Volume 7 • Issue 2 • PP: 31–37 • 2024
Predicting Student Outcomes: Evaluating Regression Techniques in Educational Data
Open Access & Copyright
© 2024 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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Student performance prediction is essential so that institutions can assist in identifying weak performers and initiate corrective measures. This research assesses different regression models by applying data from Kaggle, which involves data cleaning like managing missing values and scaling of the data, hence feature extraction, then model imposition and authenticity. The models followed are Linear Regression, SVR, MLPRegressor, Gradient Boosting, CatBoost, XGBoost, Random Forest, Extra Trees, Decision Tree and K-neighbors. The analysis shows that Linear Regression produced the best result as it has the lowest MSE score of 0.000521 and high accuracy regarding other measures, including RMSE, MAE, and R2. The results reveal that regression models can be used to predict students’ performance and be helpful to the various stakeholders in the system. The findings of this study will help develop required models for decision-making to improve students’ performance.
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