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Mastering Data Mining with Python ??? Find patterns hidden in your data

You're reading from   Mastering Data Mining with Python ??? Find patterns hidden in your data Find patterns hidden in your data

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Product type Paperback
Published in Aug 2016
Publisher
ISBN-13 9781785889950
Length 268 pages
Edition 1st Edition
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Author (1):
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Megan Squire Megan Squire
Author Profile Icon Megan Squire
Megan Squire
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What you need for this book

To complete the projects in this book, you will need a version of Python 3.5 or higher. I recommend using Anaconda Python, but any Python distribution will do as long as it is updated and contains the following packages: Numpy, Matplotlib, NetworkX, PyMySQL, Gensim, and NLTK. In Chapter 1, Expanding Your Data Mining Toolbox, we will walk through an easy installation of Python and all these libraries, and each time a library is used later in the book, we will install it or upgrade it together.

Because data mining is obviously data-centric, and because the data sets we are working with are sometimes large or require some type of persistent data storage, I chose to implement some of the data mining algorithms alongside a relational database system. I chose MySQL for accomplishing this since it is an established, easy-to-download and install piece of infrastructure. The chapters where MySQL comes into play are in working with the memory-intensive algorithms in Chapter 2, Association Rule Mining, and Chapter 3, Entity Matching. I also use MySQL for some of the examples in Chapter 9, Mining for Data Anomalies, but it is possible to go through that chapter without MySQL.

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