Predicting Student Academic Performance Using Combined Machine Learning Algorithms

Abstract

Accurate prediction of student academic outcomes supports early intervention strategies and enhances institutional planning. This study evaluates multiple machine learning algorithms—Random Forest, XGBoost, Support Vector Regression, Linear Regression, and Ridge Regression—using a comprehensive student dataset containing demographic, academic, behavioural, and lifestyle attributes. An ensemble voting regressor combining the five models shows improved accuracy over individual algorithms. Two sets of results are retained: the first demonstrating ensemble superiority with an R² of 0.32, and the second presenting regression coefficients and an alternative R² of 0.19. Across models, attendance, sleep patterns-habits, Behaviour, Regularity and previous grades emerge as significant predictors. The study highlights the importance of integrated modelling approaches and identifies key factors influencing academic performance.