Dynamic Predictive Churn Model with Multivariate Feature Engineering

Design a comprehensive churn prediction framework that integrates behavioral, demographic, and interaction data with at least 12 feature engineering techniques. Create a modular pipeline that can automatically detect and weight feature importance, using ensemble machine learning methods like gradient boosting and random forest. Include a robust validation strategy with cross-validation, stratified sampling, and a clear model interpretability component using SHAP values.