Python Feature Engineering Cookbook

How to do it...

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

1. Import the required Python packages, classes, and function:

import numpy as npimport pandas as pdfrom sklearn.datasets import load_bostonfrom sklearn.model_selection import train_test_splitfrom sklearn.preprocessing import Normalizer

2. Let's load the Boston House Prices dataset from scikit-learn into a pandas dataframe:

boston_dataset = load_boston()data = pd.DataFrame(boston_dataset.data, columns=boston_dataset.feature_names)data['MEDV'] = boston_dataset.target

3. Let's divide the data into train and test sets:

X_train, X_test, y_train, y_test = train_test_split( data.drop('MEDV', axis=1), data['MEDV'], test_size=0.3, random_state=0) ...