Analyzing and Predicting Employee Turnover in the Restaurant Industry

Employee turnover in the restaurant industry poses significant operational and financial challenges, primarily due to the high costs associated with hiring and training new staff, as well as the negative impact on team cohesion and customer service. This paper employs a comprehensive datadriven approach using machine learning (ML) and deep learning (DL) techniques to analyze employee turnover, aiming to identify and understand the key predictors and underlying patterns. Utilizing a dataset of 778 responses from restaurant employees, the study explores various factors, including job satisfaction, income levels, and perceptions of management. Through rigorous data preprocessing and the application of advanced analytical models such as Decision Trees, Random Forests, and Deep Neural Networks, the study enhances the predictive accuracy and reliability of turnover predictions. The results not only highlight the effectiveness of specific models but also shed light on significant turnover predictors. This research contributes to the development of targeted strategies for reducing turnover rates, ultimately aiding in improving both operational efficiency and employee retention in the restaurant industry.