Principal component analysis (PCA) is a technique to bring out strong patterns in a dataset by supressing variations. It is used to clean data sets to make it easy to explore and analyse. The steps involved are:
- Step 1: Get your data
- Step 2: Give your data a structure
- Step 3: Standardize your data
- Step 4: Get Covariance of Z
- Step 5: Calculate Eigen Vectors and Eigen Values
- Step 6: Sort the Eigen Vectors
- Step 7: Calculate the new features
- Step 8: Drop unimportant features from the new set
Read the article for the detailed algorithm
This is a companion discussion topic for the original entry at http://iq.opengenus.org/algorithm-principal-component-analysis-pca/