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Predicting Academic Performance: Machine Learning Insights into GPA Determinants

Students & Supervisors

Student Authors
Sakib Hossain
Bachelor of Science in Computer Science & Engineering, FST
Afzalul Abid Nazir
Bachelor of Science in Computer Science & Engineering, FST
Subrina Islam Prity
Bachelor of Science in Computer Science & Engineering, FST
Islam Saiful
Bachelor of Science in Computer Science & Engineering, FST
Supervisors
Dr. Md. Saef Ullah Miah
Associate Professor, Faculty, FST
Abhijit Bhowmik
Associate Professor, Faculty, FST

Abstract

This study looks at predicting student GPA using machine learning models based on academic and non-academic data. Random Forest (RF), Gradient Boosting (GB), AdaBoost, Linear Regression (LR), Ridge, Lasso, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), and XGBoost are among the models evaluated. R-squared (R²), Mean Squared Error (MSE) and Mean Absolute Error (MAE) are used to assess performance. The most accurate models were Ridge and Linear Regression with accuracy of 95.68%, which suggested a significant linear connection between variables and GPA. Support Vector Regression (SVR) and XGBoost also performed well but did not surpass the linear models. Because of its simplicity and accuracy, the research identifies Ridge Regression as the best model for predicting GPA, providing insightful information that may help educational institutions support student interventions.

Keywords

GPA Prediction Machine Learning Ridge Regression Linear Regression Gradient Boosting Random Forest Support Vector Regression XGBoost Lasso AdaBoost K-Nearest Neighbours Educational Data Student Performance.

Publication Details

  • DOI: 10.1109/AICDMB64359.2025.11277879
  • Type of Publication:
  • Conference Name: Annual International Conference on Data Science, Machine Learning & Blockchain Technology (AICDMB-2025)
  • Date of Conference: 27/06/2025 - 27/06/2025
  • Venue: Vidyavardhaka College of Engineering, Mysuru, Karnataka, India
  • Organizer: Department of Computer Science and Engineering, Vidyavardhaka College of Engineering and IEEE Mysore Subsection
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