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Reseach Article

Using Data Mining Classifier for Predicting Student’s Performance in UG Level

by Surbhi Agrawal, Santosh K. Vishwakarma, Akhilesh K. Sharma
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 172 - Number 8
Year of Publication: 2017
Authors: Surbhi Agrawal, Santosh K. Vishwakarma, Akhilesh K. Sharma
10.5120/ijca2017915201

Surbhi Agrawal, Santosh K. Vishwakarma, Akhilesh K. Sharma . Using Data Mining Classifier for Predicting Student’s Performance in UG Level. International Journal of Computer Applications. 172, 8 ( Aug 2017), 39-44. DOI=10.5120/ijca2017915201

@article{ 10.5120/ijca2017915201,
author = { Surbhi Agrawal, Santosh K. Vishwakarma, Akhilesh K. Sharma },
title = { Using Data Mining Classifier for Predicting Student’s Performance in UG Level },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2017 },
volume = { 172 },
number = { 8 },
month = { Aug },
year = { 2017 },
issn = { 0975-8887 },
pages = { 39-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume172/number8/28275-2017915201/ },
doi = { 10.5120/ijca2017915201 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:19:50.600582+05:30
%A Surbhi Agrawal
%A Santosh K. Vishwakarma
%A Akhilesh K. Sharma
%T Using Data Mining Classifier for Predicting Student’s Performance in UG Level
%J International Journal of Computer Applications
%@ 0975-8887
%V 172
%N 8
%P 39-44
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the realm of digitalization, the competition in the field of education has expanded drastically. To analyze this increment the education data mining has played a vital role. In this paper, student’s historical record and the relevant features like their living habits, backgrounds and so forth are utilized as data set (corpus). The performance of students is evaluated using four distinct classifiers named as decision tree, random forest, naive bayes and rule induction. Different classifiers show different accuracy depending on different algorithms used in it. These analyzed results are explicitly used to predict the upcoming grades of the students and the relevant features (like access to the Internet, study time, etc.) which affect the academic performance of the students.

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Index Terms

Computer Science
Information Sciences

Keywords

Corpus decision tree random forest naïve bayes rule induction Data mining.