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

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  • Akintunde Adetoye Fadare

    (Geoplex Drillteq Limited, Port Harcourt, Rivers, Nigeria)

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.

Suggested Citation

  • Akintunde Adetoye Fadare, 2024. "Prediction of HR Employee Attrition with Machine Learning: Bagging and Random Forest Application," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 11(8), pages 410-419, August.
    • Handle: RePEc:bjc:journl:v:11:y:2024:i:8:p:410-419
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