A Data Mining–Based Model for Predicting Student Academic Performance
Keywords:
Student Performance Prediction, Data Mining, Machine Learning, Educational Data Mining, ClassificationAbstract
Predicting student academic performance is essential for improving educational outcomes and enabling early academic interventions. With the increasing availability of educational data, data mining techniques provide effective tools for analyzing student-related information and identifying performance patterns. This paper proposes a data mining–based model for predicting student academic performance using classification techniques. The proposed framework includes data preprocessing, feature selection, model training, and performance evaluation. Several data mining algorithms, including Logistic Regression, Decision Tree, Support Vector Machine, Random Forest, and K-Nearest Neighbors, are implemented and evaluated using standard performance metrics such as accuracy, precision, recall, and F1-score. Experimental results indicate that ensemble-based classifiers achieve superior prediction accuracy compared to individual models. The findings demonstrate the potential of data mining techniques in supporting educational decision-making and early identification of students at risk of poor academic performance
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