Predicting Employee Attrition Using Machine Learning Approaches

Employee attrition is a costly problem for organizations, leading to loss of institutional knowledge, reduced productivity, and increased recruitment expenses. Machine learning offers a powerful approach to predicting employee attrition, enabling proactive interventions and improved talent retention strategies. This research explores different ensemble machine learning algorithms for employee attrition prediction. We utilize a comprehensive dataset of employee attributes and apply the following models: Logistic Regression, Naive Bayes, Nearest Neighbor, Bagging (Random Forest), Boosting (Gradient Boosting), Boosting (AdaBoost), and Boosting (Logit Boost); the models are considered based on their accuracy in predicting employee attrition. Our analysis reveals that Bagging, Random Forest, achieves the highest accuracy of 99%, effectively identifying employees at risk of leaving. This insight enables organizations to develop targeted retention strategies, addressing specific factors contributing to attrition and improving overall employee satisfaction. We believe that by leveraging the predictive power of employee attrition using machine learning, organizations can proactively address the challenge of employee attrition, fostering a more engaged and productive workforce while minimizing associated costs.