In this video, we walk through a complete end-to-end Credit Risk Modeling process — building a Probability of Default (PD) model and creating a credit scorecard using real-world techniques from the banking and finance industry.
Colab: https://colab.research.google.com/drive/1jByu78jQQM3pbFfnCboeOobs3F42tv3Y#scrollTo=ORCWxDC_-Pzu
🔍 Topics Covered:
Feature selection using Information Value (IV), VIF, and correlation analysis
Scorecard binning and transformation using Weight of Evidence (WoE)
Logistic Regression model for PD prediction
Model validation using metrics like KS statistic, AUC, and Gini
Creating a scorecard with scaling and score mapping
🧠 Key Concepts: ✔ IV-based variable selection
✔ VIF for multicollinearity detection
✔ Correlation filtering
✔ WoE Binning & Transformation
✔ Logistic Regression for PD
✔ Model evaluation (ROC, AUC, KS, Gini)
✔ Scorecard generation using score scaling
📊 Tools Used: Python, Pandas, Scikit-learn, Matplotlib, Scorecardpy (or equivalent)
This project is ideal for anyone interested in credit risk, banking analytics, and interpretable machine learning models.