PCA stands for Principal Component Analysis and is a statistical technique used to identify patterns in data by reducing the number of variables while still retaining as much of the variation in the data as possible.
In PCA, the original variables are transformed into a set of new orthogonal variables called principal components. These components are ranked in order of how much of the variance in the data they explain, with the first component explaining the most variance and each subsequent component explaining less.
PCA is commonly used in data analysis, machine learning, and data visualization to explore and understand relationships between variables. It can also be used for data compression and noise reduction.
By identifying the most important components, PCA can help simplify complex data sets, detect outliers, and highlight patterns that may not be immediately obvious. It is a powerful tool for dimensionality reduction and feature extraction in data analysis.
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