Credit risk assessment and loan portfolio analytics using Python classification models and automated KPI reporting in Jupyter Notebook

End-to-end Python notebook that engineers a proxy “bad-client” label, explores drivers of credit risk, and builds a leakage-free XGBoost scorecard with threshold tuning and cross-validation.

📊 End-to-end data analytics project analyzing a bank’s loan portfolio to identify profitable segments, high-risk borrowers, and strategic insights using Python, Jupyter Notebook, and data visualization.