Open AccessChurn Prediction of Employees Using Machine Learning Techniques
Author(s) -
Nilasha Bandyopadhyay,
Anil Jadhav
Publication year - 2021
Publication title -
tehnički glasnik
Language(s) - English
Resource type - Journals
eISSN - 1848-5588
pISSN - 1846-6168
DOI - 10.31803/tg-20210204181812
Subject(s) - random forest , confusion matrix , attrition , machine learning , naive bayes classifier , computer science , support vector machine , classifier (uml) , artificial intelligence , workload , recall rate , recall , psychology , medicine , dentistry , cognitive psychology , operating system
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.