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Comparison of Feature Selection Techniques for Predicting Student’s Academic Performance

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  • Olukoya, Bamidele Musiliu

    (Ph.D Student, Federal University Oye-Ekiti, Nigeria (FUOYE))

Abstract

In recent time, educational data mining (EDM) has received substantial considerations. Many techniques of data mining have been proposed to dig out out-of-sight knowledge in educational data. The Knowledge obtained assists the academic institutions to further enhance their process of learning and methods of passing knowledge to students. Powerful tools are required to analyze and predict the performance of students scientifically. This paper focuses on comparing two feature selection techniques in identifying major factors among the numerous affecting students’ academic that could give accurate prediction. Student educational data was retrieved from Kaggle data repository and feature selection on is done by applying Information Gain Attribute Evaluator and Correlation Based Features Selection (CFS) using WEKA as an Open Source Tool. Further a comparison is made among these two feature selections algorithm to select best attributes for prediction among all.

Suggested Citation

  • Olukoya, Bamidele Musiliu, 2020. "Comparison of Feature Selection Techniques for Predicting Student’s Academic Performance," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 7(8), pages 97-101, August.
    • Handle: RePEc:bjc:journl:v:7:y:2020:i:8:p:97-101
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