An approach for predicting employee churn by using data mining

TLDR

Employee churn prediction, closely related to customer churn, is a major issue for companies yet receives little attention in the literature. The study applies multiple classification algorithms and a feature selection method to HR data to predict employee churn. We evaluate the algorithms by computing accuracy, precision, recall, and F‑measure, and compare results before and after feature selection. The approach enables companies to predict employee churn and reduce HR costs.

Abstract

Employee churn prediction which is closely related to customer churn prediction is a major issue of the companies. Despite the importance of the issue, there is few attention in the literature about. In this study, we applied well-known classification methods including, Decision Tree, Logistic Regression, SVM, KNN, Random Forest, and Naive Bayes methods on the HR data. Then, we analyze the results by calculating the accuracy, precision, recall, and F-measure values of the results. Moreover, we implement a feature selection method on the data and analyze the results with previous ones. The results will lead companies to predict their employees' churn status and consequently help them to reduce their human resource costs.