Reduce dimensionality using PCA in Python

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.
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[ProjectProDataScienceProjects]

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[srinathganesh]

Hi. nice video.

Do you have any videos to explain role of PCA in Feature Engineering and Feature Selection. like when to use etc?

[vt]

How can I apply this PCA feature extraction method, for image dataset!!

[alejandroholguinmora]

Careful, the video has a code completely different from the code in the Github repo....