| ---
|
| license: cc0-1.0
|
| task_categories:
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| - tabular-classification
|
| language:
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| - en
|
| tags:
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| - Data-science
|
| - Machine-learning
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| - Risk-prediction
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| - Finance
|
| pretty_name: "LendingClub Loan Data (2007-2018)"
|
| size_categories:
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| - 1M<n<10M
|
| configs:
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| - config_name: default
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| data_files:
|
| - split: train
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| path: data/before_feature_engineering/train.parquet
|
| - split: validation
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| path: data/before_feature_engineering/val.parquet
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| - split: test
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| path: data/before_feature_engineering/test.parquet
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| default: true
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| - config_name: feature_engineered
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| data_files:
|
| - split: train
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| path: data/after_feature_engineering/train.parquet
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| - split: validation
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| path: data/after_feature_engineering/val.parquet
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| - split: test
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| path: data/after_feature_engineering/test.parquet
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| - config_name: data_dictionary_summary
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| data_files: data_dictionary/data_dictionary_summary.csv
|
| - config_name: missing_values_summary
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| data_files: data_dictionary/missing_values_summary.csv
|
| - config_name: missing_and_leakage_manual_review
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| data_files: data_dictionary/missing_and_leakage_manual_review.csv
|
| ---
|
|
|
| 
|
|
|
| # LendingClub Loan Data (2007-2018)
|
|
|
| [](https://creativecommons.org/publicdomain/zero/1.0/) [](https://builderslab.dev/) [](https://www.linkedin.com/company/builderslabdev) [](https://github.com/BuildersLab/Credit-Risk-Default) [](https://portfolio-risk-prediction.streamlit.app/)
|
|
|
| Personal loan applications and originations from LendingClub (2007-2018), sourced from Kaggle. Used by [BuildersLab](https://github.com/BuildersLab)'s **Credit Risk Default** project to build an explainable model that predicts **loan application defaults**, not a credit card product, for a fictional bank case study.
|
|
|
| - **Project repo:** https://github.com/BuildersLab/Credit-Risk-Default
|
| - **Live demo:** https://portfolio-risk-prediction.streamlit.app/
|
| - **Source:** [Kaggle: All Lending Club loan data](https://www.kaggle.com/datasets/wordsforthewise/lending-club) (`wordsforthewise/lending-club`)
|
| - **License:** CC0 1.0 Universal (Public Domain)
|
|
|
| ## About the project
|
|
|
| [BuildersLab](https://builderslab.dev/) is a community that turns learners into builders through real projects, not just tutorials. It gives students and early-career people real-world experience, mentorship, and guidance so they can grow from learners into confident builders ready for the first day of their careers.
|
|
|
| This dataset backs **Credit Risk Default**: an explainable credit-risk platform that predicts the probability a personal loan will default, using only borrower and loan characteristics available at origination, and surfaces the result through an interactive review dashboard for credit officers. NorthBay Bank is a fictional company created for this exercise.
|
|
|
| ### Team
|
|
|
| | Name | Role |
|
| |---|---|
|
| | Nafisat Ibrahim | Project Lead & Data Scientist |
|
| | Marienne Dosso | Data Scientist |
|
| | Bintou Ba | Data Scientist |
|
|
|
| ## Dataset Splits
|
|
|
| Two versions of the same train/validation/test splits are available as separate configs:
|
|
|
| - **`default`** (used automatically if you don't pass a config name): the cleaned,
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| target-filtered, leakage-removed, imputed dataset from
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| [`notebooks/00_cleaning.ipynb`](https://github.com/BuildersLab/Credit-Risk-Default/blob/nafisat/notebooks/00_cleaning.ipynb),
|
| before feature engineering. Files under `data/before_feature_engineering/`.
|
| - **`feature_engineered`**: the same splits after
|
| [`notebooks/02_feature_engineering.ipynb`](https://github.com/BuildersLab/Credit-Risk-Default/blob/nafisat/notebooks/02_feature_engineering.ipynb)
|
| (engineered features added, several raw columns consolidated or dropped).
|
| Files under `data/after_feature_engineering/`.
|
|
|
| ```python
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| from datasets import load_dataset
|
|
|
| before = load_dataset("BuildersLab/loan-application-dataset") # default config
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| after = load_dataset("BuildersLab/loan-application-dataset", "feature_engineered") # after feature engineering
|
| ```
|
|
|
| ## Data Dictionary
|
|
|
| Reference files under `data_dictionary/`, produced while cleaning and engineering the LendingClub data (see [`notebooks/00_cleaning.ipynb`](https://github.com/BuildersLab/Credit-Risk-Default/blob/nafisat/notebooks/00_cleaning.ipynb) and [`notebooks/02_feature_engineering.ipynb`](https://github.com/BuildersLab/Credit-Risk-Default/blob/nafisat/notebooks/02_feature_engineering.ipynb)), not Kaggle's raw column list. Each has a different schema, so each CSV is declared as its own config above (see `configs:` in the metadata) rather than being auto-merged.
|
|
|
| - **`data_dictionary_summary.csv`**: one row per column: `Variable`, `Description`
|
| (LendingClub's official column definition), `Missing Values` (count),
|
| `Percentage (%)`, and `Missingness Label` (e.g. "Very High (95-100%)").
|
| - **`missing_values_summary.csv`**: missing-value counts and percentages per column,
|
| used to identify and drop columns with structural missingness before modeling.
|
| - **`missing_and_leakage_manual_review.csv`**: the full manual review of all 151
|
| original columns, `Keep` (boolean), `Reason`, and whether each column is
|
| available at time of application (pre-loan) versus only during the loan
|
| lifecycle (post-origination). This is the source review behind the leakage
|
| and structural-missingness decisions in `00_cleaning.ipynb`.
|
| - **`missing_values_decisions.xlsx`**: per-column imputation decision for the 87
|
| columns that survive the leakage/keep review (median fill, median plus a
|
| missing-flag column, mode plus flag, or drop rows), with the mechanism and
|
| reasoning behind each call.
|
| - **`feature_engineering_data_dictionary.xlsx`**: 4-sheet workbook covering the
|
| `feature_engineered` split from `02_feature_engineering.ipynb`: a data
|
| dictionary, dictionary plus per-column statistics (min/max/mean/std/percentiles
|
| for numeric columns, unique/top/freq for categorical columns, missing value
|
| count for all), a full feature x feature correlation matrix, and feature x
|
| `default` target correlation.
|
|
|
| ## Outputs
|
|
|
| Analysis artifacts produced by the notebooks, not part of the model-ready dataset
|
| itself, live under `outputs/`. Currently the feature x feature correlation matrix
|
| (`feature_correlation_matrix.csv`) and its heatmap (`feature_correlation_matrix.png`)
|
| from `02_feature_engineering.ipynb`; later notebooks will add artifacts like SHAP
|
| plots and evaluation curves here too.
|
|
|
