Python Feature Engineering Cookbook Detailed Introduction

Overview

Python Feature Engineering Cookbook is a professional technical book published by Packt Publishing. It provides over 70 practical recipes for creating, engineering, and transforming features to build machine learning models. The book's code repository is hosted on GitHub, offering learners complete practical code.

Author Introduction

Soledad Galli is an experienced Lead Data Scientist with over 10 years of experience in world-class academic institutions and renowned companies. She has researched, developed, and deployed machine learning models for insurance claims, credit risk assessment, and fraud prevention into production. Soledad received the Data Science Leader Award in 2018 and was recognized as one of LinkedIn's voices in Data Science and Analytics in 2019.

Key Content Features

Core Skills Covered

This book covers the following exciting features:

• Streamlining Feature Engineering Pipelines: Simplify the feature engineering process using powerful Python packages.
• Missing Value Handling: Master techniques for imputing missing values.
• Categorical Variable Encoding: Encode categorical variables using various techniques.
• Text Feature Extraction: Quickly and efficiently extract insights from text.
• Time Series Feature Development: Develop features from transactional and time-series data.
• Feature Combination: Derive new features by combining existing variables.
• Variable Transformation: Learn how to transform, discretize, and scale variables.
• Time Feature Creation: Create informative variables from dates and times.

Technical Architecture

# Example code structure
def get_first_cabin(row):
    try:
        return row.split()[0]
    except:
        return np.nan

Technical Requirements

Software Requirements

Chapter	Required Software	Operating System Requirements
1-11	Python 3.5+, Anaconda Distribution, IDE (personal preference)	Windows, Mac OS X, Linux (any version)

Prerequisites for Learning

This book is suitable for machine learning professionals, AI engineers, data scientists, and NLP and Reinforcement Learning engineers who want to optimize and enrich their machine learning models using the best features. Prior knowledge of machine learning and Python programming will be beneficial for understanding the concepts covered in this book.

Content Organization

Chapter Structure

All code is organized into folders, comprising 11 chapters arranged in a natural progression from basic to advanced. Each chapter provides detailed practical recipes to help readers gradually master various aspects of feature engineering.

Practical Approach

The book adopts a "Cookbook" format, where each recipe is a complete practical case, including:

• Problem description
• Solution
• Code implementation
• Result explanation

Learning Value

Practicality

Feature engineering is invaluable for developing and enriching machine learning models. In this book, you will use the best Python tools to streamline the feature engineering process, master feature engineering techniques, and simplify and improve code quality.

Production-Ready

This book not only provides theoretical knowledge but, more importantly, offers practical skills and code directly applicable to production environments, helping readers build end-to-end feature engineering pipelines.

Additional Resources

Supplementary Materials

A PDF file containing color images of the book's screenshots/diagrams is also provided to enhance the learning experience.

Version Updates

This project has multiple versions:

• First Edition (Original)
• Second Edition (Enhanced)
• Third Edition (Latest)

Each version has a corresponding code repository on GitHub, continuously updated and maintained.

Summary

Python Feature Engineering Cookbook is a highly practical technical book that systematically introduces various aspects of Python feature engineering through over 70 hands-on recipes. Whether you are a beginner or an experienced data scientist, you will gain valuable practical experience and skill enhancement from it.