Rumor has it that the ultra-wealthy community consists of either investment bankers or entrepreneurs in the tech industry that dropped out of college. Is that stereotype really true? Ever wonder if the top billionaires in the world share anything in common? Although, we can't say with certainty what it takes to be one, we do have a way to determine if any patterns exist among the richest people in the world. 

In this guided Project, you will explore Kernel Principal Component Analysis (Kernel PCA) - an extension of principal component analysis (PCA) - to extract key feature patterns in the dataset, which is usually of higher dimension. In addition to analyzing billionaires around the globe, we will also use this unsupervised learning technique to denoise images. 

This guided project is ideal for data scientists, machine learning practitioners, and enthusiasts eager to explore non-linear dimensionality reduction techniques. Participants should have a basic understanding of linear algebra and programming fundamentals. While some familiarity with traditional KPCA is beneficial, it is not a prerequisite, as we will cover the necessary theoretical foundations and practical implementations.