Machine Learning in the National Economy

This article examines the application of machine learning in the national economy. It describes the main concepts and methods of machine learning, including supervised, unsupervised, and reinforcement learning. Key areas of using this technology in the economy are analyzed, such as market trend forecasting, financial risk management, and economic data analysis. Special attention is given to the advantages of machine learning, including improved decision-making efficiency, process automation, and handling large volumes of data. At the same time, the challenges of implementing this technology are considered, such as the need for high-quality data, legal and ethical aspects, and the shortage of qualified specialists. The paper provides recommendations for developing machine learning infrastructure, investing in research, and training professionals, which can contribute to economic growth and increase the country’s competitiveness.

Materials and methods: Various methods and approaches to examine machine learning in the national economy were used in this paper. The main methods include an analysis of scientific literature, statistical data analysis, modeling using machine learning algorithms, and practical implementation of economic models with programming languages such as Python and machine learning libraries.

To analyze economic data, methods such as linear regression, decision trees, and neural networks were selected, as they effectively predict changes in key macroeconomic indexes such as GDP, inflation, exchange rates, and unemployment levels. Pandas, NumPy, Scikitlearn, and Matplotlib libraries were used as tools to process, analyze, and visualize the data. The research is based on data from official statistical agencies and financial institutions, including historical data on macroeconomic indexes, market trends, and financial risks. Methods of cleaning, normalization, and data transformation were used for data processing to improve model accuracy. The practical part of the study included the development of machine learning algorithms for predicting economic indexes. A linear regression model was used to forecast GDP growth, while more complex models, such as random forests and gradient boosting, were applied to analyze more intricate economic relationships. Thus, the use of modern machine learning methods in economics allows us to obtain accurate forecasts, identify patterns in economic data, and make strategic decisions based on objective analysis.

Conclusion. The application of machine learning methods in the national economy offers significant potential for improving economic analysis and decision-making. Through the use of advanced algorithms and tools, such as linear regression, decision trees, and neural networks, it is possible to effectively model and predict key macroeconomic indexes, including GDP growth, inflation, and financial risks. These methods allow for a more detailed and accurate understanding of economic trends and relationships, leading to better strategic decisions by governments, businesses, and financial institutions. By using modern technologies such as Python, Pandas, NumPy, and Scikit-learn, the research demonstrated the ability to process and analyze large volumes of economic data with high precision. Machine learning provides a valuable approach for predicting economic indexes, managing risks, and optimizing resource allocation. However, the effectiveness of these models depends on the quality of the data used, and there are challenges related to data completeness, model interpretability, and computational resources. In conclusion, machine learning is a powerful tool for enhancing economic forecasting and risk management. For its successful integration into national economic systems, countries must invest in research, improve digital infrastructure, and develop educational programs to prepare skilled professionals. The proper implementation of machine learning can contribute to rapid economic growth, more efficient decision-making, and a stronger competitive position in the global economy.

1. Knyaz’kov V.A. Mashinnoye obucheniye: Teoriya i praktika = Machine Learning: Theory and Practice. Moscow: Dialectics; 2020. (In Russ.)

2. Gusev S.V. Metody mashinnogo obucheniya v ekonomike = Machine Learning Methods in Economics. Saint Petersburg: Piter; 2021. (In Russ.)

3. Timofeyev V.N. Vvedeniye v mashinnoye obucheniye i yego prilozheniya v ekonomike = Introduction to Machine Learning and its Applications in Economics. Moscow: KNORUS; 2019. (In Russ.)

4. Lebedev A.I. Iskusstvennyy intellekt i mashinnoye obucheniye v ekonomike = Artificial Intelligence and Machine Learning in Economics. Moscow: Science; 2018. (In Russ.)

5. Petrov D.V. Modeli mashinnogo obucheniya dlya analiza dannykh v ekonomike = Machine Learning Models for Data Analysis in Economics. Kazan: Kazan University; 2020. (In Russ.)

6. Sergeyeva N.A. Analiz dannykh i mashinnoye obucheniye v ekonomike i biznese = Data Analysis and Machine Learning in Economics and Business. Moscow: Yurayt; 2019. (In Russ.)

7. Shevchenko YU.I. Ispol’zovaniye neyronnykh setey v ekonomicheskikh issledovaniyakh = Using Neural Networks in Economic Research. Rostovon-Don: Phoenix; 2021. (In Russ.)

8. Bogdanov I.V. Prognozirovaniye rynochnykh trendov s pomoshch’yu mashinnogo obucheniya = Forecasting Market Trends Using Machine Learning. Moscow: Alfa-Bank; 2020. (In Russ.)

9. Litvinova I.S. Upravleniye riskami s ispol’zovaniyem mashinnogo obucheniya = Risk Management Using Machine Learning. Ekaterinburg: UrFU; 2022. (In Russ.)

10. Kuznetsova T.A. Mashinnoye obucheniye v ekonomicheskom analize = Machine Learning in Economic Analysis. Moscow: Finance and Statistics; 2021. (In Russ.)

11. Gamidov M.SH. Modeli analiza dannykh i mashinnoye obucheniye v ekonomike = Data Analysis Models and Machine Learning in Economics. Dushanbe: Tajik National University; 2021.

12. Radzhabov R.M. Informatsionnyye tekhnologii i mashinnoye obucheniye v ekonomicheskom razvitii = Information Technology and Machine Learning in Economic Development. Dushanbe: Donish; 2020.

13. Dzhabborov F.Z. Mashinnoye obucheniye i yego ispol’zovaniye v ekonomicheskom prognozirovanii v Tadzhikistane = Machine Learning and Its Use in Economic Forecasting in Tajikistan. Dushanbe: Academy of Sciences of the Republic of Tajikistan; 2022.

14. Kurbanov A.M. Metody analiza dannykh v ekonomike = Methods of data analysis in economics. Dushanbe: Donish; 2019.

15. Sharifov R.M. Algoritmy mashinnogo obucheniya i ikh primeneniye v finansakh = Machine learning algorithms and their application in finance. Dushanbe: Donish; 2020.

16. Gasanov S.T. Ispol’zovaniye iskusstvennykh neyronov v ekonomicheskom analize = Using artificial neurons in economic analysis. Dushanbe: Library; 2021.

17. Boboyev A.I. Mashinnoye obucheniye i analiz dannykh v ekonomicheskoy praktike = Machine learning and data analysis in economic practice. Dushanbe: Donish; 2022.

18. Sodikov A.M. Tekhnologii ispol’zovaniya analiza dannykh v ekonomike = Technologies for using data analysis in economics. Dushanbe: Jakhon; 2021.

19. Mukhtorov KH.I. Ispol’zovaniye prakticheskikh modeley v mashinnom obuchenii = Using practical models in machine learning. Dushanbe: Sado; 2019.

20. Fayzulloyev A.M. Rol’ mashinnogo obucheniya v otsenke riskov v ekonomike = The Role of Machine Learning in Risk Assessment in the Economy. Dushanbe: Istiklol; 2020.

21. Abdulloyev M.T. Rukovodstvo po ispol’zovaniyu modeley analiza dannykh i mashinnogo obucheniya = Guide to Using Data Analysis and Machine Learning Models. Dushanbe: Manzara; 2021.

22. Dzhurayev M.N. Tsifrovaya analitika i teoriya mashinnogo obucheniya = Digital Analytics and Machine Learning Theory. Dushanbe: Literature; 2022.

23. Kholov R.A. Obuchayushchiyesya mashiny v analize ekonomicheskikh potokov v Tadzhikistane = Learning Machines in the Analysis of Economic Flows in Tajikistan. Dushanbe: Kulol; 2020.

24. Niyozov I.K. Sposoby analiza slozhnykh dannykh v ekonomike i biznese = Methods for Analyzing Complex Data in Economics and Business. Dushanbe: Sharq; 2019.