Course Content

  • PCA
  • PCA

Course Content

FAQs

Principal Component Analysis (PCA) is a statistical process that turns a set of correlated variables into a set of uncorrelated variables using an orthogonal transformation. In exploratory data analysis and machine learning for predictive models, PCA is the most extensively used tool.

Principal Component Analysis is an unsupervised learning approach used in machine learning to reduce dimensionality. With the help of orthogonal transformation, it is a statistical technique that turns observations of correlated features into a set of linearly uncorrelated data.

The major components are orthogonal because they are the eigenvectors of a covariance matrix. The dataset on which the PCA approach will be applied must be scaled. The relative scale has an impact on the outcomes. It is a means of describing data in layman's terms.

PCA is a method for lowering the dimensionality of such datasets, boosting interpretability while minimising information loss. It accomplishes this by generating new uncorrelated variables that optimise variance in a sequential manner.

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