Predictive Modeling and Risk Scoring for Bank Customer Churn is a Machine Learning and Explainable AI project designed to identify customers at risk of churn and support proactive retention strategies. The project includes predictive modeling, risk scoring, SHAP-based explainability, interactive Streamlit dashboards, and customer analytics. The final model achieved 97.35% accuracy and 98.54% ROC-AUC, providing reliable churn prediction and actionable business insights for the banking sector.