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A Review on Various Algorithms for Student Performance Prediction

Author

Listed:
  • Mayuri S. Dongre
  • Neha S.Vidya
  • Vanita D. Telrandhe
  • Shweta R. Chaudhari
  • Anup A. Umrikar
  • Prachiti V. Adghulkar

Abstract

Data in educational institutions are growing progressively along these lines there is a need of progress this tremendous data into helpful data and information utilizing data mining. Educational data mining is the zone of science where diverse techniques are being produced for looking and investigating data and this will be valuable for better comprehension of understudies and the settings they learned. Classification of data objects in view of a predefined learning of the articles is a data mining and information administration procedure utilized as a part of collection comparable data questions together. Decision Tree is a valuable and well known classification method that inductively takes in a model from a given arrangement of data. One explanation behind its prominence comes from the accessibility of existing calculations that can be utilized to assemble decision trees. In this paper we will survey the different ordinarily utilized decision tree calculations which are utilized for classification. We will likewise contemplating how these decision tree calculations are appropriate and valuable for educational data mining and which one is ideal.

Suggested Citation

  • Mayuri S. Dongre & Neha S.Vidya & Vanita D. Telrandhe & Shweta R. Chaudhari & Anup A. Umrikar & Prachiti V. Adghulkar, 2019. "A Review on Various Algorithms for Student Performance Prediction," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 5(1), pages 235-239, February.
  • Handle: RePEc:jbh:ijsrcs:v5:y2019:i1:id:hcseit195167
    Note: Article URL: https://ijsrcseit.com/CSEIT195167
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