التنبؤ بالعوائد السكنية لدعم اتخاذ القرار باستخدام خوارزميات شجرة القرار

Abstract

This study aims to analyze residential returns in the Umbadda locality using data mining techniques, with a focus on applying decision tree algorithms to extract patterns and identify the factors influencing return values. The (WEKA) software was used to apply the (Random Tree) algorithm, and its performance was analyzed. The results showed that the (Random Tree) algorithm achieved the best performance in terms of accuracy and predictive capability. The study found that the key factors affecting residential returns include the availability of water, electricity, and other services. The study recommends the use of decision tree models as a supportive tool in investment decision-making and urban planning in the area, due to their ability to interpret relationships between variables and accurately predict returns.