Student’s Academic Performance Prediction in Academic using Data Mining Techniques

5 Pages Posted: 3 Apr 2020

See all articles by Nidhi Walia

Nidhi Walia

Chitkara University Institute of Engineering & Technology

Mukesh Kumar

Chitkara University Institute of Engineering & Technology

Nandini Nayar

Chitkara University Institute of Engineering & Technology

Gaurav Mehta

Chitkara University Institute of Engineering & Technology

Date Written: April 1, 2020

Abstract

Data Mining has adopted by many areas like education, telecommunication, retail management etc. to resolve their business problems. Due to features likes classification, clustering and association rule mining, it becomes imperative. In this paper, for building predictive classification models algorithms like Naive-Bayes, Decision Tree, Random-Forest, JRip, and ZeroR are implemented on student academic performance dataset. In our implementation results, we found that school, as well as study-time, also affect the final student grade. Classification algorithms like One Rule, Joint Reserve Intelligence Program and Decision Tree have more than 80.00 % accuracy for predicting student result, and they perform equally well.

Keywords: Naive-Bayes, Decision Tree, RandomForest, JRip, ZeroR

Suggested Citation

Walia, Nidhi and Kumar, Mukesh and Nayar, Nandini and Mehta, Gaurav, Student’s Academic Performance Prediction in Academic using Data Mining Techniques (April 1, 2020). Proceedings of the International Conference on Innovative Computing & Communications (ICICC) 2020, Available at SSRN: https://ssrn.com/abstract=3565874 or http://dx.doi.org/10.2139/ssrn.3565874

Nidhi Walia (Contact Author)

Chitkara University Institute of Engineering & Technology ( email )

Mukesh Kumar

Chitkara University Institute of Engineering & Technology ( email )

Nandini Nayar

Chitkara University Institute of Engineering & Technology ( email )

Gaurav Mehta

Chitkara University Institute of Engineering & Technology ( email )

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