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Matt Curcio
Matt Curcio

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Day 4: Why Use Principal Component Analysis?

I am working on an article that discusses Principal Component Analysis. Here is a sneak-peak.

Principal components analysis is a valuable tool for revealing hidden structure in a dataset with many features/variables. By using PCA, one may be able to:

  1. Identify which variables are important and shape the dynamics of a system

  2. Reduce the dimensionality of the data

  3. Maximize the variance that lies hidden in a dataset and rank them

  4. Filter noise from data

  5. Compress the data

  6. Preprocess data for further analysis or model building.

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