Employee Attrition Prediction Framework Using HR Analytics and AI for Workforce Management and Strategic Planning

Abstract

Employee attrition is a major challenge faced by organizations, as high employee turnover leads to increased recruitment costs, productivity loss, and organizational instability. Traditional methods of analyzing employee attrition rely on manual analysis and basic statistical techniques, which often fail to predict employee behavior accurately. This project proposes an AI-driven Employee Attrition Prediction system using HR Analytics. The system utilizes machine learning algorithms to analyze employee-related data such as job role, salary, experience, work-life balance, performance, and satisfaction levels. By identifying patterns and key factors influencing employee turnover, the system predicts the likelihood of employee attrition. This helps HR departments take proactive measures to retain valuable employees, reduce costs, and improve workforce planning. KEYWORDS: HR Analytics, Employee Attrition, LogisticRegression, Random Forest Classifier, K Nearest Neighbors Classifier, SVC