Advanced Customer Churn Predictive Model with ML Pipeline

Design a comprehensive machine learning pipeline for predicting customer churn using a telecommunications dataset. Implement feature engineering that includes behavioral scoring, interaction frequency, and financial metrics. Develop a stacked ensemble model using XGBoost, Random Forest, and Logistic Regression, with cross-validation achieving minimum 85% precision. Include model interpretability using SHAP values and create a deployment strategy for real-time scoring in a production environment.