This thesis explores the refinement of SEO classification models by integrating advanced machine learning technologies, notably XGBoost and CatBoost, and leveraging large-scale, relational data to enhance the predictive accuracy of search engine rankings. The research examines how feature engineering and relational data comparisons between web pages influence the effectiveness of SEO strategies, highlighting a clear improvement in model accuracy when contextual variables are considered.

In addition to technical advancements, the study also explores the ethical implications of web scraping and the transparency required in manipulating SEO algorithms. By systematically analyzing the performance of enhanced models on varied dataset, this work reveals critical insights into the underlying mechanisms of search engines and the factors influencing web page visibility. The thesis argues that a deeper understanding of these factors, supported by robust empirical data, can drive more targeted and effective SEO practices.

Overall, the research contributes to both academic literature and practical applications in digital marketing, offering a framework for developing more sophisticated SEO tools that can adapt to the ever changing digital landscape. It opens up new avenues for future research, particularly in the exploration of off-page SEO factors and the integration of natural language processing to automate and optimize content creation for better search engine performance.