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 s...
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| Format: | Article |
| Language: | Russian |
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State University of Management
2024-09-01
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| Series: | Управление |
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| Online Access: | https://upravlenie.guu.ru/jour/article/view/732 |
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| author | A. V. Kuznetsova L. R. Borisova V. M. Khadartsev |
| author_facet | A. V. Kuznetsova L. R. Borisova V. M. Khadartsev |
| author_sort | A. V. Kuznetsova |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8c2036ae88f848c591e418e52f89aaa5 |
| institution | Kabale University |
| issn | 2309-3633 2713-1645 |
| language | Russian |
| publishDate | 2024-09-01 |
| publisher | State University of Management |
| record_format | Article |
| series | Управление |
| spelling | doaj-art-8c2036ae88f848c591e418e52f89aaa52025-02-04T09:04:41ZrusState University of ManagementУправление2309-36332713-16452024-09-0112310.26425/2309-3633-2024-12-3-58-73464Application of multiparametric methods of data science for the classification of Russian subjects on the basis of subsidisationA. V. Kuznetsova0L. R. Borisova1V. M. Khadartsev2N.M. Emanuel Institute of Biochemical Physics of the Russian Academy of SciencesFinancial University under the Government of the Russian FederationState enterprise “TSNIITEITYAZHMASH”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.https://upravlenie.guu.ru/jour/article/view/732russian regionssubsidiesregional budgetinvestmentsmanagement of budgetary resourceseconomy of subsidised regionsdemographymachine learningstatistical methodsdata science |
| spellingShingle | A. V. Kuznetsova L. R. Borisova V. M. Khadartsev Application of multiparametric methods of data science for the classification of Russian subjects on the basis of subsidisation Управление russian regions subsidies regional budget investments management of budgetary resources economy of subsidised regions demography machine learning statistical methods data science |
| title | Application of multiparametric methods of data science for the classification of Russian subjects on the basis of subsidisation |
| title_full | Application of multiparametric methods of data science for the classification of Russian subjects on the basis of subsidisation |
| title_fullStr | Application of multiparametric methods of data science for the classification of Russian subjects on the basis of subsidisation |
| title_full_unstemmed | Application of multiparametric methods of data science for the classification of Russian subjects on the basis of subsidisation |
| title_short | Application of multiparametric methods of data science for the classification of Russian subjects on the basis of subsidisation |
| title_sort | application of multiparametric methods of data science for the classification of russian subjects on the basis of subsidisation |
| topic | russian regions subsidies regional budget investments management of budgetary resources economy of subsidised regions demography machine learning statistical methods data science |
| url | https://upravlenie.guu.ru/jour/article/view/732 |
| work_keys_str_mv | AT avkuznetsova applicationofmultiparametricmethodsofdatasciencefortheclassificationofrussiansubjectsonthebasisofsubsidisation AT lrborisova applicationofmultiparametricmethodsofdatasciencefortheclassificationofrussiansubjectsonthebasisofsubsidisation AT vmkhadartsev applicationofmultiparametricmethodsofdatasciencefortheclassificationofrussiansubjectsonthebasisofsubsidisation |