Abstract

Employee churn prediction which is closely related to customer churn prediction is a major issue of the companies. In this project, we are applying well-known classification methods including, Decision Tree, Logistic Regression, SVM, KNN, Random Forest, and Naïve Bayes methods on the datasets. Then, we analyse the results by calculating the accuracy and precision of the results. Moreover, we implement a feature selection method on the data and analyze the results with previous ones. The results will lead companies to predict their employees churn status and consequently help them to reduce their human resource costs