pca

Principal Component Analysis (PCA)

Description: PCA reduces the dimensionality of data by transforming it to a new set of orthogonal features (principal components) that capture the maximum [[Variance]].

Key Points:

• Reduces complexity of data.
• Helps in visualizing high-dimensional data.
• Assumes linear relationships among features.

Applications: Data compression, noise reduction, feature extraction.