## 4 Results and Analysis
While they might not directly translate back to the original features started with, they represent the most important underlying trends or patterns within the data. To gain a clearer understanding of what these principal components (PC) represent and how the data were distributed in this new space, a scatter plot using PC1 and PC2 was created as axes. Additionally, calculating the variance explained by each component helped quantify the amount of information retained after dimensionality reduction. This helped determine if the reduction had significantly compromised the data's integrity. By analyzing these aspects, the research leveraged PCA as shown in Figure 6, to gain valuable insights from the data without getting inundated by too many features. PC4 seemed to represent a contrast between respondents with \"A Levels or College\" education versus those with \"Bachelor's degrees and professional certifications\". This component might be capturing a dimension related to the level and type of education, particularly distinguishing between vocational and higher academic education. PC5 appeared to highlight a distinction between those with standalone \"professional certifications\" and those with a combination of \"A Levels/College and professional certifications\". This component might have represented a dimension related to the pathway of professional development, contrasting direct professional certification against a mix of academic and professional qualifications. This analysis investigated the potential interpretations of principal components (PCs) derived from a dataset on SMEs and their adoption of machine learning (ML) and cybersecurity practices. The first two principal components (PC1 and PC2) may hold information regarding educational attainment variations among survey respondents within the SME sector. Additionally, these components could reflect contrasting approaches to professional development and qualification acquisition within SMEs. PC1 and PC2 might also reveal underlying disparities in how individuals with different educational backgrounds perceive or engage with ML and cybersecurity within their organizations. To gain a more comprehensive understanding, it was crucial to examine the \"loadings\" associated with other variables in the dataset. Of particular interest were variables directly related to awareness of ML and cybersecurity practices within SMEs. The loadings quantify the contribution of each original feature to the formation of each principal component. In order to synthesize the investigation of educational background and adoption rates, the inquiry regarding the implications of educational background and professional development (captured by PC4 and PC5) on the adoption of ML and cybersecurity in  
Figure 6: PCA distribution. SMEs is a valuable one. To explore this relationship further, there is a need to analyze the correlation between these components (PC4 and PC5) and relevant variables in the dataset, such as those measuring ML awareness and cybersecurity practices. However, this encountered a data pre-processing challenge. Some text data, likely from columns like \"ML_Awareness\" and \"Cybersecurity_Package,\" needed conversion into numerical values for compatibility with PCA. Therefore, the need to address this issue by transforming these categorical variables (e.g., \"High Awareness\" or \"Basic Package\") into numerical representations before proceeding with the correlation matrix calculation proved vital. The correlation matrix ultimately helped elucidate the relationships between these components and other variables within the dataset as shown in Figure 7.  
This section delves into the implications of principal components (PCs) on machine learning (ML) adoption within SMEs. To begin, the relationship between educational background/development and ML awareness is analyzed. An intriguing observation is that the average PC4 and PC5 scores (representing educational background and professional development) for various cybersecurity package choices exhibit values very close to zero.