This work introduces an innovative predictive model for employee dismissal risks, highlighting its critical role in enhancing organizational resilience and efficiency. By combining statistical analysis and machine learning algorithms, including Logistic Regression and Neural Networks, the model integrates a wide range of predictors, from performance data to socio-economic factors like salary and demographic information. The research underscores the economic benefits of accurately identifying at-risk employees, enabling targeted retention strategies that reduce turnover costs and optimize HR resource allocation. Unique in its approach, the model offers a holistic understanding of dismissal risks, surpassing traditional methods by considering a broader spectrum of factors affecting employee

Карамова И.И., Мориков И.Д. (науч. рук. Наконечная О.В.) PREDICTING EMPLOYEE DISMISSAL RISKS // Сборник тезисов докладов конгресса молодых ученых. Электронное издание. – СПб: Университет ИТМО, [2024]. URL: https://kmu.itmo.ru/digests/article/13329