Predicting Student Performance Through Machine Learning

Author(s)	Mr. Chandan Kumar
Country	India
Abstract	The rapid evolution of Educational Data Mining(EDM) has transformed how academic institutions addressstudent retention and success. This research focuses on thedevelopment of a predictive framework designed toidentify student performance outcomes by leveragingMachine Learning (ML) algorithms. By analyzing amultifaceted dataset—encompassing demographicattributes, socio-economic backgrounds, and historicalacademic records—the study evaluates the efficacy ofvarious supervised learning models, including RandomForest, Support Vector Machines (SVM), and GradientBoosting.Pre-processing techniques such as SMOTE for classbalancing and Recursive Feature Elimination (RFE) wereemployed to enhance model precision. Preliminaryresults indicate that ensemble methods significantly outperformtraditional classifiers, providing high accuracy inidentifying "at-risk" students before the conclusion of anacademic term. The findings offer a scalable solution foreducators toimplement data-driven intervention strategies,ultimately fostering a more personalized and proactiveeducational environment. This study underscores the potential of ML as a cornerstone for institutional decision-making and academic excellence
Keywords	Student Performance Prediction, Educational Data Mining, Support Vector Machine, Smote
Field	Engineering
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-05-11

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International Journal For Multidisciplinary Research