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Liburkina L. M.

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Modeling the Development Status of the Construction Sector in EU Countries Using Classification Trees
Oriekhova T. Y., Chagovets L. O.

Oriekhova, Tetiana Ye., and Chagovets, Liubov O. (2026) “Modeling the Development Status of the Construction Sector in EU Countries Using Classification Trees.” Business Inform 1:125–134.
https://doi.org/10.32983/2222-4459-2026-1-125-134

Section: Economic and Mathematical Modeling

Article is written in Ukrainian
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UDC 004.94

Abstract:
The article discusses the issue of modeling the development status of the construction sector in European Union countries using classification trees. Based on previous research on construction dynamics trends for 2015–2024, it was found that the development of the construction sector within the European Union is heterogeneous, largely depends on the structure of activity segments, is influenced by a number of factors and market conditions under modern economic instability, and therefore is in a state of stagnation. One of the key directions identified to overcome this is the development of innovations and digital technologies. In this context, the introduction of modern methods for modeling the development status of the construction sector in EU countries using machine learning methods in the process of developing economic strategies becomes relevant. The article describes the stages of building a model using the classification tree method, specifically the CART algorithm, which is one of the most widely used methods in the field of data mining and an efficient tool for constructing analytical models capable of generating efficient decisions based on input data, including determining the state (high, sufficient, or medium) of the construction sector in EU countries. As a result of the study, rules for recognizing the state of the construction sector in EU countries were obtained, assigning countries to specific clusters, which will help predict the future state of the construction sector. The high quality of the constructed classification model confirms the possibility of dividing EU countries, based on the built tree, into three clusters with high, sufficient, or medium levels of development in the construction sector. The obtained results indicate that all analyzed EU countries were correctly classified according to the established tree rules: the distribution demonstrated a high quality of object recognition based on the constructed model.

Keywords: model, classification and regression trees, decision trees, Data Science models, CART algorithm, sustainable development, EU countries, construction sector.

Fig.: 8. Formulae: 2. Bibl.: 25.

Oriekhova Tetiana Ye. – Masters Student, Simon Kuznets Kharkiv National University of Economics (9a Nauky Ave., Kharkiv, 61166, Ukraine)
Email: [email protected]
Chagovets Liubov O. – Candidate of Sciences (Economics), Associate Professor, Associate Professor, Department of Economic Cybernetics and Systems Analysis, Simon Kuznets Kharkiv National University of Economics (9a Nauky Ave., Kharkiv, 61166, Ukraine)
Email: [email protected]

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