Book Image

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

By : Megan Squire
Book Image

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

By: Megan Squire

Overview of this book

Data mining is an integral part of the data science pipeline. It is the foundation of any successful data-driven strategy – without it, you'll never be able to uncover truly transformative insights. Since data is vital to just about every modern organization, it is worth taking the next step to unlock even greater value and more meaningful understanding. If you already know the fundamentals of data mining with Python, you are now ready to experiment with more interesting, advanced data analytics techniques using Python's easy-to-use interface and extensive range of libraries. In this book, you'll go deeper into many often overlooked areas of data mining, including association rule mining, entity matching, network mining, sentiment analysis, named entity recognition, text summarization, topic modeling, and anomaly detection. For each data mining technique, we'll review the state-of-the-art and current best practices before comparing a wide variety of strategies for solving each problem. We will then implement example solutions using real-world data from the domain of software engineering, and we will spend time learning how to understand and interpret the results we get. By the end of this book, you will have solid experience implementing some of the most interesting and relevant data mining techniques available today, and you will have achieved a greater fluency in the important field of Python data analytics.

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Mastering Data Mining with Python - Find patterns hidden in your data
Megan Squire
Aug, 2016 | 268 pages
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Table of Contents (16 chapters)
Mastering Data Mining with Python – Find patterns hidden in your data
Mastering Data Mining with Python – Find patterns hidden in your data
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Expanding Your Data Mining Toolbox
Expanding Your Data Mining Toolbox
What is data mining?
How do we do data mining?
What are the techniques used in data mining?
How do we set up our data mining work environment?
Summary
2
Association Rule Mining
Association Rule Mining
What are frequent itemsets?
Towards association rules
A project – discovering association rules in software project tags
Summary
3
Entity Matching
Entity Matching
What is entity matching?
Entity matching project
Summary
4
Network Analysis
Network Analysis
What is a network?
Measuring a network
Representing graph data
A real project
Summary
5
Sentiment Analysis in Text
Sentiment Analysis in Text
What is sentiment analysis?
The basics of sentiment analysis
Sentiment analysis algorithms
Sentiment mining application
Summary
6
Named Entity Recognition in Text
Named Entity Recognition in Text
Why look for named entities?
Techniques for named entity recognition
Building and evaluating NER systems
Named entity recognition project
Summary
7
Automatic Text Summarization
Automatic Text Summarization
What is automatic text summarization?
Tools for text summarization
Summary
8
Topic Modeling in Text
Topic Modeling in Text
What is topic modeling?
Latent Dirichlet Allocation
Gensim for topic modeling
Gensim LDA for a larger project
Summary
9
Mining for Data Anomalies
Mining for Data Anomalies
What are data anomalies?
Summary
Index
Index

About the Reviewers

Sanjeev Jaiswal is a computer graduate with 7 years of industrial experience. His works involves Perl, Python, and GNU/Linux. He is currently working on projects involving penetration testing, source code review, and security design and implementations.

He is very much interested in web and cloud security. He is also learning NodeJS and cloud security.

Sanjeev loves teaching engineering students and IT professionals. He has been teaching for the last 8 years in his free time. He founded Alien Coders (http://www.aliencoders.org), based on the learning through sharing principle for computer science students and IT professionals in 2010, which became a huge hit in India among engineering students.

You can follow him on Facebook at http://www.facebook.com/aliencoders, on Twitter at @aliencoders, and on GitHub at https://github.com/jassics.

Sanjeev wrote Instant PageSpeed Optimization and co-authored Learning Django Web Development for Packt Publishing. He has reviewed more than 5 books for Packt and looks forward to more such opportunities.

Ron Mitsugo Zacharski is a computational linguist working in the areas of information extraction and machine learning (zacharski.org). He has a BFA in music from the University of Wisconsin at Milwaukee and a PhD in computer science from the University of Minnesota, and he completed a post doctorate in linguistics at the University of Edinburgh. He authored the free online book A Programmer's Guide to Data Mining: The Ancient Art of the Numerati (www.guidetodatamining.com) and co-edited The Grammar-Pragmatics Interface: Essays in Honor of Jeanette K. Gundel, published by John Benjamins. For the majority of his academic life, he has focused on multilingual natural language processing, particularly with lesser-studied languages. Dr. Zacharski is a Zen monk in the Sōtō School lineage of Soyu Matsuoka. He lives in New Mexico.

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