Factor Analysis and PCA

PCA and Factor analysis is the most famous technique for Dimensions Reductions. Principal component analysis (PCA) is a technique used to emphasize variation and bring out strong patterns in a dataset. PCA is a most widely used tool in exploratory data analysis and in machine learning for predictive models. The module teaches the complex calculations of PCA involving Eigenvalues and Eigenvectors through simple excel settings.
The Zip contains 3 excel sheets covering the following
•Principal Component Analysis and Principal Component Regression
•Exploratory Factor analysis
•Confirmatory Factor Analysis
The excel sheets comes accompanying with a PPT explaining all the steps.