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An In-depth Guide to PCA with NumPy
Through eigenvalue analysis
Principal Component Analysis (hereafter, PCA) is one of the most popular dimensionality reduction techniques used in machine learning.
It is considered a linear dimensionality reduction technique as it finds a linear combination of input features in a lower dimensional form.
More precisely,
PCA is a linear dimensionality reduction technique that transforms the p number of input variables into a smaller k (k << p) number of uncorrelated variables called principal components by taking advantage of the existing correlations between the input variables in the dataset[ref¹]
ref¹: 3 Easy Steps to Perform Dimensionality Reduction Using Principal Component Analysis (PCA)
We’ve performed PCA so many times before in my previous articles. There, we always used the Scikit-learn popular PCA() function to perform PCA.
Today, we will apply PCA to a dataset without using the PCA() function, but using some specific functions in NumPy. The entire process seems to be tedious, but it is a great way to get an in-depth…