## 4 Data Analysis
### Data Overview  
This study compared the performance of various ML algorithms in terms of accuracy. Table 1 summarizes the results, including the mean value and standard deviation for each algorithm we examined. Table 1 reveals that the SVM algorithm achieved the highest accuracy, trailed by the random forest algorithm. KNN ranked last in accuracy. In terms of standard deviation, the random forest algorithm demonstrated the lowest value, with KNN following in second place, and the SVM algorithm exhibiting the highest standard deviation. The data presented are the output accuracy for the K-nearest neighbor, random forest, and SVM algorithms. The tables for these algorithm's data are portrayed in Appendix A, Appendix B, and Appendix C, respectively. Figures 10 and 11 depict the KNN accuracy results for the different matrices. As depicted, the accuracy had a maximum of approximately 97% and a minimum of around 96%. Figures 12 and 13 depict the accuracy for the random forest algorithm and the SVM algorithm, respectively. When comparing the performance of the different ML algorithms regarding accuracy, Table 1 depicts the results revealing the mean value and standard deviation for each examined ML algorithm, highlighting that the SVM algorithm had the highest accuracy, followed by the random forest algorithm. KNN ranked last in terms of accuracy. Regarding standard deviation, the random forest algorithm bears the lowest standard deviation; KNN ranks second, followed by the SVM algorithm, having the highest standard deviation. Figures 14-17 depict the different performance outcomes of all the examined algorithms. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **K Nearest Neighbor** & **Support Vector Machine** & **Random Forest** \\ \hline Mean Value & 96.759 & 99.394 & 99.213 \\ \hline Std. Dev. & 0.0038 & 0.0061 & 0.0029 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mean value and standard deviation comparison. Figure 11: Line chart for KNN accuracy values with trends. Figure 12: Random forest accuracy values. Figure 10: KNN accuracy values. accuracies ranging between 96% and 97.5%, with 96.6 having the highest frequency of occurring. While training the three ML algorithms, the SVM algorithm needed a relatively long time compared with the random forest and the KNN algorithm. It is important to notice that the SVM algorithm was taking too long to train on the matrices [11; 13; 31; 57], so we decided to skip these matrices. The average time the SVM took to train on the other matrices was about 2 min. The normal distribution graph for the support vector machine algorithm shown in Figure 19 indicates that the algorithm has a high probability of producing an accuracy value between 99.5% and 99.9% with a relatively low probability of having an accuracy between 97% and 99%. During SCM training of the three ML algorithms, the SVM algorithm needed a relatively long time compared with the random forest and the KNN algorithm. It is noted that the SVM algorithm also took too long to train on the matrices for [PERSON] et al. [57], [PERSON] et al. [31], [PERSON] and [PERSON] [13], and [PERSON] et al. [11]. After several days of not finishing with the computation, we decided to skip these matrices. The average time the SVM took to train on the other matrices was about 2 min. The normal distribution graph for the support vector machine algorithm shown in Figure 19 indicates that the algorithm has a high probability of producing an accuracy value between 99.5% and 100% with a relatively low probability of having an accuracy between 97% and 99%. In the case of the Random Forest algorithm it has a high probability of producing a value with an accuracy between 97% and 99%. Figure 16: Accuracy values for KNN and random forest. Figure 17: Accuracy values for KNN, SVM, and random forest. 99.1% and 99.4% as shown in Figure 20. Random forest had high accuracy rates and the lowest standard deviation value among the machine learning algorithms.