Predicting Employee Retention and Satisfaction in IT Companies Using  Random Forest–Based HR Analytics

Rashi Dubey and Manish Dhingra

Predicting Employee Retention and Satisfaction in IT Companies Using Random Forest–Based HR Analytics

Authors

Rashi Dubey and Manish Dhingra

Keywords:

Employee Retention; Job Satisfaction; HR Analytics; Random Forest; Machine Learning; Explainable AI; SHAP; Workforce Planning; IT Industry; Predictive Modeling

Abstract

Employee retention and satisfaction remain two of the most critical challenges facing informationtechnology (IT) organizations in the modern, knowledge-driven economy. High attrition rates notonly increase recruitment and training costs but also disrupt productivity and knowledge continuity.

References

Al-Mashaqbeh, I. A., & Alshurideh, M. T. (2023). Using machine learning to predict employee satisfaction in the information technology industry. International Journal of Data and Network Science, 7(2), 47–56. https://doi.org/10.5267/j.ijdns.2023.2.005 Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M., & Stua

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Published

2024-11-20

How to Cite

Rashi Dubey and Manish Dhingra. (2024). Predicting Employee Retention and Satisfaction in IT Companies Using Random Forest–Based HR Analytics. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 6784–6800. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/3987