Student Performance Predictive Model with Machine Learning

Design a comprehensive predictive analytics pipeline to forecast student academic performance using multivariate regression and ensemble learning techniques. Develop a model that integrates historical academic data, demographic information, attendance records, and extracurricular engagement metrics. Create a feature importance ranking and develop a probabilistic risk scoring system that identifies students at risk of academic underperformance with 85%+ accuracy. Include recommendations for intervention strategies and visualize potential performance trajectories using interactive dashboards.