Employee turnover prediction using machine learning models

Abstract

With respect to the 16 characteristics of the workers, the objective of this study is to investigate how employee turnover can be classified using various machine learning algorithms (Support Vector Classification, Decision Tree Classifier, AdaBoost Classifier, Random Forest Classifier, Extra Trees Classifier, Logistic Regression and Gradient Boosting Classifiers). The information comes from the Employee Turnover dataset by E. Babushkin. Seven distinct classification models were developed and contrasted, including naive Bayes, random forest, logistic regression, support vector machines, and XGBoost. Numerous experiments validate the effectiveness of machine learning model. Among all the models, we find that the random forest model achieves the best results, which can be furtherly utilized in real-world prediction.