PCA
PCA (Principal Component Analysis) 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 the data onto a lower-dimensional subspace formed by these directions. This results in a new set of uncorrelated variables, called principal components, which can be used for further analysis or visualization. PCA is commonly used in data preprocessing, exploratory data analysis, and feature extraction. It can also be used for noise reduction, outlier detection, and data compression.
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