EMPLOYEE ATTRITION PREDICTION USING HR ANALYTICS

Abstract

Employee Attrition Prediction using HR Analytics is a data-driven approach to identify employees who are likely to leave an organization. Employee attrition causes financial loss, reduced productivity, and operational instability. The objective of this project is to predict employee attrition using supervised machine learning techniques by analyzing historical HR data. The system considers multiple factors such as job satisfaction, salary, experience, working hours, performance rating, and work-life balance. Data preprocessing and feature selection are applied to improve prediction accuracy. Machine learning models such as Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine are used for prediction. The system provides accurate results and actionable insights to HR managers, helping organizations reduce attrition and improve employee retention strategies.