Book Image

Python Feature Engineering Cookbook - Second Edition

By : Soledad Galli
Book Image

Python Feature Engineering Cookbook - Second Edition

By: Soledad Galli

Overview of this book

Feature engineering, the process of transforming variables and creating features, albeit time-consuming, ensures that your machine learning models perform seamlessly. This second edition of Python Feature Engineering Cookbook will take the struggle out of feature engineering by showing you how to use open source Python libraries to accelerate the process via a plethora of practical, hands-on recipes. This updated edition begins by addressing fundamental data challenges such as missing data and categorical values, before moving on to strategies for dealing with skewed distributions and outliers. The concluding chapters show you how to develop new features from various types of data, including text, time series, and relational databases. With the help of numerous open source Python libraries, you'll learn how to implement each feature engineering method in a performant, reproducible, and elegant manner. By the end of this Python book, you will have the tools and expertise needed to confidently build end-to-end and reproducible feature engineering pipelines that can be deployed into production.
Table of Contents (14 chapters)

Technical requirements

In this chapter, we will use the pandas, Matplotlib, and scikit-learn Python libraries. We will also use NLTK from Python, a comprehensive library for NLP and text analysis. You can find the instructions to install NLTK here: http://www.nltk.org/install.html. If you are using the Python Anaconda distribution, follow these instructions to install NLTK: https://anaconda.org/anaconda/nltk.

After you have installed NLTK, open up a Python console and execute the following:

import nltk
nltk.download('punkt')
nltk.download('stopwords')

These commands will download the necessary data for you to be able to run the recipes in this chapter successfully.

Note

If you haven’t downloaded these or other data sources necessary for NLTK functionality, NLTK will raise an error. Read the error message carefully because it will direct you to download the data required to run the command you are trying to execute.