Employability Analysis using Data Mining

Authors(4) :-Shubham Kambli, Shravani Joshi, Gaurav Chauhan, Deepali Nayak

In today's competitive world, it's getting difficult to get a job of suitable profile. It may be because of automation, recession or maybe the students themselves are not knowledgeable enough for the work expected from a particular profile. It can also be that students arenít aware of what the requirements are to work in a particular domain. Our system has been created to help students in all such scenarios. From a few assessment tests, it can provide you with an employability factor which shows how much employable you are for a particular domain, and also recommend courses, certifications to work on your weaknesses and gain an edge over others in the field. It can also suggest jobs which suit your current overall profile. This system can be used by students to assess themselves, by placement cells to measure employability of their student, and also by companies to check employability potential of prospective employees.

Authors and Affiliations

Shubham Kambli
B.E. Student Department of Information Technology, Vidyalankar Institute of Technology, Mumbai, India
Shravani Joshi
Assistant Professor Department of Information Technology, Vidyalankar Institute of Technology, Mumbai, India
Gaurav Chauhan
Assistant Professor Department of Information Technology, Vidyalankar Institute of Technology, Mumbai, India
Deepali Nayak

Data Mining, Employability Analysis, Recommendation

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Publication Details

Published in : Volume 3 | Issue 3 | March-April 2018
Date of Publication : 2018-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 812-817
Manuscript Number : CSEIT1833228
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Shubham Kambli, Shravani Joshi, Gaurav Chauhan, Deepali Nayak, "Employability Analysis using Data Mining", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp.812-817, March-April-2018.
Journal URL : http://ijsrcseit.com/CSEIT1833228

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