When we handle large volumes of data, some issues occur spontaneously. How to build a representative model of a set of hundreds of variables? How to view data across countless dimensions? To address these issues, we must adopt a series of techniques called dimensionality reduction. Dimensionality reduction is the process of converting a set of data with many variables into data with lesser dimensions while ensuring similar information. The aim is to reduce the number of dimensions in a dataset through either feature selection or feature extraction without significant loss of details. Feature selection approaches try to find a subset of the original variables. Feature extraction reduces the dimensionality of the data by transforming it into new features.
Dimensionality reduction techniques are used...
