*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 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**:

** See user guide how to setup PCA**: Data Science Toolkit PCA User Guide: How to setup PCA (Principal Components Analysis)

For additional Information watch the video:

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