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Feature Understanding – What's in My Dataset?

Finally! We can start to jump into some real data, some real code, and some real results. Specifically, we will be diving deeper into the following ideas:

Structured versus unstructured data
Quantitative versus qualitative data
The four levels of data
Exploratory data analysis and data visualizations
Descriptive statistics

Each of these topics will give us a better sense of the data given to us, what is present within the dataset, what is not present within the dataset, and some basic notions on how to proceed from there.

If you're familiar with, Principles of Data Science, much of this echoes Chapter 2, Types of Data of that book. That being said, in this chapter, we will specifically look at our data less from a holistic standpoint, and more from a machine-learning standpoint.

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Key benefits

Design, discover, and create dynamic, efficient features for your machine learning application

Understand your data in-depth and derive astonishing data insights with the help of this Guide

Grasp powerful feature-engineering techniques and build machine learning systems

Description

Feature engineering is the most important step in creating powerful machine learning systems. This book will take you through the entire feature-engineering journey to make your machine learning much more systematic and effective. You will start with understanding your data—often the success of your ML models depends on how you leverage different feature types, such as continuous, categorical, and more, You will learn when to include a feature, when to omit it, and why, all by understanding error analysis and the acceptability of your models. You will learn to convert a problem statement into useful new features. You will learn to deliver features driven by business needs as well as mathematical insights. You'll also learn how to use machine learning on your machines, automatically learning amazing features for your data. By the end of the book, you will become proficient in Feature Selection, Feature Learning, and Feature Optimization.

What you will learn

Identify and leverage different feature types

Clean features in data to improve predictive power

Understand why and how to perform feature selection, and model error analysis

Leverage domain knowledge to construct new features

Deliver features based on mathematical insights

Use machine-learning algorithms to construct features

Master feature engineering and optimization

Harness feature engineering for real world applications through a structured case study

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

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Amazon Customer Sep 16, 2020

This is a very good book to understand the basics of features engineering, highly recommended for beginners. I enjoyed reading it

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Temitope Aug 11, 2020

Amazing book..

J. Felipe Ortega Soto Jan 10, 2020

The content in the book is OK. Today, I think we have other alternatives in Python, like the book by Zheng and Casari (2018), or explaining similar aspects of Feature Engineering along with ML algorithms (VanderPlas, 2016; Géron, 2019).In my opinion, a big drawback of this book (hence, 3 stars) is that **all examples are created for Python 2.7**, as it is clearly stated in the Preface (and you can confirm with a quick look at the example code). Well, Python 2 is officially deprecated as of January 1, 2020. I think this is not acceptable for a practical, "hands-on" guide like this, which should have been written (I think) for Python 3, instead.

gopi Nov 17, 2019

Very good book.

Metodi Todorov Sep 30, 2019

I really like these kind of books, which explains complex topics - short and clear with simple examples. Thank You.

Feature Engineering Made Easy: Identify unique features from your dataset in order to build powerful machine learning systems

What do you get with Print?

Feature Engineering Made Easy

Feature Understanding – What's in My Dataset?

The structure, or lack thereof, of data

An example of unstructured data – server logs

Quantitative versus qualitative data

The four levels of data

The nominal level

Recap of the levels of data

Summary

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Feature Engineering Made Easy: Identify unique features from your dataset in order to build powerful machine learning systems

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What you will learn

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What do you get with Print?

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