I would like to explain about Dimensionality Reduction using PCA using a Real time dataset.
Here is a short brief about Dimensionality Reduction and PCA.
Dimensionality Reduction with PCA
Principal Component Analysis (PCA) is a statistical technique used to reduce the dimensionality of high-dimensional datasets while preserving most of the original variance. It works by transforming the original variables into a new set of uncorrelated variables called principal components.
These components are ordered by the amount of variance they explain, allowing for the selection of a subset that captures the most important information in the data. PCA is widely used in various fields, including image processing, genetics, finance, and more, for tasks such as data visualization, noise reduction, and feature extraction.
I would like to explain about Dimensionality Reduction using PCA using a Real time dataset. Here is a short brief about Dimensionality Reduction and PCA.
Dimensionality Reduction with PCA
Principal Component Analysis (PCA) is a statistical technique used to reduce the dimensionality of high-dimensional datasets while preserving most of the original variance. It works by transforming the original variables into a new set of uncorrelated variables called principal components. These components are ordered by the amount of variance they explain, allowing for the selection of a subset that captures the most important information in the data. PCA is widely used in various fields, including image processing, genetics, finance, and more, for tasks such as data visualization, noise reduction, and feature extraction.