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This document explains Dimensionality Reduction using Principal Component Analysis (PCA), a machine learning technique that reduces the number of features in a dataset while preserving important information. PCA is particularly useful when dealing with high-dimensional data that contains correlated features, allowing for improved speed and visualization. The document outlines the steps of PCA, its mathematical foundation, and provides examples to illustrate how PCA transforms data into principal components.

Author

Vedika Srivastava

Language

EN