International Journal of Scientific Research and Engineering Development

Student Performance Prediction is an imperative research topic of educational data mining, as researchers are seeking some factors that contribute to academic success and they can be turned into actionable information for the instructors. As student data (demographics, attendance, prior year grades and behaviors traits) becomes available on mass scales the ability to use machine learning techniques to analyze patterns and predict what is likely to happen becomes too powerful a tool. The supervised learning methods such as decision trees, random forests (RF), support vector machines (SVM) with different kernels and the artificial neural networks are used in this research to predict the student performance. The process includes raw educational data set pre-processing, feature selection to identify its significant features and model training with cross-validation method.

Priya Dharshini.V, Mrs.N.Vaishnavi,"Student Performance Prediction Using Machine Learning" International Journal of Scientific Research and Engineering Development, V9(2): Page(259-263) Mar-Apr 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.