*2.2.2.2 Principal component analysis (PCA)*

Principal component analysis is the most effective unsupervised learning technique for reducing the dimensionality of data. It simultaneously reduces information loss while increasing interpretability. It facilitates the identification of the dataset's most crucial qualities and makes data easier to plot in 2D and 3D. PCA facilitates the discovery of a series of linear combinations of variables. The Main Components are the names given to these newly altered functions. One of the most well-known pieces of equipment for exploratory information evaluation and predictive modelling is this [20].

Typically, PCA looks for the surface with the lowest dimensionality onto which to project the high-dimensional data. PCA functions by taking into account each attribute's variance since a high attribute demonstrates a solid split between classes, which results in low dimensionality.

Since it uses a feature extraction technique, it keeps the crucial variables and discards the unimportant ones.

Some of the important additives of principal components are given below:

