In order to predict second-hand housing prices more accurately and quickly,a prediction algorithm based on the Gradient Boosting Decision Tree(GBDT)is proposed.Firstly,collect the latest second-hand housing data from Shenyang and pre-process the data.Secondly,a housing price prediction model is established based on the processed data and Gradient Boosting De-cision Tree method.Finally,prediction methods such as ridge regression,random forest,and linear regression were chosen as com-parison methods.GBDT model outperformed other methods such as ridge regression,random forest,and linear regression in terms of evaluation indicators.It has certain practicality in predicting housing prices.
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