Python Feature Engineering Cookbook

How to do it...

Let's begin by importing the required libraries and preparing the Boston House Prices dataset from scikit-learn:

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

import pandas as pdimport matplotlib.pyplot as pltfrom sklearn.datasets import load_bostonfrom sklearn.model_selection import train_test_splitfrom sklearn.decomposition import PCA

2. Load the Boston House Prices dataset 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. Separate the dataset 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, ...