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.

Revisión exhaustiva y multidimensional de la industria azucarera, abarcando desde el análisis histórico de complejos agroindustriales en Argentina hasta la competitividad global, innovación biotecnológica y desafíos en salud pública.

Poster Presentation (Article Summary) for Advanced Data Computing at Lawrence University

all steps of my project, including revisions.