Multi-Source Student Retention Predictive Model

Build a comprehensive machine learning pipeline to predict student dropout risk by integrating data from multiple sources including academic records, financial aid information, and behavioral metrics. Use advanced feature engineering techniques with pandas, implement multiple classification algorithms (random forest, gradient boosting), and create a model that provides probabilistic dropout risk scores. Include a detailed interpretability framework that explains key contributing factors to student attrition.