Prediction of Student Academic Performance in Higher Education Using Machine Learning Techniques

Author(s)	Ms. Bal jeet Kaur
Country	India
Abstract	Predicting student academic performance is a critical task in higher education institutions, as it helps educators identify at-risk students and implement timely interventions to improve learning outcomes. With the rapid growth of educational data, machine learning techniques have emerged as powerful tools for analysing complex patterns in student behaviour and performance. This paper presents a machine learning–based approach for predicting student academic performance using historical academic records and demographic data. Various machine learning algorithms, including Decision Tree, Random Forest, Support Vector Machine, and Logistic Regression, are applied and evaluated to determine their prediction accuracy. The proposed model aims to assist academic institutions in enhancing decision-making processes, improving student retention rates, and optimizing teaching strategies. Experimental results demonstrate that ensemble-based models achieve higher prediction accuracy compared to traditional methods, highlighting the effectiveness of machine learning in educational data mining.
Keywords	Machine Learning, Academic Performance Prediction, Higher Education, Educational Data Mining, Student Analytics
Field	Engineering
Published In	Volume 7, Issue 6, November-December 2025
Published On	2025-12-23
DOI	https://doi.org/10.36948/ijfmr.2025.v07i06.64390

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