Data preprocessing moves on to the transformation stage after dealing with the concerns. You use it to convert data into relevant conformations for analysis. Normalization, attribute selection, discretization, and Concept Hierarchy Generation are some of the approaches that can be used to accomplish this. Even for automated methods, sifting through large datasets can take a long time. That is why the data reduction stage is so crucial: it reduces the size of data sets by limiting them to the most important information, increasing storage efficiency while lowering the financial and time expenses of working with them.