Student Performance Prediction ML Pipeline with Feature Engineering

Design a comprehensive machine learning pipeline using pandas and scikit-learn that predicts student academic performance across multiple subjects. The system must handle feature engineering for categorical and numerical data, implement cross-validation with stratified k-fold, and produce an interpretable model with feature importance ranking. Include automated preprocessing to handle missing values, outlier detection, and create a modular architecture that can be easily retrained with new datasets.