Predicting Employee Attrition Using Logistic Regression With Feature Selection

Abstract

Employee attrition is a reduction in employees that happens gradually. Employee attrition can damage the organization of a company, including the projects and its employee structure. This study aims to predict employee attrition in a company using the logistic regression method. Employee attrition can be predicted using machine learning because the machine learning approach is not biased due to human interference. In addition, human resources in a company need to know the most influential factors that cause the occurrence of employee attrition. In this study, we proposed feature selection methods to identify those influential factors and simplify the data training. Our approach is to predict employee attrition with three kinds of feature selection methods, namely information gain, select k-best, and recursive feature elimination (RFE). The 10-fold cross-validation was performed as an evaluation method. Prediction of employee attrition using the logistic regression method without applying feature selection gets an accuracy value of 0.865 and an AUC score of 0.932. However, by applying the RFE feature selection showed the highest evaluation result than information gain and select k-best, with an accuracy value of 0.853 and an AUC score of 0.925