Machine Learning-Powered Dropout Prevention System

Create a predictive system that identifies students at risk of dropping out using advanced machine learning techniques. Develop a comprehensive feature engineering pipeline that incorporates academic performance, engagement metrics, demographic data, and psychological indicators. Implement ensemble learning methods with model interpretability, generate actionable intervention recommendations, and create a modular system that can be integrated with existing student management platforms.