SMU Data Science Review


Albeit to varying degrees, employee attrition is a costly challenge faced by many employers \cite{kantor}. In this paper, we present a model for predicting employee attrition, as well as discuss the serious ethical implications of using such a model within organizations. To accomplish this, we examined publicly available data from the Office of Personnel Management, the Bureau of Labor Statistics, and IBM. With these sources, we determined a set of statistically significant factors that correlate to an employee’s decision to quit, and determined to which types of occupations our model may be applied. After applying Principal Component Analysis and classification methods K-Nearest Neighbors and Random Forest, it was Logistic Regression that allowed us to simplify the model and predict employee quits with the highest accuracy of our testing methods, achieving a greater than seventy-four percent success rate.

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License