Book description
Learn how to create more powerful data mining applications with this comprehensive Python guide to advance data analytics techniques
About This Book
 Dive deeper into data mining with Python ? don't be complacent, sharpen your skills!
 From the most common elements of data mining to cuttingedge techniques, we've got you covered for any datarelated challenge
 Become a more fluent and confident Python dataanalyst, in full control of its extensive range of libraries
Who This Book Is For
This book is for data scientists who are already familiar with some basic data mining techniques such as SQL and machine learning, and who are comfortable with Python. If you are ready to learn some more advanced techniques in data mining in order to become a data mining expert, this is the book for you!
What You Will Learn
 Explore techniques for finding frequent itemsets and association rules in large data sets
 Learn identification methods for entity matches across many different types of data
 Identify the basics of network mining and how to apply it to realworld data sets
 Discover methods for detecting the sentiment of text and for locating named entities in text
 Observe multiple techniques for automatically extracting summaries and generating topic models for text
 See how to use data mining to fix data anomalies and how to use machine learning to identify outliers in a data set
In Detail
Data mining is an integral part of the data science pipeline. It is the foundation of any successful datadriven 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 easytouse 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 stateoftheart and current best practices before comparing a wide variety of strategies for solving each problem. We will then implement example solutions using realworld 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.
Style and approach
This book will teach you the intricacies in applying data mining using realworld scenarios and will act as a very practical solution to your data mining needs.
Publisher resources
Table of contents

Mastering Data Mining with Python – Find patterns hidden in your data
 Table of Contents
 Mastering Data Mining with Python – Find patterns hidden in your data
 Credits
 About the Author
 About the Reviewers
 www.PacktPub.com
 Preface
 1. Expanding Your Data Mining Toolbox
 2. Association Rule Mining

3. Entity Matching
 What is entity matching?
 Entity matching project
 Summary
 4. Network Analysis
 5. Sentiment Analysis in Text
 6. Named Entity Recognition in Text
 7. Automatic Text Summarization
 8. Topic Modeling in Text
 9. Mining for Data Anomalies
 Index
Product information
 Title: Mastering Data Mining with Python – Find patterns hidden in your data
 Author(s):
 Release date: August 2016
 Publisher(s): Packt Publishing
 ISBN: 9781785889950
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