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Principal Component Analysis in Unsupervised Machine Learning. It is an unsupervised machine learning technique used for dimensionality reduction while preserving as much variance as possible in a dataset It transforms the original correlated variables into a new set of uncorrelated variables called principal components The process begins by centering the data (which is subtracting the mean), then computing the covariance matrix to capture the relationships between variables Eigenvectors and the
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