钻石价格预测模型比较及优化路径OA

To accurately predict diamond prices,this paper constructs four regression models:multiple linear regression,random forest,GBDT,and SVM,and systematically compares their prediction performance and optimization effects.The research first performs data preprocessing,divides the dataset,and standardizes the features.Subsequently,through three stages:initial evaluation,K-fold cross-validation,and grid search hyperparameter optimization,using RMSE,MAE,R²,and MAPE as evaluation metrics,combined with visualization methods,the model performance is analyzed.The results show that the optimized random forest performs best in terms of R²(0.9775)and MAPE(6.92%);GBDT shows significant optimization effects,with RMSE reduced by about 27%;multiple linear regression consistently performs well in various indicators but has the worst overall performance;SVM shows performance degradation after optimization.The research results can provide a reference for diamond pricing and regression model selection.