Learn how to use PCA , a popular unsupervised technique, to transform high-dimensional data into a lower-dimensional representation. See the steps, advantages, disadvantages, and an example of PCA in Python. Want to know about Principal Component Analysis ( PCA) in Machine Learning ? Check out this guide for a complete understanding of PCA in Machine Learning . Read on! PCA (Principal Component Analysis) is a dimensionality reduction technique used in data analysis and machine learning . It helps you to reduce the number of features in a dataset while keeping the most important information. Principal Component Analysis ( PCA ) stands as one of the most powerful techniques for tackling the curse of dimensionality in machine learning . Imagine trying to visualize a dataset with 100 features—it’s impossible for human minds to comprehend 100-dimensional space. PCA elegantly solves this problem by finding a way to represent your high-dimensional data in fewer dimensions while retaining most of the important information. It’s like taking a 3D object and finding the best angle to ...
