyst,80000,USD,80000.0,US,100,US,M -567,2022,MI,FT,Data Analyst,50000,GBP,65438.0,GB,0,GB,M -490,2022,SE,FT,Head of Data,200000,USD,200000.0,MY,100,US,M -30,2020,MI,FT,Data Engineering Manager,51999,EUR,59303.0,DE,100,DE,S -382,2022,SE,FT,Data Analyst,128875,USD,128875.0,US,100,US,M -105,2021,MI,FT,Data Analyst,37456,GBP,51519.0,GB,50,GB,L -83,2021,MI,FT,Machine Learning Engineer,40000,EUR,47282.0,ES,100,ES,S -37,2020,EN,FT,Machine Learning Engineer,250000,USD,250000.0,US,50,US,L -360,2022,SE,FT,Data Analyst,130000,USD,130000.0,CA,100,CA,M -130,2021,EN,FT,Machine Learning Developer,100000,USD,100000.0,IQ,50,IQ,S -455,2022,MI,FT,NLP Engineer,240000,CNY,37236.0,US,50,US,L -317,2022,SE,FT,Data Scientist,120160,USD,120160.0,US,100,US,M -459,2022,MI,FT,Data Scientist,2400000,INR,31615.0,IN,100,IN,L -412,2022,MI,FT,Data Engineer,60000,EUR,65949.0,GR,100,GR,M -266,2021,MI,FT,Data Engineer,93150,USD,93150.0,US,0,US,M -91,2021,EN,FT,Data Science Consultant,65000,EUR,76833.0,DE,100,DE,S -52,2020,EN,FT,AI Scientist,300000,DKK,45896.0,DK,50,DK,S -491,2022,MI,FT,Principal Data Analyst,75000,USD,75000.0,CA,100,CA,S -374,2022,MI,FT,ETL Developer,50000,EUR,54957.0,GR,0,GR,M -505,2022,EN,FT,Data Scientist,120000,AUD,86703.0,AU,50,AU,M -500,2022,SE,FT,Machine Learning Engineer,57000,EUR,62651.0,NL,100,NL,L -254,2021,MI,FT,Data Analyst,93000,USD,93000.0,US,100,US,L -472,2022,SE,FT,Data Scientist,220000,USD,220000.0,US,100,US,M -60,2020,MI,FT,Data Engineer,110000,USD,110000.0,US,100,US,L -293,2022,MI,FT,Data Scientist,90000,USD,90000.0,US,0,US,M -349,2022,SE,FT,Data Analyst,135000,USD,135000.0,US,100,US,M -341,2022,SE,FT,Data Engineer,93700,USD,93700.0,US,100,US,M -397,2022,MI,FT,Data Engineer,90000,GBP,117789.0,GB,0,GB,M -144,2021,MI,FT,Data Engineer,100000,USD,100000.0,US,100,US,L -502,2022,EN,FT,Data Scientist,40000,USD,40000.0,JP,100,MY,L -576,2022,SE,FT,Data Scientist,210000,USD,210000.0,US,100,US,M -244,2021,EN,FT,AI Scientist,1335000,INR,18053.0,IN,100,AS,S -73,2021,EX,FT,BI Data Analyst,150000,USD,150000.0,IN,100,US,L -101,2021,SE,FT,Data Engineer,150000,USD,150000.0,US,100,US,L -230,2021,EN,FT,Big Data Engineer,1200000,INR,16228.0,IN,100,IN,L -237,2021,MI,FT,Data Scientist,39600,EUR,46809.0,ES,100,ES,M -383,2022,SE,FT,Data Analyst,93700,USD,93700.0,US,100,US,M -54,2020,SE,FL,Computer Vision Engineer,60000,USD,60000.0,RU,100,US,S -281,2021,EN,FT,Research Scientist,100000,USD,100000.0,JE,0,CN,L -482,2022,EX,FT,Data Engineer,324000,USD,324000.0,US,100,US,M -300,2022,SE,FT,Data Scientist,90000,GBP,117789.0,GB,0,GB,M -70,2020,MI,FT,Data Scientist,55000,EUR,62726.0,FR,50,LU,S -108,2021,SE,FT,Data Engineer,150000,USD,150000.0,US,100,US,M -81,2021,MI,FT,Data Engineer,140000,USD,140000.0,US,100,US,L -564,2022,SE,FT,Data Analyst,116150,USD,116150.0,US,100,US,M -487,2022,EN,PT,Data Scientist,100000,USD,100000.0,DZ,50,DZ,M -269,2021,EN,FT,Data Engineer,55000,EUR,65013.0,DE,50,DE,M -303,2022,SE,FT,Data Scientist,123000,USD,123000.0,US,100,US,M -283,2021,SE,CT,Staff Data Scientist,105000,USD,105000.0,US,100,US,M -389,2022,MI,FT,Machine Learning Engineer,95000,GBP,124333.0,GB,0,GB,M -506,2022,MI,FT,Applied Machine Learning Scientist,75000,USD,75000.0,BO,100,US,L -51,2020,EN,FT,Data Analyst,91000,USD,91000.0,US,100,US,L -489,2022,EN,CT,Applied Machine Learning Scientist,29000,EUR,31875.0,TN,100,CZ,M -430,2022,MI,FT,Data Analyst,40000,EUR,43966.0,ES,100,ES,M -204,2021,MI,FT,Data Scientist,160000,USD,160000.0,US,100,US,L -409,2022,SE,FT,Data Scientist,180000,USD,180000.0,US,100,US,M -499,2022,EN,FT,Data Scientist,66500,CAD,52396.0,CA,100,CA,L -529,2022,SE,FT,Data Analyst,90320,USD,90320.0,US,100,US,M -296,2022,SE,FT,Data Analyst,102100,USD,102100.0,US,100,US,M -183,2021,SE,FT,Finance Data Analyst,45000,GBP,61896.0,GB,50,GB,L -497,2022,SE,FT,Data Scientist,165000,USD,165000.0,US,100,US,M -501,2022,MI,FT,Head of Data,30000,EUR,32974.0,EE,100,EE,S -65,2020,EN,FT,Data Scientist,55000,EUR,62726.0,DE,50,DE,S -431,2022,MI,FT,Data Analyst,30000,EUR,32974.0,ES,100,ES,M -35,2020,MI,FT,Data Engineer,65000,EUR,74130.0,AT,50,AT,L -256,2021,MI,FT,Data Engineer,200000,USD,200000.0,US,100,US,L -514,2022,EN,FT,Data Analytics Engineer,20000,USD,20000.0,PK,0,PK,M -470,2022,MI,FT,Data Analyst,135000,USD,135000.0,US,100,US,M -354,2022,SE,FT,Data Engineer,60000,GBP,78526.0,GB,0,GB,M -259,2021,EX,FT,Director of Data Science,120000,EUR,141846.0,DE,0,DE,L -226,2021,SE,FT,Data Scientist,110000,CAD,87738.0,CA,100,CA,S -528,2022,SE,FT,Data Analyst,100000,USD,100000.0,US,100,US,M -95,2021,MI,FT,Cloud Data Engineer,120000,SGD,89294.0,SG,50,SG,L -331,2022,SE,FT,Data Analyst,90320,USD,90320.0,US,100,US,M -401,2022,SE,FT,Data Engineer,154600,USD,154600.0,US,100,US,L -571,2022,SE,FT,Data Scientist,140000,USD,140000.0,US,100,US,M -332,2022,SE,FT,Data Analyst,112900,USD,112900.0,US,100,US,M -184,2021,MI,FL,Machine Learning Scientist,12000,USD,12000.0,PK,50,PK,M -543,2022,MI,FT,Data Engineer,99100,USD,99100.0,US,0,US,M -174,2021,SE,FT,Research Scientist,51400,EUR,60757.0,PT,50,PT,L -454,2022,EN,FT,Computer Vision Engineer,125000,USD,125000.0,US,0,US,M -464,2022,SE,FT,Principal Data Scientist,148000,EUR,162674.0,DE,100,DE,M -407,2022,SE,FT,Data Engineer,183600,USD,183600.0,US,100,US,L -102,2021,MI,FT,BI Data Analyst,11000000,HUF,36259.0,HU,50,US,L -165,2021,SE,FT,Data Specialist,165000,USD,165000.0,US,100,US,L -147,2021,MI,FT,Data Engineer,90000,USD,90000.0,US,100,US,L -488,2022,MI,FL,Data Scientist,100000,USD,100000.0,CA,100,US,M -344,2022,EX,FT,Analytics Engineer,175000,USD,175000.0,US,100,US,M -404,2022,SE,FT,Data Engineer,175000,USD,175000.0,US,100,US,M -494,2022,SE,FT,Data Scientist,100000,USD,100000.0,BR,100,US,M -111,2021,SE,FT,Director of Data Engineering,82500,GBP,113476.0,GB,100,GB,M -496,2022,EN,FT,Data Engineer,52800,EUR,58035.0,PK,100,DE,M -604,2022,SE,FT,Data Analyst,129000,USD,129000.0,US,0,US,M -587,2022,SE,FT,Data Scientist,140000,USD,140000.0,US,100,US,M -220,2021,MI,FT,Machine Learning Engineer,180000,PLN,46597.0,PL,100,PL,L -297,2022,SE,FT,Data Analyst,84900,USD,84900.0,US,100,US,M -530,2022,MI,FT,Data Analyst,85000,USD,85000.0,CA,0,CA,M -234,2021,MI,FT,Data Architect,180000,USD,180000.0,US,100,US,L -310,2022,EX,FT,Data Engineer,200000,USD,200000.0,US,100,US,M -291,2022,SE,FT,Data Analyst,120600,USD,120600.0,US,100,US,M -10,2020,EN,FT,Data Scientist,45000,EUR,51321.0,FR,0,FR,S -603,2022,SE,FT,Data Engineer,126000,USD,126000.0,US,100,US,M -156,2021,MI,FT,Data Scientist,160000,SGD,119059.0,SG,100,IL,M -463,2022,EN,FT,Data Scientist,1400000,INR,18442.0,IN,100,IN,M -315,2022,SE,FT,Data Scientist,165220,USD,165220.0,US,100,US,M -477,2022,SE,FT,Machine Learning Engineer,220000,USD,220000.0,US,100,US,M -402,2022,SE,FT,Data Analyst,115934,USD,115934.0,US,0,US,M -45,2020,EN,PT,ML Engineer,14000,EUR,15966.0,DE,100,DE,S -292,2022,MI,FT,Data Scientist,130000,USD,130000.0,US,0,US,M -99,2021,MI,FT,Computer Vision Software Engineer,81000,EUR,95746.0,DE,100,US,S -273,2021,EN,FT,Machine Learning Engineer,85000,USD,85000.0,NL,100,DE,S -352,2022,SE,FT,Data Scientist,167000,USD,167000.0,US,100,US,M -187,2021,EX,FT,Data Science Consultant,59000,EUR,69741.0,FR,100,ES,S -350,2022,SE,FT,Data Science Manager,161342,USD,161342.0,US,100,US,M -555,2022,SE,FT,Data Engineer,160000,USD,160000.0,US,100,US,M -403,2022,SE,FT,Data Analyst,81666,USD,81666.0,US,0,US,M -87,2021,MI,FT,Data Analytics Engineer,110000,USD,110000.0,US,100,US,L -34,2020,MI,FT,Data Analyst,41000,EUR,46759.0,FR,50,FR,L -228,2021,SE,FT,Data Scientist,135000,USD,135000.0,US,0,US,L -4,2020,SE,FT,Machine Learning Engineer,150000,USD,150000.0,US,50,US,L -559,2022,SE,FT,Data Scientist,140400,USD,140400.0,US,0,US,M -1,2020,SE,FT,Machine Learning Scientist,260000,USD,260000.0,JP,0,JP,S -27,2020,SE,FT,Data Engineer,720000,MXN,33511.0,MX,0,MX,S -200,2021,MI,FT,Data Scientist,52000,EUR,61467.0,DE,50,AT,M -182,2021,MI,FT,Data Engineer,22000,EUR,26005.0,RO,0,US,L -188,2021,SE,FT,Data Engineer,65000,EUR,76833.0,RO,50,GB,S -458,2022,MI,FT,Business Data Analyst,1400000,INR,18442.0,IN,100,IN,M -90,2021,SE,FT,Marketing Data Analyst,75000,EUR,88654.0,GR,100,DK,L -58,2020,SE,FT,Data Scientist,120000,USD,120000.0,US,50,US,L -141,2021,SE,FT,Data Science Manager,240000,USD,240000.0,US,0,US,L -33,2020,MI,FT,Research Scientist,450000,USD,450000.0,US,0,US,M -48,2020,MI,FT,Data Scientist,105000,USD,105000.0,US,100,US,L -19,2020,MI,FT,Lead Data Engineer,56000,USD,56000.0,PT,100,US,M -380,2022,SE,FT,Data Engineer,136994,USD,136994.0,US,100,US,M -66,2020,EN,FT,Data Scientist,43200,EUR,49268.0,DE,0,DE,S -112,2021,SE,FT,Lead Data Engineer,75000,GBP,103160.0,GB,100,GB,S -12,2020,EN,FT,Data Scientist,35000,EUR,39916.0,FR,0,FR,M -345,2022,SE,FT,Data Engineer,156600,USD,156600.0,US,100,US,M -86,2021,EN,FT,Data Analyst,50000,EUR,59102.0,FR,50,FR,M -585,2022,SE,FT,Data Analyst,110925,USD,110925.0,US,100,US,M -163,2021,EN,FT,Data Science Consultant,54000,EUR,63831.0,DE,50,DE,L -581,2022,SE,FT,Data Analyst,106260,USD,106260.0,US,100,US,M -109,2021,EN,FT,Data Engineer,2250000,INR,30428.0,IN,100,IN,L -554,2022,SE,FT,Data Engineer,200100,USD,200100.0,US,100,US,M -595,2022,SE,FT,Data Analytics Manager,109280,USD,109280.0,US,100,US,M -552,2022,SE,FT,Data Scientist,176000,USD,176000.0,US,100,US,M -238,2021,EN,FT,Data Scientist,4000,USD,4000.0,VN,0,VN,M -295,2022,MI,FT,Data Engineer,150000,USD,150000.0,US,100,US,M -207,2021,SE,FT,Data Engineer,165000,USD,165000.0,US,0,US,M -531,2022,MI,FT,Data Analyst,75000,USD,75000.0,CA,0,CA,M -343,2022,EX,FT,Head of Data Science,167875,USD,167875.0,US,100,US,M -508,2022,EN,FT,Research Scientist,120000,USD,120000.0,US,100,US,L -246,2021,EN,FT,Data Scientist,31000,EUR,36643.0,FR,50,FR,L -478,2022,MI,FT,Data Scientist,200000,USD,200000.0,US,100,US,M -247,2021,MI,FT,Data Engineer,108000,TRY,12103.0,TR,0,TR,M -20,2020,MI,FT,Machine Learning Engineer,299000,CNY,43331.0,CN,0,CN,M -460,2022,MI,FT,Machine Learning Infrastructure Engineer,53000,EUR,58255.0,PT,50,PT,L -127,2021,MI,FT,Data Scientist,700000,INR,9466.0,IN,0,IN,S -474,2022,MI,FT,Data Scientist,140000,GBP,183228.0,GB,0,GB,M -517,2022,MI,FT,Data Engineer,62000,EUR,68147.0,FR,100,FR,M -113,2021,EN,PT,AI Scientist,12000,USD,12000.0,PK,100,US,M -148,2021,SE,FT,Data Engineering Manager,153000,USD,153000.0,US,100,US,L -205,2021,MI,FT,Data Scientist,69600,BRL,12901.0,BR,0,BR,S -546,2022,SE,FT,Data Engineer,110500,USD,110500.0,US,100,US,M -330,2022,SE,FT,Data Analyst,112900,USD,112900.0,US,100,US,M -314,2022,MI,FT,Data Engineer,40000,GBP,52351.0,GB,0,GB,M -583,2022,SE,FT,Data Engineer,160080,USD,160080.0,US,100,US,M -131,2021,EN,FT,Data Scientist,42000,EUR,49646.0,FR,50,FR,M -579,2022,SE,FT,Data Engineer,25000,USD,25000.0,US,100,US,M -452,2022,EX,FT,Director of Data Science,250000,CAD,196979.0,CA,50,CA,L -62,2020,EN,PT,Data Scientist,19000,EUR,21669.0,IT,50,IT,S -257,2021,SE,FT,Principal Data Scientist,147000,EUR,173762.0,DE,100,DE,M -523,2022,SE,FT,Data Analytics Lead,405000,USD,405000.0,US,100,US,L -462,2022,MI,PT,Data Engineer,50000,EUR,54957.0,DE,50,DE,L -40,2020,MI,FT,Data Scientist,45760,USD,45760.0,PH,100,US,S -420,2022,MI,FT,Machine Learning Scientist,112300,USD,112300.0,US,100,US,L -336,2022,MI,FT,Data Analyst,167000,USD,167000.0,US,100,US,M -513,2022,SE,FT,Machine Learning Engineer,65000,EUR,71444.0,IE,100,IE,S -450,2022,SE,FT,Data Engineer,80000,USD,80000.0,US,100,US,M -177,2021,MI,FT,Data Scientist,30400000,CLP,40038.0,CL,100,CL,L -422,2022,MI,FT,Data Science Manager,159000,USD,159000.0,US,100,US,M -263,2021,SE,FT,Machine Learning Engineer,4900000,INR,66265.0,IN,0,IN,L -103,2021,MI,FT,Data Analyst,62000,USD,62000.0,US,0,US,L -545,2022,SE,FT,Data Engineer,115000,USD,115000.0,US,100,US,M -278,2021,SE,FT,Data Scientist,180000,TRY,20171.0,TR,50,TR,L -270,2021,EN,FT,Data Engineer,72500,USD,72500.0,US,100,US,L -250,2021,MI,FT,Data Scientist,115000,USD,115000.0,US,50,US,L -5,2020,EN,FT,Data Analyst,72000,USD,72000.0,US,100,US,L -118,2021,EN,FT,Data Analyst,90000,USD,90000.0,US,100,US,S -252,2021,EX,FT,Principal Data Engineer,600000,USD,600000.0,US,100,US,L -114,2021,MI,FT,Data Engineer,38400,EUR,45391.0,NL,100,NL,L -369,2022,SE,FT,Data Scientist,170000,USD,170000.0,US,100,US,M -221,2021,MI,FT,Data Scientist,85000,GBP,116914.0,GB,50,GB,L -572,2022,SE,FT,Data Analyst,100000,USD,100000.0,US,100,US,M -302,2022,SE,FT,Data Scientist,146000,USD,146000.0,US,100,US,M -155,2021,SE,FT,Data Science Engineer,159500,CAD,127221.0,CA,50,CA,L -427,2022,MI,FT,Data Engineer,45000,EUR,49461.0,ES,100,ES,M -193,2021,SE,FT,Data Science Manager,174000,USD,174000.0,US,100,US,L -365,2022,SE,FT,Data Scientist,138600,USD,138600.0,US,100,US,M -13,2020,MI,FT,Lead Data Analyst,87000,USD,87000.0,US,100,US,L -437,2022,MI,FT,Data Engineer,80000,EUR,87932.0,GR,100,GR,M -69,2020,SE,FT,Data Scientist,80000,EUR,91237.0,AT,0,AT,S -538,2022,MI,FT,Data Scientist,141300,USD,141300.0,US,0,US,M -253,2021,EN,FT,Data Scientist,2100000,INR,28399.0,IN,100,IN,M -136,2021,MI,FT,ML Engineer,7000000,JPY,63711.0,JP,50,JP,S -38,2020,EN,FT,Data Analyst,10000,USD,10000.0,NG,100,NG,S -526,2022,MI,FT,Data Scientist,78000,USD,78000.0,US,100,US,M -159,2021,EN,FT,Machine Learning Engineer,125000,USD,125000.0,US,100,US,S -57,2020,MI,FT,Data Scientist,118000,USD,118000.0,US,100,US,M -157,2021,MI,FT,Applied Machine Learning Scientist,423000,USD,423000.0,US,50,US,L -42,2020,MI,FT,Machine Learning Infrastructure Engineer,44000,EUR,50180.0,PT,0,PT,M -245,2021,MI,FT,Data Engineer,52500,GBP,72212.0,GB,50,GB,L -338,2022,SE,FT,Data Analyst,136600,USD,136600.0,US,100,US,M -578,2022,SE,FT,Data Engineer,100000,USD,100000.0,US,100,US,M -594,2022,SE,FT,Data Analytics Manager,150260,USD,150260.0,US,100,US,M -446,2022,SE,FT,Data Engineer,209100,USD,209100.0,US,100,US,L -562,2022,SE,FT,Data Engineer,175100,USD,175100.0,US,100,US,M -525,2022,SE,FT,Applied Data Scientist,177000,USD,177000.0,US,100,US,L -551,2022,SE,FT,Data Scientist,140400,USD,140400.0,US,0,US,L -329,2022,MI,FT,Data Analyst,115500,USD,115500.0,US,100,US,M -298,2022,SE,FT,Data Scientist,136620,USD,136620.0,US,100,US,M -378,2022,SE,FT,Data Architect,208775,USD,208775.0,US,100,US,M -453,2022,MI,FT,Machine Learning Engineer,120000,USD,120000.0,US,100,US,S -206,2021,SE,FT,Machine Learning Engineer,200000,USD,200000.0,US,100,US,L -213,2021,EN,FT,Big Data Engineer,435000,INR,5882.0,IN,0,CH,L -356,2022,SE,FT,Data Scientist,150000,USD,150000.0,US,0,US,M -367,2022,MI,FT,Data Analyst,58000,USD,58000.0,US,0,US,S -387,2022,SE,FT,Data Analyst,164000,USD,164000.0,US,0,US,M -128,2021,EN,FT,Machine Learning Engineer,20000,USD,20000.0,IN,100,IN,S -469,2022,SE,FT,Data Scientist,140000,USD,140000.0,US,100,US,M -549,2022,SE,FT,Data Engineer,160000,USD,160000.0,US,100,US,M -124,2021,EN,PT,Data Analyst,8760,EUR,10354.0,ES,50,ES,M -11,2020,MI,FT,Data Scientist,3000000,INR,40481.0,IN,0,IN,L -162,2021,MI,FT,Data Engineer,24000,EUR,28369.0,MT,50,MT,L -563,2022,SE,FT,Data Engineer,140250,USD,140250.0,US,100,US,M -414,2022,MI,FT,Data Engineer,60000,GBP,78526.0,GB,100,GB,M -471,2022,MI,FT,Data Analyst,50000,USD,50000.0,US,100,US,M -222,2021,MI,FT,Data Scientist,2500000,INR,33808.0,IN,0,IN,M -180,2021,MI,FT,Big Data Engineer,1672000,INR,22611.0,IN,0,IN,L -311,2022,MI,FT,Data Scientist,50000,GBP,65438.0,GB,0,GB,M -434,2022,MI,FT,Data Engineer,80000,GBP,104702.0,GB,100,GB,M -605,2022,SE,FT,Data Analyst,150000,USD,150000.0,US,100,US,M -492,2022,MI,FT,Data Scientist,150000,PLN,35590.0,PL,100,PL,L -592,2022,SE,FT,Data Scientist,230000,USD,230000.0,US,100,US,M -194,2021,SE,FT,Research Scientist,120500,CAD,96113.0,CA,50,CA,L -8,2020,MI,FT,Business Data Analyst,135000,USD,135000.0,US,100,US,L -214,2021,EN,FT,Machine Learning Engineer,21000,EUR,24823.0,DE,50,DE,M -447,2022,SE,FT,Data Engineer,154600,USD,154600.0,US,100,US,L -43,2020,MI,FT,Data Engineer,106000,USD,106000.0,US,100,US,L -588,2022,SE,FT,Data Analyst,99000,USD,99000.0,US,0,US,M -203,2021,SE,FT,Research Scientist,50000,USD,50000.0,FR,100,US,S -305,2022,SE,FT,Data Analyst,99000,USD,99000.0,US,0,US,M -286,2021,SE,FT,Head of Data,87000,EUR,102839.0,SI,100,SI,L -394,2022,SE,FT,Data Analytics Manager,145000,USD,145000.0,US,100,US,M -542,2022,MI,FT,Data Engineer,206699,USD,206699.0,US,0,US,M -377,2022,SE,FT,Data Engineer,165400,USD,165400.0,US,100,US,M -59,2020,MI,FT,Data Scientist,138350,USD,138350.0,US,100,US,M -7,2020,MI,FT,Data Scientist,11000000,HUF,35735.0,HU,50,HU,L -379,2022,SE,FT,Data Architect,147800,USD,147800.0,US,100,US,M -520,2022,MI,FT,Data Scientist,88000,CAD,69336.0,CA,100,CA,M -357,2022,SE,FT,Data Scientist,211500,USD,211500.0,US,100,US,M -14,2020,MI,FT,Data Analyst,85000,USD,85000.0,US,100,US,L -561,2022,SE,FT,Analytics Engineer,184700,USD,184700.0,US,0,US,M -77,2021,MI,PT,3D Computer Vision Researcher,400000,INR,5409.0,IN,50,IN,M -479,2022,MI,FT,Data Scientist,120000,USD,120000.0,US,100,US,M -574,2022,SE,FT,Data Scientist,210000,USD,210000.0,US,100,US,M -9,2020,SE,FT,Lead Data Engineer,125000,USD,125000.0,NZ,50,NZ,S -219,2021,SE,FT,Data Analytics Manager,140000,USD,140000.0,US,100,US,L -539,2022,MI,FT,Data Scientist,102100,USD,102100.0,US,0,US,M -243,2021,SE,FT,Data Scientist,165000,USD,165000.0,US,100,US,L -388,2022,SE,FT,Data Engineer,155000,USD,155000.0,US,100,US,M -376,2022,SE,FT,Data Analyst,132000,USD,132000.0,US,0,US,M -93,2021,SE,FT,Lead Data Engineer,276000,USD,276000.0,US,0,US,L -152,2021,MI,FT,Data Scientist,95000,CAD,75774.0,CA,100,CA,L -272,2021,EN,FT,Data Science Consultant,65000,EUR,76833.0,DE,0,DE,L -285,2021,SE,FT,Data Science Manager,7000000,INR,94665.0,IN,50,IN,L -444,2022,SE,FT,Data Scientist,215300,USD,215300.0,US,0,US,L -195,2021,MI,FT,Data Scientist,147000,USD,147000.0,US,50,US,L -410,2022,MI,FT,Data Scientist,55000,GBP,71982.0,GB,0,GB,M -486,2022,SE,FT,Data Scientist,230000,USD,230000.0,US,100,US,M -202,2021,MI,FT,Data Scientist,32000,EUR,37825.0,ES,100,ES,L -29,2020,SE,FT,Machine Learning Manager,157000,CAD,117104.0,CA,50,CA,L -0,2020,MI,FT,Data Scientist,70000,EUR,79833.0,DE,0,DE,L -271,2021,SE,FT,Computer Vision Engineer,102000,BRL,18907.0,BR,0,BR,M -466,2022,SE,FT,Research Scientist,144000,USD,144000.0,US,50,US,L -150,2021,SE,FT,Director of Data Science,168000,USD,168000.0,JP,0,JP,S -393,2022,SE,FT,Data Analyst,90320,USD,90320.0,US,100,US,M -405,2022,MI,FT,Data Engineer,75000,GBP,98158.0,GB,0,GB,M -239,2021,EN,FT,Data Engineer,1600000,INR,21637.0,IN,50,IN,M -601,2022,EN,FT,Data Analyst,52000,USD,52000.0,CA,0,CA,M -24,2020,MI,FT,Lead Data Scientist,115000,USD,115000.0,AE,0,AE,L -72,2021,EN,FT,Research Scientist,60000,GBP,82528.0,GB,50,GB,L -137,2021,MI,FT,ML Engineer,8500000,JPY,77364.0,JP,50,JP,S -509,2022,MI,FT,Applied Data Scientist,157000,USD,157000.0,US,100,US,L -294,2022,MI,FT,Data Engineer,170000,USD,170000.0,US,100,US,M -120,2021,MI,FT,Big Data Engineer,60000,USD,60000.0,ES,50,RO,M -255,2021,SE,FT,Big Data Architect,125000,CAD,99703.0,CA,50,CA,M -129,2021,SE,FT,Lead Data Scientist,3000000,INR,40570.0,IN,50,IN,L -98,2021,EN,FT,Computer Vision Software Engineer,70000,USD,70000.0,US,100,US,M -371,2022,SE,FT,Machine Learning Engineer,189650,USD,189650.0,US,0,US,M -565,2022,SE,FT,Data Engineer,54000,USD,54000.0,US,0,US,M -198,2021,SE,FT,Data Science Manager,4000000,INR,54094.0,IN,50,US,L -46,2020,MI,FT,Data Scientist,60000,GBP,76958.0,GB,100,GB,S -534,2022,SE,FT,Data Architect,266400,USD,266400.0,US,100,US,M -504,2022,SE,FT,Data Engineer,115000,USD,115000.0,US,100,US,M -56,2020,MI,FT,Data Scientist,34000,EUR,38776.0,ES,100,ES,M -557,2022,SE,FT,Data Engineer,70500,USD,70500.0,US,0,US,M -139,2021,EN,FT,Data Scientist,80000,USD,,US,100,US,M -74,2021,EX,FT,Head of Data,235000,USD,,US,100,US,L -390,2022,MI,FT,Machine Learning Engineer,75000,GBP,,GB,0,GB,M -476,2022,SE,FT,Data Scientist,185100,USD,,US,100,US,M -168,2021,EN,FT,BI Data Analyst,55000,USD,,US,50,US,S -435,2022,MI,FT,Data Engineer,70000,GBP,,GB,100,GB,M -224,2021,SE,FT,Machine Learning Scientist,225000,USD,,US,100,CA,L -248,2021,SE,FT,Data Engineer,70000,GBP,,GB,50,GB,L -326,2022,EX,FT,Data Analyst,130000,USD,,US,100,US,M -110,2021,SE,FT,Machine Learning Engineer,80000,EUR,,DE,50,DE,L -299,2022,SE,FT,Data Scientist,99360,USD,,US,100,US,M -441,2022,MI,FT,Data Analyst,30000,EUR,,GR,100,GR,M -322,2022,SE,FT,Data Engineer,160080,USD,,US,0,US,M -417,2022,SE,FT,Data Science Engineer,60000,USD,,AR,100,MX,L -313,2022,MI,FT,Data Engineer,60000,GBP,,GB,0,GB,M -316,2022,EN,FT,Data Engineer,35000,GBP,,GB,100,GB,M -398,2022,SE,FT,Data Scientist,215300,USD,,US,100,US,L -78,2021,MI,CT,ML Engineer,270000,USD,,US,100,US,L -537,2022,SE,FT,Data Engineer,155000,USD,,US,100,US,M -218,2021,MI,FT,Machine Learning Engineer,75000,EUR,,BE,100,BE,M -21,2020,MI,FT,Product Data Analyst,450000,INR,,IN,100,IN,L -445,2022,MI,FT,Data Engineer,70000,EUR,,GR,100,GR,M -22,2020,SE,FT,Data Engineer,42000,EUR,,GR,50,GR,L -115,2021,EN,FT,Machine Learning Scientist,225000,USD,,US,100,US,L -406,2022,MI,FT,Data Analyst,58000,USD,,US,0,US,S -280,2021,MI,FT,Data Engineer,112000,USD,,US,100,US,L -532,2022,SE,FT,Machine Learning Engineer,214000,USD,,US,100,US,M -154,2021,SE,FT,Data Science Manager,144000,USD,,US,100,US,L -363,2022,SE,FT,Data Analyst,61300,USD,,CA,100,CA,M -231,2021,SE,FT,ML Engineer,256000,USD,,US,100,US,S -327,2022,EX,FT,Data Analyst,110000,USD,,US,100,US,M -433,2022,MI,FT,Data Engineer,70000,EUR,,ES,100,ES,M -164,2021,EX,FT,Director of Data Science,110000,EUR,,DE,50,DE,M -158,2021,SE,FT,Data Analytics Manager,120000,USD,,US,100,US,M -236,2021,MI,FT,Research Scientist,80000,CAD,,CA,100,CA,M -400,2022,SE,FT,Data Engineer,209100,USD,,US,100,US,L -192,2021,MI,FT,Big Data Engineer,18000,USD,,MD,0,MD,S -568,2022,SE,FT,Data Analyst,80000,USD,,US,100,US,M -413,2022,MI,FT,Data Engineer,45000,EUR,,GR,100,GR,M -132,2021,MI,FT,Applied Machine Learning Scientist,38400,USD,,VN,100,US,M -362,2022,SE,FT,Data Analyst,130000,USD,,CA,100,CA,M -381,2022,SE,FT,Data Engineer,101570,USD,,US,100,US,M -71,2020,MI,FT,Data Scientist,37000,EUR,,FR,50,FR,S -518,2022,MI,FT,Data Scientist,115000,CHF,,CH,0,CH,L -432,2022,MI,FT,Data Engineer,80000,EUR,,ES,100,ES,M -375,2022,EX,FT,Lead Data Engineer,150000,CAD,,CA,100,CA,S -346,2022,SE,FT,Data Engineer,108800,USD,,US,0,US,M -185,2021,MI,FT,Data Engineer,4000,USD,,IR,100,IR,M -519,2022,SE,FT,Applied Data Scientist,380000,USD,,US,100,US,L -126,2021,SE,FT,Machine Learning Scientist,120000,USD,,US,50,US,S -451,2022,MI,FT,Machine Learning Developer,100000,CAD,,CA,100,CA,M -368,2022,EX,FT,Analytics Engineer,135000,USD,,US,100,US,M -181,2021,MI,FT,Data Scientist,76760,EUR,,DE,50,DE,L -64,2020,SE,FT,Machine Learning Engineer,40000,EUR,,HR,100,HR,S -178,2021,EN,FT,Machine Learning Engineer,81000,USD,,US,50,US,S -323,2022,SE,FT,Data Scientist,180000,USD,,US,0,US,L -211,2021,MI,FT,Research Scientist,48000,EUR,,FR,50,FR,S -104,2021,MI,FT,Data Scientist,73000,USD,,US,0,US,L -449,2022,EN,FT,ML Engineer,20000,EUR,,PT,100,PT,L -318,2022,SE,FT,Data Analyst,90320,USD,,US,100,US,M -208,2021,MI,FL,Data Engineer,20000,USD,,IT,0,US,L -215,2021,SE,FT,Principal Data Engineer,185000,USD,,US,100,US,L -275,2021,EN,FT,Data Scientist,100000,USD,,US,100,US,M -544,2022,SE,FT,Data Engineer,130000,USD,,US,100,US,M -586,2022,MI,FT,Data Analyst,35000,GBP,,GB,0,GB,M -416,2022,SE,FT,Data Scientist,260000,USD,,US,100,US,M -370,2022,SE,FT,Data Scientist,123000,USD,,US,100,US,M -429,2022,MI,FT,Data Analyst,30000,GBP,,GB,100,GB,M -191,2021,EN,FT,Machine Learning Engineer,21844,USD,,CO,50,CO,M -319,2022,SE,FT,Data Engineer,181940,USD,,US,0,US,M -262,2021,MI,FT,Data Scientist,1250000,INR,,IN,100,IN,S -61,2020,MI,FT,Data Engineer,130800,USD,,ES,100,US,M -240,2021,SE,FT,Data Scientist,130000,CAD,,CA,100,CA,L -473,2022,SE,FT,Data Scientist,140000,USD,,US,100,US,M -351,2022,SE,FT,Data Science Manager,137141,USD,,US,100,US,M -44,2020,MI,FT,Data Engineer,88000,GBP,,GB,50,GB,L -364,2022,SE,FT,Data Engineer,160000,USD,,US,0,US,L -84,2021,EX,FT,Director of Data Science,130000,EUR,,IT,100,PL,L -418,2022,MI,FT,Data Engineer,63900,USD,,US,0,US,M -510,2022,EN,FT,Computer Vision Software Engineer,150000,USD,,AU,100,AU,S -199,2021,EN,FT,Data Science Consultant,90000,USD,,US,100,US,S -287,2021,MI,FT,Data Scientist,109000,USD,,US,50,US,L -17,2020,SE,FT,Big Data Engineer,100000,EUR,,PL,100,GB,S -411,2022,MI,FT,Data Scientist,35000,GBP,,GB,0,GB,M -335,2022,SE,FT,Data Engineer,132320,USD,,US,100,US,M -386,2022,EN,FT,Machine Learning Engineer,28500,GBP,,GB,100,GB,L -483,2022,EX,FT,Data Engineer,216000,USD,,US,100,US,M -439,2022,SE,FT,Machine Learning Engineer,164996,USD,,US,0,US,M -76,2021,MI,FT,BI Data Analyst,100000,USD,,US,100,US,M -89,2021,SE,FT,Data Analyst,80000,USD,,BG,100,US,S -602,2022,SE,FT,Data Engineer,154000,USD,,US,100,US,M -94,2021,EN,FT,Data Scientist,2200000,INR,,IN,50,IN,L -31,2020,EN,FT,Big Data Engineer,70000,USD,,US,100,US,L -260,2021,MI,FT,Data Scientist,130000,USD,,US,50,US,L -396,2022,MI,FT,Machine Learning Engineer,80000,EUR,,FR,100,DE,M -171,2021,MI,FT,Data Engineer,60000,GBP,,GB,100,GB,L -88,2021,SE,FT,Lead Data Analyst,170000,USD,,US,100,US,L -146,2021,MI,FT,Research Scientist,53000,EUR,,FR,50,FR,M -465,2022,EN,FT,Data Engineer,120000,USD,,US,100,US,M -493,2022,SE,FT,Machine Learning Developer,100000,CAD,,CA,100,CA,M -598,2022,MI,FT,Data Scientist,160000,USD,,US,100,US,M -333,2022,SE,FT,Data Analyst,90320,USD,,US,100,US,M -423,2022,SE,FT,Data Scientist,180000,USD,,US,0,US,M -593,2022,SE,FT,Data Scientist,150000,USD,,US,100,US,M -279,2021,EN,FT,Business Data Analyst,50000,EUR,,LU,100,LU,L 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Analytics Engineer,USD,110000.0,US,100,US,L +34,2020,MI,FT,Data Analyst,EUR,46759.0,FR,50,FR,L +228,2021,SE,FT,Data Scientist,USD,135000.0,US,0,US,L +4,2020,SE,FT,Machine Learning Engineer,USD,150000.0,US,50,US,L +559,2022,SE,FT,Data Scientist,USD,140400.0,US,0,US,M +1,2020,SE,FT,Machine Learning Scientist,USD,260000.0,JP,0,JP,S +27,2020,SE,FT,Data Engineer,MXN,33511.0,MX,0,MX,S +200,2021,MI,FT,Data Scientist,EUR,61467.0,DE,50,AT,M +182,2021,MI,FT,Data Engineer,EUR,26005.0,RO,0,US,L +188,2021,SE,FT,Data Engineer,EUR,76833.0,RO,50,GB,S +458,2022,MI,FT,Business Data Analyst,INR,18442.0,IN,100,IN,M +90,2021,SE,FT,Marketing Data Analyst,EUR,88654.0,GR,100,DK,L +58,2020,SE,FT,Data Scientist,USD,120000.0,US,50,US,L +141,2021,SE,FT,Data Science Manager,USD,240000.0,US,0,US,L +33,2020,MI,FT,Research Scientist,USD,450000.0,US,0,US,M +48,2020,MI,FT,Data Scientist,USD,105000.0,US,100,US,L +19,2020,MI,FT,Lead Data Engineer,USD,56000.0,PT,100,US,M +380,2022,SE,FT,Data Engineer,USD,136994.0,US,100,US,M +66,2020,EN,FT,Data Scientist,EUR,49268.0,DE,0,DE,S +112,2021,SE,FT,Lead Data Engineer,GBP,103160.0,GB,100,GB,S +12,2020,EN,FT,Data Scientist,EUR,39916.0,FR,0,FR,M +345,2022,SE,FT,Data Engineer,USD,156600.0,US,100,US,M +86,2021,EN,FT,Data Analyst,EUR,59102.0,FR,50,FR,M +585,2022,SE,FT,Data Analyst,USD,110925.0,US,100,US,M +163,2021,EN,FT,Data Science Consultant,EUR,63831.0,DE,50,DE,L +581,2022,SE,FT,Data Analyst,USD,106260.0,US,100,US,M +109,2021,EN,FT,Data Engineer,INR,30428.0,IN,100,IN,L +554,2022,SE,FT,Data Engineer,USD,200100.0,US,100,US,M +595,2022,SE,FT,Data Analytics Manager,USD,109280.0,US,100,US,M +552,2022,SE,FT,Data Scientist,USD,176000.0,US,100,US,M +238,2021,EN,FT,Data Scientist,USD,4000.0,VN,0,VN,M +295,2022,MI,FT,Data Engineer,USD,150000.0,US,100,US,M +207,2021,SE,FT,Data Engineer,USD,165000.0,US,0,US,M +531,2022,MI,FT,Data Analyst,USD,75000.0,CA,0,CA,M +343,2022,EX,FT,Head of Data Science,USD,167875.0,US,100,US,M 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Engineer,USD,25000.0,US,100,US,M +452,2022,EX,FT,Director of Data Science,CAD,196979.0,CA,50,CA,L +62,2020,EN,PT,Data Scientist,EUR,21669.0,IT,50,IT,S +257,2021,SE,FT,Principal Data Scientist,EUR,173762.0,DE,100,DE,M +523,2022,SE,FT,Data Analytics Lead,USD,405000.0,US,100,US,L +462,2022,MI,PT,Data Engineer,EUR,54957.0,DE,50,DE,L +40,2020,MI,FT,Data Scientist,USD,45760.0,PH,100,US,S +420,2022,MI,FT,Machine Learning Scientist,USD,112300.0,US,100,US,L +336,2022,MI,FT,Data Analyst,USD,167000.0,US,100,US,M +513,2022,SE,FT,Machine Learning Engineer,EUR,71444.0,IE,100,IE,S +450,2022,SE,FT,Data Engineer,USD,80000.0,US,100,US,M +177,2021,MI,FT,Data Scientist,CLP,40038.0,CL,100,CL,L +422,2022,MI,FT,Data Science Manager,USD,159000.0,US,100,US,M +263,2021,SE,FT,Machine Learning Engineer,INR,66265.0,IN,0,IN,L +103,2021,MI,FT,Data Analyst,USD,62000.0,US,0,US,L +545,2022,SE,FT,Data Engineer,USD,115000.0,US,100,US,M +278,2021,SE,FT,Data Scientist,TRY,20171.0,TR,50,TR,L +270,2021,EN,FT,Data Engineer,USD,72500.0,US,100,US,L +250,2021,MI,FT,Data Scientist,USD,115000.0,US,50,US,L +5,2020,EN,FT,Data Analyst,USD,72000.0,US,100,US,L +118,2021,EN,FT,Data Analyst,USD,90000.0,US,100,US,S +252,2021,EX,FT,Principal Data Engineer,USD,600000.0,US,100,US,L +114,2021,MI,FT,Data Engineer,EUR,45391.0,NL,100,NL,L +369,2022,SE,FT,Data Scientist,USD,170000.0,US,100,US,M +221,2021,MI,FT,Data Scientist,GBP,116914.0,GB,50,GB,L +572,2022,SE,FT,Data Analyst,USD,100000.0,US,100,US,M +302,2022,SE,FT,Data Scientist,USD,146000.0,US,100,US,M +155,2021,SE,FT,Data Science Engineer,CAD,127221.0,CA,50,CA,L +427,2022,MI,FT,Data Engineer,EUR,49461.0,ES,100,ES,M +193,2021,SE,FT,Data Science Manager,USD,174000.0,US,100,US,L +365,2022,SE,FT,Data Scientist,USD,138600.0,US,100,US,M +13,2020,MI,FT,Lead Data Analyst,USD,87000.0,US,100,US,L +437,2022,MI,FT,Data Engineer,EUR,87932.0,GR,100,GR,M +69,2020,SE,FT,Data Scientist,EUR,91237.0,AT,0,AT,S +538,2022,MI,FT,Data Scientist,USD,141300.0,US,0,US,M 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Scientist,INR,33808.0,IN,0,IN,M +180,2021,MI,FT,Big Data Engineer,INR,22611.0,IN,0,IN,L +311,2022,MI,FT,Data Scientist,GBP,65438.0,GB,0,GB,M +434,2022,MI,FT,Data Engineer,GBP,104702.0,GB,100,GB,M +605,2022,SE,FT,Data Analyst,USD,150000.0,US,100,US,M +492,2022,MI,FT,Data Scientist,PLN,35590.0,PL,100,PL,L +592,2022,SE,FT,Data Scientist,USD,230000.0,US,100,US,M +194,2021,SE,FT,Research Scientist,CAD,96113.0,CA,50,CA,L +8,2020,MI,FT,Business Data Analyst,USD,135000.0,US,100,US,L +214,2021,EN,FT,Machine Learning Engineer,EUR,24823.0,DE,50,DE,M +447,2022,SE,FT,Data Engineer,USD,154600.0,US,100,US,L +43,2020,MI,FT,Data Engineer,USD,106000.0,US,100,US,L +588,2022,SE,FT,Data Analyst,USD,99000.0,US,0,US,M +203,2021,SE,FT,Research Scientist,USD,50000.0,FR,100,US,S +305,2022,SE,FT,Data Analyst,USD,99000.0,US,0,US,M +286,2021,SE,FT,Head of Data,EUR,102839.0,SI,100,SI,L +394,2022,SE,FT,Data Analytics Manager,USD,145000.0,US,100,US,M +542,2022,MI,FT,Data Engineer,USD,206699.0,US,0,US,M 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Scientist,USD,,US,100,US,M +74,2021,EX,FT,Head of Data,USD,,US,100,US,L +390,2022,MI,FT,Machine Learning Engineer,GBP,,GB,0,GB,M +476,2022,SE,FT,Data Scientist,USD,,US,100,US,M +168,2021,EN,FT,BI Data Analyst,USD,,US,50,US,S +435,2022,MI,FT,Data Engineer,GBP,,GB,100,GB,M +224,2021,SE,FT,Machine Learning Scientist,USD,,US,100,CA,L +248,2021,SE,FT,Data Engineer,GBP,,GB,50,GB,L +326,2022,EX,FT,Data Analyst,USD,,US,100,US,M +110,2021,SE,FT,Machine Learning Engineer,EUR,,DE,50,DE,L +299,2022,SE,FT,Data Scientist,USD,,US,100,US,M +441,2022,MI,FT,Data Analyst,EUR,,GR,100,GR,M +322,2022,SE,FT,Data Engineer,USD,,US,0,US,M +417,2022,SE,FT,Data Science Engineer,USD,,AR,100,MX,L +313,2022,MI,FT,Data Engineer,GBP,,GB,0,GB,M +316,2022,EN,FT,Data Engineer,GBP,,GB,100,GB,M +398,2022,SE,FT,Data Scientist,USD,,US,100,US,L +78,2021,MI,CT,ML Engineer,USD,,US,100,US,L +537,2022,SE,FT,Data Engineer,USD,,US,100,US,M +218,2021,MI,FT,Machine Learning Engineer,EUR,,BE,100,BE,M +21,2020,MI,FT,Product Data 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Developer,CAD,,CA,100,CA,M +598,2022,MI,FT,Data Scientist,USD,,US,100,US,M +333,2022,SE,FT,Data Analyst,USD,,US,100,US,M +423,2022,SE,FT,Data Scientist,USD,,US,0,US,M +593,2022,SE,FT,Data Scientist,USD,,US,100,US,M +279,2021,EN,FT,Business Data Analyst,EUR,,LU,100,LU,L +337,2022,SE,FT,Data Engineer,USD,,US,100,US,M +276,2021,EN,FT,Data Scientist,USD,,US,50,US,L +212,2021,MI,FT,Data Engineer,GBP,,HK,50,GB,S +173,2021,SE,FT,Principal Data Scientist,USD,,US,100,US,L +225,2021,EX,CT,Principal Data Scientist,USD,,US,100,US,S +63,2020,SE,FT,Data Scientist,USD,,US,100,US,L +186,2021,SE,FT,Data Analytics Engineer,USD,,VN,100,GB,M +289,2022,SE,FT,Data Engineer,USD,,US,100,US,M +138,2021,SE,FT,Principal Data Scientist,USD,,US,0,US,L +372,2022,SE,FT,Machine Learning Engineer,USD,,US,0,US,M +498,2022,SE,FT,Research Scientist,EUR,,FR,50,FR,L +547,2022,SE,FT,Data Engineer,USD,,US,100,US,M +197,2021,SE,FT,Machine Learning Engineer,INR,,IN,100,IN,L +167,2021,EX,FT,Director of Data Science,USD,,US,0,US,L +366,2022,SE,FT,Data Engineer,USD,,US,0,US,M +324,2022,SE,FT,Data Scientist,USD,,US,0,US,L diff --git a/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/info.json b/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/info.json index 973ccc0d3adf80cf1b8def4e8285a7e47ca05d6b..3bbed89f5698870fe3fd2a1ed2f40ae78a21f554 100644 --- a/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/info.json +++ b/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/info.json @@ -1,252 +1,247 @@ { - "name": "Data Science Job Salaries", - "source": "https://www.kaggle.com/datasets/ruchi798/data-science-job-salaries/data", - "data_intro": "A dataset contain information about Salaries of jobs in the Data Science domain", - "is_splited": false, - "overall_size": 607, - "train_size": 0, - "test_size": 0, - "c_classes": 7, - "n_classes": 5, - "task_type": "regression", - "target": { - "salary_in_usd": "Given the job title, experience level, employment type, salary currency, company size, and remote ratio, predict the salary in USD." - }, - "cat_feature_intro": { - "experience_level": "- experience_level: The experience level in the job during the year with the following possible values: EN Entry-level / Junior MI Mid-level / Intermediate SE Senior-level / Expert EX Executive-level / Director.", - "employment_type": "- employment_type: The type of employement for the role: PT Part-time FT Full-time CT Contract FL Freelance.", - "job_title": "- job_title : The role worked in during the year.", - "salary_currency": "- salary_currency: The currency of the salary paid as an ISO 4217 currency code.", - "employee_residence": "- employee_residence: Employee's primary country of residence in during the work year as an ISO 3166 country code.", - "company_location": "- company_location: The country of the employer's main office or contracting branch as an ISO 3166 country code. ", - "company_size": "- company_size: The average number of people that worked for the company during the year: S less than 50 employees (small) M 50 to 250 employees (medium) L more than 250 employees (large)" - }, - "num_feature_intro": { - "#": "- #: The order of the data in the dataset.", - "work_year": "- work_year: The year the salary was paid.", - "salary": "- salary_in_usd: The salary in USD (FX rate divided by avg. USD rate for the respective year via fxdata.foorilla.com).", - "salary_in_usd": "- salary_in_usd: The salary in USD (FX rate divided by avg. USD rate for the respective year via fxdata.foorilla.com). ", - "remote_ratio": "- remote_ratio: The overall amount of work done remotely, possible values are as follows: 0 No remote work (less than 20%) 50 Partially remote 100 Fully remote (more than 80%)" - }, - "evaluation_metric": null, - "num_feature_value": { - "#": [], - "remote_ratio": [ - 0.0, - 100.0 - ], - "salary": [ - 4000.0, - 30400000.0 - ], - "salary_in_usd": [ - 2859.0, - 600000.0 - ], - "work_year": [ - 2020.0, - 2022.0 - ] - }, - "cat_feature_value": { - "company_location": [ - "AE", - "AS", - "AT", - "AU", - "BE", - "BR", - "CA", - "CH", - "CL", - "CN", - "CO", - "CZ", - "DE", - "DK", - "DZ", - "EE", - "ES", - "FR", - "GB", - "GR", - "HN", - "HR", - "HU", - "IE", - "IL", - "IN", - "IQ", - "IR", - "IT", - "JP", - "KE", - "LU", - "MD", - "MT", - "MX", - "MY", - "NG", - "NL", - "NZ", - "PK", - "PL", - "PT", - "RO", - "RU", - "SG", - "SI", - "TR", - "UA", - "US", - "VN" - ], - "company_size": [ - "L", - "M", - "S" - ], - "employee_residence": [ - "AE", - "AR", - "AT", - "AU", - "BE", - "BG", - "BO", - "BR", - "CA", - "CH", - "CL", - "CN", - "CO", - "CZ", - "DE", - "DK", - "DZ", - "EE", - "ES", - "FR", - "GB", - "GR", - "HK", - "HN", - "HR", - "HU", - "IE", - "IN", - "IQ", - "IR", - "IT", - "JE", - "JP", - "KE", - "LU", - "MD", - "MT", - "MX", - "MY", - "NG", - "NL", - "NZ", - "PH", - "PK", - "PL", - "PR", - "PT", - "RO", - "RS", - "RU", - "SG", - "SI", - "TN", - "TR", - "UA", - "US", - "VN" - ], - "employment_type": [ - "CT", - "FL", - "FT", - "PT" - ], - "experience_level": [ - "EN", - "EX", - "MI", - "SE" - ], - "job_title": [ - "3D Computer Vision Researcher", - "AI Scientist", - "Analytics Engineer", - "Applied Data Scientist", - "Applied Machine Learning Scientist", - "BI Data Analyst", - "Big Data Architect", - "Big Data Engineer", - "Business Data Analyst", - "Cloud Data Engineer", - "Computer Vision Engineer", - "Computer Vision Software Engineer", - "Data Analyst", - "Data Analytics Engineer", - "Data Analytics Lead", - "Data Analytics Manager", - "Data Architect", - "Data Engineer", - "Data Engineering Manager", - "Data Science Consultant", - "Data Science Engineer", - "Data Science Manager", - "Data Scientist", - "Data Specialist", - "Director of Data Engineering", - "Director of Data Science", - "ETL Developer", - "Finance Data Analyst", - "Financial Data Analyst", - "Head of Data", - "Head of Data Science", - "Head of Machine Learning", - "Lead Data Analyst", - "Lead Data Engineer", - "Lead Data Scientist", - "Lead Machine Learning Engineer", - "ML Engineer", - "Machine Learning Developer", - "Machine Learning Engineer", - "Machine Learning Infrastructure Engineer", - "Machine Learning Manager", - "Machine Learning Scientist", - "Marketing Data Analyst", - "NLP Engineer", - "Principal Data Analyst", - "Principal Data Engineer", - "Principal Data Scientist", - "Product Data Analyst", - "Research Scientist", - "Staff Data Scientist" - ], - "salary_currency": [ - "AUD", - "BRL", - "CAD", - "CHF", - "CLP", - "CNY", - "DKK", - "EUR", - "GBP", - "HUF", - "INR", - "JPY", - "MXN", - "PLN", - "SGD", - "TRY", - "USD" - ] - } -} \ No newline at end of file + "name": "Data Science Job Salaries", + "source": "https://www.kaggle.com/datasets/ruchi798/data-science-job-salaries/data", + "data_intro": "A dataset contain information about Salaries of jobs in the Data Science domain", + "is_splited": false, + "overall_size": 607, + "train_size": 0, + "test_size": 0, + "c_classes": 7, + "n_classes": 4, + "task_type": "regression", + "target": { + "salary_in_usd": "Given the job title, experience level, employment type, salary currency, company size, and remote ratio, predict the salary in USD." + }, + "cat_feature_intro": { + "experience_level": "- experience_level: The experience level in the job during the year with the following possible values: EN Entry-level / Junior MI Mid-level / Intermediate SE Senior-level / Expert EX Executive-level / Director.", + "employment_type": "- employment_type: The type of employement for the role: PT Part-time FT Full-time CT Contract FL Freelance.", + "job_title": "- job_title : The role worked in during the year.", + "salary_currency": "- salary_currency: The currency of the salary paid as an ISO 4217 currency code.", + "employee_residence": "- employee_residence: Employee's primary country of residence in during the work year as an ISO 3166 country code.", + "company_location": "- company_location: The country of the employer's main office or contracting branch as an ISO 3166 country code. ", + "company_size": "- company_size: The average number of people that worked for the company during the year: S less than 50 employees (small) M 50 to 250 employees (medium) L more than 250 employees (large)" + }, + "num_feature_intro": { + "#": "- #: The order of the data in the dataset.", + "work_year": "- work_year: The year the salary was paid.", + "salary_in_usd": "- salary_in_usd: The salary in USD (FX rate divided by avg. USD rate for the respective year via fxdata.foorilla.com). ", + "remote_ratio": "- remote_ratio: The overall amount of work done remotely, possible values are as follows: 0 No remote work (less than 20%) 50 Partially remote 100 Fully remote (more than 80%)" + }, + "evaluation_metric": null, + "num_feature_value": { + "#": [], + "remote_ratio": [ + 0.0, + 100.0 + ], + "salary_in_usd": [ + 2859.0, + 600000.0 + ], + "work_year": [ + 2020.0, + 2022.0 + ] + }, + "cat_feature_value": { + "company_location": [ + "AE", + "AS", + "AT", + "AU", + "BE", + "BR", + "CA", + "CH", + "CL", + "CN", + "CO", + "CZ", + "DE", + "DK", + "DZ", + "EE", + "ES", + "FR", + "GB", + "GR", + "HN", + "HR", + "HU", + "IE", + "IL", + "IN", + "IQ", + "IR", + "IT", + "JP", + "KE", + "LU", + "MD", + "MT", + "MX", + "MY", + "NG", + "NL", + "NZ", + "PK", + "PL", + "PT", + "RO", + "RU", + "SG", + "SI", + "TR", + "UA", + "US", + "VN" + ], + "company_size": [ + "L", + "M", + "S" + ], + "employee_residence": [ + "AE", + "AR", + "AT", + "AU", + "BE", + "BG", + "BO", + "BR", + "CA", + "CH", + "CL", + "CN", + "CO", + "CZ", + "DE", + "DK", + "DZ", + "EE", + "ES", + "FR", + "GB", + "GR", + "HK", + "HN", + "HR", + "HU", + "IE", + "IN", + "IQ", + "IR", + "IT", + "JE", + "JP", + "KE", + "LU", + "MD", + "MT", + "MX", + "MY", + "NG", + "NL", + "NZ", + "PH", + "PK", + "PL", + "PR", + "PT", + "RO", + "RS", + "RU", + "SG", + "SI", + "TN", + "TR", + "UA", + "US", + "VN" + ], + "employment_type": [ + "CT", + "FL", + "FT", + "PT" + ], + "experience_level": [ + "EN", + "EX", + "MI", + "SE" + ], + "job_title": [ + "3D Computer Vision Researcher", + "AI Scientist", + "Analytics Engineer", + "Applied Data Scientist", + "Applied Machine Learning Scientist", + "BI Data Analyst", + "Big Data Architect", + "Big Data Engineer", + "Business Data Analyst", + "Cloud Data Engineer", + "Computer Vision Engineer", + "Computer Vision Software Engineer", + "Data Analyst", + "Data Analytics Engineer", + "Data Analytics Lead", + "Data Analytics Manager", + "Data Architect", + "Data Engineer", + "Data Engineering Manager", + "Data Science Consultant", + "Data Science Engineer", + "Data Science Manager", + "Data Scientist", + "Data Specialist", + "Director of Data Engineering", + "Director of Data Science", + "ETL Developer", + "Finance Data Analyst", + "Financial Data Analyst", + "Head of Data", + "Head of Data Science", + "Head of Machine Learning", + "Lead Data Analyst", + "Lead Data Engineer", + "Lead Data Scientist", + "Lead Machine Learning Engineer", + "ML Engineer", + "Machine Learning Developer", + "Machine Learning Engineer", + "Machine Learning Infrastructure Engineer", + "Machine Learning Manager", + "Machine Learning Scientist", + "Marketing Data Analyst", + "NLP Engineer", + "Principal Data Analyst", + "Principal Data Engineer", + "Principal Data Scientist", + "Product Data Analyst", + "Research Scientist", + "Staff Data Scientist" + ], + "salary_currency": [ + "AUD", + "BRL", + "CAD", + "CHF", + "CLP", + "CNY", + "DKK", + "EUR", + "GBP", + "HUF", + "INR", + "JPY", + "MXN", + "PLN", + "SGD", + "TRY", + "USD" + ] + } +} diff --git a/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/info_mod.json b/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/info_mod.json index 559566f19815f04d9343bb8163afb0360621701f..5fea19978eea1c30e703a06cbfbb92334b36abd6 100644 --- a/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/info_mod.json +++ b/treatment_effect_analysis/finance/regression/Data_Science_Job_Salaries_B3/info_mod.json @@ -22,7 +22,6 @@ "num_feature_intro": { "Unnamed: 0": "Unnamed: 0: numeric feature inferred from dataset columns", "work_year": "- work_year: The year the salary was paid.", - "salary": "- salary_in_usd: The salary in USD (FX rate divided by avg. USD rate for the respective year via fxdata.foorilla.com).", "remote_ratio": "- remote_ratio: The overall amount of work done remotely, possible values are as follows: 0 No remote work (less than 20%) 50 Partially remote 100 Fully remote (more than 80%)" }, "evaluation_metric": null, @@ -35,10 +34,6 @@ 2020.0, 2022.0 ], - "salary": [ - 4000.0, - 30400000.0 - ], "remote_ratio": [ 0.0, 100.0 @@ -290,4 +285,4 @@ "missing_from_original_info": [ "Unnamed: 0" ] -} \ No newline at end of file +}