## 5 Results
### Classifiers Performance Assessment
Finally, from the visualization, k-NN reaches the highest accuracy, while despite its overall lower accuracy, NB shows a big recall. After the evaluation, the best method is applied to the selected dataset to classify the instances. The DTs classifier was successfully trained, evaluated, and tested, scoring an accuracy of 97.98% when, due to an imbalanced dataset, the precision for the conversions remained at the lowest value of zero (0). The recall remained also at a low value of zero (0), signifying that there are no correctly identified conversions. The NB classifier was successfully trained, evaluated, and tested on the validation set, scoring an accuracy of 65.00% while maintaining a low precision score of 0.01, indicating a high false positive ratio and scoring 100%. NB's current performance is the best out of a series of seven runs to reach maximum accuracy. Finally, the k-NN classifier was successfully trained, evaluated, and tested, scoring an accuracy of 97.90% while scoring precision and recall at the level of zero (0) [11; 13; 15; 16]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **Accuracy 1** & **Precision 2** & **Recall 3** & **F1-Score 4** \\ \hline DTs & 97.98\% & 0 & 0 & 0.00 \\ NB & 65.00\% & 0.01 & 1.00 & 0.02 \\ k-NN & 97.90\% & 0 & 0 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Classifiers performance assessment. Figure 9 visualizes the classification performance differences among classifiers. The graphical representation indicates the slight accuracy difference between DTs and k-NN, with the DTs classifier scoring the highest accuracy among all. The accuracy scores are shown in each bar. These results highlight the difficulty of accurately predicting rare events (conversions) in a highly imbalanced dataset, with each algorithm showing different pros and cons. Adjusting the models or dataset for imbalance, or incorporating additional features or ensemble methods, could potentially improve these results [11; 13; 15; 16]. Figure 10 visualizes the receiver operating characteristic (ROC) curve, which shows the prediction ability of a binary classifier based on its discrimination threshold. The curve plots show the true positive rate (TPR) against the false positive rate (FPR) at various thresholds. The AUC is defined as the area under the ROC curves offering a single scale  
Figure 8: Classifiers overall performance assessment. Figure 9: Classifiers accuracy performance assessment comparison. of values, from 0 to 1, to show the model's performance. The DTs succeeded at an AUC value of 0.88, showing that the DTs can effectively distinguish the sessions that are above or below the median. NB succeeded at an AUC value of 0.50, indicating that the NB classifier does not perform well compared to DTs. Finally, k-NN succeeded at an AUC value of 0.79. This suggests that k-NN has a fair ability to identify classes. The overlap between the ROC curves of DTs and k-NN in the range from 0 to 0.2 can be explained by both models' capability to recognize true positives at low false positive rates. This resemblance starts from their dependence on distinguished characteristics in the dataset, where DTs' hierarchical split criteria and k-NN's neighborhood-based distance variables are narrowly aligned. A deeper analysis of characteristics, contributions, and decision boundaries assists performance differentiation in overlapping regions. The dotted line in the ROC curve demonstrates the no-discrimination line, corresponding to a random classifier with an AUC of 0.5. Both the DTs and k-NN classifiers surpass this baseline, showing significant predictability. The higher position of the ROC curve for the DTs implies higher performance compared to k-NN across the majority of the thresholds. Regarding Figure 11, the DT confusion matrix demonstrates how relatively well the model identifies different classes referring to above or below the median number of the number of sessions (class). The k-NN also performs well with a high number of correct predictions.