Dropout Risk Prediction and Intervention Framework

Design a comprehensive Python-based early warning system to predict and mitigate student dropout risks. Develop machine learning models using gradient boosting and neural networks that analyze multiple risk factors including academic performance, engagement metrics, and socio-economic indicators. Create an automated intervention recommendation system that provides tailored support strategies for students with high dropout probability. Implement a real-time monitoring dashboard for institutional administrators.