A Comparative Study of Machine Learning Techniques for Predicting Student Academic Performance

This study examines the effectiveness of different machine learning algorithms, specifically Decision Trees, Random Forests, and Support Vector Machines (SVM), in forecasting the academic performance of students. By utilizing a dataset that includes demographic information, past academic records, and socioeconomic factors, the study seeks to determine the most efficient algorithm for educational data analysis. Our approach involves employing feature selection techniques to refine the dataset and employing cross-validation to evaluate algorithmic effectiveness. The findings reveal that while SVM demonstrated the highest accuracy in predicting performance, Decision Trees and Random Forests offered valuable insights into the importance of features. These findings hold significant implications for educators and policymakers aiming to implement data-driven strategies to enhance student achievement. Future research directions involve the assessment of additional machine learning algorithms and feature selection methods to optimize prediction accuracy.