Career Path Prediction Using Machine Learning

Authors

  • Prathamesh Gavhane  Department of Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegaon, Maharashtra India
  • Dhanraj Shinde  Department of Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegaon, Maharashtra India
  • Ashwini Lomte  Department of Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegaon, Maharashtra India
  • Naveen Nattuva  Department of Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegaon, Maharashtra India
  • Monika Munjal  Department of Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegaon, Maharashtra India

Keywords:

Machine Learning, Classification algorithms, Career path prediction.

Abstract

In today’s era, choosing the right career option is the challenging task. Starting at the early stage of life students usually fail to grasp the idea of which career to pursue as they lack maturity and the experience related to that field. Furthermore, students suffer greatly in deciding which career would result the highest benefit. Students do not have sufficient knowledge to take the decision on their own which may lead to complications in future. Choosing proper career can help the student to be successful by excelling his/her skills in that particular field. In this paper we present the detail study for choosing the right career option for an individual. Survey has been done for analyzing the different techniques for making right career choice.

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International Journal of Scientific Research in Science and Technology

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Published

2020-12-18

Issue

Volume 5, Issue 8, November-December-2020

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Prathamesh Gavhane, Dhanraj Shinde, Ashwini Lomte, Naveen Nattuva, Monika Munjal, " Career Path Prediction Using Machine Learning, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 5, Issue 8, pp.300-304, November-December-2020.