What is the difference between SVD and PCA?

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What is the difference between SVD and PCA?​

PCA uses the SVD in its calculation, clearly there is some ‘extra’ analysis done. PCA is map the data to lower dimensional. In order for PCA to do that it should calculate and rank the importance of features/dimensions. There are 2 ways to do so.
Is the covariance matrix orthonormal in PCA?
Since the covariance matrix is symmetric, the matrix is diagonalizable, and the eigenvectors can be normalized such that they are orthonormal. In case PCA used SVD to rank the importance of features, then U matrix will have all features ranked, we choose the first k columns which represent the most important one.
What happens if SVD_solver == ‘ARPACK’?
If svd_solver == ‘arpack’, the number of components must be strictly less than the minimum of n_features and n_samples. Hence, the None case results in: If False, data passed to fit are overwritten and running fit (X).transform (X) will not yield the expected results, use fit_transform (X) instead.

What are the weights in PCA_components_?​

The pca.components_ object contains the weights (also called as ‘loadings’) of each Principal Component. It is using these weights that the final principal components are formed. But what exactly are these weights? how are they related to the Principal components we just formed and how it is calculated?
How to implement PCA in scikit-learn?
Using scikit-learn package, the implementation of PCA is quite straight forward. The module named sklearn.decomposition provides the PCA object which can simply fit and transform the data into Principal components.
What is the use of PCA in machine learning?
Practically PCA is used for two reasons: Dimensionality Reduction: The information distributed across a large number of columns is transformed into principal components (PC) such that the first few PCs can explain a sizeable chunk of the total information (variance). These PCs can be used as explanatory variables in Machine Learning models.