Machine Learning in the National Economy
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| Názov: | Machine Learning in the National Economy |
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| Autori: | Azamjon A. Usmonov |
| Zdroj: | Открытое образование (Москва), Vol 29, Iss 3, Pp 4-10 (2025) |
| Informácie o vydavateľovi: | Plekhanov Russian University of Economics (PRUE), 2025. |
| Rok vydania: | 2025 |
| Predmety: | education, machine learning, LC8-6691, data analysis, forecasting, regression, intelligent algorithms, economics, neural networks, risks, financial technologies, Special aspects of education |
| Popis: | 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. |
| Druh dokumentu: | Article |
| ISSN: | 2079-5939 1818-4243 |
| DOI: | 10.21686/1818-4243-2025-3-4-10 |
| Prístupová URL adresa: | https://doaj.org/article/b7d665a83ec2454aaa2abd3586e0a7ee |
| Rights: | URL: https://openedu.rea.ru/jour/about/editorialPolicies#openAccessPolicy |
| Prístupové číslo: | edsair.doi.dedup.....41a97e00dce586e6c1514660c68f67ed |
| Databáza: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstrakt: | 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. |
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| ISSN: | 20795939 18184243 |
| DOI: | 10.21686/1818-4243-2025-3-4-10 |