Employee attrition is a significant issue for organizations across the globe, as it results in increased recruitment costs, diminished skilled employees, and decreased organizational productivity. Early identification of employees that are likely to leave the organization can provide proactive action and enable Human Resource (HR) departments to undertake retention strategies. In this paper, we provide a machine learning-based solution to predict employee attrition, and we focus specifically on the Support Vector Machine (SVM) algorithm as the primary machine learning model to create the predictive model. We also trained several SVM kernels and conducted hyperparameter tuning to enhance the accuracy and capability of generalization. We used multiple performance metrics to evaluate the models such as accuracy, precision, recall, F1-score and ROC-AUC. Our experiments demonstrated that the SVM predictive model consistently outperformed the performance of other models by accurately identifying the cases of higher risk of attrition, allowing us to conclude that it was a sufficiently reliable model. To ensure ease of use and accessibility we developed a web application utilizing Streamlit, while maintaining a user-friendly interface. The application allows human resource professionals to enter employee characteristics and receive real-time predictions of potential employee attrition and benefits HR professionals by comparison of predictive models and performance measures used, as well as visualizations of performance metrics and various datasets. This paper aims to connect the gap between data science and HR decision making. This paper applies powerful machine learning methods to an interactive software interface and produces a straightforward, scaleable yet intelligent system to assist organizations with employee retention, workforce planning, and long-term strategic growth.
Panduri et al. (Thu,) studied this question.