Machine Learning Churn Risk Prediction Pipeline

Build a comprehensive Python data pipeline for predicting customer churn risk using advanced ensemble machine learning techniques. Integrate multiple data sources including behavioral logs, support interactions, usage patterns, and demographic information. Implement a stacked ensemble model combining random forest, gradient boosting, and neural network approaches. Include feature importance analysis, model interpretability metrics, and a robust cross-validation strategy that handles class imbalance.