Housing Price Intelligence Using Decision Trees

A data-driven framework was designed to convert real estate datasets into reliable housing price insights using Decision Tree learning techniques. The implementation involved structuring multi-source data, identifying influential market variables, and training a transparent predictive model capable of explaining price behavior across different locations. By emphasizing interpretability and practical application, the solution equips decision-makers with clarity around value drivers, enabling smarter property evaluation, investment planning, and real estate market assessment.