House Price Prediction Model Using Bridge Memristors Recurrent Neural Network

In recent decay, the house price prediction plays important role because of it's the volatile of house price which makes significant impact on property valuation and economic growth. It characterizes are attracted the numerous researchers, businessman and people who buy or sell house towards it. The volatile of house price is occurred based on various factors like location, facility, neighborhood, etc. In this way, researchers are evaluating the factors using machine and deep learning process to analysis the information. Although, regression-based analysis has problem due to its nonlinear and linear information in neural network. Thus, we have proposed a novel Bridge Memristors Recurrent Neural Network to forecast the house price prediction in this paper. In addition, RBP algorithm is used on Bridge Memristors RNN for train the neural network in efficient manner. Besides, our proposed model carried out outstanding performance than existing models to attain the high prediction rate by analyzing the correlation coefficient.