Employee attrition prediction system

Employee attrition is one of the most critical challenges faced by modern organizations.High turnover rates lead to increased recruitment costs, loss of organizational knowledge, reduced productivity, and lower customer satisfaction.This research proposes an Employee Attrition Prediction System using Machine Learning integrated with Customer Feedback Analytics.The framework combines employee demographic data, job-related factors, satisfaction indicators, and customer feedback information to predict employee turnover risk.Logistic Regression, Decision Tree, Random Forest, and XGBoost models are evaluated.Experimental results demonstrate accuracy between 85% and 92%, while customer feedback integration improves prediction performance by 3-5%.The proposed system assists HR departments in proactive retention planning and workforce management.Employee attrition is one of the most critical challenges faced by modern organizations.High turnover rates lead to increased recruitment costs, loss of organizational knowledge, reduced productivity, and lower customer satisfaction.This research proposes an Employee Attrition Prediction System using Machine Learning integrated with Customer Feedback Analytics.The framework combines employee demographic data, job-related factors, satisfaction indicators, and customer feedback information to predict employee turnover risk.Logistic Regression, Decision Tree, Random Forest, and XGBoost models are evaluated.Experimental results demonstrate accuracy between 85% and 92%, while customer feedback integration improves prediction performance by 3-5%.The proposed system assists HR departments in proactive retention planning and workforce management.