Student Dropout Risk Prediction System

Build a sophisticated machine learning pipeline using Python to predict student dropout risks with high accuracy. Develop a multi-stage model that integrates academic performance, socioeconomic indicators, attendance data, and psychological assessment scores. Use advanced ensemble techniques like XGBoost and create a comprehensive reporting system that not only predicts risk but provides actionable intervention recommendations for each student profile.