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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM28674

ML Enhanced Workforce Management

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM28674

Volume:

8 |

2 |

Feb 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM28674

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

PSAN

Prof. S. A. Nagtilak1

AK

Anushka Kulkarni

MK

Mihir Kulkarni

AM

Anand Mali

RN

Rutvik Navale

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Abstract

The field of machine learning (ML)-enhanced workforce management offers a transformative potential for organizations aiming to revolutionize their employee recruitment, retention, and management processes. ML algorithms provide automation, pattern recognition, and predictive capabilities, significantly enhancing various aspects of workforce management. This paper presents developing an intuitive website tailored for HR professionals, harnessing diverse ML models to optimize user experience and operational efficiency. These models offer valuable insights, such as predicting employee attrition, objectively evaluating performance, and ensuring fair appraisal distribution. By enabling HR professionals to effortlessly manipulate employee data and leverage ML-generated predictions, organizations can make informed, data-driven decisions seamlessly. This innovative approach equips HR teams to proactively tackle challenges, fostering a cohesive, motivated, and high-performing workforce. This paper serves as a guide, steering organizations towards the future where HR decisions are not only intuitive but also deeply enriched by the analytical prowess of ML technology.

KEYWORDS

HR Employee Workforce Management Performance Projects Appraisal ML.

Cite This Article

Prof. S. A. Nagtilak1, Anushka Kulkarni, Mihir Kulkarni, Anand Mali, Rutvik Navale, "ML Enhanced Workforce Management," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 2, Feb 2024. https://doi.org/10.55041/IJSREM28674

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Publication Date

12/31/45441

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Double-Blind Peer Review

Review Rounds: 2

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5

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The authors declare no conflict of interest related to this research work.

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