Student Performance Prediction Using Machine Learning Pipeline

Design a comprehensive Python machine learning pipeline to predict student academic performance using multi-source data. Integrate data from learning management systems, student information systems, and historical academic records. Implement feature engineering techniques to handle categorical and numerical variables, use cross-validation with stratified k-fold, and develop an ensemble model combining RandomForestClassifier, GradientBoostingClassifier, and XGBoost. Include model interpretability using SHAP values to understand key predictive factors for student success.