## 3 Methods and Results
### Evaluation  
In the following section, we will observe the performance indicators received from all the algorithms applied for training associated with the two sets of data that we have considered during the analysis. The end results can be seen with all the associated details in Table 5.  
Therefore, analyzing the performance of Naive Bayes on the two datasets, we observed that the accuracy was 82% for the first dataset and 77% for the second one. Therefore, for this specific algorithm, having more observations did not lead to a better accuracy, but on the contrary, it decreased its performance, proving that Naive Bayes can perform on small-sized datasets as well. Moreover, for applying the same algorithm on the second dataset, we have obtained a better value for recall; however, all the other metrics are showing lower performance again. In the case of Logistic Regression, the algorithm obtained an accuracy over 90% in both cases; however, the accuracy on the second dataset surpassed the results of the first one by 2 pp. We can also see that F1-score is better for the second set of data. For K-Nearest Neighbor, the accuracy obtained for the first dataset was 63%, while it was 73% for the second dataset. Even though we have observed an increase of 10 pp when the number of observations was higher, none of the results are remarkable. For the SVMs, the accuracies obtained for both datasets were over 90%, the first run resulting in an accuracy of 90% and the second one resulting in an accuracy of 92%. Training the algorithm on more observations led to an increase in accuracy of 2 pp. Comparing the performance of the algorithms on the two datasets, we can see that the second one managed to provide a better performance than the first for three out of \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Algorithm** & **Dataset** & **TN** & **FP** & **FN** & **TP** & **Accuracy** & **Precision** & **Recall** & **F1-score** \\ \hline \multirow{3}{*}{Naïve Bayes} & Dataset 1 & 3329 & 1235 & 1094 & 6943 & 82\% & 85\% & 86\% & 86\% \\ \cline{2-10}  & Dataset 2 & 6562 & 7254 & 1063 & 21,751 & 77\% & 75\% & 95\% & 84\% \\ \hline \multirow{3}{*}{Logistic Regression} & Dataset 1 & 3986 & 578 & 551 & 7486 & 91\% & 93\% & 93\% & 93\% \\ \cline{2-10}  & Dataset 2 & 12,575 & 1241 & 1482 & 21,332 & 93\% & 95\% & 94\% & 94\% \\ \hline \multirow{3}{*}{K-NN} & Dataset 1 & 3176 & 1388 & 3227 & 4810 & 63\% & 78\% & 60\% & 68\% \\ \cline{2-10}  & Dataset 2 & 8043 & 5773 & 4203 & 18,611 & 73\% & 76\% & 82\% & 79\% \\ \hline \multirow{3}{*}{SVM} & Dataset 1 & 3947 & 617 & 594 & 7443 & 90\% & 92\% & 93\% & 92\% \\ \cline{2-10}  & Dataset 2 & 12,526 & 1290 & 1499 & 21,315 & 92\% & 94\% & 93\% & 94\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of results after initial training. the four algorithms, which means that the best results can be obtained while more data are used, and also when this is collected from multiple sources, hence being more diverse. At the same time, it is well known that machine learning, especially supervised learning, performs better when the amount of data is higher. Finally, in the short analysis above, we have seen all models from the perspective of Precisions, Recalls, F1 Scores and Balances Accuracies. Precision calculates the ratio between the true positives and all the positives, more exactly [25]:  
\[Precision=\frac{TP}{TP+FP}\]  
where:  
\(TP\) is the number of true positive cases;  
\(FP\) is the number of false positive cases;  
Recall is the measure of the model correctly identifying true positives.