Principal Component Analysis (PCA) is a statistical dimensionality reduction technique used to identify underlying patterns and structures in multivariate data sets. PCA transforms a set of correlated variables into a set of uncorrelated variables called principal components, which account for most of the variability in the original data.
The objective of PCA is to reduce the dimensionality of the data by projecting them into a lower dimensional space while retaining as much information as possible. Principal components are calculated from a covariance or correlation matrix of the original variables and ordered according to their relative contribution to the total variability of the data set. The principal components are then used to reconstruct the original data, allowing for a reduced representation of the original data set.
PCA is commonly used in machine learning applications to simplify and compress data, facilitating analysis and visualisation. It is also used in data exploration to discover underlying patterns and structures in large multivariate datasets.
Machine learning is a branch of artificial intelligence (AI) that is based on making a system capable of learning from the information it receives.
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