HR Analytics: Employee Attrition Analysis using Random Forest

doi:10.23940/ijpe.22.04.p5.275281

Int J Performability Eng ›› 2022, Vol. 18 ›› Issue (4): 275-281.doi: 10.23940/ijpe.22.04.p5.275281

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HR Analytics: Employee Attrition Analysis using Random Forest

Shobhanam Krishna and Sumati Sidharth^*

Department of Technology Management, Defence Institute of Advanced Technology, Pune, 411025, India

Submitted on ; Revised on ; Accepted on
Contact: * E-mail address: sumatisidharth@gmail.com
About author:Shobhanam Krishna is a B.tech graduate in Electrical Engineering from C.V Raman College of Engineering, Bhubaneswar. She is Certified in Artificial Intelligence & Machine Learning from NIT Warangal, HR Analytics, Project Management Leader, Six Sigma Green Belt[CSSGBP]. She is currently pursuing her M.Tech in ’Technology Management’from from DIAT, Pune.
Dr. Sumati Sidharth is an Assistant Professor in the Department of Technology Management in Defence Institute of Advanced Technology. She holds degrees MSc, MBA and Ph.D. in Management. She is Certified Six Sigma Green Belt and IPMA Level “C”. She is an Accredited Management Teacher from AIMA, New Delhi. She is a life member of various management and research associations. She has eight book chapters to her credit. She has a number of publications in national and international journals. Her focus area is Human Resource Management, Project management, Strategic Management.

Abstract

Abstract: Employee attrition has indeed been viewed as a crucial concern for businesses due to the negative impact it has on workplace motivation and productivity as well as prolonged growth strategies. Organizations are using machine learning(ml) algorithms to predict employee turnover to address the problem. In this paper, an effort has been made to build up a model for predicting employee turnover rates using HR analytics data provided by IBM Analytics. The actual dataset includes 35 features as well as 1470 samples. Random Forest is being used to make accurate predictions. The model built using Random Forest Classifier is transformed by SMOTE mechanism to improve target class imbalance. After SMOTE mechanism metrics of the training model are improved however, validation metrics are improved slightly specially sensitivity has very little impact. This paper also introduces the factors that influence employee attrition within any organization, giving top management a better perspective when attempting to make major decisions regarding the engagement of the majority of workers in the company. The study may be obligated in prospective research to reduce the prediction error margin.

Key words: machine learning (ML), employee attrition, turnover, classification, organization, artificial intelligence (AI), random forest (RF)

Shobhanam Krishna and Sumati Sidharth. HR Analytics: Employee Attrition Analysis using Random Forest [J]. Int J Performability Eng, 2022, 18(4): 275-281.

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References

1. PM, U. and Balaji, N.V., Analysing Employee attrition using machine learning. Karpagam Journal Of Computer Science, 13, 277-282, 2019.
2. Ponnuru S., Merugumala G., Padigala S., Vanga R. and Kantapalli B.Employee attrition prediction using logistic regression. International Journal for Research in Applied Science & Engineering Technology, 8(5), pp.2871-2875, 2020.
3. Alao D.A.B.A. and Adeyemo, A.B. Analyzing employee attrition using decision tree algorithms. Computing, Information Systems, Development Informatics and Allied Research Journal, 4(1), pp.17-28, 2013.
4. Alduayj, S.S. and Rajpoot, K. Predicting employee attrition using machine learning. In2018 international conference on innovations in information technology (iit), pp. 93-98, 2018.
5. Frye A., Boomhower C., Smith M., Vitovsky L. and Fabricant S.Employee Attrition: What Makes an Employee Quit?. SMU Data Science Review, 1(1), p.9, 2018.
6. Jantan, H., Hamdan, A.R. and Othman, Z.A.Towards applying data mining techniques for talent management. In International Conference on Computer Engineering and Applications, IPCSIT, 2, pp. 2011, 2011.
7. Nagadevara, V., Srinivasan, V. and Valk, R. Establishing a link between employee turnover and withdrawal behaviours: Application of data mining techniques, 2008.
8. Hong, W.C., Wei, S.Y. and Chen, Y.F.A comparative test of two employee turnover prediction models. International Journal of Management, 24(4), p.808, 2007.
9. Kane-Sellers, M.L. Predictive models of employee voluntary turnover in a North American professional sales force using data-mining analysis. Texas A&M University, 2007.
10. Saradhi, V.V. and Palshikar, G.K.Employee churn prediction. Expert Systems with Applications, 38(3), pp.1999-2006, 2011.
11. IBM HR Analytics Employee. Available online: https://www.kaggle.com/zainsr4/ibm-hr-analytics

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HR Analytics: Employee Attrition Analysis using Random Forest

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