Abstract
The purpose of this study is to examine the suitability of machine learning (ML) techniques for predicting students’ performance. By analyzing various ML algorithms, the authors assess the accuracy and reliability of these approaches, considering factors such as data quality, feature selection, and model complexity. The findings indicate that certain ML methods are more effective for student performance forecasting, emphasizing the need for a deliberate evaluation of these factors. This study provides significant contributions to the field of education and reinforces the growing use of ML in decision-making and student performance prediction.
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