МОДЕЛЮВАННЯ ЕКОНОМІЧНОГО РОСТУ МЕТОДАМИ ІНТЕЛЕКТУАЛЬНОГО АНАЛІЗУ ДАНИХ

Abstract

The article substantiates the expediency of using intelligent data analysis in the study and forecasting of economic growth. Intelligent data analysis is a powerful tool that can help researchers and politicians understanding economic growth more deeply and making reasonable decisions by forming state policy. In addition, intelligent data analysis can be used to determine the potential areas of economic growth and investment opportunities. Attention is focused on using CART (predictive modeling tool, which has proved its high efficiency in understanding the non-obvious relationships between several variable statistical importance) and associative rules (intelligent data analysis, which can work with big data arrays and generate clear and effective conclusions, supported by statistical significance indicators). It is concluded that the methods of intelligent data analysis, such as CART and associative rules, are crucial for understanding the complex patterns that exist in large arrays of economic data. The advantages of these methods make them appropriate and effective tools for modeling and understanding of levers and tools of economic growth. Based on the analysis of data for 119 countries of the world for 2021 (the period of post-pandemic recovery), it was established that in order to promote economic growth, countries with low levels of export and import need to focus on the development of national industry. In addition, developing an economic policy aimed at promoting the recovery of the economy, it is necessary to increase the democratic status of the country. In general, countries that are less dependent on exports and imports tend to have more stable and self-sufficient economies. This can be explained by a number of factors, such as a strong domestic market, the development of national industry and less vulnerability to global economic shocks. In addition, a low level of imports may indicate that a country is able to produce and consume a significant portion of its own goods, which may contribute to further economic growth and development. It is important to note that these relationships are based on highlevel statistical correlations.