Python Feature Engineering Cookbook

How to do it...

Let's begin by importing the required libraries and getting the dataset ready:

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

import pandas as pdfrom sklearn.datasets import load_bostonfrom sklearn.model_selection import train_test_splitfrom sklearn.tree import DecisionTreeRegressorfrom sklearn.model_selection import GridSearchCV

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. Let's separate the dataset into train and test sets to conform to machine learning best practices:

X_train, X_test, y_train, y_test = train_test_split( ...