How it works...

The sklearn.preprocessing package provides several common utility functions and transformer classes to modify the features available in a representation that best suits our needs. In this recipe, the scale() function has been used (z-score standardization).  In summary, the z-score (also called the standard score) represents the number of standard deviations by which the value of an observation point or data is greater than the mean value of what is observed or measured. Values more than the mean have positive z-scores, while values less than the mean have negative z-scores. The z-score is a quantity without dimensions that is obtained by subtracting the population's mean from a single rough score and then dividing the difference by the standard deviation of the population.