Principal Component Analysis (PCA)

Principal Component Analysis (PCA) #

  • dimensionality reduction technique
  • helps us to reduce the number of features in a dataset while keeping the most important information.
  • changes complex datasets by transforming correlated features into a smaller set of uncorrelated components.
  • uses linear algebra to transform data into new features called principal components.
  • finds these by calculating eigenvectors (directions) and eigenvalues (importance) from the covariance matrix.
  • PCA selects the top components with the highest eigenvalues and projects the data onto them simplify the dataset.

PCA prioritizes the directions where the data varies the most because more variation = more useful information.

  1. Standardize the Data
  2. Calculate Covariance Matrix
  3. Find the Principal Components
  4. Pick the Top Directions & Transform Data

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