Predicting higher education tuition fees using machine learning methods

Serdar Çelik; Cevriye Gencer

doi:10.31801/cfsuasmas.1685330

Predicting higher education tuition fees using machine learning methods

Abstract

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.

Keywords

References

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Details

Primary Language

English

Subjects

Applied Statistics , Statistics (Other)

Journal Section

Research Article

Authors

Serdar Çelik ^*
0000-0003-4530-9055
Türkiye

Cevriye Gencer
0000-0002-3373-8306
Türkiye

Publication Date

December 24, 2025

Submission Date

April 28, 2025

Acceptance Date

June 6, 2025

Published in Issue

Year 1970 Volume: 74 Number: 4

DOI

https://doi.org/10.31801/cfsuasmas.1685330

IZ

https://izlik.org/JA92UK47UJ

Cite

RIS / Bibtex

APA

Çelik, S., & Gencer, C. (2025). Predicting higher education tuition fees using machine learning methods. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics, 74(4), 608-620. https://doi.org/10.31801/cfsuasmas.1685330

AMA

1.Çelik S, Gencer C. Predicting higher education tuition fees using machine learning methods. Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat. 2025;74(4):608-620. doi:10.31801/cfsuasmas.1685330

Chicago

Çelik, Serdar, and Cevriye Gencer. 2025. “Predicting Higher Education Tuition Fees Using Machine Learning Methods”. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics 74 (4): 608-20. https://doi.org/10.31801/cfsuasmas.1685330.

EndNote

Çelik S, Gencer C (December 1, 2025) Predicting higher education tuition fees using machine learning methods. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics 74 4 608–620.

IEEE

[1]S. Çelik and C. Gencer, “Predicting higher education tuition fees using machine learning methods”, Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat., vol. 74, no. 4, pp. 608–620, Dec. 2025, doi: 10.31801/cfsuasmas.1685330.

ISNAD

Çelik, Serdar - Gencer, Cevriye. “Predicting Higher Education Tuition Fees Using Machine Learning Methods”. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics 74/4 (December 1, 2025): 608-620. https://doi.org/10.31801/cfsuasmas.1685330.

JAMA

1.Çelik S, Gencer C. Predicting higher education tuition fees using machine learning methods. Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat. 2025;74:608–620.

MLA

Çelik, Serdar, and Cevriye Gencer. “Predicting Higher Education Tuition Fees Using Machine Learning Methods”. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics, vol. 74, no. 4, Dec. 2025, pp. 608-20, doi:10.31801/cfsuasmas.1685330.

Vancouver

1.Serdar Çelik, Cevriye Gencer. Predicting higher education tuition fees using machine learning methods. Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat. 2025 Dec. 1;74(4):608-20. doi:10.31801/cfsuasmas.1685330