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Before training a model, most applications need first transforming the data into a new representation. Applying pre-processing modifications to input data before presenting it to a network is almost always helpful, and the choice of pre-processing will be one of the most important variables in determining the final system’s performance. The reduction of the dimensionality of the input data is another key method in which network performance can be enhanced, sometimes dramatically. To produce inputs for the network, dimensionality reductions entail creating linear or nonlinear combinations of the original variables. Feature extraction is the process of creating such input combinations, which are frequently referred to as features. The main motivation for dimensionality reduction is to help mitigate the worst impacts of high dimensionality.