Standardizing the features

Standardization is the process of centering the variable at 0 and standardizing the variance to 1. To standardize features, we subtract the mean from each observation and then divide the result by the standard deviation:

The result of the preceding transformation is called the z-score and represents how many standard deviations a given observation deviates from the mean. In this recipe, we will implement standardization with 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 the required Python packages, classes, and functions:

   import matplotlib.pyplot as plt
   import pandas as pd
   from sklearn.datasets import fetch_california_housing
   from sklearn.model_selection import train_test_split
   from sklearn.preprocessing import StandardScaler

2. Let’s load the California housing dataset from scikit-learn into a dataframe, and drop...