## 4 Experimental Results
RMSE is computed as in Equation (3):  
\[\text{RMSE}=\sqrt{\sum_{i=1}^{n}\frac{\left(\text{Actual}-\text{Predicted} \right)^{2}}{n}}. \tag{3}\]  
Accuracy is calculated as percent variation of a predicted value, with actual values up to an acceptable limit of error. This is given in Equation (4), where, \(p_{i}\) is the \(i\)th predicted target, ai is the \(i\)th original value, err is the acceptable error and \(n\) is the total count of samples. \[\text{Accuracy}=\frac{100}{n}\sum_{i=1}^{n}d_{i}, \tag{4}\] \[d_{i}=\left\{ \begin{array}{ll}1&if\ abs(p_{i}-a_{i})\leq\text{ err}\\ 0&\text{other wise}\end{array} \right.\]  
Total time refers to the time it takes the entire program to execute the model, compute the different parameters of the regression data, and evaluate the outcome. Table 6 depicts the average results of various performance metrics obtained from the seven different machine-learning models. The top models were selected based on RMSE parameters. The parameters measured the quality of prediction. The lower values of RMSE indicated better fit models. From the results of the year 2014, it was clear that the SVR model achieved the lowest RMSE value, 0.37, with the highest accuracy, 90%, compared to the rest of the algorithms. The model's elastic net was found to be the worst performer, with an RMSE value of 0.81 and accuracy of 70.9%. The case for the years 2015 to 2017 were similar. The SVR model gave the best results, in terms of RMSE value, 0.35, 0.32, and 0.38 for 2015, 2016 and 2017, respectively. Also, the accuracy, using the SVR model, was, on average, above 90%. The RMSE value for the cubist, elastic net and linear models was higher than 0.80 for the year 2015. For the year 2016, both elastic net and cubist give a lower RMSE value, 0.87, whereas random forest and neural networks had RMSE values near 0.40. SVR had the lowest RMSE value, 0.32. Furthermore, the same model was able to be exceedingly specific in predicting life satisfaction from the dataset, with an RMSE of 0.38 and accuracy of 91.58%, for the year 2017. The performance assessment of all the models considered was graphically envisioned with a sketching boxplot [37]. It portrayed the empirical distribution of the data. Figure 4a-d represents the boxplot for the base models. In the plot, the x-axis represents the model names and the y-axis labels the RMSE parameter. It is clear from the boxplots that the SVR gave improved and consistent results compared to the other models, in terms of the RMSE metric for each year. The absence of outliers also added to the steadiness of the models. The boxplot depicts RF and NN to be the second-best performers, of all the years, in terms of RMSE. As each category of the model has potential and shortcomings, consequently, the top models (based on RMSE) were combined to generate ensemble models. Ensembles use a number of different algorithms in order to achieve superior predictive performance than could be achieved by using any of the individual learners [38]. Based on the results, the top four models, namely decision tree (DT), support vector regressor (SVR), neural network (NN), and random forest (RF), were selected for the development of the ensemble models and three models (out of these four models) were combined in each ensemble. The following are the top four ensemble models chosen based on the values of RMSE. E1--(DT, NN, RF)  
E2--(NN, RF, SVR)  
E3--(DT, RF, SVR)  
All models were combined in the three groups (making four different combinations) to improve the performance of the models using the stacking technique. Table 7 shows the performance parameter values obtained from the four ensembles after the application of the stacking scheme on the three prediction results. The table depicts the combination of the DT, RF and SVR models and gives the best