Student Churn Risk Prediction Pipeline

Design a comprehensive Python-based machine learning pipeline to predict student dropout risks in educational institutions. Utilize advanced feature engineering techniques with pandas, implement multiple classification algorithms (Random Forest, XGBoost, Logistic Regression), and create a probabilistic risk scoring system. The model should handle imbalanced datasets, provide feature importance analysis, and generate actionable insights for early intervention strategies.