When taking measurements of real-world objects, we can often get features in very different ranges. For instance, if we are measuring the qualities of an animal, we might have several features, as follows:

For a mathematical-based algorithm to compare each of these features, the differences in the scale, range, and units can be difficult to interpret. If we used the above features in many algorithms, the weight would probably be the most influential feature due to only the larger numbers and not anything to do with the actual effectiveness of the feature.

One of the methods to overcome this is to use a process called preprocessing to normalize the features so that they...