Educational Data Mining: Student Performance Prediction in Academic
Y. K. Salal1, S. M. Abdullaev2, Mukesh Kumar3
1Mr. Y. K Salal, Department of System Programming, South Ural State University, National Research University, Chelyabinsk, Russian Federation.
2S.M. Abdullaev, Doctor of Geographical Sciences, Professor, Chair System Programming, South Ural State University, National Research University, Chelyabinsk, Russian Federation.
3Mr. Mukesh Kumar, Assistant Professor, Chitkara University, Himachal Pradesh.
Manuscript received on 22 March 2019 | Revised Manuscript received on 05 April 2019 | Manuscript Published on 11 April 2019 | PP: 54-59 | Volume-8 Issue-4C, April 2019 | Retrieval Number: D24210484C19/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: At present data mining techniques become very popular among the data analyst. It became an effective tool for finding the uncovered information from a big database. Due to this feature data mining are adopted by many areas like education, telecommunication, retail management etc to resolve their business problems. In this paper, for building classification models for ‘student performance’ dataset consisting of 649 different instances with 33 different attributes implement algorithms like Naive Bayes, Decision Tree (J48), Random Forest, Random Tree, REP Tree, JRip, One R, Simple Logistic and Zero R. After implementing these algorithms on student performance dataset, we evaluate and compare the implementation result for better accuracy of prediction. The result of this study is extremely significant and hence provides a greater insight for evaluating the student performance and underlines the significance of data mining in education. It also shows that how students attributes affect the student performance.
Keywords: Naive Bayes, K-Nearest Neighbor, Logistic Regression, J4.8, Randomforest.
Scope of the Article: Data Mining