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Application of Decision Tree for Predictive Analysis of Student’s Self Satisfaction with Multivariate Parameters

  • Aniket MuleyEmail author
  • Parag Bhalchandra
  • Govind Kulkarni
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1037)

Abstract

Decision trees are better known for automatic feature selection in predictive analysis. There is no need of variable transformation while processing data in decision tree as such trees work on selection of important variables as parent nodes and they act as split node. An attempt is made herein to enumerate decision tree over customized educational dataset. The work is based on the hypothesis that distinctiveness, routine and sensitivity related variables of students have close association together with self satisfaction and motivation. This paper demonstrates better feature selection using tree analytics. Experimental analytics were carried out with R software package.

Keywords

Educational analytics Decision trees Classification Multivariate analysis 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Aniket Muley
    • 1
    Email author
  • Parag Bhalchandra
    • 2
  • Govind Kulkarni
    • 2
  1. 1.School of Mathematical SciencesSRTM UniversityNandedIndia
  2. 2.School of Computational ScienceS.R.T.M. UniversityNandedIndia

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