Predicting Customer Churn Before It Happens: An End-to-End ML Project with SHAP Explainability

An end-to-end churn prediction pipeline using XGBoost and SHAP for explainability — deployed on both Streamlit Cloud and HuggingFace Spaces. Covers EDA insights, feature engineering, model comparison, and a real Docker deployment debugging story.