Student Success Early Warning System

Develop a probabilistic machine learning model for early identification of students at risk of academic underperformance or dropout. Create a comprehensive feature engineering pipeline that integrates academic, behavioral, and contextual data sources. Implement advanced ensemble learning techniques like gradient boosting and random forest to maximize predictive accuracy. Design a clear intervention recommendation framework that provides actionable insights for academic support teams, with explicit confidence intervals and risk stratification.