A Machine Learning Approach for Predicting Student Performance

Authors

  • 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

DOI:

https://doi.org//10.32628/CSEIT1952106

Keywords:

Machine learning - supervised learning - prediction models - EPP Algorithm - naïve bayes algorithm - Decision tress - Regression models - Statistics

Abstract

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.

References

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2019-04-30

Issue

Volume 5, Issue 2, March-April-2019

Section

Research Articles

License

Copyright (c) IJSRCSEIT

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
C. Selvi, R. Shalini, V. Navaneethan, L. Santhiya, " A Machine Learning Approach for Predicting Student Performance, IInternational 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