Introduction

Principal Component Analysis (PCA) is a popular data analysis technique used to reduce the dimensionality of a dataset. It is widely used in many fields, including machine learning, computer vision, and bioinformatics. PCA is a statistical method that transforms a dataset into a new coordinate system, where the variables are uncorrelated and ranked in order of their contribution to the variance in the data. This blog discusses the details of PCA, its applications, and an example of how to perform PCA. The need for PCA is due to the problems posed by high-dimensional data, such as the curse of dimensionality, sparsity, and overfitting.