Published Paper Details:

Employee attrition poses a significant challenge for organizations, impacting productivity, morale, and overall success. Predicting and mitigating attrition can be achieved through the application of machine learning techniques, leveraging the power of data-driven insights. This research paper explores the development of an attrition prediction model using machine learning algorithms, aiming to identify influential factors and provide actionable recommendations for talent management. A comprehensive dataset of employee information, encompassing demographics, job-related factors, performance metrics, and attrition status, was collected. Various machine learning algorithms, including random forests, XGBoost, Naive Bayes and Decision tree, were employed to develop attrition prediction models. The performance of these models was evaluated using accuracy, precision, recall, and F1 score. The results reveal significant predictors of attrition and provide valuable insights for organizations to proactively manage employee retention. This study contributes to the understanding of talent management by harnessing the potential of machine learning, enabling organizations to anticipate and address attrition risks effectively. The findings serve as a foundation for evidence-based decision-making and the development of tailored retention strategies. The outcomes of this research have implications for enhancing workforce stability and optimizing organizational performance. Future research directions include the exploration of advanced machine learning techniques, incorporation of additional data sources, and validation of the developed models across diverse industries and organizational contexts.