Student Performance Predictive Model with Machine Learning

Develop a comprehensive predictive model using Python's scikit-learn to forecast student academic performance across multiple courses. The model should incorporate historical grade data, attendance records, learning platform engagement metrics, and demographic information. Create a modular pipeline that can handle feature engineering, handle missing data, perform cross-validation, and generate interpretable results with feature importance rankings. Include a method to visualize predicted vs. actual performance and calculate model accuracy using multiple metrics.