Análisis de factores predominantes en la rotación temprana de empleados de grupo Difare de la región sierra aplicando machine learning.

Abstract

Artificial Intelligence (AI) focuses on the development of systems that can perform tasks that normally require human intelligence, including activities such as learning, problem solving, pattern recognition and decision making. Artificial Intelligence has a variety of applications, from virtual assistants and chatbots to recommendation algorithms. Its continuous development has had a significant impact in areas such as healthcare, industry and research, changing the way we interact with technology and creating new opportunities in a variety of fields. The main objective of this research work was to develop a Machine Learning model to classify the predominant factors that influence the early turnover of employees of Difare Group, in the Andean region of Ecuador, which in turn allows making strategic decisions about the forecast of future behavior. This analysis was carried out through the implementation of the Random Forest algorithm in the statistical software RStudio. This allowed us to evaluate the main causes of voluntary resignations of talented personnel of the commercial entity, and consequently, to provide recommendations to prevent the growing trend of the phenomenon of staff turnover, which has an impact on productivity and organizational dynamics.