Comprehensive Student Engagement Risk Prediction System

Develop an advanced predictive analytics system that identifies potential student disengagement and dropout risks using machine learning and complex feature engineering. Create a data pipeline integrating academic performance, attendance records, psychological assessment data, socioeconomic indicators, and digital learning platform interactions. The model must generate real-time risk scores with interpretable feature importance and actionable early intervention strategies.