Abstract
As corporations go through the Great Resignation, a post-pandemic economic trend in the surge of employee resignations, employee attrition has become one of the most significant problems for any organization. Employee attrition is defined as a reduction in the number of workers from various causes such as retirement, resignation, and termination. Because employees are important human resources (HR) of the organization and the subjects who own other valuable resources that the organization need, diverse opportunity costs occur when employee attrition takes place. To prevent such unwanted loss of valuable assets, various efforts have been made to predict and prevent employee attrition. In this study, three machine learning methods, Random Forest, XGBoost, and Artificial Neural Network, were used to predict employee attrition. Kaggle’s IBM HR Analytics Employee Attrition and Performance dataset which is composed of 1470 employee information was used as the data set. The variable to be predicted was whether or not employees leave the organization and a total of 35 variables such as academic background and environment satisfaction were considered. ‘Accuracy’, ‘Precision’, ‘Sensitivity’, and ‘F-1 Score’ were used as measures to calculate the prediction performance of the models. The result showed that XGBoost has the best performance in Accuracy while Random Forest showed the best performance in Precision. Artificial Neural Network showed the best performance in both Sensitivity and F1-Score.
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Gim, S., Im, E.T. (2023). A Study on Predicting Employee Attrition Using Machine Learning. In: Lee, R. (eds) Big Data, Cloud Computing, and Data Science Engineering. BCD 2022. Studies in Computational Intelligence, vol 1075. Springer, Cham. https://doi.org/10.1007/978-3-031-19608-9_5
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