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credit-scorecard

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Completed as part of the 365 Data Science Credit Risk Modeling in Python Udemy course. Developed an end-to-end credit risk modeling pipeline for consumer lending, covering data preprocessing, feature engineering, Probability of Default , Loss Given Default , Exposure at Default , scorecard development, model validation, population stability

  • Updated Jun 10, 2026
  • Jupyter Notebook

Consumer credit risk, PD scorecard (logistic regression + WOE/IV) on Home Credit data, with discrimination, calibration and population-stability validation, ongoing monitoring, and model governance. Includes an EAD analysis reframed as a documented data-quality finding.

  • Updated Jun 23, 2026
  • Jupyter Notebook

End-to-End Python implementation of Kanziga et. al's (2026) credit-risk research method: monotonic binning, Weight-of-Evidence encoding, Information Value filtering, residual gradient boosting with hybrid-AUC early stopping, adaptive weighting via a 27-point CV search, Platt calibration, TreeSHAP, DeLong tests & stratified paired bootstrapping.

  • Updated Sep 4, 2026
  • Jupyter Notebook

💰 Credit Risk Scorecard — Gradient Boosting + Logistic Regression + Decision Tree on 5,000 loans. Industry-standard metrics: Gini 0.521 · KS 0.395 · AUC 0.761 · IV/WoE table · Credit grades A-E. Basel III aligned. Production-realistic metrics. Python · scikit-learn

  • Updated May 16, 2026
  • Python

IFRS 9 Credit Risk Scorecard & Expected Credit Loss (ECL = PD * LGD * EAD) Engine under Basel III / EBA standards. Features R Weight of Evidence (WoE) binning & Information Value, Python PD models (Logistic Regression Gini=0.7467 vs XGBoost), 3-Stage Staging, PostgreSQL, automated Excel financial models, and a 2-page Power BI Dashboard.

  • Updated Aug 21, 2026
  • Python

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