Principal Component Analysis
Principal Component Analysis (PCA) is a statistical technique used to reduce the dimensionality of a dataset while retaining as much of the original variation as possible. It works by identifying the directions of maximum variance in the data and projecting it onto a new coordinate system. The new coordinate system is defined by a set of orthogonal axes called principal components. The first principal component captures the most variation in the data, followed by the second, and so on. PCA is commonly used in data preprocessing and exploratory data analysis to identify patterns and relationships in high-dimensional datasets. It can also be used for feature extraction and data compression.
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