Empirical Study of Student Performance Prediction Using Machine Learning Models

Predicting student academic performance has become an important research area due to increasing dropout rates, academic stress, and the need for personalized learning systems. Educational institutions generate large volumes of student data related to attendance, assessment scores, learning behavior, and demographic characteristics. Machine Learning (ML) techniques provide effective tools for analyzing such data and predicting student performance outcomes. This study presents a mathematical and empirical investigation of student performance prediction using supervised machine learning models. A comprehensive dataset containing academic, behavioral, and demographic attributes was analyzed using regression and classification techniques. Mathematical formulations of prediction models were developed, and performance was evaluated using statistical accuracy measures. The results indicate that machine learning models significantly improve prediction accuracy and can support early academic intervention and personalized education strategies