Practical Data Science using Python - Principal Component Analysis - Computations 1

The video tutorial explains Principal Component Analysis (PCA), a technique used to reduce the dimensionality of data while retaining most of the variance. It covers the motivations for using PCA, such as simplifying complex models and improving visualization. The process involves standardizing data, calculating the covariance matrix, and deriving eigenvectors and eigenvalues. These eigenvectors form new axes that capture the most variance, allowing for data compression by selecting the most significant components.

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