Methods of Data Mining in the Study of Economic Development of Countries

The paper presents a demonstration of the analysis of a specific data set using machine learning methods to solve the research problem of finding a link between relative economic indicators (development indices) of the world’s countries according to the World Bank data for 2023 using machine learning methods and the use of structural equations.This approach was applied to the analysis of 205 countries using the weighted syndrome method and 180 countries using oneand twofactor confirmatory analysis. The advantage of using machine learning methods is the ability to use data with gaps, unlike regression models, which are the basis of factor analysis, when data gaps are not acceptable. To use the weighted syndromes method, eight main economic relative indicators of the countries’ development were used. The % GDP growth rate for 2023 was chosen as the grouping indicator. The quality of the model was assessed by the ROC AUK indicator. This indicator is 0.92, which indicates that the selected features really make it possible to divide the countries. Scattering diagrams showing a clear division of countries into two groups according to the analyzed indicators also illustrate the quality of recognition. The use of factor analysis made it possible to build two models (one-factor and two-factor) using not eight indicators, but only four, so that the models measured by the indices of comparative conformity and Tucker-Lewis were statistically significant. However, the loads of the one-factor model (λ) are extremely low (0.258, 0.131), which indicates a weak relationship between the observed variables and the latent factor. The factor practically does not explain the variance of the variables. These results indicate a poor correspondence of the model to the data, especially for the two—factor model, since the value of the TLI index is 0.474, which also cannot be a satisfactory result with an adequate threshold of >0.90–0.95). A meaningful economic interpretation of the latent factors obtained is not provided due to the poor correspondence of the results of factor analysis and the inability to compare machine learning and factor analysis methods with simultaneous analysis, since there was less data suitable for factor analysis than when using machine learning methods that demonstrated their adequacy.

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