Credit Risk Scoring & Loan Default Prediction
End-to-end ML pipeline predicting loan defaults with SHAP explainability and 0.788 AUC.
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Key Metrics
0.788
Best AUC Score
67
Engineered Features
3
Models Compared
SMOTE
Class Imbalance Method
Approach
Technical Overview
Dataset & Preprocessing
The German Credit Dataset presents a realistic class imbalance — a common challenge in credit scoring. SMOTE (Synthetic Minority Oversampling Technique) was applied to the training set to ensure the model learned meaningful default patterns rather than simply predicting the majority class.
Feature Engineering
Raw features were expanded from 20 to 67 through interaction terms, polynomial features, and domain-driven transformations. This expansion gave tree-based models additional signal while the logistic regression benefited from the interaction terms directly.
Model Comparison
Three classifiers were trained and evaluated — Logistic Regression, Random Forest, and XGBoost — each with cross-validated hyperparameter tuning. Logistic Regression achieved the best generalization at 0.788 AUC, outperforming the more complex tree ensembles on this dataset size.
Explainability with SHAP
SHAP (SHapley Additive exPlanations) values were computed for the best model to produce both global feature importance rankings and individual prediction explanations. In credit risk contexts, model interpretability is not optional — regulators require lenders to justify adverse decisions. SHAP provides that audit trail.
Gallery
Output & Visualizations
Demo screenshots and output visualizations — coming soon.
ROC Curves — Logistic Regression vs. Random Forest vs. XGBoost
SHAP Summary Plot — Global feature importance across all predictions
Confusion Matrix — Best model classification results on holdout set
Model Comparison — AUC and F1 across all three classifiers
Feature Importance — Top features driving default predictions
Risk Distribution — Predicted probability distribution by outcome
Stack