Using machine learning algorithms with improved accuracy to analyze and predict employee attrition

Abstract: Human migration is based on pull factors that individuals evaluate when it comes to moving to a different territory. Likewise, employee attrition is a phenomenon that represents the tendency to a reduction in employees within an organization. This research paper aims to develop and evaluate machine learning algorithms, namely Decision Tree, Random Forest, and Binary Logistic Regression, to predict employee attrition using the IBM dataset available on Kaggle. The objective is to provide organizations with a proactive approach to employee retention and human resource management by creating accurate predictive models. Employee attrition has significant implications for an organization's reputation, profitability, and overall structure. By accurately predicting employee attrition, organizations can identify the factors contributing to it and implement data-driven human resources management practices. This study contributes to improving decision-making processes, including hiring and firing decisions, and ultimately enhances an organization's capital. The IBM dataset used in this study consists of anonymized employee records and their employment outcomes. It provides a comprehensive HR data representation for analysis and prediction. Three machine learning algorithms, Decision Tree, Random Forest, and Binary Logistic Regression, were utilized in this research. These algorithms were selected for their potential to improve accuracy in predicting employee attrition. The Logistic Regression model yielded the highest accuracy of 87.44% among the tested algorithms. By leveraging this study's findings, organizations can develop predictive models to identify factors contributing to employee attrition. These insights can inform strategic decisions and optimize human resource management practices.

How to cite this paper

 Alsubaie, F & Aldoukhi, M. (2024). Using machine learning algorithms with improved accuracy to analyze and predict employee attrition.Decision Science Letters , 13(1), 1-18.

Refrences

Al-Darraji, S., Honi, D. G., Fallucchi, F., Abdulsada, A. I., Giuliano, R., & Abdulmalik, H. A. (2021). Employee attrition prediction using deep neural networks. Computers, 10(11), 1–11. https://doi.org/10.3390/computers10110141
Alao, D. A. B. A., & Adeyemo, A. B. (2013). Analyzing employee attrition using decision tree algorithms. Information Systems & Development Informatics, 4(1), 17–28.
Almomani, A., Alauthman, M., Shatnawi, M. T., Alweshah, M., Alrosan, A., Alomoush, W., & Gupta, B. B. (2022). Phishing Website Detection With Semantic Features Based on Machine Learning Classifiers: A Comparative Study. International Journal on Semantic Web and Information Systems, 18(1), 1–24. https://doi.org/10.4018/IJSWIS.297032
Bhartiya, N., Jannu, S., Shukla, P., & Chapaneri, R. (2019). Employee Attrition Prediction Using Classification Models. 2019 IEEE 5th International Conference for Convergence in Technology, I2CT 2019. https://doi.org/10.1109/I2CT45611.2019.9033784
Branham, L. (2005). Planning to become an employer of choice. Journal of Organizational Excellence, 24(3), 57–68. https://doi.org/10.1002/joe.20060
Breiman, L. (1996). Out-of-Bag Estimation. In Statistics Department: University of California Berkeley.
Ceriani, L., & Verme, P. (2012). The origins of the Gini index: Extracts from Variabilità e Mutabilità (1912) by Corrado Gini. Journal of Economic Inequality, 10(3), 421–443. https://doi.org/10.1007/s10888-011-9188-x
Dalton, D. R., & Mesch, D. J. (1990). The Impact of Flexible Scheduling on Employee Attendance and Turnover. Administrative Science Quarterly, 370–387. Retrieved from http://www.jstor.org/stable/pdf/3150242.pdf?_=1467266017307
Desboulets, L. D. D. (2018). A review on variable selection in regression analysis. Econometrics. https://doi.org/10.3390/econometrics6040045
Fallucchi, F., Coladangelo, M., Giuliano, R., & De Luca, E. W. (2020). Predicting employee attrition using machine learning techniques. Computers, 9(4). https://doi.org/10.3390/computers9040086
Gaurav, A., Gupta, B. B., & Panigrahi, P. K. (2023). A comprehensive survey on machine learning approaches for malware detection in IoT-based enterprise information system. Enterprise Information Systems, 17(3). https://doi.org/10.1080/17517575.2021.2023764
Here’s what your turnover and retention rates should look like. (n.d.). Retrieved October 14, 2022, from https://www.ceridian.com/blog/turnover- and-retention-rates-benchmark (accessed
Joseph, R., Udupa, S., Jangale, S., Kotkar, K., & Pawar, P. (2021). Employee attrition using machine learning and depression analysis. Proceedings - 5th International Conference on Intelligent Computing and Control Systems, ICICCS 2021. https://doi.org/10.1109/ICICCS51141.2021.9432259
Lazzari, M., Alvarez, J. M., & Ruggieri, S. (2022). Predicting and explaining employee turnover intention. International Journal of Data Science and Analytics, 14(3), 279–292. https://doi.org/10.1007/s41060-022-00329-w
Liu, J. L. (2014). Main causes of voluntary employee turnover: A study of factors and their relationship with expectations and preferences. University Of Chile.
Nagadevara, V., & Srinivasan, V. (2007). Early Prediction of Employee Attrition in Software Companies-Application of Data Mining Techniques. The 10th International Conference of the Society of Global Business and Economic Development.
Najafi-Zangeneh, S., Shams-Gharneh, N., Arjomandi-Nezhad, A., & Zolfani, S. H. (2021). An improved machine learning-based employees attrition prediction framework with emphasis on feature selection. Mathematics, 9(11). https://doi.org/10.3390/math9111226
Ponnuru, S., Merugumala, G., Padigala, S., Vanga, R., & Kantapalli, B. (2020). Employee Attrition Prediction using Logistic Regression. International Journal for Research in Applied Science and Engineering Technology, 8(5), 2871–2875. https://doi.org/10.22214/ijraset.2020.5481
Qutub, A., Al-Mehmadi, A., Al-Hssan, M., Aljohani, R., & Alghamdi, H. S. (2021). Prediction of Employee Attrition Using Machine Learning and Ensemble Methods. International Journal of Machine Learning and Computing, 11(2), 110–114. https://doi.org/10.18178/ijmlc.2021.11.2.1022
Rombaut, E., & Guerry, M. A. (2018). Predicting voluntary turnover through human resources database analysis. Management Research Review, 41(1), 96–112. https://doi.org/10.1108/MRR-04-2017-0098
Subhash Pavan. (2017). IBM HR Analytics Employee Attrition & Performance. Retrieved from https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset
Subhashini, M., & Gopinath, R. (2020). Employee Attrition Prediction in Industry Using Machine Learning Techniques. International Journal of Advanced Research in Engineering and Technology, 11(12), 3329–3341. Retrieved from https://doi.org/10.34218/IJARET.11.12.2020.313
Vasa, J., & Masrani, K. (2019). Foreseeing employee attritions using diverse data mining strategies. International Journal of Recent Technology and Engineering, 8(3), 620–626. https://doi.org/10.35940/ijrte.B2406.098319
Wassan, S., Suhail, B., Mubeen, R., Raj, B., Agarwal, U., Khatri, E., … Dhiman, G. (2022). Gradient Boosting for Health IoT Federated Learning. Sustainability, 14(24). https://doi.org/10.3390/su142416842
Zhao, Y., Hryniewicki, M. K., Cheng, F., Fu, B., & Zhu, X. (2018). Employee turnover prediction with machine learning: A reliable approach. In Advances in Intelligent Systems and Computing (Vol. 2). https://doi.org/10.1007/978-3-030-01057-7_56