Machine Learning Feature Engineering for Student Retention

Create a PostgreSQL stored procedure that preprocesses and engineers machine learning features from student academic and behavioral data. The procedure must aggregate complex signals including grade trends, course withdrawal patterns, attendance frequency, and demographic factors. Design the feature engineering pipeline to output a normalized dataset suitable for predictive retention modeling, with built-in data quality checks and anomaly detection.