 Applying machine learning algorithms to determine and predict the reasons and models for employee turnoverShardul Shankar, Ranjana Vyas and Vijayshri Tewari International Journal of Information Technology and Management, 2024, vol. 23, issue 1, 48-63 Abstract: In recent years, organisations have struggled with the turnover of employees, which has become one of the biggest issues that not only has inadvertent consequences for an organisation's growth, productivity, and performance but also has negative implications for the intrinsic cost associated with it. To cater to this problem, one such method is the use of machine learning algorithms. But one of the biggest issues in HR information system (HRIS) analysis is the presence of noise in data, leading to inaccurate predictions. This paper tries to examine the efficiency of six such algorithms, to determine the robustness, accuracy in real-time analysis of data, and then use that company's historical data to predict employee turnover for the present year. The dataset was mined from the HRIS database of a global organisation in the USA and Canada in the span of ten years to compare these algorithms to examine voluntary turnover, using Python and RStudio analytical tools. Keywords: employee turnover; machine learning; predictive algorithms; classification; voluntary turnover. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijitma:v:23:y:2024:i:1:p:48-63 Access Statistics for this article More articles in International Journal of Information Technology and Management from Inderscience Enterprises Ltd |
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