CLUSTERING AND CLASSIFICATION OF UZBEKISTAN'S REGIONS BY AGRICULTURAL INDICATORS USING A MACHINE LEARNING APPROACH
DOI:
https://doi.org/10.47390/ts-v4i3y2026N12Keywords:
K-Means, Hierarchical Clustering, clustering, agriculture, Machine Learning, Uzbekistan.Abstract
In this article, the agricultural indicators of 12 regions of the Republic of Uzbekistan and the Republic of Karakalpakstan for the years 2010–2024 were analyzed using machine learning, and the regions — including the Republic of Karakalpakstan — were divided into clusters based on their performance efficiency. The database was obtained from the official website of the National Statistics Committee of the Republic of Uzbekistan at https://stat.uz. A total of 36 agricultural indicators were collected and used for analysis. The K-Means and Hierarchical Clustering machine learning algorithms were applied. Based on the agricultural data, the regions and the Republic of Karakalpakstan were divided into 3 clusters using K-Means and Hierarchical Clustering algorithms
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