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This project focuses on predicting whether a client will experienceserious delinquency within the next two years using classification models. The dataset was preprocessed to ensure high data quality, including imputation, outlier treatment, and transformation usingWeight of Evidence (WOE) to enhance interpretability and model effectiveness—especially for logistic regression.

Three machine learning models were evaluated to identify the best-performing approach in terms of accuracy, recall, and business relevance.

• Logistic Regression (with and without WOE)
• Random Forest Classifier
• XGBoost Classifier

1. Raw Features – Cleaned but untransformed dataset
2. WOE-Transformed Features – Variables transformed using Weight of Evidence to support interpretability and improve logistic regression performance

• Random Forest delivered the best balance of performance metrics:
  • ROC AUC: 0.8569
  • Recall: 0.72
  • Precision: 0.22
• Logistic Regression performed well with WOE-transformed features:
  • Recall: 0.70
  • Precision: 0.19
  • Enabled creation of an interpretablescorecard
• XGBoost had the highest precision for non-delinquents but a low recall (0.16), making it less suitable for minimizing false negatives.
• Key predictors across models included:
  • Revolving Utilization of Unsecured Lines
  • Past Due Counts (30–59, 60–89, 90+)
  • Age

• Python, Jupyter Notebook
• pandas, numpy, seaborn, matplotlib
• scikit-learn, XGBoost
• WOE Binning tools, scikit-plot

credit-risk-prediction/

1. data/ # Raw dataset
2. notebooks/ # Model training and evaluation (LR, RF, XGBoost)
3. results/ # Evaluation metrics, ROC curves, confusion matrices, scorecard
4. README.md # Project overview

This project demonstrates that:

• Random Forest is the most effective model for credit delinquency prediction, offering high recall and balanced precision.
• Logistic Regression with WOE remains highly interpretable and practical for deployment via scorecards.
• CombiningRandom Forest's accuracy withLogistic Regression's explainability provides a strong, business-ready solution for credit scoring systems.