Robert Layton - Learning Data Mining with Python
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Harness the power of Python to analyze data and create insightful predictive models
About This Book- Learn data mining in practical terms, using a wide variety of libraries and techniques
- Learn how to find, manipulate, and analyze data using Python
- Step-by-step instructions on creating real-world applications of data mining techniques
If you are a programmer who wants to get started with data mining, then this book is for you.
What You Will Learn- Apply data mining concepts to real-world problems
- Predict the outcome of sports matches based on past results
- Determine the author of a document based on their writing style
- Use APIs to download datasets from social media and other online services
- Find and extract good features from difficult datasets
- Create models that solve real-world problems
- Design and develop data mining applications using a variety of datasets
- Set up reproducible experiments and generate robust results
- Recommend movies, online celebrities, and news articles based on personal preferences
- Compute on big data, including real-time data from the Internet
The next step in the information age is to gain insights from the deluge of data coming our way. Data mining provides a way of finding this insight, and Python is one of the most popular languages for data mining, providing both power and flexibility in analysis.
This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. Next, we move on to more complex data types including text, images, and graphs. In every chapter, we create models that solve real-world problems.
There is a rich and varied set of libraries available in Python for data mining. This book covers a large number, including the IPython Notebook, pandas, scikit-learn and NLTK.
Each chapter of this book introduces you to new algorithms and techniques. By the end of the book, you will gain a large insight into using Python for data mining, with a good knowledge and understanding of the algorithms and implementations.
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Copyright 2015 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
First published: July 2015
Production reference: 1230715
Published by Packt Publishing Ltd.
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Birmingham B3 2PB, UK.
ISBN 978-1-78439-605-3
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Author
Robert Layton
Reviewers
Asad Ahamad
P Ashwin
Christophe Van Gysel
Edward C. Delaporte V
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Robert Layton has a PhD in computer science and has been an avid Python programmer for many years. He has worked closely with some of the largest companies in the world on data mining applications for real-world data and has also been published extensively in international journals and conferences. He has extensive experience in cybercrime and text-based data analytics, with a focus on behavioral modeling, authorship analysis, and automated open source intelligence. He has contributed code to a number of open source libraries, including the scikit-learn library used in this book, and was a Google Summer of Code mentor in 2014. Robert runs a data mining consultancy company called dataPipeline, providing data mining and analytics solutions to businesses in a variety of industries.
Asad Ahamad is a data enthusiast and loves to work on data to solve challenging problems.
He did his master's degree in industrial mathematics with computer application at Jamia Millia Islamia, New Delhi. He admires mathematics a lot and always tries to use it to gain maximum profit for businesses.
He has good experience working in data mining, machine learning, and data science and has worked for various multinationals in India. He mainly uses R and Python to perform data wrangling and modeling. He is fond of using open source tools for data analysis.
He is an active social media user. Feel free to connect with him on Twitter at @asadtaj88.
P Ashwin is a Bangalore-based engineer who wears many different hats depending on the occasion. He graduated from IIIT, Hyderabad at in 2012 with an M Tech in computer science and engineering. He has a total of 5 years of experience in the software industry, where he has worked in different domains such as testing, data warehousing, replication, and automation. He is very well versed in DB concepts, SQL, and scripting with Bash and Python. He has earned professional certifications in products from Oracle, IBM, Informatica, and Teradata. He's also an ISTQB-certified tester.
In his free time, he volunteers in different technical hackathons or social service activities. He was introduced to Raspberry Pi in one of the hackathons and he's been hooked on it ever since. He writes a lot of code in Python, C, C++, and Shell on his Raspberry Pi B+ cluster. He's currently working on creating his own Beowulf cluster of 64 Raspberry Pi 2s.
Christophe Van Gysel is pursuing a doctorate degree in computer science at the University of Amsterdam under the supervision of Maarten de Rijke and Marcel Worring. He has interned at Google, where he worked on large-scale machine learning and automated speech recognition. During his internship in Facebook's security infrastructure team, he worked on information security and implemented measures against compression side-channel attacks. In the past, he was active as a security researcher. He discovered and reported security vulnerabilities in the web services of Google, Facebook, Dropbox, and PayPal, among others.
Edward C. Delaporte V leads a software development group at the University of Illinois, and he has contributed to the documentation of the Kivy framework. He is thankful to all those whose contributions to the open source community made his career possible, and he hopes this book helps continue to attract enthusiasts to software development.
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If you have ever wanted to get into data mining, but didn't know where to start, I've written this book with you in mind.
Many data mining books are highly mathematical, which is great when you are coming from such a background, but I feel they often miss the forest for the treesthat is, they focus so much on how the algorithms work, that we forget about why we are using these algorithms.
In this book, my aim has been to create a book for those who can program and want to learn data mining. By the end of this book, my aim is that you have a good understanding of the basics, some best practices to jump into solving problems with data mining, and some pointers on the next steps you can take.
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