Student Performance Predictive Model with Machine Learning

Design a comprehensive predictive analytics framework to forecast student academic performance using multivariate regression. Develop a model that incorporates historical academic data, engagement metrics, socioeconomic indicators, and learning platform interactions. Create a feature engineering pipeline that can handle both categorical and continuous variables, with explicit attention to handling missing data and potential bias. Include model interpretability techniques like SHAP values to explain individual predictions, and develop a recommended intervention strategy for at-risk students based on the predictive insights.