Dynamic Student Performance Predictive Analytics Pipeline

Design a comprehensive Python-based machine learning pipeline using pandas and scikit-learn that predicts student academic performance risk with 85%+ accuracy. The system must integrate multiple data sources including historical grades, attendance records, learning management system interaction logs, and demographic information. Implement feature engineering techniques to identify leading indicators of potential academic challenges, and create an automated reporting system that generates personalized intervention recommendations for educators. Include robust error handling, cross-validation mechanisms, and a Flask-based dashboard for real-time model insights.