Kategorik verilerde sınıf dengesizliği durumunda makine öğrenimi algoritmalarının karşılaştırılması: Öğrencilerin başarı durumları üzerine bir uygulama

Merve Dunder; Emre Dünder

doi:10.5281/zenodo.12637330

Authors

Merve Dunder Ondokuz Mayıs Üniversitesi, Fen Fakültesi https://orcid.org/0000-0003-0230-8968
Emre Dünder Ondokuz Mayıs Üniversitesi, Fen Fakültesi https://orcid.org/0000-0003-0230-8968

DOI:

https://doi.org/10.5281/zenodo.12637330

Keywords:

Predicting success in education, machine learning algorithms, class imbalance

Abstract

Machine learning algorithms are applied in educational sciences for various purposes to evaluate student performance. Given that educational datasets often consist of categorical data, addressing class imbalance issues requires the use of alternative data generation techniques. This study aims to address this issue by comparing the performance of various machine learning algorithms in predicting student success. In this application, the SmoteNC technique is used to address class imbalance, and the analysis findings are evaluated using five different machine learning techniques. The results of the data analysis indicate that if class imbalance is mitigated, machine learning algorithms can be successfully applied to datasets with a limited number of observations.

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