Data-Driven Insights into Student Performance: Benchmarking Machine Learning Models for Grade Prediction using Regression and Classification Approaches
classification; machine learning; regressions models; student performance
This research explores the effectiveness of 17 machine learning models in predicting student performance across Mathematics and Portuguese datasets. The primary goal of this study was to evaluate and compare regression and classification models to identify the most accurate predictors of student grades. A range of algorithms was tested, including linear models (Linear Regression, Elastic Net, Ridge, Lasso), tree-based models (Random Forest, Gradient Boosting, CatBoost, LightGBM), and advanced techniques (Neural Networks, SVM, XGBoost, Naive Bayes, SVR). The methodology involved data preprocessing, feature engineering, and splitting data into training and test sets. Base models were implemented, followed by hyperparameter tuning to optimize performance. Metrics like RMSE, MAE, MSE, R2 (for regression), and accuracy, precision, recall, F1 score (for classification) were used to assess performance. The study found that Gradient Boosting and Elastic Net consistently outperformed other models in regression tasks, achieving the highest R2 scores. For classification, Logistic Regression proved to be the most accurate, followed by Naive Bayes. These findings provide valuable insights for model selection in educational performance prediction, establishing Gradient Boosting and Logistic Regression as benchmark models. 2025 IEEE.
Christina, Sowmya; Sowjanya, S.; Lakshmhyma, Ch.; Prathiba, L.; Basha, Md Shaik Amzad
International Conference on Intelligent Systems and Computational Networks, ICISCN 2025;
Institute of Electrical and Electronics Engineers Inc.
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/ICISCN64258.2025.10934398 https://www.scopus.com/pages/publications/105002698034?origin=resultslist
Christina S., Christ (Deemed to be University), Department of Professional Studies, Bengaluru, India; Sowjanya S., Rajeev Gandhi Memorial College of Engineering, Department of Management Studies, Nandyal, India; Lakshmhyma Ch., Maris Stella College (Autonomous), Department of Business Administration, Vijayawada, India; Prathiba L., Ashoka Women's Engineering College (Autonomous), Kurnool, India; Basha M.S.A., Gitam (Deemed to be University), Gitam School of Business, Hyderabad, India