## 4 Results and Analysis
**Feature Importance:** The \"Feature Importance\" table reveals how different features contribute to each principal component. For example, \"Education\" has a high importance in PC1 and PC3, suggesting a strong association with these components. Conversely, \"Age\" has a high importance in PC2, indicating a distinct influence on this component. Relating this to the research and the data collected from SMEs through the survey gave reference and showed the following outcome:  
1. **Identifying Key Factors:** By reducing the data into a few principal components, the study can pinpoint the most critical factors influencing ML awareness and adoption in SMEs. The varying contributions of features (e.g., high importance of \"Education\" in certain components) highlight their significance. 2. **Data Visualization:** Scatter plots utilizing the first two principal components can visually represent the positioning of different SMEs based on their characteristics. This helped identify patterns and potential clusters within the data. 3. **Practical Implications:** Policymakers and business leaders can leverage these insights to prioritize factors when promoting ML adoption in cybersecurity for SMEs. Tailored training programs based on factors like age or educational background can be developed to enhance ML awareness. The PCA analysis condenses the complex data into a manageable number of principal components, facilitating the identification of key factors impacting ML awareness and implementation in SMEs. By focusing on these key factors, the study can develop more effective strategies to promote ML use in cybersecurity advancements. Figures 11 and 12 show variations in the data captured between ML Awareness and Company size and Industry. The final section of results was extracted from the third data sample across 44 SMEs, as shown in Figures 13-16. Figure 17 shows the box plots providing a summary of the distribution for each encoded variable. They show the median, quartiles, and potential outliers for each variable. From these visualizations, it was observed that the pair plot's diagonal elements showed that most variables have somewhat uneven distributions, with some having multiple peaks. This suggested that the data were not normally distributed for most variables. The scatter plots in the pair plot did not show strong linear relationships between most variables. This aligned with our earlier observation from the correlation matrix. The box plots revealed that some variables, particularly 'Education' and 'Business_Sector', had outliers (points beyond the ideals). The encoded variables had different ranges, which was expected due  
Figure 12: Company Size vs. ML Awareness. Figure 13: ML Awareness by Age Range. to the encoding process. For example, 'Education' seemed to have fewer unique values compared to 'Business_Sector'. Figure 14: ML Usage by Age Range. Figure 15: ML Awareness by Industry. These visualizations provided a good overview of the data's structure and relationships. They confirmed that there were no strong linear relationships between variables, which could have impacted the performance of certain machine learning models.