Abstract
This paper describes the creation of a database and a machine learning model to predict employee attrition. Our proposal deals with attrition by considering 3 classes (voluntary, involuntary and no attritors) giving a more complete view of the loss of qualified personnel to the Human Resources Management. Of the several machine learning models tested to solve the problem, XGBoost stood out as the best performing one on a dataset with more than four thousand employees and twenty-one features collected from three independent companies from different industrial sectors. The model, evaluated on a 20-run experiment, achieved an overall mean accuracy of 78.5%, corresponding to the correct classification of 52.6% of the voluntary attritors, 78.9% of the involuntary attritors and 81.6% of the non-attritors, showing that voluntary attritors are harder to discriminate.
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Notes
- 1.
Literature also use employee churn or employee turnover.
References
Ali, M.: PyCaret: an Open Source, Low-Code Machine Learning Library in Python (2020). https://www.pycaret.org, pyCaret version 1.0.0
Basariya, R., Ahmed, R.R.: A study on attrition-turnover intentions of employees. Int. J. Civ. Eng. Technol. 10(1), 2594–2601 (2019)
Brown, T., Green, B.: Is “The Great Resignation” Real? We Analyzed Salary Increases and Turnover to Make Sense of a Competitive Labor Market. https://humancapital.aon.com/insights/articles/2021/is-the-great-resignation-real
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002). https://doi.org/10.1613/jair.953
GeoPy: Geopy (2.2.0) (2021). https://geopy.readthedocs.io/en/stable/
Holtom, B.C., Mitchell, T.R., Lee, T.W., Eberly, M.B.: Turnover and retention research: a glance at the past, a closer review of the present, and a venture into the future. Acad. Manag. Ann. 2(1), 231–274 (2008)
Hom, P.W., Griffeth, R.W., Sellaro, C.L.: The validity of mobley’s (1977) model of employee turnover. Organ. Behav. Hum. Perform. 34(2), 141–174 (1984)
Kavakli, E., Buenabad-Chavez, J., Tountopoulos, V., Loucopoulos, P., Sakellariou, R.: Specification of a software architecture for an industry 4.0 environment. In: 2018 Sixth International Conference on Enterprise Systems (ES), pp. 36–43. IEEE (2018)
March, J.G., Simon, H.A.: Organizations. John Wiley & Sons (1958)
Mobley, W.H., Griffeth, R.W., Hand, H.H., Meglino, B.M.: Review and conceptual analysis of the employee turnover process. Psychol. Bull. 86(3), 493 (1979)
OpenRouteService: Openrouteservice (2.2.2) (2020). https://openrouteservice.org/
Rosenbaum, E.: IBM Artificial Intelligence can Predict with 95% Accuracy Which Workers are About to Quit Their Jobs. https://www.cnbc.com/2019/04/03/ibm-ai-can-predict-with-95-percent-accuracy-which-employees-will-quit.html
Rubenstein, A.L., Kammeyer-Mueller, J.D., Wang, M., Thundiyil, T.G.: “Embedded’’ at hire? Predicting the voluntary and involuntary turnover of new employees. J. Organ. Behav. 40(3), 342–359 (2019)
Salvadorinho, J., Teixeira, L.: Organizational knowledge in the i4. 0 using BPMN: a case study. Procedia Comput. Sci. 181, 981–988 (2021)
Saradhi, V.V., Palshikar, G.K.: Employee churn prediction. Expert Syst. Appl. 38(3), 1999–2006 (2011)
Takeuchi, N., Takeuchi, T.: A longitudinal investigation on the factors affecting newcomers’ adjustment: evidence from Japanese organizations. Int. J. Hum. Resour. Manag. 20(4), 928–952 (2009)
Wolpert, D.H.: The lack of a priori distinctions between learning algorithms. Neural Comput. 8(7), 1341–1390 (1996). https://doi.org/10.1162/neco.1996.8.7.1341
Zhou, Z.H.: Machine Learning, 1st edn. Springer Singapore (2021). https://doi.org/10.1007/978-981-15-1967-3
Zhu, X.: Forecasting Employee Turnover in Large Organizations (2016). https://trace.tennessee.edu/utk_graddiss/3985/
Zhu, X., et al.: Employee turnover forecasting for human resource management based on time series analysis. J. Appl. Stat. 44(8), 1421–1440 (2017)
Acknowledgments
The present work was developed in the scope of the Project Augmented Humanity [POCI-01-0247-FEDER-046103 and LISBOA-01-0247-FEDER-046103], financed by Portugal 2020, under the Competitiveness and Internationalization Operational Program, the Lisbon Regional Operational Program, and by the European Regional Development Fund. Second and third authors were also supported by CIDMA and funded by the Fundação para a Ciência e a Tecnologia, I.P. (FCT, Funder ID = 50110000187) under Grants UIDB/04106/2020 and UIDP/04106/2020 (https://doi.org/10.54499/UIDB/04106/2020) (https://doi.org/10.54499/UIDP/04106/2020).
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Gomes, A., Silva, L.M., Cruz, J.P. (2025). Predicting Employee Attrition in a Multi-company Setting. In: Julian, V., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2024. IDEAL 2024. Lecture Notes in Computer Science, vol 15346. Springer, Cham. https://doi.org/10.1007/978-3-031-77731-8_31
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