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Principal Component Analysis (PCA) is a very useful tool that is widely used for dimensionality reduction and data visualization. It helps in finding patterns and relationships in data by turning high dimensional input into a lower dimensional form with important information being retained.
The number of variables in a dataset may be reduced using a statistical technique called PCA while retaining its main characteristics.
It does this by transforming the original variables into new ones known as principal components, which are used to reduce the dimensions of the data set. They are linear combinations of the original variables, and they possess orthogonality among them.
To carry out PCA we first compute eigenvalues and eigenvectors of the covariance matrix of our data.
Also read:Understanding the Mathematics Behind Principal Component Analysis(PCA)
Python NumPy, scikit-learn R, MATLAB and many others are among programming languages that have implemented this technique called PCA already
Data analysis, machine learning, and statistics are among many fields where PCA can be used meaning it is versatile. It is good to try and learn what this technique is about that can aid in the analysis of high-dimensional data sets. The mastery of PCA comes with experiment and practical application which determines how one applies PCA in real life situations.