Data Dreamland, Subash Palvel, Data Science, Dimensionality Reduction, Machine Learning.
variance in the data. PCA finds linear combinations of the original features and projects the data onto these new axes. By selecting a subset of the principal components, we can reduce the dimensionality while retaining most of the information. PCA is particularly useful for visualizing