Paper Contents
Abstract
Abstract Employee attrition poses a significantchallenge to organizations, impacting productivity,cost, and overall workforce stability. This researchaims to develop a machine learning-basedpredictive model to identify employees at risk ofleaving the organization. Using the IBM HRAnalytics Employee Attrition dataset, variousfeatures such as job satisfaction, salary, years atcompany, overtime, and work-life balance wereanalyzed. The dataset was preprocessed andclassified using algorithms like Logistic Regression,Decision Tree, and Random Forest. Among them,the Random Forest model delivered the highestaccuracy, making it suitable for real-timeprediction. The model was further deployed usingStreamlit to create a user-friendly interface for HRdepartments to make data-driven decisions. Thesystem enables proactive employee retentionstrategies, reducing attrition risk and enhancingorganizational performance..
Copyright
Copyright © 2025 AMRUTA RAJENDRA SHINDE. This is an open access article distributed under the Creative Commons Attribution License.
