Implementing maximum absolute scaling

Maximum absolute scaling scales the data to its maximum value; that is, it divides every observation by the maximum value of the variable:

The result of the preceding transformation is a distribution in which the values vary approximately within the range of -1 to 1. In this recipe, we will implement maximum absolute scaling with scikit-learn.

Scikit-learn recommends using this transformer on data that is centered at zero or on sparse data.

How to do it...

Let's begin by importing the required packages, loading the dataset, and preparing the train and test sets:

1. Import pandas and the required scikit-learn classes and function:

import pandas as pd
from sklearn.datasets import load_boston...