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

Learn how to create more powerful data mining applications with this comprehensive Python guide to advance data analytics techniques
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Mastering Data Mining with Python - Find patterns hidden in your data

Megan Squire

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Learn how to create more powerful data mining applications with this comprehensive Python guide to advance data analytics techniques
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Book Details

ISBN 139781785889950
Paperback268 pages

Book Description

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.

Table of Contents

Chapter 1: 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
Chapter 2: Association Rule Mining
What are frequent itemsets?
Towards association rules
A project – discovering association rules in software project tags
Summary
Chapter 3: Entity Matching
What is entity matching?
Entity matching project
Summary
Chapter 4: Network Analysis
What is a network?
Measuring a network
Representing graph data
A real project
Summary
Chapter 5: Sentiment Analysis in Text
What is sentiment analysis?
The basics of sentiment analysis
Sentiment analysis algorithms
Sentiment mining application
Summary
Chapter 6: 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
Chapter 7: Automatic Text Summarization
What is automatic text summarization?
Tools for text summarization
Summary
Chapter 8: Topic Modeling in Text
What is topic modeling?
Latent Dirichlet Allocation
Gensim for topic modeling
Gensim LDA for a larger project
Summary
Chapter 9: Mining for Data Anomalies
What are data anomalies?
Summary

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 real-world 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

Authors

Table of Contents

Chapter 1: 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
Chapter 2: Association Rule Mining
What are frequent itemsets?
Towards association rules
A project – discovering association rules in software project tags
Summary
Chapter 3: Entity Matching
What is entity matching?
Entity matching project
Summary
Chapter 4: Network Analysis
What is a network?
Measuring a network
Representing graph data
A real project
Summary
Chapter 5: Sentiment Analysis in Text
What is sentiment analysis?
The basics of sentiment analysis
Sentiment analysis algorithms
Sentiment mining application
Summary
Chapter 6: 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
Chapter 7: Automatic Text Summarization
What is automatic text summarization?
Tools for text summarization
Summary
Chapter 8: Topic Modeling in Text
What is topic modeling?
Latent Dirichlet Allocation
Gensim for topic modeling
Gensim LDA for a larger project
Summary
Chapter 9: Mining for Data Anomalies
What are data anomalies?
Summary

Book Details

ISBN 139781785889950
Paperback268 pages
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