## 4 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Year** & **Models Used** & \(r\) & \(R\) & **RMSE** & **Accuracy** & **Total Time** \\ \hline \multirow{8}{*}{2014} & Cubist & 0.77 & 0.60 & 0.8 & 73.92 & 32.42 \\  & Decision tree & 0.24 & 0.06 & 0.537 & 81.66 & 30.49 \\  & Elastic net & 0.57 & 0.35 & 0.816 & 70.88 & 45.79 \\  & Linear model & 0.84 & 0.71 & 0.748 & 77.93 & 33.72 \\  & Neural network & 0.73 & 0.54 & 0.48 & 84.99 & 31.35 \\  & Random forest & 0.84 & 0.71 & 0.507 & 83.22 & 30.77 \\  & Support vector regressor & 0.79 & 0.62 & 0.371 & 90.00 & 30.66 \\ \hline \multirow{8}{*}{2015} & Cubist & 0.76 & 0.61 & 0.832 & 72.96 & 35.22 \\  & Decision tree & 0.36 & 0.13 & 0.763 & 81.66 & 34.91 \\  & Elastic net & 0.57 & 0.34 & 0.850 & 70.50 & 43.50 \\  & Linear model & 0.81 & 0.65 & 0.803 & 77.59 & 38.75 \\  & Neural network & 0.69 & 0.49 & 0.594 & 83.33 & 28.77 \\  & Random forest & 0.83 & 0.71 & 0.408 & 86.47 & 31.00 \\  & Support vector regressor & 0.83 & 0.69 & 0.352 & 88.33 & 30.43 \\ \hline \multirow{8}{*}{2016} & Cubist & 0.62 & 0.39 & 0.869 & 77.33 & 32.12 \\  & Decision Tree & 0.54 & 0.35 & 0.614 & 83.32 & 32.67 \\  & Elastic net & 0.73 & 0.54 & 0.867 & 77.55 & 45.86 \\  & Linear Model & 0.69 & 0.48 & 0.808 & 78.79 & 40.14 \\  & Neural Network & 0.78 & 0.63 & 0.464 & 85.00 & 33.52 \\  & Random Forest & 0.82 & 0.68 & 0.406 & 86.66 & 33.14 \\  & Support vector regressor & 0.81 & 0.66 & 0.322 & 90.00 & 28.95 \\ \hline \multirow{8}{*}{2017} & Cubist & 0.63 & 0.40 & 0.843 & 77.97 & 35.45 \\  & Decision tree & 0.68 & 0.48 & 0.44 & 88.26 & 32.89 \\ \cline{1-1}  & Elastic net & 0.71 & 0.52 & 0.879 & 77.84 & 43.98 \\ \cline{1-1}  & Linear model & 0.69 & 0.48 & 0.727 & 79.33 & 40.14 \\ \cline{1-1}  & Neural network & 0.78 & 0.61 & 0.463 & 83.33 & 36.20 \\ \cline{1-1}  & Random forest & 0.82 & 0.67 & 0.45 & 86.67 & 37.02 \\ \cline{1-1}  & Support vector regressor & 0.81 & 0.66 & 0.378 & 91.58 & 30.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average results of base models. results in terms of RMSE compared to the rest of the ensembles for each year. Moreover, the ensemble combining DT, NN and SVR gave the highest RMSE value for the year 2014 and 2015. Both the ensemble with DT, NN, and SVR and that with DT, NN, and RF obtained the highest RMSEs, 0.43, for the year 2016. The ensemble DT, NN, and RF gave the worst RMSE value, 0.40, for the year 2017. Figure 5 shows the comparative analysis of ensemble models in terms of average RMSE. The ensemble model (combining DT, RF, and SVR) gave the lowest RMSE values, i.e., 0.34, 0.30, \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Year** & **Models** & \(\mathbf{r}\) & \(\mathbf{R}\) & **RMSE** & **Accuracy (\%)** & **Total Time** \\ \hline \multirow{4}{*}{2014} & DT, NN, RF & 0.74 & 0.55 & 0.450 & 85.24 & 30.25 \\  & NN, RF, SVR & 0.95 & 0.91 & 0.394 & 91.56 & 40.13 \\  & DT, NN, SVR & 0.68 & 0.46 & 0.478 & 85.00 & 40.70 \\  & DT, RF, SVR & 0.78 & 0.61 & 0.342 & 94.50 & 30.29 \\ \hline \multirow{4}{*}{2015} & DT, NN, RF & 0.79 & 0.62 & 0.417 & 88.41 & 31.43 \\  & NN, RF, SVR & 0.90 & 0.81 & 0.402 & 90.00 & 42.32 \\  & DT, NN, SVR & 0.87 & 0.76 & 0.453 & 88.00 & 39.16 \\  & DT, RF, SVR & 0.94 & 0.87 & 0.306 & 93.00 & 29.16 \\ \hline \multirow{4}{*}{2016} & DT, NN, RF & 0.75 & 0.56 & 0.438 & 86.44 & 33.14 \\  & NN, RF, SVR & 0.82 & 0.66 & 0.442 & 89.23 & 39.50 \\ \cline{1-1}  & DT, NN, SVR & 0.71 & 0.50 & 0.437 & 89.23 & 37.43 \\ \cline{1-1}  & DT, RF, SVR & 0.73 & 0.53 & 0.329 & 92.43 & 32.44 \\ \hline \multirow{4}{*}{2017} & DT, NN, RF & 0.80 & 0.64 & 0.401 & 90.00 & 32.76 \\ \cline{1-1}  & NN, RF, SVR & 0.83 & 0.68 & 0.386 & 92.54 & 40.29 \\ \cline{1-1}  & DT, NN, SVR & 0.94 & 0.88 & 0.378 & 90.00 & 40.02 \\ \cline{1-1}  & DT, RF, SVR & 0.82 & 0.67 & 0.292 & 96.42 & 30.22 \\ \hline \end{tabular}
\end{table} 
Table 7: Results of ensemble models. Figure 4: (a)–(d): RMSE values of various models for the years 2014–2017, respectively. 0.32 and 0.29 for each year from 2014 to 2017 respectively, in the prediction of countrywide life satisfaction.