000			a
999			_c22062 _d22062
003			OSt
005			20250108104416.0
008			250108b xxu||||| |||| 00| 0 eng d
040			_aAIKTC-KRRC _cAIKTC-KRRC
100			_924997 _aPatil, Kamlesh V.
245			_aStudy on regression based machine learning models to predict the student performance
250			_aVol.38(2), Oct
260			_aSangli _bK.E. Society's Rajarambapu Institute of Technology _c2024
300			_a177-186p.
520			_aThis 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.
650		0	_94642 _aHumanities and Applied Sciences
700			_924998 _aYesugade, Kiran D.
773	0		_tJournal of engineering education transformations (JEET) _x2349-2473 _dSangli Rajarambapu Institute Of Technology
856			_uhttps://journaleet.in/articles/a-study-on-regression-based-machine-learning-models-to-predict-the-student-performance _yClick here
942			_2ddc _cAR