Application of Machine Learning Methods for Employee Turnover Prediction Based on Open Data

Abstract

The application of machine learning methods for predicting staff turnover in organizations using open data is studied. An analysis of existing approaches to predicting staff turnover is conducted, the need to use modern machine learning algorithms is substantiated. Based on an open data set, a model is developed that allows for a high-precision determination of the probability of employee dismissal. The results of the study demonstrate the practical significance of the proposed approach and can be used to improve the efficiency of human resource management in organizations. Formal descriptions and architecture of the applied machine learning models are presented, which ensures the transparency and reproducibility of the approach under consideration.