Predicting Employee Attrition with Big Five Personality Involved Using Machine Learning

Peijia Yu; Yuntian Gao; Yifan Fan

doi:10.54254/2754-1169/2024.26743

Advances in Economics, Management and Political SciencesOpen access

Predicting Employee Attrition with Big Five Personality Involved Using Machine Learning

Research Article

Open Access

Predicting Employee Attrition with Big Five Personality Involved Using Machine Learning

Peijia Yu ^1* Yuntian Gao ², Yifan Fan ³

¹ Singapore University of Technology and Design

² Shanghai United International School Gubei Campus

³ University of International Business and Economics

^*Corresponding author: peijia_yu@alumni.sutd.edu.sg

Published on 9 September 2025

AEMPS Vol.215

ISSN (Print): 2754-1177

ISSN (Online): 2754-1169

ISBN (Print): 978-1-80590-357-4

ISBN (Online): 978-1-80590-358-1

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Abstract

The paper aims to find whether factors related to personalities can influence the attrition of employees and how important those factors are compared with other factors using a real-world dataset. It also aims to figure out the most suitable machine learning models to predict employee attrition given both objective and subjective factors of the employee. Six machine learning models are applied and evaluated. Random Forest has the best performance given this dataset, and the big five personality plays an important role for the model in making decisions, the position of which is just behind the age and working experience of an employee. These findings provide insights from a different perspective to examine how internal factors of an employee can affect attrition besides external factors like income. At the same time, the result emphasizes the attention to the personality of an employee during the hiring and working process for human resource managers in an organization.

Keywords:

Employee attrition, Attrition prediction, Big five personality, Machine Learning, Random Forest.

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References

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References

[2]. Tolles, J., & Meurer, W. J. (2016). Logistic regression: relating patient characteristics to outcomes. Jama, 316(5), 533-534.

[5]. Mahesh, B. (2020). Machine learning algorithms-a review. International Journal of Science and Research (IJSR). [Internet], 9(1), 381-386.

[6]. Ho, T. K. (1995, August). Random decision forests. In Proceedings of 3rd international conference on document analysis and recognition (Vol. 1, pp. 278-282). IEEE.

[9]. Frye, A., Boomhower, C., Smith, M., Vitovsky, L., & Fabricant, S. (2018). Employee attrition: what makes an employee quit?. SMU Data Science Review, 1(1), 9.

[10]. Fallucchi, F., Coladangelo, M., Giuliano, R., & William De Luca, E. (2020). Predicting employee attrition using machine learning techniques. Computers, 9(4), 86.

[11]. Jain, P. K., Jain, M., & Pamula, R. (2020). Explaining and predicting employees’ attrition: a machine learning approach. SN Applied Sciences, 2(4), 757.

[14]. Buckland, M., & Gey, F. (1994). The relationship between recall and precision. Journal of the American society for information science, 45(1), 12-19.

Cite this article

Yu,P.;Gao,Y.;Fan,Y. (2025). Predicting Employee Attrition with Big Five Personality Involved Using Machine Learning. Advances in Economics, Management and Political Sciences,215,20-29.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of the 4th International Conference on Financial Technology and Business Analysis

ISBN: 978-1-80590-357-4(Print) / 978-1-80590-358-1(Online)

Editor: Lukáš Vartiak

Conference website: https://2025.icftba.org/

Conference date: 12 December 2025

Series: Advances in Economics, Management and Political Sciences

Volume number: Vol.215

ISSN: 2754-1169(Print) / 2754-1177(Online)

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