Abstract
Machine Learning (ML) has profoundly impacted various domains, including speech recognition, healthcare, and automotive safety. Acknowledging its pervasive influence, our project aims to harness ML's capabilities for housing price prediction. In the volatile real estate market, prospective buyers strive to make informed decisions within budget constraints, often hindered by the absence of reliable future market trend forecasts. Our project's primary goal is to provide accurate house price predictions, mitigating potential financial losses. To achieve this, we are developing a housing cost prediction model employing ML algorithms such as Linear Regression, Decision Tree Regression, KMeans Regression, and Random Forest Regression. This model empowers individuals to invest in real estate without intermediaries. Our research highlights Random Forest Regression as the most accurate model, offering a promising avenue for confident real estate investment.
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: International Journal of Engineering Applied Sciences and Technology
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.
