## 4 Results and Analysis
This suggests a potentially weak correlation between these principal components and the selection of cybersecurity packages by SMEs. The key findings in the analyzed data suggest:  
1. Educational Background and ML Awareness: A positive correlation is observed between PC4 (educational background/development) and ML awareness in SMEs. This implies that companies with higher levels of education and professional development personnel are more likely to be aware of, and potentially adopt, ML technologies. 2. Cybersecurity Packages and the PC Model: The relationship between PC4/PC5 and cybersecurity package adoption remains unclear. This suggests that other factors might hold a greater influence on the cybersecurity choices made by SMEs. 3. Beyond Education and Development: The overall weak correlations indicate that while educational background and professional development play a role in ML awareness and adoption, they are not the sole determinants for SMEs. Other aspects, such as company size, industry, or specific business needs, might also be significant. 4. Nuances of Professional Development: The varying PC5 scores across different ML awareness levels suggest that this component might capture some subtle aspects of professional development that have a complex relationship with ML adoption. Figure 7: Explained variance by PC. Figure 8 shows that in order to gain further insights, the exploration of the specific variables contributing most heavily to PC4 and PC5 is additionally analyzed using other factors that could influence ML and cybersecurity adoption in SMEs would be beneficial. Figure 8 continues to explore data on how company characteristics, adaptation rates, company size, industry, and business needs influence ML and cybersecurity adoption in SMEs. To analyze how these factors influence adoption rates, there was a need to examine the relevant data points within our dataset further, specifically focusing on columns related to company size, industry, and business needs. Extracting this information and creating data visualizations will help gain a clearer understanding of these relationships. In order to further analyze the principal components (PCs), this section explores the results of PCA applied to the dataset, investigating factors influencing machine learning (ML) adoption in SMEs for cybersecurity purposes. Table 1 shows the Variance Ratio explained cumulatively. Figure 9 shows the corresponding scatterplot. \begin{table}
\begin{tabular}{l c} \hline \hline
**Principal Components (PC)** & **Explained Variance Ratio** \\ \hline PC1 & 0.2854 \\ \hline PC2 & 0.5024 \\ \hline PC3 & 0.6969 \\ \hline PC4 & 0.8674 \\ \hline PC5 & 1.0000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cumulative explained variance ratio. Figure 8: Correlation between PC4 and PC5. Table 2 shows the importance of the features declared in the data sample research. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature** & **PC1** & **PC2** & **PC3** & **PC4** & **PC5** \\ \hline Education & 0.3783 & 0.2733 & 0.7607 & 0.2997 & 0.3372 \\ \hline Age & 0.1031 & 0.8711 & 0.1674 & 0.0065 & 0.4499 \\ \hline Business Sector & 0.5028 & 0.0560 & 0.1292 & 0.8512 & 0.0536 \\ \hline Company Size & 0.5642 & 0.3902 & 0.0245 & 0.3938 & 0.6114 \\ \hline ML Awareness & 0.5245 & 0.1051 & 0.6132 & 0.1749 & 0.5543 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Feature importance. Figure 10: Feature importance. Figure 9: Cumulative explained variance ratio. Each row represents a feature (e.g., Education), and each column represents a principal component (PC). The values indicate the contribution of each feature to the corresponding principal component. From these tables, the key observations explained are as follows:  
1. **Explained Variance:** The first two principal components capture a significant portion (50.24%) of the data's variance, indicating that these components represent the most important underlying factors. All five components are required to capture the entirety of the data's variance. 2.