*The purpose of this article is to explain PCA (Principal Component Analysis)*

*The purpose of this article is to explain PCA (Principal Component Analysis)*

## What is PCA in the Data Science Toolkit?

#### Purpose

The purpose of PCA analysis is to convert a set of columns that are possibly correlated into a set of uncorrelated variables called principal components. It makes sense to combine multiple columns into principal components in order to speed up the run time of a model, reduce noise or otherwise optimize a model. PCA is a one step in the predictive modeling process.

#### Additional Information on PCA

#### Outputs: (4 Visualization):

- PC Values on Original Data - Scatter Plot
- PCA Rotation Matrix table
- Variance explained by each PC bar chart. There will be one bar for each principal component.
- PCA Plot for top components - Scatter Plot

**Note**: Columns are also appended to the original data for PC

**Example**:

Data Science Toolkit PCA User Guide: How to set up PCA (Principal Components Analysis)

See the PCA in action below:

Data Science Toolkit: PCA from Ruths.ai on Vimeo.

For additional information on RAI Data Science Toolkit documentation, click here.