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  In previous articles, we read about Feature Scaling and two of the most important techniques used for feature scaling, i.e. Standardization  & MinMaxScaling . Here we will see another feature scaling technique that can be used to scale the variables and is somewhat similar to the MinMaxScaling technique. This technique is popularly known as MaxAbsScaling or Maximum Absolute Scaling . What is MaxAbsScaling? Maximum Absolute Scaling is the technique of scaling the data to its absolute maximum value. The logic used here is to divide each value by the Absolute Maximum Value for each variable/column. Doing so will scale down all the values between -1 to 1.  It can be implemented easily in a few lines of code, as shown below in the  practical section.   Note:- Scikit-learn recommends using this transformer on data that is centred at zero or on sparse data. Formula Used:-  MaxAbsScaling Formula Features of MaxAbsScaling:-  1.  Minimum and Maximum values are scaled between [-1,1]:-  Sin

In our previous article/blog we had an overview of Feature Scaling . We saw What is Feature Scaling and How we can use it for our benefit. Basically, we discussed all the theory related to Feature Scaling, lets move ahead and see What are the various ways to achieve Feature Scaling and How we can implement them.  The very first and most important technique is " Standardization " also known as " Z-Score Normalization ".  The basic idea of this technique is to subtract the mean from each value and divide it by the standard deviation. Doing so will centre the data around its mean with unit standard deviation.  Formula Used:-  standardization formula This formula is also known as Z-Score, hence the name  Z-Score Normalization.  What is Z-Score? Internet Defines it as:-  A Z-score is a numerical measurement that describes a value's relationship to the mean of a group o