In the world of data analysis and machine learning, Principal Component Analysis (PCA) is a powerful technique that finds patterns and relationships in high-dimensional datasets.

By reducing the dimensionality of the data, PCA helps in visualizing and understanding complex datasets.

In this article, we will explore PCA in detail, from its definition and working principles to its applications and limitations.

Definition and Explanation of PCA

PCA is a dimensionality reduction technique that transforms a dataset into a new set of variables called principal components. These components are linear combinations of the original variables and are chosen in such a way that they capture the maximum variance in the data. The first principal component accounts for the largest variance, followed by the second, and so on.