Abstract
Abstract The price of commercial housing is related to the process of urbanization in China and the living standard of residents, so the prediction of the price of commercial housing is very important. A major difficulty in predicting regression problems is how to handle different attribute types and fuse them. This paper proposes a house price prediction model based on multi-dimensional data fusion and a fully connected neural network. The model building steps are: First, normalize the data involved in the sample; then, interpolate the normalized data to increase the data density; subsequently, the normalized sample data is converted into a pixel matrix; finally, a fully connected neural network model is established from the pixel matrix to the price of the commercial house. After the neural network model has been established, the price of house can be obtained by entering the attributes of the house into the neural network model.
Highlights
Urbanization[1], known as urbanization and urbanization, refers to the process of population gathering towards cities, the expansion of cities, and the series of economic and social changes that result from it
A major factor affecting young people's entry into big cities is the price of local commercial housing
A major factor affecting China's urbanization process is the price of urban house
Summary
Urbanization[1], known as urbanization and urbanization, refers to the process of population gathering towards cities, the expansion of cities, and the series of economic and social changes that result from it. With the further progress of China's urbanization process, more and more young people have begun to enter second-tier, third tier and even first-tier cities. A major factor affecting young people's entry into big cities is the price of local commercial housing. A major factor affecting China's urbanization process is the price of urban house. This shows that it is necessary to forecast house prices. The input information of this model is the seven factors that affect house prices, and the output information is the price of commercial housing. After the data fusion model has been established, only the attributes that affect house prices are entered into the data fusion model, and the price of the commercial house can be obtained
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