Algorithm of Principal Component Analysis (PCA)

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
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