A Comparative Study of the Applicability of Regression Models in Predicting Student Academic Performance
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Abstract
Educational Data Mining (EDM) has witnessed a surge in educational systems, as it enables the analysis and prediction of student performance, facilitating proactive measures. This paper aims to present a comparative study of regression prediction using eight mainstream models: Linear Regression, Ridge Regression, Lasso Regression, Huber Regression, Support Vector Regression (SVR), K-Nearest Neighbors Regression (KNN), Decision Tree Regression (DT), and Neural Network Regression. The applicability of these eight models is analyzed across different courses and semester GPAs, considering three distinct scenarios. Our thorough analysis underscores the substantial influence of data granularity and integrity on bolstering the precision of final CGPA predictions. In the third scenario where Semester GPAs were utilized, Lasso Regression achieved an R-value of 0.9901 with remarkably low RMSE and MAE, establishing its dominance across all scenarios. Neural Network Regression, with an R-value of 0.9832 and minimal error metrics in the same scenario, also demonstrated robust predictive capabilities. These insights highlight the imperative of tailoring regression model selection to align with specific scenario nuances and the targeted predictive precision.
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