How Does PCA Dimension Reduction Work For Images?

In machine learning, we need lots of data to build an efficient model, but dealing with a larger dataset is not an easy task we need to work hard in preprocessing the data and as a data scientist we will come across a situation dealing with a large number of variables here PCA (principal component analysis) is dimension reduction technique helps in dealing with those problems. In this article, we will demonstrate how to work on larger data and images using a famous dimension reduction technique PCA( principal component analysis). PCA is a dimensionality reduction that is often used to reduce the dimension of the variables of a larger dataset that is compressed to the smaller one which contains most of the information to build an efficient model. In a real-time scenario when you are working reducing the number of variables in the dataset you need compromise on model accuracy but using PCA will give good accuracy. The idea of PCA is to reduce the variables in the dataset and preserve data as much as possible.