Use Kernel PCA To Find Why Are You Poor
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Explore Kernel PCA for dimensionality reduction. Learn how this advanced method can uncover hidden patterns in financial data and provide insights into socio-economic factors that contribute to wealth disparities.
At a Glance
Learn to identify patterns in data using Python programming and Data Science. Explore Kernel Principal Component Analysis by uncovering non linear trends, and draw valuable insights from your datasets. It’s a powerful extension of traditional PCA that can unravel complex patterns and structures in non-linear data. Maping the data into a higher-dimensional feature space, where non-linear relationships become linear allows KPCA to capture the intricate structures and similarities in the data that may otherwise remain hidden.
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.
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