Customer churn prediction model with feature engineering

Build production churn prediction system. Pipeline: 1. Perform exploratory data analysis and visualization. 2. Engineer features (RFM, engagement scores, usage patterns). 3. Handle class imbalance with SMOTE or class weights. 4. Train multiple models (XGBoost, Random Forest, Neural Network). 5. Implement cross-validation and hyperparameter tuning. 6. Create SHAP values for model interpretability. 7. Build prediction API with FastAPI. 8. Set up monitoring for model drift. Include feature importance analysis and business impact metrics.