Coding is a skill that is now essential in every industry, even apart from IT, since it is the base of machine learning and AI, which are the two cornerstones of future innovations. Python, one of the most used programming languages in the world, has a vast library and is used in almost all sectors unanimously because of its ease and flexibility. Given below is a short account of how to perform two important algorithms, and why they’re necessary.

What is StandardScaler Transform?

In simplest terms, StandardScaler is used so that the data (distribution) is arranged so as to have a standard deviation 1 and mean value 0. For data with multiple variables, this is done independently for each variable, or each column in the table.

How can it be implemented?

This algorithm uses the strictest form of standardization to transform data, and uses the following formula:

a_scaled = (a-m) / d

where m is the mean and d is the standard deviation.

First, define a StandardScaler instance with default hyperparameters. After that, a fit_transform() function can be called to pass this instance to a dataset, thus creating a new dataset, which is basically just a transformed version of the previous one.

What is MinMaxScaler Transform?

This transform algorithm uses a given range to scale and transform each feature. The feature_range parameter is specified with default at (0,1), and works better for cases with non-Gaussian distribution or with small standard deviation, and best for data with no outliers.

a_scaled = (a – min(a)) / (max(a) – min(a))

Importing and usage of the MinMaxScaler is exactly the same as of StandardScaler, with only a few parameters being different on a new instance initiation.

A MinMaxScaler instance has to be defined by default hyperparameters. Then, the fit_transform() function can be called to pass it to the main dataset, which now becomes a transformed version of itself.