Student Performance Predictive Model Using Machine Learning

Design a comprehensive Python pipeline using scikit-learn to predict student academic performance risk. Develop a model that integrates historical academic data, attendance records, demographic information, and learning platform engagement metrics. Create a predictive scoring system that identifies students at risk of academic underperformance with at least 85% accuracy. Implement feature importance analysis to understand key predictive variables and generate interpretable recommendations for intervention strategies.