REVIEW ON CLASSIFICATION AND PREDICTION OF STUDENT PERFORMANCE USING MACHINE LEARNING AND DEEP LEARNING ALGORITHM
Keywords:
Logistic Regression, Random Forest Classifier, MLP Classifier, GaussianNB, SVC, Decision Tree Classifier
Abstract
Data mining methods are being applied to a greater extent in the education sector to predict and classify both student and teacher performance, assisting in the development of effective teaching and learning strategies and individualized learning systems. These technologies assist students with career choices, educational planning, early intervention, and individualized instruction. In this research, different machine learning and deep learning models—Logistic Regression, Decision Tree, Random Forest, SVC, KNN, GaussianNB, and MLPClassifier—were experimented with on a labeled student performance dataset. Performance was evaluated in terms of accuracy, precision, recall, F1-score, and cross- validation. The highest accuracy (96.8%) was achieved by Random Forest, followed by Decision Tree (96.2%), with MLPClassifier scoring 90.8%. The findings indicate that ensemble and deep learning models are powerful tools for educational data mining in enhancing student support and institutional decision-making.
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[18] A. Villar and C. R. V. de Andrade, “Supervised machine learning algorithms for predicting student dropout and academic success: a comparative study,” SpringerA Villar, CRV de AndradeDiscover Artificial Intelligence, 2024•Springer, vol. 4, no. 1, Dec. 2024, doi: 10.1007/S44163-023-00079-Z.
[19] E. S. M. El-Kenawy, A. A. Alhussan, N. Khodadadi, S. Mirjalili, and M. M. Eid, “Predicting potato crop yield with machine learning and deep learning for sustainable agriculture,” SpringerESM El-Kenawy, AA Alhussan, N Khodadadi, S Mirjalili, MM EidPotato Research, 2024•Springer, vol. 68, pp. 759–792, Mar. 2024, doi: 10.1007/S11540-024-09753-W.
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Students Performance Dataset
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https://www.kaggle.com/datasets/rabieelkharoua/students-performance-dataset
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Published
2025-12-24
How to Cite
Anuj Raj, Er. Ankit Kumar, & Er. Narinder Kaur. (2025). REVIEW ON CLASSIFICATION AND PREDICTION OF STUDENT PERFORMANCE USING MACHINE LEARNING AND DEEP LEARNING ALGORITHM. IJRDO -Journal of Computer Science Engineering, 11(5), 10-16. https://doi.org/10.53555/cse.v11i5.6547
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