Application of machine learning methods to assess the socio-economic development of Russian regions

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.