Data Analysis and Visualisation Review | Principal Component Analysis

Principal Component Analysis | Learn the Basics of Data Analysis and Machine Learning

A linear dimensionality-reduction technique:

Transforming variables (or features) of a large dataset (i.e., multivariate data) into a smaller one that still contains most of the information in the large dataset

By using PCA, we reduce the number of features of a dataset, while preserving as much information as possible.

Reducing data by projecting (geometrically) into a lower dimensions which called principal components (PCs)

Principal components are the directions where there is the most variance, or the directions where the data is most spread out.

PCA is used to extract the important information from a multivariate data table and to express this information as a set of few new variables called PCs (principal components). These new variables correspond to a linear combination of the originals. The number of principal components is less than or equal to the number of original variables.

#PCA
#MachineLearning
#DataAnalysis
#Eigenvector
#Eigenvalue
#R_Programming

PCA analysis using R packages

Created by Dr Reza Rafiee
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