Evaluating Machine Learning Algorithms for Enhanced Prediction of Student Academic Performance

This study aims to evaluate and compare the predictive performance of decision trees, random forests, support vector machines, and neural networks in forecasting student academic outcomes based on academic and demographic factors. The research utilizes a dataset from the UCI Machine Learning Repository, encompassing student performance data from Portuguese secondary schools. The results indicate that neural networks and random forests achieved the highest accuracy rates of 87.4% and 85.6%, respectively, suggesting their potential for effective educational analytics and early intervention strategies. These findings underscore the importance of leveraging machine learning techniques to enhance educational outcomes through targeted support and resource allocation.