Book Image

Python Feature Engineering Cookbook

By : Soledad Galli
Book Image

Python Feature Engineering Cookbook

By: Soledad Galli

Overview of this book

Feature engineering is invaluable for developing and enriching your machine learning models. In this cookbook, you will work with the best tools to streamline your feature engineering pipelines and techniques and simplify and improve the quality of your code. Using Python libraries such as pandas, scikit-learn, Featuretools, and Feature-engine, you’ll learn how to work with both continuous and discrete datasets and be able to transform features from unstructured datasets. You will develop the skills necessary to select the best features as well as the most suitable extraction techniques. This book will cover Python recipes that will help you automate feature engineering to simplify complex processes. You’ll also get to grips with different feature engineering strategies, such as the box-cox transform, power transform, and log transform across machine learning, reinforcement learning, and natural language processing (NLP) domains. By the end of this book, you’ll have discovered tips and practical solutions to all of your feature engineering problems.
Table of Contents (13 chapters)

Implementing random sample imputation

Random sampling imputation consists of extracting random observations from the pool of available values in the variable. Random sampling imputation preserves the original distribution, which differs from the other imputation techniques we've discussed in this chapter and is suitable for numerical and categorical variables alike. In this recipe, we will implement random sample imputation with pandas and Feature-engine.

How to do it...

Let's begin by importing the required libraries and tools and preparing the dataset:

  1. Let's import pandas, the train_test_split function from scikit-learn, and RandomSampleImputer from Feature-engine:
import pandas as pd
from...