A Machine Learning based Approach on Employee Attrition Prediction with an Emphasize on predicting Leaving Reasons

Abstract

Using Vitesco Technologies as an example, this article examines whether machine learning models are suitable for detecting employee attrition at an early stage, with the aim of uncovering underlying reasons for leaving. Nine different machine learning algorithms were examined: K-nearest-neighbors, Naive Bayes, logistic regression, a support vector machine, a neural network, a random forest, adaptive boosting, and two gradient boosting models. A three-way-holdout validation method was implemented to assess the quality of the results and measure both the f-score and the degree of model generalization. Initially, it was found that tree-based methods are best suited for classifying employees. A multiclass classification approach showed that under certain conditions it is even possible to predict the underlying leaving reasons.