Resume Screening using Machine Learning and NLP: A proposed system

Authors

  • Bhushan Kinge  UG Student, Information Technology, SPPU, Pune, Maharashtra, India
  • Shrinivas Mandhare  UG Student, Information Technology, SPPU, Pune, Maharashtra, India
  • Pranali Chavan  UG Student, Information Technology, SPPU, Pune, Maharashtra, India
  • S. M. Chaware  Professor, Information Technology, SPPU, Pune, Maharashtra, India

DOI:

https://doi.org/10.32628/CSEIT228240

Keywords:

NLP, Resume, CV, KNN, SVM, NER

Abstract

The Indian Recruitment market has grown substantially over the last half-decade as the need for cheap labor grows and the number of job openings is increasing. And as the job market increases so does the recruitment industry which is a new way of hiring people by outsourcing the hiring process itself to other companies whose sole purpose is to give the correct talent required for the company. This is done because these companies are hiring in bulk and doing such a thing in-house will require a lot of company resources which will hamper productivity. As such companies emerge even for them manually going through all of the Resume of candidates is very time-consuming and tedious so these Talent Acquisition Companies use various Machine Learning models to filter out top resumes according to the job roles, which reduces the efforts for the Human Resource team

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2022-04-30

Issue

Volume 8, Issue 2, March-April-2022

Section

Research Articles

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

How to Cite

[1]
Bhushan Kinge, Shrinivas Mandhare, Pranali Chavan, S. M. Chaware, " Resume Screening using Machine Learning and NLP: A proposed system" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 2, pp.253-258, March-April-2022. Available at doi : https://doi.org/10.32628/CSEIT228240