Housing price forecast based on XGBoost algorithm house price: advanced regression techniques

Abstract

House prices have always been a major concern in people's daily lives, not only for individuals, but also for the stability of society as a whole. Accurate prediction of house prices can help people to buy houses with a great degree of reference and help the state and society to control the overall consumer prices. With the development of machine learning, we are able to make greater use of machine learning tools to obtain accurate forecasting of house prices in recent years, in order to attempt to predict the house price, this article utilize the machine learning algorithms. The house price problem is a typical regression problem in machine learning. In this paper, we obtain data from Kaggle, look at the data and find that there are many factors affecting house prices. By pre-processing the original data, engineering and standardizing the features, finally, machine learning algorithm XGBoost is selected to establish a prediction model for housing price prediction. We selected the root mean square error (RMSE) ranging from the logarithm of the expected value to the logarithm of the real selling price as the final result of the forecast. The final results we obtained can help one to judge the accuracy of the housing price forecast.