Subject. The article discusses the use of mathematical and computer modeling methods to create fundamentally new tools for solving the problems of finding optimal ways of demographic and socio-economic development of Russian regions, given their specifics. Objectives. The study aims at modeling and forecasting the demographic and socio-economic development of Russian regions, using machine learning algorithms, including methods of cluster and regression analysis. Methods. I created a database of socio-economic indicators of Russian regions for 2022, which included three interrelated blocks: demographic processes, economic development, quality of life and the environment. The information was processed using the high-level Python programming language, including specialized libraries for data analysis and visualization, like Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn. Results. I developed new predicative analytical models based on machine learning algorithms. They enable to evaluate the main demographic and socio-economic indicators of Russian regions. Conclusions. The interconnection of all social processes necessitates the search for a point of optimal (balanced) equilibrium of the economy, since only in this case the State will be able to ensure the required rates of economic growth and a decent standard of living for its citizens in compliance with all social guarantees. The designed algorithms can help analyze and predict various indicators of demographic and socio-economic development of territories, and create regional policy and strategic documents.
Keywords: region, socio-economic development, regional policy, data analysis, mathematical modeling
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