Predicting Students’ Results Using Machine Learning
Keywords:
machine learning, student outcome prediction, educational data analysis, Random Forest, neural network, GPA prediction, academic analytics.Abstract
This article examines the methodology for predicting students’ academic results in higher education institutions using machine learning algorithms. The study used a database of 1,200 students of Tashkent University of Information Technologies (TUIT), which includes study activity, course duration, previous GPA, and many other factors. Six popular ML algorithms were tested, including Logistic Regression, Decision Tree, Random Forest, SVM, Gradient Boosting, and Deep Neural Network. The Neural Network showed the highest accuracy rate — 89.4%, while Random Forest showed 85.3%. The results show that course duration, previous GPA, and exam results are the most complete determinants of student success. This study has practical significance for implementing early warning systems in educational institutions.
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