Application of multiparametric methods of data science for the classification of Russian subjects on the basis of subsidisation

The relevance of the study is justified by the importance of monitoring and forecasting the subsidisation of the Russian regions in order to identify the main criteria for classifying subjects on the basis of subsidisation. In a brief review of the literature, mathematical models used to model the subsidisation of the Russian regions are considered. They have mainly fixed socio-economic indicators that need to be given attention while applying, and also regression models are used, but mathematically sound recommendations for the withdrawal of regions from clusters of subsidisation are not provided. The paper analyses the socio-economic and demographic indicators of the Russian regions applying methods that identify patterns in a multiparametric dataset. The methods of traditional statistical analysis and machine learning, including the author’s ones, are used. Statistically significant patterns have been identified, reflecting the relationship of subsidisation with such indicators as investments in fixed capital, fixed assets, average per capita income and average size of assigned pensions, unemployment rate, etc. The performed logical and statistical analysis strongly supports the use of machine learning (Data Science) methods in identifying statistically significant relationships between various indicators characterising the development of the regions of the Russian Federation.