Min-max normalization (usually called feature scaling) performs a linear transformation on the original data. This technique gets all the scaled data in the range (0, 1). The formula to achieve this is the following:
Min-max normalization preserves the relationships among the original data values. The cost of having this bounded range is that we will end up with smaller standard deviations, which can suppress the effect of outliers.
To better understand how to perform a min-max normalization, just analyze an example. We will use a dataset contained in the Airquality.csv file.
This dataset is available at the UCI machine ...
