Student Performance Predictive Pipeline with Machine Learning

Design a comprehensive Python-based predictive analytics pipeline that forecasts individual student academic performance using multi-source data. Integrate data from learning management systems, historical grade records, student engagement metrics, and demographic information. Implement advanced feature engineering techniques using pandas, develop a machine learning model with scikit-learn that predicts student risk of academic failure with at least 85% accuracy, and create an automated reporting system that generates personalized intervention recommendations. Include comprehensive error handling, cross-validation strategies, and model interpretability using SHAP values.