ITM Web Conf.
Volume 44, 2022International Conference on Automation, Computing and Communication 2022 (ICACC-2022)
|Number of page(s)||7|
|Published online||05 May 2022|
Resume Classification using various Machine Learning Algorithms
1,2,3 Ramrao Adik Institute of Technology, Nerul, Navi Mumbai, India
4 D.Y. Patil Deemed to be University, Ramrao Adik Institute of Technology, Nerul, Navi Mumbai, India
* Corresponding author: firstname.lastname@example.org
With the onset of the epidemic, everything has gone online, and individuals have been compelled to work from home. There is a need to automate the hiring process in order to enhance efficiency and decrease manual labour that may be done electronically. If resume categorization were done online, it would significantly save paperwork and human error. The recruiting process has several steps, but the first is resume categorization and verification. Automating the first stage would greatly assist the interview process in terms of speedy applicant selection. Classification of resumes will be performed using Machine Learning Algorithms such as Nave Bayes, Random Forest, and SVM, which will aid in the extraction of skills and show diverse capabilities under appropriate job profile classes. While the abilities are being extracted, an appropriate job profile may be retrieved from the categorised and pre-processed data and shown on the interviewer’s screen. During video interviews, this will aid the interviewer in the selection of candidates.
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.