Study on regression based machine learning models to predict the student performance
By: Patil, Kamlesh V
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Contributor(s): Yesugade, Kiran D.
Publisher: Sangli K.E. Society's Rajarambapu Institute of Technology 2024Edition: Vol.38(2), Oct.Description: 177-186p.Subject(s): Humanities and Applied SciencesOnline resources: Click here
In:
Journal of engineering education transformations (JEET)Summary: This article discusses the use of three regression models (Linear Regression, Decision Tree Regression, and Random Forest Regression) to study the performance of high school students in India across three subjects: Physics, Chemistry, and Mathematics. The study identifies various factors that affect student performance, such as access to good internet connectivity, parental educational background, and lunch quality. The data was obtained from an educational firm and analyzed based on principles and methods that aid decision-making processes. The results showed that all three regression models produced accurate and plausible results, with an overall accuracy of approximately 95%. The study's primary objective was to provide a clear and concise comparative analysis of various Machine Learning techniques and their impact on the dataset and the predictive attributes analyzed. The findings from this study underscore the importance of considering various factors when analyzing student performance and highlight the effectiveness of Machine Learning techniques in this domain.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2025-0002 |
This article discusses the use of three regression models (Linear Regression, Decision Tree Regression, and Random Forest Regression) to study the performance of high school students in India across three subjects: Physics, Chemistry, and Mathematics. The study identifies various factors that affect student performance, such as access to good internet connectivity, parental educational background, and lunch quality. The data was obtained from an educational firm and analyzed based on principles and methods that aid decision-making processes. The results showed that all three regression models produced accurate and plausible results, with an overall accuracy of approximately 95%. The study's primary objective was to provide a clear and concise comparative analysis of various Machine Learning techniques and their impact on the dataset and the predictive attributes analyzed. The findings from this study underscore the importance of considering various factors when analyzing student performance and highlight the effectiveness of Machine Learning techniques in this domain.
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