What is the primary goal of Principal Component Analysis (PCA) in data analysis?
- Clustering data
- Maximizing the variance of the data
- Reducing computation time
- Removing all outliers
The primary goal of PCA is to transform the data into a new coordinate system where the variance is maximized. This helps in reducing dimensions while preserving as much information as possible in the main components.
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