The Prediction of Workplace Turnover Using Machine Learning Technique

Youngkeun Choi and Jae Won Choi
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Youngkeun Choi: Sangmyung University, South Korea
Jae Won Choi: University of Texas at Dallas, USA

International Journal of Business Analytics (IJBAN), 2021, vol. 8, issue 4, 1-10

Abstract: Turnover in the workplace is a significant cause of lost productivity of the organization and the root cause of the company's performance to many employers. Managing turnover is inevitable, but making sudden changes without knowing the cause of the problem is a terrible mistake. This paper aims to develop a reliable workplace turnover prediction model using machine learning and natural language processing techniques. In the results, first, satisfaction level, last evaluation, number of projects, average monthly hours, time spent at the company, work accident, and salary are shown to increase employee turnover at the workplace, while promotion in the 5 years and sale have no influence employee turnover at the workplace. Second, for the full model, the accuracy rate is 0.976, which implies that the error rate is 0.024. Among the patients who predicted not to be left, the accuracy that would not be left was 98.57%, and the accuracy that was left was 97.36% among the patients predicted to be left.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jban00:v:8:y:2021:i:4:p:1-10

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