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