Student Performance Predictive Model with Machine Learning

Design a comprehensive predictive analytics pipeline to forecast student academic performance using multi-source data. Develop a machine learning model that integrates historical academic records, demographic information, engagement metrics, and learning platform interactions. The model should provide a probabilistic risk score for student dropout or academic underperformance, with at least 85% accuracy. Include feature importance analysis, model interpretability techniques, and a recommendation framework for targeted interventions.