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Hands-On Data Science and Python Machine Learning

You're reading from Hands-On Data Science and Python Machine Learning Perform data mining and machine learning efficiently using Python and Spark

Product type Paperback

Published in Jul 2017

Publisher Packt

ISBN-13 9781787280748

Length 420 pages

Edition 1st Edition

Languages

Python

Tools

NumPy

Concepts

Data Mining

Author (1):

Frank Kane

View More author details

Table of Contents (16) Chapters

Title Page

Credits

About the Author

www.PacktPub.com

Customer Feedback

Preface

1. Getting Started FREE CHAPTER

2. Statistics and Probability Refresher, and Python Practice

3. Matplotlib and Advanced Probability Concepts

4. Predictive Models

5. Machine Learning with Python

6. Recommender Systems

7. More Data Mining and Machine Learning Techniques

8. Dealing with Real-World Data

9. Apache Spark - Machine Learning on Big Data

10. Testing and Experimental Design

Covariance and correlation

Next, we're going to talk about covariance and correlation. Let's say I have two different attributes of something and I want to see if they're actually related to each other or not. This section will give you the mathematical tools you need to do so, and we'll dive into some examples and actually figure out covariance and correlation using Python. These are ways of measuring whether two different attributes are related to each other in a set of data, which can be a very useful thing to find out.

Defining the concepts

Imagine we have a scatter plot, and each one of the data points represents a person that we measured, and we're plotting their age on one axis versus their income on another. Each one of these dots would represent a person, for example their x value represents their age and the y value represents their income. I'm totally making this up, this is fake data.

Now if I had a scatter plot that looks like the left one in the preceding image, you see that these...

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Authors (1)

Frank Kane

Frank Kane

Frank Kane has spent nine years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers all the time. He holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology and teaches others about big data analysis.

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