Abstract. The article is devoted to the study of the application of machine learning algorithms for the classification of regions of Kazakhstan using demographic data for 2024. The study considers the Decision Tree, Random Forest and k-Nearest Neighbors (KNN) algorithms. They demonstrate high efficiency in solving this problem. Data preprocessing included the calculation of the urban population ratio (urban_ratio), which was used to construct the binary target variable. All three evaluated algorithms demonstrated high performance under the reported experimental settings. The observed differences between the models were limited, with Decision Tree, Random Forest, and KNN showing comparably strong results across the tested partitions. The results indicate the potential of machine learning methods for territorial classification based on demographic indicators; however, the findings should be interpreted with regard to the selected feature set and target construction. In addition, K-means clustering and principal component analysis identified three distinct demographic profiles among the districts, providing a clearer understanding of regional differences.

Keywords: machine learning, classification, demographics, urbanization, Principal Component Analysis, clustering.