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)

Performing mean normalization

In mean normalization, we center the variable at zero and rescale the distribution to the value range. This procedure involves subtracting the mean from each observation and then dividing the result by the difference between the minimum and maximum values:

This transformation results in a distribution centered at 0, with its minimum and maximum values within the range of -1 to 1. In this recipe, we will implement mean normalization with pandas and then with scikit-learn.

How to do it...

We'll begin by importing the required libraries, loading the dataset, and preparing the train and test sets:

  1. Import pandas and the required scikit-learn class and function:
import pandas as pd
from sklearn...