Student Progression Predictive Model Using Machine Learning

Design a comprehensive predictive analytics framework to forecast student graduation likelihood using multivariate regression. Develop a model that integrates historical academic performance, socioeconomic indicators, course enrollment patterns, and extracurricular engagement. Create a scalable Python pipeline using scikit-learn that can generate probabilistic graduation risk scores with 85%+ accuracy, including feature importance visualization and confidence intervals.