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Principal Component Analysis (PCA)

Principal Component Analysis (PCA) is a technique that finds underlying variables (known as principal components) that best differentiate the data points. Example: if the dataset is a collection of food items, vitamin c can be a principal component as it is present in vegetables but absent in meat, then the meat should be separated by another component, etc.

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