Principal Component Analysis, or PCA for short, is an unsupervised, non-parametric statistical method for reducing the dimensionality of data.
PCA is a projection method where data with n features or columns are projected into a subspace with fewer columns, while retaining as much variability of the original dataset.
If you want to learn and implement PCA, check out this Machine Learning Course by Intellipaat.
