Abstract
With the rapid development of machine learning and related fields, it has been widely used in various industries. House price is a hot topic in the nation’s livelihood, and the influencing factors are very complex. Therefore, a dataset with a large number of features is chosen as the data for processing in this paper. Firstly, the least-squares and random forest regression algorithms in machine learning are introduced; afterwards, the training and test set data are processed and analysed to identify the seven main factors affecting housing prices; finally, the two models are evaluated, and the results show that both algorithms can accurately predict housing prices.
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