Python Feature Engineering Cookbook

How to do it...

Let's begin by importing the necessary libraries:

1. Import the required Python libraries and a linear regression class:

import pandas as pdimport numpy as npimport matplotlib.pyplot as pltimport seaborn as snsfrom sklearn.linear_model import LinearRegression

To proceed with this recipe, let's create a toy dataframe with an x variable that follows a normal distribution and shows a linear relationship with a y variable.

2. Create an x variable with 200 observations that are normally distributed:

np.random.seed(29)x = np.random.randn(200)

Setting the seed for reproducibility using np.random.seed() will help you get the outputs shown in this recipe.

3. Create a y variable that is linearly related to x with some added random noise: ...