Table 4.

Regression Model Performance Metrics on Training and Testing Data.

MODEL	MAE		MSE		RMSE		R2
	Testing	Training	Testing	Training	Testing	Training	Testing	Training
AdaBoost	0.194	0.19	0.073	0.06	0.271	0.246	0.986	0.989
Decision Tree	0.03	0.01	0.038	0.02	0.194	0.01	0.993	0.998
Extra Tree	0.03	0.01	0.027	0.02	0.163	0.049	0.995	0.997
Gradient Boosting	0.035	0.032	0.017	0.01	0.131	0.097	0.997	0.998
K-Nearest Neighbours	0.295	0.225	0.176	0.107	0.42	0.327	0.967	0.98
Linear Regression	0.481	0.473	0.349	0.34	0.591	0.583	0.935	0.937
Neural Network	0.463	0.457	0.372	0.364	0.61	0.603	0.931	0.933
Random Forest	0.027	0.009	0.026	0.002	0.16	0.047	0.995	0.999
XGBoost	0.035	0.014	0.014	0.001	0.119	0.026	0.997	0.999
Stacking Ensemble (RF + XGB)	0.026	0.012	0.013	0.001	0.11	0.024	0.998	0.999