Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Apache Spark Machine Learning Blueprints
  • Table Of Contents Toc
Apache Spark Machine Learning Blueprints

Apache Spark Machine Learning Blueprints

By : Alex Liu
1 (1)
close
close
Apache Spark Machine Learning Blueprints

Apache Spark Machine Learning Blueprints

1 (1)
By: Alex Liu

Overview of this book

There's a reason why Apache Spark has become one of the most popular tools in Machine Learning – its ability to handle huge datasets at an impressive speed means you can be much more responsive to the data at your disposal. This book shows you Spark at its very best, demonstrating how to connect it with R and unlock maximum value not only from the tool but also from your data. Packed with a range of project "blueprints" that demonstrate some of the most interesting challenges that Spark can help you tackle, you'll find out how to use Spark notebooks and access, clean, and join different datasets before putting your knowledge into practice with some real-world projects, in which you will see how Spark Machine Learning can help you with everything from fraud detection to analyzing customer attrition. You'll also find out how to build a recommendation engine using Spark's parallel computing powers.
Table of Contents (13 chapters)
close
close
12
Index

Data cleaning

In this section, we will review some methods for data cleaning on Spark with a focus on data incompleteness. Then, we will discuss some of Spark's special features for data cleaning and also some data cleaning solutions made easy with Spark.

After this section, we will be able to clean data and make datasets ready for machine learning.

Dealing with data incompleteness

For machine learning, the more the data the better. However, as is often the case, the more the data, the dirtier it could be—that is, the more the work to clean the data.

There are many issues to deal with data quality control, which can be as simple as data entry errors or data duplications. In principal, the methods of treating them are similar—for example, utilizing data logic for discovery and subject matter knowledge and analytical logic to correct them. For this reason, in this section, we will focus on missing value treatment so as to illustrate our usage of Spark for this topic. Data cleaning...

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Apache Spark Machine Learning Blueprints
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon