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.