Prediction of House Price Based on RBF Neural Network Algorithms of Principal Component Analysis

When the traditional BP neural network is used for prediction, the convergence speed is too slow, the prediction accuracy is low, and it is easy to fall into the local optimal solution. Aiming at these problems, a PCA-based RBF neural network prediction algorithm is proposed and verified. Firstly, PCA is used to recombine the influencing factors of housing prices to generate new comprehensive indicators. Then use the RBF neural network algorithm with strong approximation ability to model and predict the house price. The experimental results show that the fitting result of the predicted value and the real value is 97%, which can be used as an effective method for forecasting house prices.