Scaling with the median and quantiles

When scaling variables to the median and quantiles, the median value is removed from the observations, and the result is divided by the Inter-Quartile Range (IQR). The IQR is the difference between the 1st quartile and the 3rd quartile, or, in other words, the difference between the 25th quantile and the 75th quantile:

This method is known as robust scaling because it produces more robust estimates for the center and value range of the variable and is recommended if the data contains outliers. In this recipe, we will implement scaling with the median and IQR by utilizing scikit-learn.

How to do it...

To begin, we will import the required packages, load the dataset, and prepare the train and test sets:

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

   import pandas as pd
   from sklearn.datasets import fetch_california_housing
   from sklearn.model_selection import train_test_split
   from sklearn...