Machine Learning Techniques for Student Performance Prediction and Academic Analytics

Abstract

Machine Learning (ML) has emerged as a powerful tool in the education sector for analyzing large volumes of academic data and predicting student performance. Educational institutions generate vast datasets including attendance records, assignment scores, examination results, and demographic details. Traditional data analysis methods are often inefficient and fail to uncover hidden patterns influencing student success. This research focuses on the application of machine learning techniques such as Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Linear Regression, and Neural Networks for predicting student academic performance. The study highlights how predictive analytics helps in early identification of atrisk students, enabling timely intervention and improving overall learning outcomes.