AI-Student Retention & Adaptive Counseling System

This project is an AI‑Driven Student Retention & Adaptive Counseling System that helps schools detect dropout risk early, understand the reasons, and take immediate action. It uses a machine‑learning model (Random Forest/XGBoost) to predict dropout probability, SHAP to explain which factors contribute to each prediction, and a rule‑based recommendation engine to suggest interventions (scholarship support, remedial classes, or parent meetings). A full‑stack web dashboard (Flask + React) visualizes risk scores, SHAP explanations, recommended actions, and intervention monitoring. Student records, predictions, and counseling actions are stored in a database for long‑term tracking and evaluation.