Predicting employee attrition using machine learning approaches

Abstract

Employee attrition poses a critical challenge to organizations, both in terms of financial costs and operational continuity, with the average replacement cost per hire estimated at USD 4,129 and a reported attrition rate of 57.3% in 2021. This study applies machine learning techniques to predict employee attrition and identify its primary organizational drivers. Four supervised learning models were evaluated, Support Vector Machine (SVM), Support Vector Machine (LR), Decision Tree Classifier (DTC), and Extra Trees Classifier (ETC), in which the optimized ETC achieving the highest prediction accuracy of 93%, surpassing existing state-of-the-art methods. An Employee Exploratory Data Analysis (EEDA) revealed that monthly income, hourly rate, job level, and age are key factors influencing attrition. These findings highlight the effectiveness of AI-driven approaches in workforce analytics and provide actionable insights for organizational leaders aiming to improve retention through data-informed strategies.