Principal Component Analysis (PCA) Explained: Simplify Complex Data for Machine Learning

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Principal Component Analysis (PCA) - GeeksforGeeks

Artificial Intelligence | AI on Instagram: "Principal Component Analysis (PCA) 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. Eige