## 3 Methodology  
### Participants  
In this study, we conducted an online survey of Filipino women ages 14 and above. The method used for acquiring respondents was a non-probability sampling method, specifically convenience sampling due to strict protocols resulting from the COVID-19 pandemic. To obtain the desired number of respondents, this study chose to use convenience and snowball sampling. A total of 553 Filipino respondents were collected, providing 22,120 data points for forecasting factors influencing women's intention to purchase self-defense items. Females of ages ranging from 14 and above provided the data obtained in Table 2; 47.92% were aged 14-18, and 35.80% were 19-25. The majority also came from the National Capital Region, NCR, comprising 54.25%, while 28.21% came from region IV-A and 11.39% from region III. Moreover, 62.21% travelled less than 2 h a day, and 31.83% travelled 2-4 h a day on average. Among the respondents, 77.76% earned less than 15,000 pesos monthly, 11.57% earned 15,000-30,000 pesos, and the rest earned more.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Category** & **Characteristics** & **Number** & **Percentage** \\ \hline \multirow{8}{*}{Age} & 14–18 & 265 & 47.92 \\  & 19–25 & 198 & 35.80 \\  & 26–30 & 23 & 4.16 \\  & 31–38 & 15 & 2.71 \\  & 39–45 & 17 & 3.07 \\  & 46–50 & 7 & 1.27 \\  & 51 and above & 28 & 5.06 \\ \hline \multirow{8}{*}{Location} & Region I & 3 & 0.54 \\  & Region II & 5 & 0.90 \\  & Region III & 63 & 11.39 \\  & Region IV-A & 156 & 28.21 \\  & MIMAROPA & 2 & 0.36 \\  & Region V & 1 & 0.18 \\  & Region VI & 8 & 1.45 \\  & Region VII & 6 & 1.08 \\  & Region VIII & 2 & 0.36 \\  & Region X & 2 & 0.36 \\  & Region XI & 3 & 0.54 \\  & Region XIII & 1 & 0.18 \\  & NCR & 300 & 54.25 \\  & BARMM & 1 & 0.18 \\ \hline \multirow{8}{*}{Average Travel Time in Hours Per Day} & Less than 2 h & 344 & 62.21 \\  & 2–4 h & 176 & 31.83 \\  & 4–6 h & 21 & 3.80 \\  & 6–8 h & 7 & 1.27 \\  & More than 8 h & 5 & 0.90 \\ \hline \multirow{8}{*}{Monthly Salary/Allowance} & Less than 15,000 & 430 & 77.76 \\  & 15,000–30,000 & 64 & 11.57 \\ \cline{1-1}  & 30,001–45,000 & 24 & 4.34 \\ \cline{1-1}  & 45,001–60,000 & 13 & 2.35 \\ \cline{1-1}  & 60,001–75,000 & 4 & 0.72 \\ \cline{1-1}  & More than 75,000 & 18 & 3.25 \\ \hline \multirow{2}{*}{Type of Residence} & Rural & 129 & 23.33 \\ \cline{1-1}  & Urban & 424 & 76.67 \\ \hline \multirow{2}{*}{Do you have Life Insurance} & Yes & 175 & 31.65 \\ \cline{1-1}  & No & 378 & 68.35 \\ \hline \end{tabular}
\end{table}
Table 2: Respondent demographics (\(n=553\)).  
Before using the MLA, data pre-processing was completed. At first, SPSS 25 software was used to check for missing data; there were no missing data. Furthermore, non-significant indications with a \(p\)-value greater than 0.05 were removed from the data using correlation analysis for the data preprocessing stage [33]. Additionally, only variables higher than 0.20 were taken into the MLA enhancement. Of the 40 indicators, SE2 was the only indicator removed. Hence, 39 indicators were considered significant according to the correlation analysis.  
Data aggregation was carried out to represent the input variables for the MLA. The unobserved variables of the latent factors taken into consideration in this study were represented by indicators in the questionnaire. Ten latent variables (U, PR, SE, PV, BC, SN, A, PF, PI, and BI) were analyzed for the MLA using Jupyter Notebook 6.3 simultaneously. After data normalization, various MLAs, including decision trees (DTs), random forest classifiers (RFC), and deep learning neural network, were implemented to predict the variables influencing the intention of women to purchase self-defense tools.