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. Learn what is principal component analysis in machine learning , its applications, and how PCA helps in dimensionality reduction. Step-by-step explanation with use cases. 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 ...
