Utilize the Machine Learning Models to Forecast Home Values.:Seattle U.S.

Abstract

Understanding housing price predictions assists the government in better adjusting and formulating relevant policies to promote economic stability and sustainable social development. In this study, researchers collected a large amount of data on the Seattle real estate market, including housing characteristics (such as area, number of bedrooms, number of bathrooms), geographical location (such as neighborhood, nearby facilities), and historical price data. This paper used this data to train and test linear regression, and KNN prediction models to forecast future housing price trends. The linear regression model, on the other hand, models the linear relationship between a single independent variable and the dependent variable, predicting housing prices by fitting an optimal line. The KNN prediction model, based on the nearest neighbor algorithm, predicts by searching for the K nearest neighbor samples closest to the target sample. Researchers will compare the accuracy and effectiveness of these three methods in predicting Seattle housing prices to determine which method is most suitable for housing price forecasting. Through this analysis, they aim to provide a reliable housing price prediction model for local residents to help them make wiser real estate decisions.