A Complete Guide to Principal Component Analysis -- PCA in Machine Learning

Principal Component Analysis or PCA is a widely used technique for dimensionality reduction of the large data set. Reducing the number of components or features costs some accuracy and on the other hand, it makes the large data set simpler, easy to explore and visualize. Also, it reduces the computational complexity of the model which makes machine learning algorithms run faster. It is always a question and debatable how much accuracy it is sacrificing to get less complex and reduced dimensions data set. In this article, we will be discussing the step by step approach to achieve dimensionality reduction using PCA and then I will also show how can we do all this using python library.