## 5 Data Analysis and Method Implementation
### Results and Analysis  
In this section, the results are visualized and analyzed using a box and whisker plot (or boxplot for short) and reference table to list the respective mean accuracy. As an alternative to histograms, boxplots help understand the distribution of sample data. Each dataset is examined using three models created against each hyperparameter tuning factor. Tables 4-15 show the mean accuracy of the models are tuning the available hyperparameters, and Figures 15-26 show the corresponding distribution of accuracy over the data. Tables 4-6 illustrate the outcomes for Ecuador after tuning the hyperparameters. The total number of features considered in the study is eight. The boxplot is formed to understand the distribution of accuracy observed across the sample data against different input features. The orange line in each box acts as the median for summarizing the sensible data, and the triangle inside the box represents the group means. The vertical line called the whisker represents the sensible distribution of the values. The small circles outside the whisker in a few of the boxplots represent the possible outliers present in the dataset. The outlier is the sample data, unlike the rest of the data, and often does not make sense for the classifier. In Figure 15, the overall performance of tuning the feature selection is better than configuring the two hyperparameters. In addition, the mean accuracy achieved in this scenario has slight growth compared to the accuracy obtained before optimizing the hyperparameters (64%). The wall area displays a high impact on attaining the required accuracy, although the accuracy differences might or might not be statistically significant. In the boxplot, feature numbers 3 and 5 on the \(x\)-axis show peaks, and 7 is low. The boxplot has different feature input data on the \(x\)-axis and accuracy scored by each of them on the \(y\)-axis. In most cases, the box plot's median is overlapping the mean score, which means that the distribution is symmetric. Table 5 illustrates that, as the number of samples per split increases, the accuracy is decreased. In between 5 to 7 number of samples, the predictive accuracy is showing a similar trend. The boxplot in the given Figure 16 has the number of samples per split on the \(x\)-axis and accuracy scores for each sample split set on the \(y\)-axis. The data distribution is not even, and the values are falling below the 25 th percentile. In addition, the mean and median are not overlapping for most cases, and there are few outliers in the case of 3, 4, and 5 number of samples per split. \begin{table}
\begin{tabular}{c c} \hline
**Feature** & **Mean Accuracy (\%)** \\ \hline No. of floor & 64 \\ Total floor area & 65 \\ Column area & 65 \\ Concrete wall area (NS) & 66 \\ Concrete wall area (EW) & 67 \\ Masonry wall area (NS) & 66 \\ Masonry wall area (EW) & 65 \\ Captive Columns & 66 \\ \hline \end{tabular}
\end{table} 
Table 4: Ecuador: Predictive accuracy score against each input feature. Figure 15: Ecuador: Boxplot to represent the mean accuracy distribution achieved by hyper-tuning the input features. Results after hyper-tuning the number of trees in an ensemble are shown in Figure 17. Table 6 shows that accuracy increases with the high number of trees; 40 tree ensemble attains 60% accuracy. The boxplot in Figure 17 is skewed left (also called negatively skewed), with 40 trees in one sample; the distribution of the sample data's value is not uniform as the mean and median are not overlapping. Figure 18 shows the result of the predictive accuracies achieved after tuning different feature inputs. The Captive Column shows the highest impact on model performance based on the accuracy value. In Table 7, the boxplots are almost symmetric except for the Column Area. There are few visible outliers as well among the input data of the Column Area. \begin{table}
\begin{tabular}{c c} \hline \hline
**Feature (No. of Tree)** & **Mean Accuracy (\%)** \\ \hline
10 & 63 \\
20 & 64 \\
30 & 65 \\
40 & 68 \\ \hline \hline \end{tabular}
\end{table}