This study aims to develop a data-based design preference prediction model that performs design preference prediction based on design preference data of a specific user or group of users. The developed prediction model can function as a design guide for expanding client engagement, facilitating comprehensive information sharing, and inducing efficient communication during the architectural design phase. By conducting a preference survey, this study first addressed the challenges of constructing a reliable design preference dataset. Then, within the configured dataset, the study utilized statistical and machine learning algorithms to create design preference prediction models, explore the significance of each design parameters that have a significant impact on the final building design preferences, and infer the accuracy of predicting users’ preference data.
