Publish audited TopBench cleaned data (2026-11-07)

Versioned cleanup, source-verified labels, clarified queries and strict heldout profiles. Paper data remains available at the previous revision.

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). The old Kaggle CSV itself contains the corrupted maker spelling; the publisher's matching reference/year/origin/cocoa/rating records support the spelling repair.

README.md

@@ -1,5 +1,4 @@
 ---
-license: mit
 pretty_name: TopBench
 task_categories:
   - tabular-classification
@@ -14,19 +13,63 @@ tags:
   - llm
 ---
 
-# TopBench Dataset
+# TopBench
 
-TopBench is a benchmark for 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},
+}
+```

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"
+}

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

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"
-        ]
-    }
-}
+{
+  "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"
+    ]
+  }
+}

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": []
-}
+}

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": {}
-}
+}

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": {}
-}
+}

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": {}
-}
+}

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": {}
-}
+}

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": {}
-}
+}

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": {}
-}
+}

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

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

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"
-        ]
-    }
-}
+{
+  "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"
+    ]
+  }
+}

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": []
-}
+}

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": {}
-}
+}

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": {}
-}
+}

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": {}
-}
+}

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": {}
-}
+}

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": {}
-}
+}

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": {}
-}
+}

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

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"
-        ]
-    }
-}
+  "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"
+    ]
+  }
+}

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": []
-}
+}

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": {}
-}
+}

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": {}
-}
+}

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

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"
-        ]
-    }
-}
+  "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"
+    ]
+  }
+}

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": []
-}
+}

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": {}
-}
+}

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": {}
-}
+}

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": {}
-}
+}

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": {}
-}
+}

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": {}
-}
+}

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": {}
-}
+}

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": {}
-}
+}

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": {}
-}
+}

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

decision_making/medical/classification/Indicators_of_Heart_Disease_(2022_UPDATE)_B2/history.csv

@@ -1,3 +1,3 @@
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-oid sha256:0087ce2112773f9b88077e6f713a683eff2540220613d8375541ee87d6ace908
-size 271694926
+oid sha256:cdeaf8ddba61c9334b356ddee78bd3ac39b6513aa999b6fc5aaa6500f6c75c59
+size 270672586

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"
-        ]
-    }
-}
+  "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"
+    ]
+  }
+}

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": []
-}
+}

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": {}
-}
+}

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": {}
-}
+}

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": {}
-}
+}

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": {}
-}
+}

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

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