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:
…


This content originally appeared on DEV Community and was authored by Matt Curcio

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.


This content originally appeared on DEV Community and was authored by Matt Curcio


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Matt Curcio | Sciencx (2022-02-08T05:03:53+00:00) Why Use Principal Component Analysis?. Retrieved from https://www.scien.cx/2022/02/08/why-use-principal-component-analysis/

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" » Why Use Principal Component Analysis?." Matt Curcio | Sciencx - Tuesday February 8, 2022, https://www.scien.cx/2022/02/08/why-use-principal-component-analysis/
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" » Why Use Principal Component Analysis?." Matt Curcio | Sciencx - Accessed . https://www.scien.cx/2022/02/08/why-use-principal-component-analysis/
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" » Why Use Principal Component Analysis?." Matt Curcio | Sciencx [Online]. Available: https://www.scien.cx/2022/02/08/why-use-principal-component-analysis/. [Accessed: ]
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» Why Use Principal Component Analysis? | Matt Curcio | Sciencx | https://www.scien.cx/2022/02/08/why-use-principal-component-analysis/ |

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