Abstract
The study focuses on the development and evaluation of predictive models for forecasting labour demand in the metallurgical industry of the East Kazakhstan Region, with particular emphasis on the impact of production volume and labour productivity. The methodological framework combines classical econometric approaches with modern machine learning techniques, which makes it possible to capture nonlinear dependencies and more accurately assess labour market dynamics. The research is based on regional statistical data for the period 2015–2023. Several modeling approaches were tested, including linear regression, a parametric specification, and a hybrid machine learning model that integrates decision trees with local linear regression. Model performance was validated using the Mean Absolute Error (MAE), followed by forecasting labour demand for 2024–2028. Results demonstrate that the hybrid model outperforms the alternatives by achieving the lowest prediction error and producing the most plausible projection of moderate employment growth. The parametric model, although less precise, offers a high level of interpretability and is well suited for strategic analysis, while the linear regression model has limited effectiveness under nonlinear conditions. The practical value of the research lies in the possibility of embedding the developed models into decision support systems for government bodies and industrial enterprises, enabling early assessment of the impact of technological changes and production dynamics on employment. The outcomes may contribute to shaping balanced human resource policies, aligning educational programs with labour market needs, and conducting scenario analyses. Furthermore, the findings establish a foundation for extending the methodology to other industries and incorporating additional variables related to digitalization and innovation activity.