A practical exploration of Interpretable Machine Learning (XAI) with Python. This repository features Jupyter notebooks covering SHAP, LIME, Anchors, Counterfactuals, Grad-CAM, and more, applied to models from Linear Regression to Convolutional Neural Networks (CNNs).
End-to-end credit risk ML pipeline with CatBoost, SHAP & LIME explainability, fairness monitoring, and auto-generated PDF reports — built for auditability over accuracy.
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