Tony Fischetti - R: Predictive Analysis
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- Book:R: Predictive Analysis
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Master the art of predictive modeling
About This Book
- Familiarize yourself with the most common data mining tools of R, such as k-means, hierarchical regression, linear regression, Nave Bayes, decision trees, text mining and so on.
- We emphasize important concepts, such as the bias-variance trade-off and over-fitting, which are pervasive in predictive modeling
Who This Book Is For
If you work with data and want to become an expert in predictive analysis and modeling, then this Learning Path will serve you well. It is intended for budding and seasoned practitioners of predictive modeling alike. You should have basic knowledge of the use of R, although its not necessary to put this Learning Path to great use.
What You Will Learn
- Get to know the basics of Rs syntax and major data structures
- Write functions, load data, and install packages
- Use different data sources in R and know how to interface with databases, and request and load JSON and XML
- Identify the challenges and apply your knowledge about data analysis in R to imperfect real-world data
- Predict the future with reasonably simple algorithms
- Understand key data visualization and predictive analytic skills using R
- Understand the language of models and the predictive modeling process
In Detail
Predictive analytics is a field that uses data to build models that predict a future outcome of interest. It can be applied to a range of business strategies and has been a key player in search advertising and recommendation engines.
The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions in the real world. This Learning Path will provide you with all the steps you need to master the art of predictive modeling with R.
We start with an introduction to data analysis with R, and then gradually youll get your feet wet with predictive modeling. You will get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. You will be able to solve the difficulties relating to performing data analysis in practice and find solutions to working with messy data, large data, communicating results, and facilitating reproducibility. You will then perform key predictive analytics tasks using R, such as train and test predictive models for classification and regression tasks, score new data sets and so on. By the end of this Learning Path, you will have explored and tested the most popular modeling techniques in use on real-world data sets and mastered a diverse range of techniques in predictive analytics.
This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:
- Data Analysis with R, Tony Fischetti
- Learning Predictive Analytics with R, Eric Mayor
- Mastering Predictive Analytics with R, Rui Miguel Forte
Style and approach
Learn data analysis using engaging examples and fun exercises, and with a gentle and friendly but comprehensive learn-by-doing approach. This is a practical course, which analyzes compelling data about life, health, and death with the help of tutorials. It offers you a useful way of interpreting the data thats specific to this course, but that can also be applied to any other data. This course is designed to be both a guide and a reference for moving beyond the basics of...
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Copyright 2017 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: March 2017
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham B3 2PB, UK.
ISBN 978-1-78829-037-1
www.packtpub.com
Authors Tony Fischetti Eric Mayor Rui Miguel Forte Reviewers Dipanjan Sarkar Ajay Dhamija Khaled Tannir Matt Wiley Prasad Kothari Dawit Gezahegn Tadesse | Content Development Editor Mayur Pawanikar Production Coordinator Nilesh Mohite |
Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. With over 7,000 user contributed packages, it's easy to find support for the latest and greatest algorithms and techniques.
Packed with engaging problems and exercises, this course begins with a review of R and its syntax. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with "messy data", large data, communicating results, and facilitating reproducibility.
The primary mission of this course is to bridge the gap between low-level introductory books and tutorials that emphasize intuition and practice over theory, and high-level academic texts that focus on mathematics, detail, and rigor. Another equally important goal is to instill some good practices in you, such as learning how to properly test and evaluate a model. We also emphasize important concepts, such as the bias-variance trade-off and over-fitting, which are pervasive in predictive modeling and come up time and again in various guises and across different models.
This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:
- Data Analysis with R
- Learning Predictive Analytics with R
- Mastering Predictive Analytics with R
, Starting with the basics of R and statistical reasoning, Data Analysis with R dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples. This course is engineered to be an invaluable resource through many stages of anyone's career as a data analyst.
, The main purpose of this book is to show you how to analyze data with reasonably simple algorithms. The book is composed of chapters describing the algorithms and their use and of an appendices with exercises and solutions to the exercises and references.
, The purpose of this course is to show how to use R tools/packages for applied predictive analytics. The course will make full use of R for Predictive models so that by the end of the course, the readers would have gained expertise in building predictive models and performing Predictive Analytics with R.
Module 1:
All code in this book has been written against the latest version of R3.2.2 at the time of writing. As a matter of good practice, you should keep your R version up to date but most, if not all, code should work with any reasonably recent version of R. Some of the R packages we will be installing will require more recent versions, though. For the other software that this book uses, instructions will be furnished pro re nata. If you want to get a head start, however, install RStudio, JAGS, and a C++ compiler (or Rtools if you use Windows).
Module 2:
All you need for this book is a working installation of R > 3.0 (on any operating system) and an active internet connection.
Following are the links for your reference:
Installing R: https://cran.r-project.org/doc/manuals/r-release/R-admin.html
R Interpreter for Apache Zeppelin: https://zeppelin.apache.org/docs/0.6.0/interpreter/r.html
Module 3:
The only strong requirement for running the code in this book is an installation of R. This is freely available from http://www.r-project.org/ and runs on all the major operating systems. The code in this book has been tested with R version 3.1.3.
All the chapters introduce at least one new R package that does not come with the base installation of R. We do not explicitly show the installation of R packages in the text, but if a package is not currently installed on your system or if it requires updating, you can install it with the install.packages() function. For example, the following command installs the tm package:
> install.packages("tm")
All the packages we use are available on CRAN. An Internet connection is needed to download and install them as well as to obtain the open source data sets that we use in our real-world examples. Finally, even though not absolutely mandatory, we recommend that you get into the habit of using an Integrated Development Environment (IDE) to work with R. An excellent offering is RStudio (http://www.rstudio.com/), which is open source.
If you work with data and want to become an expert in predictive analysis and modeling, then this Learning Path will serve you well. It is intended for budding and seasoned practitioners of predictive modeling alike. You should have basic knowledge of the use of R, although it's not necessary to put this Learning Path to great use.
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To send us general feedback, simply e-mail <>, and mention the course's title in the subject of your message.
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