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This project analyses apartment prices in Warsaw using spatial data analysis and machine learning methods. The dataset was cleaned, transformed into a spatial object, and enriched with OpenStreetMap accessibility variables such as distance to subway stations, tram infrastructure, and parks. The analysis includes exploratory maps, spatial clustering, a Spatial Random Forest model, variable-importance interpretation, residual analysis, and kriging interpolation. The results show that apartment prices in Warsaw are strongly influenced by location, accessibility, building characteristics, and spatial-market segmentation.
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