M Shirisha, P Vamshi, A Nagamani, M Kranthi, K Swayam 03 09 The Employee Attrition Analysis and Prediction system is designed to analyze employeerelated data and predict the likelihood of employees leaving an organization Employee attrition can negatively affect productivity increase recruitment and training costs and create difficulties in maintaining an experienced workforce Therefore identifying employees who may have a higher probability of leaving can help organizations take preventive actions The proposed system uses data analysis and machine learning techniques to examine various factors associated with employee attrition such as age job role salary job satisfaction overtime years of experience work environment distance from home and job involvement Historical employee data is collected and preprocessed by handling missing values encoding categorical variables and preparing the data for machinelearning algorithms Different classification algorithms such as Logistic Regression Decision Tree Random Forest and Support Vector Machine can be applied to the prepared dataset The models are trained using historical employee information and evaluated using performance measures such as accuracy precision recall F1score and confusion matrix The bestperforming model can then be used to predict whether an employee is likely to stay or leave The system can also provide visualizations that help management understand the major factors influencing employee attrition These insights can support HR teams in developing suitable strategies such as improving job satisfaction workload management career growth opportunities and employee engagement Overall the Employee Attrition Analysis and Prediction system provides a datadriven approach to understanding and predicting employee turnover It can help organizations make informed HR decisions reduce avoidable attrition improve employee retention and support better workforce planning Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026