Abstract
With the increasing complexity of the urban real estate market, accurate prediction of housing prices has become an important task. One of the key applications of machine learning is how to raise and accurately estimate costs. Various factors will affect the price of houses. Most of the current frameworks are all using as detailed features as possible to increase their predicting accuracy. But in real-life conditions, many non-local clients also want to have a clear prediction of the house price. These consumers are not from the area; thus they are unaware of the house's surroundings, including nearby amenities. What they know about the house is only its housing characteristics. The objective of the paper is to help these clients put their resources into a bequest properly. The paper collected a wide range of Shanghai real estate data about housing characteristics as characteristics. By harnessing the ensemble learning capabilities of random forests, the paper aims to capture complex relationships and non-linear effects between features, thereby improving prediction accuracy.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Advances in Economics, Management and Political Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
