Journal of Artificial Intelligence and Metaheuristics
Journal DOI
2833-5597ISSN (Online)
Volume 7 , Issue 2 , PP: 62-72, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Abdelhameed Ibrahim 1 * , Abdelaziz A. Abdelhamid 2 , Ehab M. Almetwally 3
Doi: https://doi.org/10.54216/JAIM.070206
Education contributes a crucial portion to the world’s development; thus, it is crucial to focus on education enrollment and quality education. It is essential not only that children enroll in school but also that they receive proper education to improve individuals and, consequently, society. This paper aims to use machine learning to predict educational outcomes based on the World Educational Data obtained from Kaggle to analyze the data, preprocess it, and evaluate the performances of the different regression models. The following models consist of Support Vector Regression (SVR), CatBoost, RandomForestRegressor, ExtraTreesRegressor, GBoost, MLPRegressor, GradientBoosting Regressor, DecisionTreeRegressor, KNeighborsRegressor, LinearRegression, and Pipeline. Evaluation measures used included MSE, RMSE, MAE, MBE, r, R2, NSE, and WI. Analyzing the performance comparison, the best accuracy was associated with CatBoost with an r value equal to 0.999996 and an R2 value of 0. 999993; The MSE score was 0.04024. The outcomes of the present paper demonstrate that the application of advanced machine learning algorithms can be used effectively to predict educational outcomes, thus enabling policymakers and educational planners to use them for designing effective educational policies and overcoming existing global challenges in the sphere of education.
Educational Data Analysis, Regression Models, Machine Learning, Predictive Modeling, Global Education Outcomes
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