Model Research on Forecast of Second-Hand House Price in Shenyang Based on XGBoost Algorithm

In order to have a deep understanding of second-hand house prices in Shenyang, this paper studied transaction data of second-hand housing from the platform ke.com. First, the qualified data was determined and collected, then processed and data parameters were set. Second, by comparing three models, Ridge regression, Lasso Regression, and XGBoot model to find which is the best model to be used in the second-hand housing market. By implementing the data and analysis with visualization graphics, the study results indicate that the XGBoost model offers more accurate predictions in understanding second-hand housing prices in Shenyang.