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