Principal-Component-Analysis

Introduction Principal component analysis (PCA) is a statistical procedure that is used to reduce the dimensionality. It uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.

It is often used as a dimensionality reduction technique.

You can reduce dimensionality by limiting the number of principal components to keep based on cumulative explained variance.
Particularly, in this, we need to transform variables into a new set of variables. As these are a linear combination of original variables. These new set of variables are known as principal components.

Steps:

-> Standardize the data
-> Covariance: Find the covariance matrix for your dataset
-> Eigenvectors: Find the eigenvectors of that matrix
-> Ordering: Sort the eigenvectors/'dimensions' from biggest to smallest variance
-> Projection / Data reduction: Use the eigenvectors corresponding to the largest variance to project the dataset into a reduced- dimensional space