Prediction and Analysis of Student Performance in Secondary Education Based on Data Mining and Machine Learning Techniques

Authors

  • Meenal Joshi  M.Tech Scholar, Computer Science and Engineering, Mewar University, Gangrar, Chittorgarh, Rajasthan, India
  • Shiv Kumar  Computer Science and Engineering, Mewar University, Gangrar, Chittorgarh, Rajasthan, India

DOI:

https://doi.org/10.32628/CSEIT20653

Keywords:

Naive Bayes, J48, Logistic Regression Classification, Prediction, WEKA

Abstract

According to modern era education is the key to achieve success in the future; it develops a human personality, thoughts, and social skills. The purpose of this research work is to focus on educational data mining (EDM) through machine learning algorithms. EDM means to discover hidden knowledge and pattern about student's performance. Machine learning can be useful to predict the learning outcomes of students. From last few years, several tools have been used to judge the student's performance from different points of view like the student's level, objectives, techniques, algorithms, and different methods. In this paper, predicting and analyzing student performance in secondary school is conducted using data mining techniques and machine learning algorithms such as Naive Bayes, Decision Tree algorithm J48, and Logistic Regression. For this the collection of dataset from "Secondary School" and then filtration is applying on desired values using WEKA, tool.

References

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Published

2020-10-30

Issue

Section

Research Articles

How to Cite

[1]
Meenal Joshi, Shiv Kumar, " Prediction and Analysis of Student Performance in Secondary Education Based on Data Mining and Machine Learning Techniques" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 5, pp.294-301, September-October-2020. Available at doi : https://doi.org/10.32628/CSEIT20653