APPLICATION OF DATA ANALYSIS TOOLS IN PYTHON TO ASSESS THE STANDARD OF LIVING OF THE POPULATION: THE CASE OF KAZAKHSTAN

The article discusses the application of Python data analysis tools to assess the standard of living of the Kazakhstani population based on statistical data for the period 1991–2021. The main focus is using modern methods and technologies, such as linear regression, correlation analysis, and time series processing. The study uses Python libraries (Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn) and the PostgreSQL relational database to store and process large amounts of information. The analysis revealed key trends, including a steady increase in per capita income and a decrease in unemployment. The constructed regression models showed that population incomes are closely related to the subsistence minimum and minimum wage. Forecasting for the coming years indicates a continued increase in living standards while maintaining current economic trends. The results confirm that information technology can improve calculation accuracy, optimize data analysis, and present the results in an easy-to-interpret form. This work can be helpful for government agencies, analysts, and researchers involved in assessing socio-economic indicators. This article highlights the importance of information technologies in data analysis and forecasting and demonstrates their potential to solve urgent social policy problems. Keywords: standard of living, data analysis, Python, regression analysis, forecasting, correlation analysis, time series.