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Mastering Predictive Analytics with Python

You're reading from   Mastering Predictive Analytics with Python Exploit the power of data in your business by building advanced predictive modeling applications with Python

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Product type Paperback
Published in Aug 2016
Publisher
ISBN-13 9781785882715
Length 334 pages
Edition 1st Edition
Languages
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Author (1):
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Joseph Babcock Joseph Babcock
Author Profile Icon Joseph Babcock
Joseph Babcock
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Table of Contents (11) Chapters Close

Preface 1. From Data to Decisions – Getting Started with Analytic Applications FREE CHAPTER 2. Exploratory Data Analysis and Visualization in Python 3. Finding Patterns in the Noise – Clustering and Unsupervised Learning 4. Connecting the Dots with Models – Regression Methods 5. Putting Data in its Place – Classification Methods and Analysis 6. Words and Pixels – Working with Unstructured Data 7. Learning from the Bottom Up – Deep Networks and Unsupervised Features 8. Sharing Models with Prediction Services 9. Reporting and Testing – Iterating on Analytic Systems Index

Conventions

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "Let's start by peeking at the beginning and end of the data using head() and tail()."

Any command-line input or output is written as follows:

rdd_data.coalesce(2).getNumPartitions()

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "Returning to the Files tab, you will notice two options in the top right-hand corner."

Note

Warnings or important notes appear in a box like this.

Tip

Tips and tricks appear like this.

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