Why Principal Component Analysis (PCA) works?

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 algorithm of Principal Component Analysis is based on a few mathematical ideas namely:

  • Variance and Convariance
  • Eigen Vectors and Eigen values

Read the full article for the intuition behind Principal Component Analysis
This is a companion discussion topic for the original entry at http://iq.opengenus.org/why-principal-component-analysis-pca-works/