{"id":4413,"date":"2025-03-07T07:30:36","date_gmt":"2025-03-07T07:30:36","guid":{"rendered":"https:\/\/interfinance.tsue.uz\/?p=4413"},"modified":"2025-03-07T07:31:20","modified_gmt":"2025-03-07T07:31:20","slug":"forecasting-the-economic-competitiveness-of-the-samarkand-region-based-on-big-data-technology","status":"publish","type":"post","link":"https:\/\/interfinance.tsue.uz\/?p=4413","title":{"rendered":"FORECASTING THE ECONOMIC COMPETITIVENESS OF THE SAMARKAND REGION BASED ON BIG DATA TECHNOLOGY"},"content":{"rendered":"

Shuhrat Kamalovich Kamalov
\nTashkent State University of Economics
\nAssociate Professor, Department of Artificial Intelligence
\nEmail: kamalov.shukhrat@gmail.com
\nTashkent, Uzbekistan
\nORCID: 0000-0003-4937-0814<\/p>\n

Diyora Bo\u2018riboyevna Absalamova
\nTashkent State University of Economics
\nAssistant, Department of Artificial Intelligence
\nEmail: absalamovadiyora@gmail.com
\nTashkent, Uzbekistan<\/p>\n

Go\u2018zal Bo\u2018riboyevna Absalamova
\nTashkent State University of Economics
\nSenior Lecturer, Department of Artificial Intelligence
\nEmail: gozalabdusalomova1996@gmail.com
\nTashkent, Uzbekistan<\/p>\n

JELClassification: R11 O24<\/p>\n

Abstract: This article is dedicated to analyzing the economic competitiveness of the Samarkand region using Big Data technologies and developing development forecasts for the years 2025-2030. The study processes large volumes of data, including the region’s Gross Regional Product (GRP), tourism sector, agriculture, industrial production, and infrastructure. Machine learning algorithms such as Decision Trees and Long Short-Term Memory (LSTM) are applied. The results identify the region’s economic growth potential, competitive advantages, and weaknesses, providing recommendations for improving regional development policies.
\nKeywords: Big Data, economic competitiveness, forecasting, Samarkand region, tourism, decision tree, LSTM, ARIMA, Apache Spark<\/p>\n\n\n