House Prices Prediction Advanced Regression Techniques

Abstract

In recent years, with the development of the real estate industry, housing prices have continued to rise. The nation, society, and individuals are all concerned about these prices. For commodity housing prices, there are many factors that influence the housing prices. Apart from national regulations, factors such as lighting, layout, and environment of the houses themselves also have a certain impact on the prices, leading to significant fluctuations in the real estate market. Therefore, researching an accurate model for predicting housing prices has practical significance. It can guide residents in housing consumption and provide policy recommendations for government price regulation. Machine learning methods have become a new type of prediction method in this regard. Based on the theories of data analysis and machine learning, a dataset consisting of 2920 data points with 81 attributes was selected from the publicly available Kaggle housing dataset. The data was normalized and analyzed for feature selection. The ranking of attributes most correlated with housing prices was obtained. Subsequently, a neural network model was built, parameters were adjusted, and the trained network was used to predict housing prices. On the Kaggle leaderboard, the RMSE test result stands at 0.1198, positioning our model among the top performers among all machine learning methods.