Employee-Attrition-Prediction

Employee attrition is a critical challenge for organizations worldwide, impacting productivity, stability, and profitability. Traditional approaches to attrition prediction often fall short in accurately identifying at-risk employees, leading to increased recruitment costs and talent loss. This paper presents a novel application of Artificial Intelligence (AI) in HR analytics to predict employee attrition. Leveraging machine learning algorithms and comprehensive employee datasets, including demographic information, performance metrics, and job satisfaction scores, this research model aims to enhance attrition prediction accuracy. It employs data preprocessing, feature engineering, and model selection techniques to develop a robust predictive framework. Through proactive identification of attrition risk factors, organizations can implement targeted retention strategies and organizational improvements to mitigate attrition and foster employee engagement. The proposed AI-driven approach not only enhances HR analytics capabilities but also provides actionable insights for strategic decision-making, contributing to organizational success in talent retention and workforce optimization.