Employee attrition is a critical challenge for modern organizations, leading to increased recruitment costs, loss of skilled talent, and reduced productivity. This paper presents TalentGuard, a machine learning-based HR analytics system designed to predict employee attrition and provide actionable insights for workforce management. The proposed system leverages historical employee data, including job role, salary, department, tenure, performance metrics, and work conditions, to train and evaluate multiple machine learning models such as Logistic Regression, Random Forest, Support Vector Machine (SVM), and Gradient Boosting algorithms of leaving, enabling organizations to take proactive measures. By combining predictive The system incorporates data preprocessing, feature engineering, and model optimization techniques to enhance prediction accuracy. Performance evaluation is conducted using metrics such as accuracy, precision, recall, and ROC-AUC score. In addition, TalentGuard integrates interactive dashboards and an AI- powered chatbot to assist HR professionals in analyzing attrition trends and generating retention strategies. The results demonstrate that machine learning models can effectively identify employees at risk primarily rely on reactive approaches, where analytics with intelligent user interaction, TalentGuard contributes to data-driven decision- making and improved employee retention strategies.
Shree et al. (Thu,) studied this question.