Datasets:
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license: cc0-1.0
task_categories:
- tabular-classification
language:
- en
tags:
- Data-science
- Machine-learning
- Risk-prediction
- Finance
pretty_name: "LendingClub Loan Data (2007-2018)"
size_categories:
- 1M<n<10M
configs:
- config_name: default
data_files:
- split: train
path: data/before_feature_engineering/train.parquet
- split: validation
path: data/before_feature_engineering/val.parquet
- split: test
path: data/before_feature_engineering/test.parquet
default: true
- config_name: feature_engineered
data_files:
- split: train
path: data/after_feature_engineering/train.parquet
- split: validation
path: data/after_feature_engineering/val.parquet
- split: test
path: data/after_feature_engineering/test.parquet
- config_name: data_dictionary_summary
data_files: data_dictionary/data_dictionary_summary.csv
- config_name: missing_values_summary
data_files: data_dictionary/missing_values_summary.csv
- config_name: missing_and_leakage_manual_review
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,
target-filtered, leakage-removed, imputed dataset from
[`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
from datasets import load_dataset
before = load_dataset("BuildersLab/loan-application-dataset") # default config
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.
|