Data Mining Techniques for Predicting Student Academic Performance

Abstract
            Predicting student academic performance is a major objective in educational data mining (EDM) and learning analytics. Accurate prediction models can help early identification of at-risk students and enable timely interventions to improve retention and outcomes. This paper surveys relevant literature, proposes a reproducible experimental framework, implements multiple data mining techniques (decision trees, random forests, gradient boosting, support vector machines, k-nearest neighbours, logistic regression, Naive Bayes, and neural networks), and evaluates them on commonly used datasets. We discuss robust data preprocessing, feature engineering, class imbalance handling, and model selection strategies. We propose evaluation metrics (classification: accuracy, precision, recall, F1, AUC; regression: RMSE, MAE, R²) and present an experimental protocol using stratified k-fold cross-validation and hyperparameter tuning via grid/random search. Finally, we discuss operational deployment considerations and ethical implications, and provide a reproducible appendix with recommended code snippets and experiment templates. All references and resources are provided.