A housing price prediction method based on neural network

Abstract

This paper proposes a housing price prediction model based on the neural network for the problem of rising housing prices. First, the data are read from Kaggle, then the data are collected for preprocessing, and 10 relevant features are obtained and the data are normalized for each feature. Then build the model, train and configure the model. After that, the loss function and optimization algorithm are set using the stochastic gradient descent method. After the model is trained on the data set, a housing price prediction model is obtained. The experimental results show that the prediction model can accurately predict housing prices, and can make a more scientific reference for the trend of housing prices.