This application predicts student dropout risk using a supervised machine learning model and provides an interface for single-student form input or batch CSV uploads. Results are sorted by predicted risk and can be exported as a CSV. The initial approach uses a Random Forest classifier to capture non-linear relationships and interactions across categorical and numerical features.
Source: UCI ML Repository — Predict Students Dropout and Academic Success