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 techniquesAbout 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 cutting-edge techniques, we've got you covered for any data-related challenge Become a more fluent and confident Python data-analyst, in full control of its extensive range of librariesWho This Book Is ForThis book is fo…
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- Full title :Mastering Data Mining with Python ' Find patterns hidden in your data.
Summary
Learn how to create more powerful data mining applications with this comprehensive Python guide to advance data analytics techniquesAbout 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 cutting-edge techniques, we've got you covered for any data-related challenge Become a more fluent and confident Python data-analyst, in full control of its extensive range of librariesWho This Book Is ForThis 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 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 In DetailData 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. Style and approach This book will teach you the intricacies in applying data mining using real-world scenarios and will act as a very practical solution to your data mining needs.
Read More ↓Edition Statements
- Publication Date:2016.
- Edition:1
Publication Statements
- Publisher(s):Packt Publishing
- Language:eng
- Publication Date:2016.
The Physical Object
- Description:1 online resource
Edition Identifiers
- ISBN:178588591X, 9781785885914, 1785889958, 9781785889950
- oclc:ocn957656005
- LC Classification:T55.4-60.8
- Dewey Decimal:006.312
Table of Contents
Cover ; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Expanding Your Data Mining Toolbox; What is data mining?; How do we do data mining?; The Fayyad et al. KDD process; The Han et al. KDD process; The CRISP-DM process; The Six Steps process; Which data mining methodology is the best?; What are the techniques used in data mining?; What techniques are we going to use in THIS book?; How do we set up our data mining work environment?; Summary; Association Rule Mining; What are frequent itemsets?; The diapers and beer urban legend.
Frequent itemset mining basicsTowards association rules; Support; Confidence; Association rules; An example with data; Added value -- fixing a flaw in the plan; Methods for finding frequent itemsets; A project -- discovering association rules in software project tags; Summary; Entity Matching; What is entity matching?; Merging data; Merging datasets vertically; Merging datasets horizontally; Techniques for matching; Attribute-based similarity matching; Be careful of pairwise comparisons; Leverage rare values; Methods for matching attributes; Range-based or distance from target.
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