Predicting Employee Turnover: A Systematic Machine Learning Approach for Resource Conservation and Workforce Stability-Reference-Cited by-同舟云学术

Predicting Employee Turnover: A Systematic Machine Learning Approach for Resource Conservation and Workforce Stability

Published:2023-12-26 Issue: Volume: Page:
ISSN:
Container-title:RAiSE-2023
language:
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Author:

Kumar Parmod¹,Gaikwad Sagar Balu²^ORCID,Ramya Shunmugavel Thanga³,Tiwari Tripti⁴^ORCID,Tiwari Mohit⁵^ORCID,Kumar Binod⁶

Affiliation:

1. Department of Electronics and Information Engineering, Jiangxi University of Engineering, Xinyu 338000, China

2. Department of Management, MET Institute of Management, Mumbai 422003, India

3. Department of Computer Science and Design, R.M.K. Engineering College, Kavaraipettai, Tiruvallur 601206, India

4. Department of Management Studies, Bharati Vidyapeeth Institute of Management and Research, Delhi 110063, India

5. Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, Delhi 110063, India

6. Department of Master of Computer Applications, JSPM’S Rajarshi Shahu College of Engineering, Pune 411033, India

Publisher

MDPI

Link

https://www.mdpi.com/2673-4591/59/1/117/pdf

Reference20 articles.

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4. Fallucchi, F., Coladangelo, M., Giuliano, R., and William De Luca, E. (2020). Predicting employee attrition using machine learning techniques. Computers, 9.

5. Comprehensive Study of Machine Learning Algorithms for Stock Market Prediction During COVID-19;Saxena;J. Comput. Mech. Manag.,2023