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Predicting higher education tuition fees using machine learning methods.

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Názov: Predicting higher education tuition fees using machine learning methods.
Autori: ÇELİK, Serdar, GENCER, Cevriye
Zdroj: Communications Series A1 Mathematics & Statistics; 2025, Vol. 74 Issue 4, p608-620, 13p
Predmety: TUITION, MACHINE learning, HIGHER education, FINANCIAL stress, FORECASTING methodology, ENSEMBLE learning, DATA scrubbing, FORECASTING
Abstrakt: University education is a critical step in preparing young individuals for their future and shaping their careers. However, this educational service often entails high costs, requiring students and their families to bear significant financial burdens. Despite the growing importance of accurately estimating tuition fees-given their impact not only on families but also on university administration and national economies-there remains a noticeable gap in the literature regarding the application of advanced machine learning (ML) methods for tuition fee prediction. This study addresses this gap by employing and comparing various ML regression techniques, including Linear Regression, Lasso Regression, Random Forest, Decision Tree, Ridge Regression, XGBoost, and ANN, which have proven successful in related forecasting tasks but are underutilized in tuition fee estimation. After a rigorous data preprocessing phase on a comprehensive dataset, the empirical results demonstrate that XGBoost stands out as a highly effective model for predicting university tuition fees. The findings contribute to the literature by expanding the methodological toolkit for tuition fee estimation and provide valuable insights for students, university administrators, economists, and policymakers. [ABSTRACT FROM AUTHOR]
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Databáza: Complementary Index
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Abstrakt:University education is a critical step in preparing young individuals for their future and shaping their careers. However, this educational service often entails high costs, requiring students and their families to bear significant financial burdens. Despite the growing importance of accurately estimating tuition fees-given their impact not only on families but also on university administration and national economies-there remains a noticeable gap in the literature regarding the application of advanced machine learning (ML) methods for tuition fee prediction. This study addresses this gap by employing and comparing various ML regression techniques, including Linear Regression, Lasso Regression, Random Forest, Decision Tree, Ridge Regression, XGBoost, and ANN, which have proven successful in related forecasting tasks but are underutilized in tuition fee estimation. After a rigorous data preprocessing phase on a comprehensive dataset, the empirical results demonstrate that XGBoost stands out as a highly effective model for predicting university tuition fees. The findings contribute to the literature by expanding the methodological toolkit for tuition fee estimation and provide valuable insights for students, university administrators, economists, and policymakers. [ABSTRACT FROM AUTHOR]
ISSN:13035991
DOI:10.31801/cfsuasmas.1685330