Python Feature Engineering Cookbook

How to do it...

Let's first import the necessary Python libraries:

1. Import the required Python libraries:

import pandas as pdimport numpy as npimport matplotlib.pyplot as pltimport seaborn as snsfrom sklearn.datasets import load_boston

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

boston_dataset = load_boston()

3. Let's capture three of the variables, RM, LSTAT, and CRIM, in a pandas dataframe:

boston = pd.DataFrame(boston_dataset.data, columns=boston_dataset.feature_names)[['RM', 'LSTAT', 'CRIM']]

You can visualize the loaded data using boston.head().

4. Let's make a function to find the limits using the inter-quartile range proximity rule:

def find_skewed_boundaries(df, variable, distance): IQR = df[variable].quantile(0.75) ...