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All Journal ComEngApp : Computer Engineering and Applications Journal
Shengjun Zhai
The University of Chicago, Illinois
Computer Science & IT Engineering

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Predicting the Occurrence and Causes of Employee Turnover with Machine Learning Xiaojun Ma; Shengjun Zhai; Yingxian Fu; Leonard Yoonjae Lee; Jingxuan Shen
Computer Engineering and Applications Journal Vol 8 No 3 (2019)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (383.959 KB) | DOI: 10.18495/comengapp.v8i3.316

Abstract

This paper looks at the problem of employee turnover, which has considerable influence on organizational productivity and healthy working environments. Using a publicly available dataset, key factors capable of predicting employee churn are identified. Six machine learning algorithms including decision trees, random forests, naïve Bayes and multi-layer perceptron are used to predict employees who are prone to churn. A good level of predictive accuracy is observed, and a comparison is made with previous findings. It is found that while the simplest correlation and regression tree (CART) algorithm gives the best accuracy or F1-score, the alternating decision tree (ADT) gives the best area under the ROC curve. Rules extracted in the if-then form enable successful identification of the probable causes of churning.
Co-Authors Jingxuan Shen Leonard Yoonjae Lee Xiaojun Ma Yingxian Fu