Abstract
The real estate prices prediction is a relatively popular data analysis problem. With the gradual increase in the competitiveness of the housing trading platform in recent years, and the basic model of house prices predicted is different from the factual transaction price. Therefore, it is necessary to design more accurate house prices prediction models. This article will adopt different linear regression models and machine learning models. By training these models of training Set, through the accuracy algorithm of Kaggle, the accuracy of each model is obtained. According to the analysis, this article finally discovered that the accuracy of the new model is improved than the accuracy of the basic model (i.e., new model predicts house price more precision), but it can be improved in the case of less data in the real environment. The reason may be related to economic and public opinion effects. Overall, these results shed light on guiding further exploration of house price.
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