Principal Component Analysis (PCA) and Factor Analysis
- 4.3
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Brief Introduction
Analytics / Machine Learning / Dimensionality Reduction : PCA & Factor Analysis using SAS and R programDescription
The course explains one of the important aspect of machine learning - Principal component analysis and factor analysis in a very easy to understand manner. It explains theory as well as demonstrates how to use SAS and R for the purpose.
The course provides entire course content available to download in PDF format, data set and code files. The detail course content is as follows.
- Intuitive Understanding of PCA 2D Case
- what is the variance in the data in different dimensions?
- what is principal component?
- Formal definition of PCs
- Understand the formal definition of PCA
- Properties of Principal Components
- Understanding principal component analysis (PCA) definition using a 3D image
- Properties of Principal Components
- Summarize PCA concepts
- Understand why first eigen value is bigger than second, second is bigger than third and so on
- Data Treatment for conducting PCA
- How to treat ordinal variables?
- How to treat numeric variables?
- Conduct PCA using SAS: Understand
- Correlation Matrix
- Eigen value table
- Scree plot
- How many pricipal components one should keep?
- How is principal components getting derived?
- Conduct PCA using R
- Introduction to Factor Analysis
- Introduction to factor analysis
- Factor analysis vs PCA side by side
- Factor Analysis Using R
- Factor Analysis Using SAS
- Theory for using PCA for Variable Selection
- Demo of using PCA for Variable Selection
Requirements
- Requirements
- The course will start with elementary concepts but knowledge of basic statistics will help
- For execution - it will help to know basic SAS or R programming
Gopal Prasad Malakar
4.3 Raiting
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