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With the arrival of the era of big data, it is very important to have big data's thought, and the application of artificial intelligence technology in various industries can be seen everywhere. In the manufacturing industry, artificial intelligence technology can greatly improve production efficiency, save labor costs and improve product quality; in the service industry, it can optimize the existing products and services of the industry, improve its quality and labor productivity; financial, medical and other fields, but also because of the addition of artificial intelligence technology and more prosperous, people's life is also more convenient.
As a necessity for every citizen, housing plays a very important role in life. Buying a house has become a topic that people often talk about, and how to buy and sell a house at the right time has also become the focus of attention. Therefore, in this context, there are problems related to the prediction of house prices. At present, the field of house price forecasting is mainly reflected in two issues: one is to choose an appropriate mathematical model to predict the trend of housing prices in order to evaluate the changes in housing prices; the other is to find out the causes of the changes in housing prices. The state can use this to help the market coordinate changes in housing prices, and citizens can judge the timing of starting according to current events. This project mainly analyzes the first problem, that is, to choose an appropriate mathematical model to help predict house prices.
This project will start from the housing price data of a certain area, take the relevant properties of the houses in the area as the characteristics, screen the important information, and deal with some information appropriately, and finally use it to predict the prices of other houses in the area.