A Machine Learning-Based Web Application for Employee Attrition and Retention Prediction

In this paper, we proposed a Machine Learning-based Employee Attrition and Retention Prediction System to help companies manage their employees. The system combines data cleaning, various machine learning algorithms, and a web-based front-end for real-time predictions. The findings from the experiments indicated that the ensemble models show superior performance compared to traditional methods, with up to 98% accuracy. The system effectively identified the major factors affecting employee attrition, including job satisfaction, salary, overtime and work environment. Also, embedding the prediction model in Streamlit based web application improves the system's accessibility, allowing HR managers to easily get real-time predictions with probability and insight. While the proposed system achieves high performance, there is room for improvement. Next steps may involve integrating real-time HR data streams and unstructured data (e.g., employee feedback, behavioral analytics) to improve predictions. Moreover, incorporating Explainable Artificial Intelligence (XAI) methods like SHAP and LIME can enhance model explainability and trust in HR professionals. The system can also integrate with enterprise HR data systems and can be deployed in the cloud. In summary, the proposed system offers a scalable, affordable and feasible solution for predicting attrition and aids in data-driven decision-making.

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