The article addresses the challenge of enhancing labor productivity in companies by analyzing objective data on economic, demographic, and social factors alongside subjective information about employees' health quality. It introduces a technology for labor productivity management that involves phased data processing and modeling relationships between quantitative and qualitative data, aimed at aiding decision-making for planning productivity growth trajectories. This technology leverages statistical analysis and machine learning to support managerial decisions on health-preserving strategies to boost productivity. It is demonstrated that the k-means method is more suitable and reliable for clustering employees into homogeneous groups compared to Kohonen neural networks. Additionally, various classification methods for predicting new employees' labor productivity profiles were tested, revealing that support vector machines offer higher accuracy when working with numerous qualitative variables such as gender, education, and health self-assessment.

data science; statistical data processing; predictive analytics; machine learning; classification; clustering; labor productivity; health management; health-saving strategies; electric power industry

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