Abstract: The article is devoted to the quantitative assessment of the tax sensitivity of particular sectors of the economy. The subject of the study is a system of indicators characterizing the sectors of the economy at the macro and micro levels. The purpose of the study is to analyze the dependence of economic sectors on the tax burden. The paper focuses on the importance of taxes for the economic growth of the country's economy and sectoral differences caused by the level of technological development, regional conditions, as well as government regulation. A methodological approach developed by the author allowed to assess the tax sensitivity of individual industries, tested on the most important sectors for the Russian budget. The author analyzes the tax burden and revenues to the consolidated budget, identifies the industries that have the greatest impact on the budget and builds models for them that allow quantifying tax sensitivity across a set of organizations. Modern machine learning methods such as decision tree, gradient boosting, nearest neighbor method, as well as the classical linear regression method were used as analysis methods. The scientific novelty of the study lies in the possibility of using the developed methodological approach to assess differences in the tax sensitivity of individual sectors of the economy for making managerial decisions differentially for each individual sector. As a result of the conducted research, the high tax sensitivity of the extractive industry, manufacturing and construction industries has been revealed. The average level of tax sensitivity is typical for wholesale and retail trade; repair of motor vehicles and motorcycles. Low tax sensitivity was found in financial and insurance activities. Based on the results of the assessment, recommendations are proposed for the introduction of tax instruments into the activities of individual industries and a conclusion is made about the need to specialize tax incentive mechanisms by economic sectors in order to increase economic growth and optimize tax revenues.

Abstract: The article analyzes the data of a set of agricultural enterprises and builds machine learning models to predict the tax burden. The subject of this study is a system of statistical indicators of agricultural enterprises that characterize the level of tax burden. The purpose of the study is to predict the tax burden using machine learning methods. The introduction of modern artificial intelligence tools is an integral and inevitable process in all spheres, including in the tax environment. Machine learning methods were used to build models: regression analysis, decision tree, random forest, gradient boosting. Models of forecasting the tax burden depending on a set of factors were built. The high quality of tax burden forecasting models will make it possible to more accurately assess the financial condition of enterprises, calculate profitability, predict profitability and make informed investment management decisions. As a result of forecasting the tax burden, the gradient boosting machine learning model turned out to have the best quality. In general, the model allows you to predict the tax burden better than traditional econometric models and make high-quality forecasts. The introduction of modern forecasting tools based on artificial intelligence methods will allow obtaining highly accurate forecasts with minimal time, which will increase the efficiency of enterprises and the level of production.