A Machine Learning Approach for Predicting Student Performance

Authors(4) :-C. Selvi, R. Shalini, V. Navaneethan, L. Santhiya

An University’s reputation and its standard are weighted by its students performance and their part in the future economic prosperity of the nation, hence a novel method of predicting the student’s upcoming academic performance is really essential to provide a pre-requisite information upon their performances. A machine learning model can be developed to predict the student’s upcoming scores or their entire performance depending upon their previous academic performances.

Authors and Affiliations

C. Selvi
Associate professor, Department of Computer Science and Engineering, Velalar College of Engineering and Technology, Erode, Anna University, Tamilnadu, India
R. Shalini
Department of Computer Science and Engineering, Velalar College of Engineering and Technology, Erode, Anna University, Tamilnadu, India
V. Navaneethan
Department of Computer Science and Engineering, Velalar College of Engineering and Technology, Erode, Anna University, Tamilnadu, India
L. Santhiya
Department of Computer Science and Engineering, Velalar College of Engineering and Technology, Erode, Anna University, Tamilnadu, India

Machine learning - supervised learning - prediction models - EPP Algorithm - na´ve bayes algorithm - Decision tress - Regression models - Statistics

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Publication Details

Published in : Volume 5 | Issue 2 | March-April 2019
Date of Publication : 2019-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 462-465
Manuscript Number : CSEIT1952106
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

C. Selvi, R. Shalini, V. Navaneethan, L. Santhiya, "A Machine Learning Approach for Predicting Student Performance", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 2, pp.462-465, March-April-2019. Available at doi : https://doi.org/10.32628/CSEIT1952106
Journal URL : http://ijsrcseit.com/CSEIT1952106

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