Predicting Employee Turnover Using Applied Machine Learning
DOI:
https://doi.org/10.26439/ing.ind2025.n049.7934Keywords:
machine learning, neural networks, labor turnover, forecasting, ensemble learning, logistic regression analysisAbstract
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.
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