Mazin Mohammed Hamid

From Prediction to Personalized Intervention: An Explainable, Lightweight SHAP-Driven Ensemble Framework for Early Academic Failure Risk Assessment

A model to interpret the model globally and instance-level using SHapley Additive exPlanations (SHAP) along with three machine learning algorithms (Random Forest, XGBoost, and Gradient Boosting) in a lightweight, easy to explain academic performance prediction framework is proposed.