## 5 Discussion  
From the three machine learning algorithms used to predict the variables influencing purchase intention, only the RFC and DLNN algorithms were accepted. The accuracy garnered from the Decision Tree was below 60%, which was considered a low accuracy rate \begin{table}
\begin{tabular}{c c c} \hline \hline
**Factor** & **Importance** & **Normalized Importance** \\ \hline A & 0.179 & 100\% \\ PR & 0.175 & 97.9\% \\ SN & 0.168 & 93.9\% \\ PBC & 0.161 & 90.4\% \\ SE & 0.157 & 88.0\% \\ PV & 0.154 & 86.4\% \\ U & 0.153 & 85.9\% \\ BI & 0.149 & 83.2\% \\ PF & 0.148 & 83.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Normalized Score of Importance. Figure 6: Taylor Diagram. making it not a desirable classification model [27]. In contrast, the RFC algorithm resulted in a 96% accuracy, while the DLNN algorithm generated a 97.70% accuracy. Studies by [PERSON] et al. [26] and [PERSON] et al. [30] justified that the accuracy results determined by the RFC and DLNN algorithms are enough to determine the factors affecting the purchase intention of women for self-defense. The normalized score of importance showed that attitude (A), perceived risk (PR), subjective norms (SN), and perceived behavioral control (PBC) were the highly significant factors. These were followed by self-efficacy (SE), perceived severity (PV), understanding safety (U), buying impulse (BI), and perceived safety (PF), which were all deemed to be significant. The two most significant factors affecting purchase intentions were attitude (100%) and perceived risk (97.9%). The indicators presented how women's anxiousness about the risk of danger and unfamiliarity of the places they are going was a factor that would make them purchase self-defense tools, thinking that it was both worth it and their responsibility. With over 27,200 reported crimes in the Philippines, safety has been and still is a concern in the country [7]. In addition, [PERSON] [103] established how knowledge of such danger increases one's risk perception, which influences their attitude. This is in line with the study of [PERSON] et al. [104], which expounded that individuals are willing to make certain preparations to lessen the risk of a threat. Aligning those findings to our results, it was seen that women's perception of risk changed their attitude, as they wanted to be safer on the streets. In doing so, these influenced them to have the intention to purchase self-defense tools to lessen their risk of danger. Following those factors are the third and fourth most significant factors, subjective norms (93.9%) and perceived behavioral control (90.4%). From the constructs, women are encouraged by their friends and family to carry a self-defense tool, believing it will benefit them by increasing their safety. According to a study by [PERSON] et al. [105], individuals under higher social pressure to do certain actions are more willing to do so, which impacts their decision-making. However, [PERSON] et al. [106] mentioned how positive subjective norms influence purchase intention if the mediating factors and perceived behavioral control, are also high. Nonetheless, the study of [PERSON] and [PERSON] [107] proved that subjective norms and perceived behavioral control strongly influence one's intention to purchase. Therefore, it can be implied that peers, their awareness, and choice highly influence a persons' intention to purchase self-defense tools. A positive influence both on other people and one's own control affects women' intention for safety tool purchase. According to the results, self-efficacy (88%) is significant in influencing purchase intention as women think they are easy targets for street crimes, so they have a safety plan for dealing with them. This is justified by the studies of [PERSON] et al. [108], which have indicated that self-efficacy behaves as a mediator in the link between perceived control and purchase intention. Additionally, [PERSON] and [PERSON] [109] concluded that self-efficacy has a significant positive correlation with preventive behaviors.