Applications of PCA

PCA is one fundamental algorithm and forms the foundation of ML. It finds use in diverse areas such as noise reduction in images, classification of data in general, anomaly detection, and other applications in medical data correlation. We will explore a couple of widely used applications of PCA in this section.

The scikit-learn library in Python provides functions for PCA. The following example code shows how to leverage PCA for dimensionality reduction while developing a predictive model that uses a PCA projection as input. We will be using PCA on a synthetic dataset while fitting a logistic regression model for classification:

from sklearn.datasets import make_classification
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import RepeatedStratifiedKFold
from sklearn.pipeline import Pipeline
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from numpy import mean
from numpy import std...