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 one step in the predictive modeling process.
Information on PCA:
Inputs:

Data table
 Columns that would be put into a predictive model (remember the PCA will attempt to combine)
 Tolerance  cutoff in variance (from 0 to 1) of the first PC, reduces the number of PCs returned. Leave blank to not enforce a tolerance cutoff.
Limitations:
 Only runs on complete case data, so is very sensitive to missing data
 Only runs on numeric data, so categorical variables would need to be converted to binary columns
Prep:
 Convert categorical variables to binary columns
Steps to Run:
 Go to the Tools menu
 Select the Data Science option
 Select the PCA option
 Enter the data table, columns to be included and a tolerance if desired
Outputs:
 There are four outputs.
 PC Values on Origianl 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
Interpretation:
Data Science Toolkit: PCA from Ruths.ai on Vimeo.