Revolutionizing Job Consulting: Harnessing the Power of Machine Learning Modules for Enhanced Recruitment Processes

Registration ID: IJNRD_224623

Published ID: IJNRD2407497

DOI: Click Here to Get

Hruhsikesh Gaikwad , Mehak Kaul , Nipun Alone

Job consulting, recruitment process, qualified candidates, consultancy companies, candidate satisfaction, employee comfort, perceived limitations, candidate accessibility, comprehensive candidate information, survey, employee perspectives, effective recruitment

This research delves into the intricate realm of job consulting, focusing on the pivotal process of recruitment, which involves identifying and hiring the most qualified candidates in a timely and costeffective manner. Consultancy companies serve as the crucial link between candidates and recruiters. To investigate the variations in the approaches of different consultancy companies and to glean insights into employees’ perspectives on the recruitment processes in their respective organizations, a survey was conducted involving 50 employees from diverse consultancy firms. The findings emphasize that candidate satisfaction, employee comfort, perceived limitations, candidate accessibility, and comprehensive candidate information are pivotal factors for effective recruitment.

Published Paper   E-Certificate

Hruhsikesh Gaikwad, Mehak Kaul, & Nipun Alone (July-2024). Revolutionizing Job Consulting: Harnessing the Power of Machine Learning Modules for Enhanced Recruitment Processes. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(7), f963-f982. https://ijnrd.org/papers/IJNRD2407497.pdf

Volume 9 Issue 7, July-2024

Pages : f963-f982

Paper Reg. ID: IJNRD_224623

Published Paper Id: IJNRD2407497

Downloads: 000121978

Research Area: Computer Science & Technology 

Country: pune, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2407497.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2407497

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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Call For Paper - Volume 10 | Issue 10 | October 2025

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