An Explainable AI Framework for Multiclass Student Performance Prediction with SMOTE and Feature Selection

An integrated framework combining SMOTE, correlation-based feature selection, and explainable AI for multiclass student performance prediction. Random Forest achieves perfect classification, with XAI methods (SHAP, LIME) identifying project and assignment scores as most influential predictors.