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Principal Component Analysis (PCA) is a dimensionality reduction method that reprojects data into a new coordinate system, where each axis - called a principal component - captures the maximum possible variance, preserving the most important information in the dataset. To compute PCA, we first calculate the covariance matrix of the data, which measures how features vary together. Then, we perform an eigenvalue decomposition on this matrix. Each eigenvalue indicates how much variance a particular
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Principal Component Analysis (PCA) is a dimensionality reduction method that reprojects data into a new coordinate system, where each axis - called a principal component - captures the maximum possible variance, preserving the most important information in the dataset. To compute PCA, we first calculate the covariance matrix of the data, which measures how features vary together. Then, we perform an eigenvalue decomposition on this matrix. Each eigenvalue indicates how much variance a particular
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