Breaking Down SEO Complexity: Bridging PCA and Bayesian-Optimized t-SNE

The complexity of Search Engine Optimization (SEO) data requires sophisticated analytical tools. This study integrates Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE), optimized by Bayesian methods, to enhance SEO data analysis. PCA is a technique that minimises the number of dimensions in data, allowing for the identification of important aspects related to search engine optimisation (SEO). On the other hand, optimised t-SNE gives a visual representation of data clustering and correlations in a way that is easy to understand and interpret. Our methodology enhances computing efficiency and interpretability, surpassing conventional techniques in analysing both linear and non-linear data. The results develop more strategic decision-making in the field of SEO, indicating a remarkable advancement in SEO analytics.