Enhancing Student Performance Prediction Using Machine Learning Models in Higher Education
Keywords:
Data Science Education, Machine Learning, Student Performance, Predictive Analytics, Educational Data MiningAbstract
This study explores the application of machine learning techniques to predict student academic performance in higher education. The research aims to develop a predictive model that can assist educators in identifying at-risk students and improving learning outcomes. A dataset consisting of student demographic and academic records was analyzed using multiple algorithms, including Random Forest, Support Vector Machine (SVM), and Logistic Regression. The results indicate that ensemble models outperform traditional approaches, achieving an accuracy of 88%. The findings demonstrate the potential of data-driven decision-making in education and highlight the importance of integrating machine learning into educational systems.
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