Student Retention Predictive Analytics Framework

Design an advanced predictive analytics model to forecast student retention risks using ensemble machine learning techniques. Integrate multiple data sources including academic performance, financial aid records, demographic information, and behavioral metrics. Develop a probabilistic risk scoring system with interpretable machine learning models (e.g., gradient boosting, random forests) that provides actionable insights for early intervention strategies.