Abstract
With the increase in the standard, houses have become an indispensable part of people’s lives. However, purchasing houses needs to consider the prices. Therefore, this paper uses machine learning to predict US house prices. Based on the random forest and linear regression method used in this research. It is found that the use of the former has a good effect on predicting multivariate nonlinear relationships in house prices. After comparing the results of actual prices and predicting prices, residents can select different room structures by contrasting them with experimental types as the numerical results are basically accurate. This research provides theoretical value to the literature on housing price prediction and brings different enlightenment to policymakers, regulators, and investors. As a result, this research plays a key role in maintaining the order of the real estate market and helping people make good choices.
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