Reduce dimensionality using PCA in Python

To view more free Data Science code recipes, visit us at: https://bit.ly/3H1ZsD7 The dataset consists of rows and columns. When you fit this data onto a model, the model visualizes the number of columns as dimensions. The more the number of columns, the greater is the dimensions which lead to an increase in time and space complexities. PCA stands for Principal Component Analysis and is used to reduce the number of columns/features while retaining the essence of those features. This video teaches you to reduce dimensionality using PCA in python. Why ProjectPro? With ProjectPro, you get access to 100,000+ lines of verified, downloadable code and 50,000+ minutes of videos and practical hands-on experience from real industry projects as well as Tech support and 1-1 sessions. So, check out ProjectPro - the only solution for solved industrial-grade projects. Also, subscribe to our channel to get video updates.