Subject. The article deals with substantiating the possibility of using mathematical and computer modeling methods to create fundamentally new tools enabling to solve problems of finding optimal ways of demographic and socio-economic development of Russia, given the global challenges of our time. Objectives. The study aims at modeling and forecasting the economic development in different countries of the world, depending on indicators of living standards. Methods. I created a database of socio-economic indicators for 50 countries with the largest population in 2022, which included four interrelated blocks: demographic processes, economic development, life quality and the environment, and social stress (social tension). Information was processed using the high-level Python programming language, including the use of specialized libraries for data analysis and visualization (Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn). Results. I developed new predictive analytical models based on machine learning algorithms. They make it possible to assess economic development in different countries worldwide, depending on indicators of life quality of the population. Conclusions. The interconnection of all social processes necessitates a 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 decent standards of living for its citizens, in compliance with all social guarantees. The findings can be used to evaluate and predict various indicators of Russia's socio-economic development, and develop strategic documents in the field of medical and demographic policy.
Keywords: demography, socio-economic development, data analysis, modeling, quality of life
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