Prediction of HR Employee Attrition with Machine Learning: Bagging and Random Forest Application

Akintunde Adetoye Fadare
Additional contact information
Akintunde Adetoye Fadare: Geoplex Drillteq Limited, Port Harcourt, Rivers, Nigeria

International Journal of Research and Scientific Innovation, 2024, vol. 11, issue 8, 410-419

Abstract: Employee attrition, the loss of valuable employees, presents a significant challenge for human resource (HR) departments in global corporations. Despite substantial investments in attracting and hiring top talent, companies often face high turnover rates. Traditional retention strategies, such as blanket incentives offered to all employees, can be resource-intensive and may not be equally effective for everyone. This research aims to develop a more targeted employee retention strategy by leveraging machine learning (ML) algorithms. The objective is to identify employees at a high risk of leaving the organization and priorities retention efforts for this specific group.

Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.rsisinternational.org/journals/ijrsi/d ... -issue-8/410-419.pdf (application/pdf)
https://rsisinternational.org/journals/ijrsi/artic ... -forest-application/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bjc:journl:v:11:y:2024:i:8:p:410-419

Access Statistics for this article

International Journal of Research and Scientific Innovation is currently edited by Dr. Renu Malsaria

More articles in International Journal of Research and Scientific Innovation from International Journal of Research and Scientific Innovation (IJRSI)
Bibliographic data for series maintained by Dr. Renu Malsaria ().