Original scientific paper

https://doi.org/10.31803/tg-20210204181812

Churn Prediction of Employees Using Machine Learning Techniques

Nilasha Bandyopadhyay orcid.org/0000-0002-1519-0105 ; Symbiosis Centre for Information Technology, Pune - Plot No: 15, Rajiv Gandhi Infotech Park, MIDC, Hinjewadi, Phase 1, Pune, Maharashtra 411057, India
Anil Jadhav ; Symbiosis Centre for Information Technology, Pune - Plot No: 15, Rajiv Gandhi Infotech Park, MIDC, Hinjewadi, Phase 1, Pune, Maharashtra 411057, India

Abstract

Employees are considered as the most valuable assets of any organization. Various policies have been introduced by the HR professionals to create a good working environment for them, but still, the rate of employees quitting the Technology Industry is quite high. Often the reason behind their early attrition could be due to company-related or personal issues, such as No satisfaction at the workplace, Fewer opportunities for learning, Undue Workload, Less Encouragement, and many others. This paper aims in discussing a structured way for predicting the churn rate of the employees by implementing various Classification techniques like SVM, Random Forest classifier, and Naives Bayes classifier. The performance of the classifiers was compared using metrics like Confusion Matrix, Recall, False Positive Rate, and Accuracy to determine the best model for the churn prediction. We found that among the models, the Random Forest classifier proved to be the best model for IT employee churn prediction. A Correlation Matrix was generated in the form of a heatmap to identify the important features that might impact the attrition rate.

Keywords

attrition; churn rate; classification techniques; confusion matrix; feature selection

Hrčak ID:

253021

URI

https://hrcak.srce.hr/253021

Publication date:

3.3.2021.

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