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This paper examines the applicability of machine learning techniques to spatial data modeling and identification of urban patterns using a limited set of demographic and geographic indicators. Drawing on sector-level data for Bucharest from the National Institute of Statistics of Romania, the analysis uses surface area, population, population density, and distance to the city center computed as Euclidean distance between sector centroids and a defined central reference point as variables. The dataset is processed ...