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Key benefits

Learn and implement feature engineering best practices

Reinforce your learning with the help of multiple hands-on recipes

Build end-to-end feature engineering pipelines that are performant and reproducible

Description

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.

What you will learn

Impute missing data using various univariate and multivariate methods

Encode categorical variables with one-hot, ordinal, and count encoding

Handle highly cardinal categorical variables

Transform, discretize, and scale your variables

Create variables from date and time with pandas and Feature-engine

Combine variables into new features

Extract features from text as well as from transactional data with Featuretools

Create features from time series data with tsfresh

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Frequently bought together

Modern Time Series Forecasting with Python

€39.99

Machine Learning with PyTorch and Scikit-Learn

€41.99

€37.99

Total € 119.97

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Amazon verified reviews

Ram Seshadri Sep 04, 2023

This is a very useful book for those who want to focus on feature engineering in their daily work. If you want you can use the Feature-Engine library which the author has created but if you wanted to understand how to improve your model performance, this is a must have book. I will highlight some of the best features of this book here:1. Imputing, encoding and Transforming features with Feature-Engine is very easy. You get to use a very simple and consistent syntax to perform these steps.2. Extracting features from date-time, text and time series data is very easy with the tools given in this book3. Finally creating new features such as splines, polynomials and periodic features is made easy.What is missing in this book is feature selection though it is covered in the feature-engine library. That’s probably a task and a book for another day. However this book comprehensively covers the large gamut of feature engineering techniques and is a great cookbook to that you can use from day one. Highly recommended!

Amazon Verified review

Avishek D. Nov 21, 2022

This is a great book for a new learner and also an experienced professional. The feature engineering techniques are explained very well. It covers almost all techniques. The addition of Time Series in this edition is like an icing on the cake. Every technique is very easy to understand. Feature Scaling, Extraction, Outlier Treatment, etc are well explained with examples. A must read of anyone in Data Science.

Rahul Raoniar Feb 03, 2023

I recently purchased "Python Feature Engineering Cookbook" (second edition) by Soledad Galli and was impressed with the updates and flawless codes. This book has become an integral part of my research work. The author did an excellent job in explaining various ways to extract good features for a model, making it easy to understand and apply in real-world scenarios. The 2nd Edition includes updated information, codes, and techniques, making it a valuable resource for staying up-to-date with the latest advancements in the field.As a mentor to master's students, I highly recommend this book to anyone looking to improve their feature engineering skills. The book is a comprehensive and well-structured guide with over 70 recipes for creating, engineering, and transforming features. It is my go-to recommendation for students looking to gain a deeper understanding of the field.Overall, I am extremely satisfied with this book and would highly recommend it to anyone looking to improve their feature engineering skills in the field of machine learning.

Mr. K. Inanam Dec 05, 2022

This book provides an excellent resource for feature engineering for machine learning. The author provides a concise explanation of each feature engineering method, which is followed by a Python implementation that makes use of open-source libraries. Like this, we can quickly understand what is possible in terms of feature engineering for a specific dataset and machine learning algorithm and also apply these transformations in a few lines of code with the available open-source implementations.

Kevin Dec 28, 2022

As other reviews have stated the book delivers what it says it will; Python code that generates a lot of feature-engineering. I find this book to be fantastic, and Sole's work overall, as it gives life to new feature-engineering possibilities and does it fast. Long gone are the days of writing your own custom transformers or unique time-series features. This book automates a lot of that headache and will absolutely be the first reference I go to when I need to handle a new feature. I personally hadn't dealt with tsfresh prior to reading through and it brought to life instantaneous time-series features I no longer have to write scripts for. A very happy customer on that knowledge alone! Per usual, Sole continues to advance the ML community for the betterment of all.

Python Feature Engineering Cookbook: Over 70 recipes for creating, engineering, and transforming features to build machine learning models , Second Edition

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