Prediction of Student Academic Performance Using a Multi-Regression and Classification-Based Model

Authors

  • Modupe Agagu Dept. of Computer Science Olusegun Agagu University of Science and Technology, Okitipupa, Ondo State, Nigeria
  • Aderonke Justina Ikuomola Dept. of Computer Science Olusegun Agagu University of Science and Technology, Okitipupa, Ondo State, Nigeria
  • Adeolu Seun Obamehinti Dept. of Computer Science Olusegun Agagu University of Science and Technology, Okitipupa, Ondo State, Nigeria

Keywords:

Academic Performance, Classification Model, Machine Learning, Prediction, Regression Model

Abstract

The Prediction of students’ performance is a necessity because it forecasts how well a student is expected to perform during a course of study. Over the years, studies have revealed that student performance has been below average, with one of the main causes being that a thorough prediction of a student's academic potential is typically not done. To choose the best model for predicting and categorizing academic achievement, a multi-regression analysis is performed using machine learning models such as Decision Tree, K-Nearest Neighbor, Random Forest, Logistic Regression, and Support Vector Machine. Furthermore, the result shows that Random Forest is the best-performing classifier in this study, with an F1 score and accuracy of 94.9%, as well as the best-performing regression model, with a Mean Absolute Error (MAE) of 0.3711 in predicting academic success. 

Published

2024-08-05

How to Cite

Agagu, M., Ikuomola, A. J., & Obamehinti, A. S. (2024). Prediction of Student Academic Performance Using a Multi-Regression and Classification-Based Model . LAUTECH JOURNAL OF COMPUTING AND INFORMATICS , 4(2), 49-57. Retrieved from http://laujci.lautech.edu.ng/index.php/laujci/article/view/122