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Danneman Nathan - R mining spatial, text, web, and social media data: create and customize data mioning algorithms: a course in three modules

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Danneman Nathan R mining spatial, text, web, and social media data: create and customize data mioning algorithms: a course in three modules

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Create data mining algorithms

About This Book

  • Develop a strong strategy to solve predictive modeling problems using the most popular data mining algorithms
    • Real-world case studies will take you from novice to intermediate to apply data mining techniques
    • Deploy cutting-edge sentiment analysis techniques to real-world social media data using R

      Who This Book Is For

      This Learning Path is for R developers who are looking to making a career in data analysis or data mining. Those who come across data mining problems of different complexities from web, text, numerical, political, and social media domains will find all information in this single learning path.

      What You Will Learn

    • Discover how to manipulate data in R
    • Get to know top classification algorithms written in R
    • Explore solutions written in R based on R Hadoop projects
    • Apply data management skills in handling large data sets
    • Acquire knowledge about neural network...
  • Danneman Nathan: author's other books


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    R: Mining Spatial, Text, Web, and Social Media Data

    R: Mining Spatial, Text, Web, and Social Media Data

    Create and customize data mining algorithms

    A course in three modules

    BIRMINGHAM - MUMBAI R Mining Spatial Text Web and Social Media Data - photo 1

    BIRMINGHAM - MUMBAI

    R: Mining Spatial, Text, Web, and Social Media Data

    Copyright 2016 Packt Publishing

    All rights reserved. No part of this course 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 course to ensure the accuracy of the information presented. However, the information contained in this course is sold without warranty, either express or implied. Neither the authors, 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 course.

    Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this course by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

    Published on: April 2017

    Published by Packt Publishing Ltd.

    Livery Place

    35 Livery Street

    Birmingham B3 2PB, UK.

    ISBN 978-1-78829-374-7

    www.packtpub.com

    Credits

    Authors

    Bater Makhabel

    Pradeepta Mishra

    Nathan Danneman

    Richard Heimann

    Reviewers

    Jason H.D. Cho

    Gururaghav Gopal

    Vibhav Kamath

    Hasan Kurban

    Alexey Grigorev

    Carlos J. Gil Bellosta

    Vibhav Vivek Kamath

    Feng Mai

    Ajay Ohri

    Yanchang Zhao

    Content Development Editor

    Trusha Shriyan

    Graphics

    Jason Monterio

    Production Coordinator

    Melwyn Dsa

    Preface

    The necessity to handle many, complex statistical analysis projects is hitting statisticians and analysts across the globe. With increasing interest in data analysis, R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions in the real world. With its constantly growing community and plethora of packages, R offers functionality for dealing with a truly vast array of problems.

    It's been decades since the R programming language was born and has become an eminent and well known not only within the community of scientist, but also in the wider community of developers. It has grown into a powerful tool to help developers produce efficient and consistent source code for data related tasks. The R development team and independent contributors have created good documentation so getting started programming with R isn't that hard.

    What this learning path covers

    , Learning Data mining with R, will teach you how to manipulate data with R using code snippets and be introduced to mining frequent patterns, association, and correlations while working with R programs. You will discover how to write code for various predication models, stream data, and time-series data. You will also be introduced to solutions written in R based on RHadoop projects. You will finish this module by feeling confident in your ability to know which data mining algorithm to apply in any situation.

    , R Data Mining Blueprints , explores data mining techniques and shows you how to apply different mining concepts to various statistical and data applications in a wide range of fields. You will learn about R and its application to data mining, and give you relevant and useful information you can use to develop and improve your applications. This module will help you complete complex data mining cases and guide you through handling issues you might encounter during projects.

    , Social Media Mining with R, begins by introducing you to the topic of social media data, including its sources and properties. It then explains the basics of R programming in a straightforward, unassuming way. Thereafter, you will be made aware of the inferential dangers associated with social media data and how to avoid them, before describing and implementing a suite of social media mining techniques.

    What you need for this learning path

    Any modern PC with installed Windows, Linux or Mac OS should be sufficient to run the code samples in the book. All the software used in the book is open source and freely available on the Web:

    http://www.r-project.org/

    Who this learning path is for

    This learning path is for R developers who are looking up to making a career in data analysis or data mining. Those who come across data mining problems of different complexities from web, text, numerical, political, and social media domains will find all information in this single learning path.

    Reader feedback

    Feedback from our readers is always welcome. Let us know what you think about this coursewhat you liked or disliked. Reader feedback is important for us as it helps us develop titles that you will really get the most out of.

    To send us general feedback, simply e-mail <>, and mention the course's title in the subject of your message.

    If there is a topic that you have expertise in and you are interested in either writing or contributing to a book, see our author guide at www.packtpub.com/authors.

    Customer support

    Now that you are the proud owner of a Packt course, we have a number of things to help you to get the most from your purchase.

    Downloading the example code

    You can download the example code files for this course from your account at http://www.packtpub.com. If you purchased this course elsewhere, you can visit http://www.packtpub.com/support and register to have the files e-mailed directly to you.

    You can download the code files by following these steps:

    1. Log in or register to our website using your e-mail address and password.
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    6. Choose from the drop-down menu where you purchased this course from.
    7. Click on Code Download .

    You can also download the code files by clicking on the Code Files button on the course's webpage at the Packt Publishing website. This page can be accessed by entering the course's name in the Search box. Please note that you need to be logged in to your Packt account.

    Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

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    The code bundle for the course is also hosted on GitHub at https://github.com/PacktPublishing/R-Mining-spatial-text-web-and-social-media-data/. We also have other code bundles from our rich catalog of books, videos, and courses available at https://github.com/PacktPublishing/. Check them out!

    Errata

    Although we have taken every care to ensure the accuracy of our content, mistakes do happen. If you find a mistake in one of our coursesmaybe a mistake in the text or the codewe would be grateful if you could report this to us. By doing so, you can save other readers from frustration and help us improve subsequent versions of this course. If you find any errata, please report them by visiting http://www.packtpub.com/submit-errata, selecting your course, clicking on the

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