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International Conference on Recent Trends in Image Processing and Pattern Recognition

RTIP2R 2018: Recent Trends in Image Processing and Pattern Recognition pp 572–579Cite as

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

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Part of the book series: Communications in Computer and Information Science ((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.

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Author information

Authors and Affiliations

  1. School of Mathematical Sciences, SRTM University, Nanded, MS, India

    Aniket Muley

  2. School of Computational Science, S.R.T.M. University, Nanded, India

    Parag Bhalchandra & Govind Kulkarni

Authors
  1. Aniket Muley
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  2. Parag Bhalchandra
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  3. Govind Kulkarni
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Corresponding author

Correspondence to Aniket Muley .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of South Dakota, Vermillion, SD, USA

    K. C. Santosh

  2. Solapur University, Solapur, India

    Ravindra S. Hegadi

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Muley, A., Bhalchandra, P., Kulkarni, G. (2019). Application of Decision Tree for Predictive Analysis of Student’s Self Satisfaction with Multivariate Parameters. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1037. Springer, Singapore. https://doi.org/10.1007/978-981-13-9187-3_51

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  • DOI: https://doi.org/10.1007/978-981-13-9187-3_51

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9186-6

  • Online ISBN: 978-981-13-9187-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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