Student's Performance Analysis with EDA and Machine Learning Models

Authors

  • Dr. A. Senthil Kumar  Department of EEE, Principal, Sanskrithi School of engineering, Puttparthi, Andhra Pradesh, India
  • K. Joshna  Department of ECE, Sanskrithi School of Engineering, Puttparthi, Andhra Pradesh, India

DOI:

https://doi.org/10.32628/IJSRST218448

Keywords:

Data Analytics, Visualization Tools, Pandas, Matplotlib, Predictive Modeling, Random Forest.

Abstract

Educational data analytics is used to study the data which is available in the educational institutions and bring out the insights from it. Analytics is a process of discovering, analyzing, and interpreting meaningful patterns from large amounts of data. Predictive analytics can help in improving the quality of education by providing right information for decision makers to take better decisions. This paper focuses on the need for implementing the data analytics in educational system, suggests some strategies to use these needs. While implementing any system, the understanding of different components and their functions is necessary. The educational data analytics has potential to discover, analyze and predict meaningful knowledge from educational data which will help to education management system for flexible planning, execution and prediction for future.

References

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  8. Author name, paper name, title ,conference name, volume, pages.

International Journal of Scientific Research in Science and Technology

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Published

2021-08-30

Issue

Volume 8, Issue 4, July-August-2021

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

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

How to Cite

[1]
Dr. A. Senthil Kumar, K. Joshna "Student's Performance Analysis with EDA and Machine Learning Models" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 8, Issue 4, pp.297-302, July-August-2021. Available at doi : https://doi.org/10.32628/IJSRST218448