R - Unleash Machine Learning Techniques (A course in three modules) PDF
Preview R - Unleash Machine Learning Techniques (A course in three modules)
R: Unleash Machine Learning Techniques Find out how to build smarter machine learning systems with R. Follow this three module course to become a more fluent machine learning practitioner A course in three modules BIRMINGHAM - MUMBAI R: Unleash Machine Learning Techniques Copyright © 2016 Packt Publishing Published on: September 2016 Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK. ISBN 978-1-78712-734-0 www.packtpub.com Preface "He who defends everything, defends nothing." — Frederick the Great Machine learning is a very broad topic. The following quote sums it up nicely: The first problem facing you is the bewildering variety of learning algorithms available. Which one to use? There are literally thousands available, and hundreds more are published each year. (Domingo, P., 2012.) It would therefore be irresponsible to try and cover everything in the chapters that follow because, to paraphrase Frederick the Great, we would achieve nothing. With this constraint in mind, we hope to provide a solid foundation of algorithms and business considerations that will allow the reader to walk away and, first of all, take on any machine learning tasks with complete confidence, and secondly, be able to help themselves in figuring out other algorithms and topics. Essentially, if this course significantly helps you to help yourself, then I would consider this a victory. Don't think of this course as a destination but rather, as a path to self-discovery. What this learning path covers Module 1, R Machine Learning By Example, Data science and machine learning are some of the top buzzwords in the technical world today. From retail stores to Fortune 500 companies, everyone is working hard to make machine learning give them data-driven insights to grow their businesses. With powerful data manipulation features, machine learning packages, and an active developer community, R empowers users to build sophisticated machine learning systems to solve real-world data problems. This module takes you on a data-driven journey that starts with the very basics of R and machine learning and gradually builds upon the concepts to work on projects that tackle real-world problems. Module 2, Machine Learning with R, Machine learning, at its core, is concerned with the algorithms that transform information into actionable intelligence. This fact makes machine learning well-suited to the present-day era of big data. Without machine learning, it would be nearly impossible to keep up with the massive stream of information. Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has never been a better time to start using machine learning. R offers a powerful but easy-to-learn set of tools that can assist you with finding data insights. By combining hands-on case studies with the essential theory that you need to understand how things work under the hood, this book provides all the knowledge that you will need to start applying machine learning to your own projects. Module 3 Mastering Machine Learning with R, The world of R can be as bewildering as the world of machine learning! There is seemingly an endless number of R packages with a plethora of blogs, websites, discussions, and papers of various quality and complexity from the community that supports R. This is a great reservoir of information and probably R's greatest strength, but I've always believed that an entity's greatest strength can also be its greatest weakness. R's vast community of knowledge can quickly overwhelm and/or sidetrack you and your efforts. Show me a problem and give me ten different R programmers and I'll show you ten different ways the code is written to solve the problem. As I've written each chapter, I've endeavored to capture the critical elements that can assist you in using R to understand, prepare, and model the data. I am no R programming expert by any stretch of the imagination, but again, I like to think that I can provide a solid foundation herein. Another thing that lit a fire under me to write this book was an incident that happened in the hallways of a former employer a couple of years ago. My team had an IT contractor to support the management of our databases. As we were walking and chatting about big data and the like, he mentioned that he had bought a book about machine learning with R and another about machine learning with Python. He stated that he could do all the programming, but all of the statistics made absolutely no sense to him. I have always kept this conversation at the back of my mind throughout the writing process. It has been a very challenging task to balance the technical and theoretical with the practical. One could, and probably someone has, turned the theory of each chapter to its own book. I used a heuristic of sorts to aid me in deciding whether a formula or technical aspect was in the scope, which was would this help me or the readers in the discussions with team members and business leaders? If I felt it might help, I would strive to provide the necessary details. I also made a conscious effort to keep the datasets used in the practical exercises large enough to be interesting but small enough to allow you to gain insight without becoming overwhelmed. This book is not about big data, but make no mistake about it, the methods and concepts that we will discuss can be scaled to big data. In short, this module will appeal to a broad group of individuals, from IT experts seeking to understand and interpret machine learning algorithms to statistical gurus desiring to incorporate the power of R into their analysis. However, even those that are well-versed in both IT and statistics—experts if you will—should be able to pick up quite a few tips and tricks to assist them in their efforts. What you need for this learning path This software applies to all the chapters of the book: • Windows / Mac OS X / Linux • R 3.2.0 (or higher) • RStudio Desktop 0.99 (or higher) For hardware, there are no specific requirements, since R can run on any PC that has Mac, Linux, or Windows, but a physical memory of minimum 4 GB is preferred to run some of the iterative algorithms smoothly. Who this learning path is for Aimed for intermediate-to-advanced people (especially data scientist) who are already into the field of data science Reader feedback Feedback from our readers is always welcome. Let us know what you think about this book—what 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 [email protected], and mention the book'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 book, 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 book from your account at http://www.packtpub.com. If you purchased this book 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. 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Module 1: R Machine Learning By Example Chapter 1: Getting Started with R and Machine Learning 3 Delving into the basics of R 4 Data structures in R 9 Working with functions 28 Controlling code flow 31 Advanced constructs 34 Next steps with R 40 Machine learning basics 42 Summary 48 Chapter 2: Let's Help Machines Learn 49 Understanding machine learning 50 Algorithms in machine learning 51 Families of algorithms 58 Summary 82 Chapter 3: Predicting Customer Shopping Trends with Market Basket Analysis 83 Detecting and predicting trends 84 Market basket analysis 85 Evaluating a product contingency matrix 92 Frequent itemset generation 99 Association rule mining 108 Summary 114 Chapter 4: Building a Product Recommendation System 115 Understanding recommendation systems 116 Issues with recommendation systems 117 Collaborative filters 118 vii Table of Contents Building a recommender engine 124 Production ready recommender engines 135 Summary 144 Chapter 5: Credit Risk Detection and Prediction – Descriptive Analytics 145 Types of analytics 146 Our next challenge 147 What is credit risk? 148 Getting the data 149 Data preprocessing 151 Data analysis and transformation 154 Next steps 183 Summary 185 Chapter 6: Credit Risk Detection and Prediction – Predictive Analytics 187 Predictive analytics 189 How to predict credit risk 191 Important concepts in predictive modeling 192 Getting the data 199 Data preprocessing 199 Feature selection 201 Modeling using logistic regression 204 Modeling using support vector machines 209 Modeling using decision trees 220 Modeling using random forests 226 Modeling using neural networks 232 Model comparison and selection 238 Summary 240 Chapter 7: Social Media Analysis – Analyzing Twitter Data 241 Social networks (Twitter) 242 Data mining @social networks 244 Getting started with Twitter APIs 250 Twitter data mining 257 Challenges with social network data mining 276 References 277 Summary 278 Chapter 8: Sentiment Analysis of Twitter Data 279 Understanding Sentiment Analysis 280 Sentiment analysis upon Tweets 289 Summary 312 viii Table of Contents Module 2: Machine Learning with R Chapter 1: Introducing Machine Learning 317 The origins of machine learning 318 Uses and abuses of machine learning 320 How machines learn 325 Machine learning in practice 332 Machine learning with R 338 Summary 341 Chapter 2: Managing and Understanding Data 343 R data structures 344 Managing data with R 355 Exploring and understanding data 358 Summary 380 Chapter 3: Lazy Learning – Classification Using Nearest Neighbors 381 Understanding nearest neighbor classification 382 Example – diagnosing breast cancer with the k-NN algorithm 391 Summary 403 Chapter 4: Probabilistic Learning – Classification Using Naive Bayes 405 Understanding Naive Bayes 406 Example – filtering mobile phone spam with the Naive Bayes algorithm 419 Summary 440 Chapter 5: Divide and Conquer – Classification Using Decision Trees and Rules 441 Understanding decision trees 442 Example – identifying risky bank loans using C5.0 decision trees 452 Understanding classification rules 465 Example – identifying poisonous mushrooms with rule learners 476 Summary 485 Chapter 6: Forecasting Numeric Data – Regression Methods 487 Understanding regression 488 Example – predicting medical expenses using linear regression 502 Understanding regression trees and model trees 517 Example – estimating the quality of wines with regression trees and model trees 521 Summary 534 ix
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