Aprendizaje automático aplicado para predecir la rotación de empleados en una empresa
DOI:
https://doi.org/10.26439/ing.ind2025.n049.7934Palabras clave:
aprendizaje automático, redes neuronales, rotación de personal, pronósticos, aprendizaje conjunto, análisis de regresión logísticaResumen
La rotación de personal es un proceso natural en las organizaciones que refleja la cantidad de empleados que dejan la empresa en un periodo determinado. Una alta rotación genera costos significativos, por lo que comprender sus causas y planificar acciones correctivas es esencial para mantener la rotación de personal en niveles aceptables. Este artículo analiza la rotación en organizaciones mediante modelos predictivos. Se desarrollaron y compararon dos algoritmos de aprendizaje automático (regresión logística binaria y bosque aleatorio) y uno de aprendizaje profundo (redes neuronales artificiales), utilizando el conjunto de datos de IBM disponible en Kaggle. El artículo se estructura en cinco partes: introducción al problema, desarrollo metodológico, análisis de resultados, discusión y conclusión. Las redes neuronales demostraron mayor eficiencia en la predicción. Se concluye que el uso de modelos predictivos puede ayudar a las empresas a anticipar la rotación, optimizar procesos de selección y fomentar una gestión de recursos humanos más ética y proactiva.
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