Automated Student Performance Predictive Analytics Pipeline

Design a comprehensive Python data pipeline using pandas and scikit-learn that can ingest multi-source student performance data (grades, attendance, demographic information) and build a predictive machine learning model to forecast student risk of academic failure. The solution must include automated feature engineering, handle missing data strategically, implement at least three different classification algorithms (logistic regression, random forest, gradient boosting), and generate an interactive dashboard using Dash that provides intervention recommendations for at-risk students.