BuildersLab/loan-application-dataset

LendingClub Loan Data (2007-2018)

Personal loan applications and originations from LendingClub (2007-2018), sourced from Kaggle. Used by 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 (wordsforthewise/lending-club)
• License: CC0 1.0 Universal (Public Domain)

About the project

BuildersLab 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, before feature engineering. Files under data/before_feature_engineering/.
• feature_engineered: the same splits after notebooks/02_feature_engineering.ipynb (engineered features added, several raw columns consolidated or dropped). Files under data/after_feature_engineering/.

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 and 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.