Housing Rental Information Management and Prediction System Based on CatBoost Algorithm - a Case Study of Halifax Region

Shuangrun Shao, Bingxi Zhao, Xiangen Cui, Yihong Dai, Beining Bao

Abstract: Considering the growing demands for efficient information retrieval from house rental markets by non-professional users, we develop a comprehensive framework for house information management, visualization, and prediction based on the CatBoost algorithm. We aim to promote the digital transformation of house rental market management and drive innovation in management methods. The conception and ideas of the Housing Rental Information Management and Prediction System are initially proposed, with subsequent application in Halifax, Canada. Integrating the Tableau server, database, and prediction model, we build a seamless web system to harmonize management, visualization, and prediction functionalities for rental house data. The details and effects of the application of the CatBoost algorithm within this system are emphasized, highlighting its precision, adaptability, and business viability in forecasting the house rental market.

External IDs:dblp:conf/rskt/ShaoZCDB24