In the real world, we build Machine Learning models on top of features and parameters. These features can be multi-dimensional and large in number. Sometimes, the features may be irrelevant and it becomes a difficult task to visualize them.
Here, we use dimensionality reduction to cut down the irrelevant and redundant features with the help of principal variables. These principal variables are the subgroup of the parent variables that conserve the feature of the parent variables.