Urban and Rural Areas Studies Difference Using Machine Learning

This research contributes a machine learning-based approach for identifying and analyzing the differences between urban and rural areas using socioeconomic and demographic features. The study compares multiple machine learning models and demonstrates that Random Forest can effectively classify urban and rural areas, achieving an accuracy of 94.20%. The findings provide data-driven insights that can support better planning, resource allocation and strategies for reducing urban–rural disparities.