[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125428-en":3,"doc-seo-125428-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125428,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Predicting Employee Turnover Using Applied Machine Learning","Employee turnover is a core organizational process representing employees who leave within a defined period, and elevated turnover creates substantial financial and operational costs. Understanding underlying drivers and enabling corrective actions is essential to sustain acceptable retention levels. The study builds predictive modeling using machine learning and deep learning approaches, developing and comparing logistic regression and random forest alongside artificial neural networks with an IBM dataset from Kaggle.","Predicting emPloyee turnover using APPlied mAchine leArning  \nMarcos Antonio Albarracin Manrique*  \n[https://orcid.org/0000-0002-3429-0267](https://orcid.org/0000-0002-3429-0267)[ ](https://orcid.org/0000-0002-3429-0267)Instituto de Física, Universidad de Sao Paulo, Brasil  \nReceived: May 19, 2025 / Accepted: July 10, 2025  \nPublished: December 19, 2025  \ndoi: [https://doi.org/10.26439/ing.ind2025.n049.7934](https://doi.org/10.26439/ing.ind2025.n049.7934)  \nABSTRACT. Employee turnover is a fundamental process within organizations, reflecting the number of employees who leave a company within a specified timeframe. High turnover incurs substantial costs, so comprehending its causes and implementing corrective actions is crucial for maintaining acceptable levels of employee retention. This article uses predictive models to analyze employee turnover. The researcher developed and compared two machine learning algorithms (Binary Logistic Regression and Random Forest) and one deep learning algorithm (Artificial Neural Networks), utilizing the IBM dataset available on Kaggle. The article is structured in five parts: an introduction to the problem, methodological development, analysis of results, discussion, and conclusion. The findings indicate that neural networks are more efficient at prediction. Ultimately, the use of predictive models can help companies anticipate turnover, optimize selection processes, and promote more ethical and proactive human resource management.  \nKEYWORDS: machine learning / neural networks / labor turnover / forecasting / ensemble learning / logistic regression analysis  \nEste estudio no fue financiado por ninguna entidad.  \n* Corresponding author.  \nE-mail address: [sagret10@usp.br](sagret10@usp.br), [sagret10@gmail.com](sagret10@gmail.com)  \nEste es un artículo de acceso abierto, distribuido bajo los términos de la licencia Creative Commons Attribution 4.0 International (CC BY 4 .0) .  \nIngeniería Industrial n. ° 49, diciembre 2025, ISSN (en línea) 2523-6326, pp. 214-238  \nPredicting Employee Turnover Using Applied Machine Learning  \nAPRENDIZAJEAUTOMÁTICO APLICADO PARA PREDECIR LA ROTACIÓN DE EMPLEADOS EN UNA EMPRESA  \nRESUMEN. 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 planificaracciones correctivas es esencial para mantener la rotación de personal en niveles aceptables. Este artículo analiza la rotación en organizaciones mediante modelos predictivos. Sedesarrollaron y compararon dos algoritmos de aprendizaje automático (regresión logísticabinaria 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 seleccióny fomentar una gestión de recursos humanos más ética y proactiva.  \nPALABRAS CLAVE: aprendizaje automático / redes neuronales / rotación de personal / pronósticos / aprendizaje conjunto / análisis de regresión logística  \nIngeniería Industrial n.o 49, diciembre 2025 215  \nM. Albarracin  \nINTRODUCTION  \nIn recent years, researchers have applied Machine Learning (ML) across various fields, including the assessment of real estate prices (Albarracin & Souza, 2021), enhancement of security and privacy protocols (Wassan et al., 2022), detection of phishing websites (Almomani et al., 2022), identification of malware detection in IoT devices (Gaurav et al., 2023), forecasting sales for companies (Soltaninejad et al., 2024), and evaluation of credit scoring (El Maanaoui et al., 2024), among many others a","cbCaimCv3syIagLp","https://ap.wps.com/l/cbCaimCv3syIagLp","pdf",413642,1,25,"English","en",105,"# Introduction\n## Employee turnover challenge and costs\n## Types of employee turnover\n# Methodology\n## Predictive modeling approach\n## Algorithms compared\n# Results and Discussion\n## Model performance\n## Practical implications for HR management\n# Conclusion","[{\"question\":\"What problem does the document address?\",\"answer\":\"It addresses employee turnover as a major organizational challenge that affects costs, productivity, and long-term success.\"},{\"question\":\"Which algorithms are developed and compared?\",\"answer\":\"The study compares binary logistic regression and random forest, and it also uses an artificial neural network as the deep learning model.\"},{\"question\":\"What data source is used for the modeling?\",\"answer\":\"The models are trained and evaluated using the IBM dataset available on Kaggle.\"}]","Predicting Employee Turnover Using Applied Machine Learning | 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