IDEAS home Printed from https://ideas.repec.org/a/ids/ijitma/v23y2024i1p48-63.html
   My bibliography  Save this article

Applying machine learning algorithms to determine and predict the reasons and models for employee turnover

Author

Listed:
  • Shardul Shankar
  • Ranjana Vyas
  • Vijayshri Tewari

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.

Suggested Citation

  • Shardul Shankar & Ranjana Vyas & Vijayshri Tewari, 2024. "Applying machine learning algorithms to determine and predict the reasons and models for employee turnover," International Journal of Information Technology and Management, Inderscience Enterprises Ltd, vol. 23(1), pages 48-63.
    • Handle: RePEc:ids:ijitma:v:23:y:2024:i:1:p:48-63
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=136187
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ids:ijitma:v:23:y:2024:i:1:p:48-63. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=18 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.