Download PDFOpen PDF in browser

House Price Prediction System Using Machine Learning and Data Science

EasyChair Preprint no. 10283

5 pagesDate: May 29, 2023


Real estate is the area with the least transparency 
in our economy. Housing costs fluctuate every day and are 
sometimes artificially exaggerated. Using real factors to 
forecast real estate values is the main goal of our research 
project. In this case, we strive to base our reviews on all the 
important aspects that go into pricing. We use several 
different regression algorithms in this strategy. Rather than 
solely relying on one technique to determine our results, we 
instead use the weighted averages of several different 
techniques that produce the most accurate results. The 
results showed that this strategy provides the lowest error 
and maximum accuracy compared to using separate 
methods. We also recommend using Google Maps to get 
accurate real-world reviews by using up-to-date local data.
Homes in Bengaluru most upscale and cheap 
neighborhoods have very different prices, as is obvious.

Keyphrases: Bengaluru Dataset, Forest Regression, hedonic price model, House Price Prediction System, SVR

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Satyam Sobhraj and S.P.S Chauhan and Anas Ghani Ur Rahman},
  title = {House Price Prediction System Using Machine  Learning and Data Science},
  howpublished = {EasyChair Preprint no. 10283},

  year = {EasyChair, 2023}}
Download PDFOpen PDF in browser