Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Feature Engineering Made Easy
Feature Engineering Made Easy

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

Arrow left icon
Profile Icon Sinan Ozdemir Profile Icon Susarla
Arrow right icon
€18.99 per month
Full star icon Full star icon Full star icon Full star icon Half star icon 4.4 (11 Ratings)
Paperback Jan 2018 316 pages 1st Edition
eBook
€17.99 €26.99
Paperback
€32.99
Subscription
Free Trial
Renews at €18.99p/m
Arrow left icon
Profile Icon Sinan Ozdemir Profile Icon Susarla
Arrow right icon
€18.99 per month
Full star icon Full star icon Full star icon Full star icon Half star icon 4.4 (11 Ratings)
Paperback Jan 2018 316 pages 1st Edition
eBook
€17.99 €26.99
Paperback
€32.99
Subscription
Free Trial
Renews at €18.99p/m
eBook
€17.99 €26.99
Paperback
€32.99
Subscription
Free Trial
Renews at €18.99p/m

What do you get with a Packt Subscription?

Free for first 7 days. $19.99 p/m after that. Cancel any time!
Product feature icon Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!
Product feature icon 50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.
Product feature icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Product feature icon Thousands of reference materials covering every tech concept you need to stay up to date.
Subscribe now
View plans & pricing
Table of content icon View table of contents Preview book icon Preview Book

Feature Engineering Made Easy

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.

...

The structure, or lack thereof, of data

When given a new dataset, it is first important to recognize whether or not your data is structured or unstructured:

  • Structured (organized) data: Data that can be broken down into observations and characteristics. They are generally organized using a tabular method (where rows are observations and columns are characteristics).

  • Unstructured (unorganized) data: Data that exists as a free-flowing entity and does not follow standard organizational hierarchy such as tabularity. Often, unstructured data appears to us as a blob of data, or as a single characteristic (column).

A few examples that highlight the difference between structured and unstructured data are as follows:

  • Data that exists in a raw free-text form, including server logs and tweets, are unstructured

  • Meteorological data, as reported by scientific instruments...

An example of unstructured data – server logs

As an example of unstructured data, we have pulled some sample server logs from a public source and included them in a text document. We can take a glimpse of what this unstructured data looks like, so we can recognize it in the future:

# Import our data manipulation tool, Pandas
import pandas as pd
# Create a pandas DataFrame from some unstructured Server Logs
logs = pd.read_table('../data/server_logs.txt', header=None, names=['Info'])

# header=None, specifies that the first line of data is the first data point, not a column name
# names=['Info] is me setting the column name in our DataFrame for easier access

We created a DataFrame in pandas called logs that hold our server logs. To take a look, let's call the .head() method to look at the first few rows:

# Look at the first 5...

Quantitative versus qualitative data

To accomplish our diagnoses of the various types of data, we will begin with the highest order of separation. When dealing with structured, tabular data (which we usually will be doing), the first question we generally ask ourselves is whether the values are of a numeric or categorical nature.

Quantitative data are data that are numerical in nature. They should be measuring the quantity of something.

Qualitative data are data that are categorical in nature. They should be describing the quality of something.

Basic examples:

  • Weather measured as temperature in Fahrenheit or Celsius would be quantitative
  • Weather measured as cloudy or sunny would be qualitative
  • The name of a person visiting the White House would be qualitative
  • The amount of blood you donate at a blood drive is quantitative

The first two examples show that we can describe...

The four levels of data

We already know that we can identify data as being either qualitative or quantitative. But, from there, we can go further. The four levels of data are:

  • The nominal level
  • The ordinal level
  • The interval level
  • The ratio level

Each level comes with a varying level of control and mathematical possibilities. It is crucial to know which level data lives on because it will dictate the types of visualizations and operations you are allowed to perform.

The nominal level

The first level of data, the nominal level, has the weakest structure. It consists of data that are purely described by name. Basic examples include blood type (A, O, AB), species of animal, or names of people. These types of data are all qualitative...

Recap of the levels of data

Understanding the various levels of data is necessary to perform feature engineering. When it comes time to build new features, or fix old ones, we must have ways of identifying how to work with every column.

Here is a quick table to summarize what is and isn't possible at every level:

Level of Measurement

Properties

Examples

Descriptive statistics

Graphs

Nominal

Discrete

Orderless

Binary Responses (True or False)

Names of People

Colors of paint

Frequencies/Percentages
Mode

Bar

Pie

Ordinal

Ordered categories

Comparisons

Likert Scales

Grades on an exam

Frequencies

Mode

Median

Percentiles

Bar

Pie

Stem and leaf

Interval

Differences between ordered values have meaning

Deg. C or F

Some Likert Scales (must be specific)

Frequencies

Mode

Median

Mean

Standard Deviation

Bar
Pie
Stem and leaf

Box plot

Histogram...

Summary

Understanding the features that we are working with is step zero of feature engineering. If we cannot understand the data given to us, we will never hope to fix, create, and utilize features in order to create well-performing, machine-learning pipelines. In this chapter, we were able to recognize, and extract the levels of data from our datasets and use that information to create useful and meaningful visuals that shine new lights on our data.

In the next chapter, we will use all of this new-found knowledge of the levels of data to start improving our features, and we will start to use machine-learning to effectively measure the impact of our feature engineering pipelines.

Left arrow icon Right arrow icon
Download code icon Download Code

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.

Who is this book for?

If you are a data science professional or a machine learning engineer looking to strengthen your predictive analytics model, then this book is a perfect guide for you. Some basic understanding of the machine learning concepts and Python scripting would be enough to get started with this book.

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

Product Details

Country selected
Publication date, Length, Edition, Language, ISBN-13
Publication date : Jan 22, 2018
Length: 316 pages
Edition : 1st
Language : English
ISBN-13 : 9781787287600
Category :
Languages :
Tools :

What do you get with a Packt Subscription?

Free for first 7 days. $19.99 p/m after that. Cancel any time!
Product feature icon Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!
Product feature icon 50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.
Product feature icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Product feature icon Thousands of reference materials covering every tech concept you need to stay up to date.
Subscribe now
View plans & pricing

Product Details

Publication date : Jan 22, 2018
Length: 316 pages
Edition : 1st
Language : English
ISBN-13 : 9781787287600
Category :
Languages :
Tools :

Packt Subscriptions

See our plans and pricing
Modal Close icon
€18.99 billed monthly
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Simple pricing, no contract
€189.99 billed annually
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just €5 each
Feature tick icon Exclusive print discounts
€264.99 billed in 18 months
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just €5 each
Feature tick icon Exclusive print discounts

Frequently bought together


Stars icon
Total 98.97
Machine Learning Solutions
€32.99
Feature Engineering Made Easy
€32.99
Python Feature Engineering Cookbook
€32.99
Total 98.97 Stars icon

Table of Contents

9 Chapters
Introduction to Feature Engineering Chevron down icon Chevron up icon
Feature Understanding – What's in My Dataset? Chevron down icon Chevron up icon
Feature Improvement - Cleaning Datasets Chevron down icon Chevron up icon
Feature Construction Chevron down icon Chevron up icon
Feature Selection Chevron down icon Chevron up icon
Feature Transformations Chevron down icon Chevron up icon
Feature Learning Chevron down icon Chevron up icon
Case Studies Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.4
(11 Ratings)
5 star 72.7%
4 star 9.1%
3 star 9.1%
2 star 0%
1 star 9.1%
Filter icon Filter
Top Reviews

Filter reviews by




Metodi Todorov Sep 30, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I really like these kind of books, which explains complex topics - short and clear with simple examples. Thank You.
Amazon Verified review Amazon
Amazon Customer Mar 26, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is step-by-step, easy to follow, and really focused on an applied approach. It's already helped me at work on a current project!
Amazon Verified review Amazon
Temitope Aug 11, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Amazing book..
Amazon Verified review Amazon
joe hoeller Aug 14, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Very good book for beginners that want to build a solid foundation, and even a bit beyond that, before moving to more advanced principals.
Amazon Verified review Amazon
Rohan Shamapant Mar 24, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A really informative read, includes some of the most relevant information pertaining to data learning.
Amazon Verified review Amazon
Get free access to Packt library with over 7500+ books and video courses for 7 days!
Start Free Trial

FAQs

What is included in a Packt subscription? Chevron down icon Chevron up icon

A subscription provides you with full access to view all Packt and licnesed content online, this includes exclusive access to Early Access titles. Depending on the tier chosen you can also earn credits and discounts to use for owning content

How can I cancel my subscription? Chevron down icon Chevron up icon

To cancel your subscription with us simply go to the account page - found in the top right of the page or at https://subscription.packtpub.com/my-account/subscription - From here you will see the ‘cancel subscription’ button in the grey box with your subscription information in.

What are credits? Chevron down icon Chevron up icon

Credits can be earned from reading 40 section of any title within the payment cycle - a month starting from the day of subscription payment. You also earn a Credit every month if you subscribe to our annual or 18 month plans. Credits can be used to buy books DRM free, the same way that you would pay for a book. Your credits can be found in the subscription homepage - subscription.packtpub.com - clicking on ‘the my’ library dropdown and selecting ‘credits’.

What happens if an Early Access Course is cancelled? Chevron down icon Chevron up icon

Projects are rarely cancelled, but sometimes it's unavoidable. If an Early Access course is cancelled or excessively delayed, you can exchange your purchase for another course. For further details, please contact us here.

Where can I send feedback about an Early Access title? Chevron down icon Chevron up icon

If you have any feedback about the product you're reading, or Early Access in general, then please fill out a contact form here and we'll make sure the feedback gets to the right team. 

Can I download the code files for Early Access titles? Chevron down icon Chevron up icon

We try to ensure that all books in Early Access have code available to use, download, and fork on GitHub. This helps us be more agile in the development of the book, and helps keep the often changing code base of new versions and new technologies as up to date as possible. Unfortunately, however, there will be rare cases when it is not possible for us to have downloadable code samples available until publication.

When we publish the book, the code files will also be available to download from the Packt website.

How accurate is the publication date? Chevron down icon Chevron up icon

The publication date is as accurate as we can be at any point in the project. Unfortunately, delays can happen. Often those delays are out of our control, such as changes to the technology code base or delays in the tech release. We do our best to give you an accurate estimate of the publication date at any given time, and as more chapters are delivered, the more accurate the delivery date will become.

How will I know when new chapters are ready? Chevron down icon Chevron up icon

We'll let you know every time there has been an update to a course that you've bought in Early Access. You'll get an email to let you know there has been a new chapter, or a change to a previous chapter. The new chapters are automatically added to your account, so you can also check back there any time you're ready and download or read them online.

I am a Packt subscriber, do I get Early Access? Chevron down icon Chevron up icon

Yes, all Early Access content is fully available through your subscription. You will need to have a paid for or active trial subscription in order to access all titles.

How is Early Access delivered? Chevron down icon Chevron up icon

Early Access is currently only available as a PDF or through our online reader. As we make changes or add new chapters, the files in your Packt account will be updated so you can download them again or view them online immediately.

How do I buy Early Access content? Chevron down icon Chevron up icon

Early Access is a way of us getting our content to you quicker, but the method of buying the Early Access course is still the same. Just find the course you want to buy, go through the check-out steps, and you’ll get a confirmation email from us with information and a link to the relevant Early Access courses.

What is Early Access? Chevron down icon Chevron up icon

Keeping up to date with the latest technology is difficult; new versions, new frameworks, new techniques. This feature gives you a head-start to our content, as it's being created. With Early Access you'll receive each chapter as it's written, and get regular updates throughout the product's development, as well as the final course as soon as it's ready.We created Early Access as a means of giving you the information you need, as soon as it's available. As we go through the process of developing a course, 99% of it can be ready but we can't publish until that last 1% falls in to place. Early Access helps to unlock the potential of our content early, to help you start your learning when you need it most. You not only get access to every chapter as it's delivered, edited, and updated, but you'll also get the finalized, DRM-free product to download in any format you want when it's published. As a member of Packt, you'll also be eligible for our exclusive offers, including a free course every day, and discounts on new and popular titles.