Makine Öğrenimi ile Öğrenci Başarı Tahmini: Akademik Başarıyı Etkileyen Faktörlerin Modellenmesi ve Performans Analizi

This study aims to analyze the factors affecting students' academic success and develop prediction models based on these factors. Many features such as "Studying Hours," "Absence," and "Family Income" that affect students' performance during the education process were evaluated. In the study, student success predictions were made using machine learning models such as Random Forest, Ridge, and Deep Neural Networks and the performance of these models were compared. The importance levels of the features were calculated with the Random Forest algorithm and the most important features were determined. As a result of the analysis, the effect of removing low-importance features from the model to increase prediction performance was also examined. The findings of the study contribute to educators making data-driven decisions and developing strategies that can increase students' success.