Big data has become a popular buzzword across many industries. An increasing number of people have been exposed to the term and are looking at how to leverage big data in their own businesses, to improve sales and profitability. However, collecting, aggregating, and visualizing data is just one part of the equation. Being able to extract useful information from data is another task, and much more challenging.
Traditionally, most researchers perform statistical analysis using historical samples of data. The main downside of this process is that conclusions drawn from statistical analysis are limited. In fact, researchers usually struggle to uncover hidden patterns and unknown correlations from target data. Aside from applying statistical analysis, machine learning has emerged as an alternative. This process yields a more accurate predictive model with the data inserted into a learning algorithm. Through machine learning, the analysis of business operations and processes is not limited to human-scale thinking. Machine-scale analysis enables businesses to discover hidden values in big data.
The most widely used tool for machine learning and data analysis is the R language. In addition to being the most popular language used by data scientists, R is open source and is free for use for all users. The R programming language offers a variety of learning packages and visualization functions, which enable users to analyze data on the fly. Any user can easily perform machine learning with R on their dataset without knowing every detail of the mathematical models behind the analysis.
Machine Learning with R Cookbook takes a practical approach to teaching you how to perform machine learning with R. Each of the 12 chapters are introduced to you by dividing this topic into several simple recipes. Through the step-by-step instructions provided in each recipe, the reader can construct a predictive model by using a variety of machine learning packages.
In this book, readers are first directed how to set up the R environment and use simple R commands to explore data. The next topic covers how to perform statistical analysis with machine learning analysis and assessing created models, which are covered in detail later on in the book. There is also content on learning how to integrate R and Hadoop to create a big data analysis platform. The detailed illustrations provide all the information required to start applying machine learning to individual projects.
With Machine Learning with R Cookbook, users will feel that machine learning has never been easier.
Chapter 1, Practical Machine Learning with R, describes how to create a ready-to-use R environment. Furthermore, we cover all the basic R operations, from reading data into R, manipulating data, and performing simple statistics, to visualizing data.
Chapter 2, Data Exploration with RMS Titanic, provides you an opportunity to perform exploratory analysis in R. In this chapter, we walk you through the process of transforming, analyzing, and visualizing the RMS Titanic data. We conclude by creating a prediction model to identify the possible survivors of the Titanic tragedy.
Chapter 3, R and Statistics, begins with an emphasis on data sampling and probability distribution. Subsequently, the chapter demonstrates how to perform descriptive statistics and inferential statistics on data.
Chapter 4, Understanding Regression Analysis, analyzes the linear relationship between a dependent (response) variable and one or more independent (predictor) sets of explanatory variables. You will learn how to use different regression models to make sense of numeric relationships, and further apply a fitted model to data for continuous value prediction.
Chapter 5, Classification (I) – Tree, Lazy, Probabilistic, teaches you how to fit data into a tree-based classifier, k-nearest neighbor classifier, logistic regression classifier, or the Naïve Bayes classifier. In order to understand how classification works, we provide an example with the purpose of identifying possible customer churns from a telecom dataset.
Chapter 6, Classification (II) – Neural Network, SVM, introduces two complex but powerful classification methods: neural networks and support vector machines. Despite the complex nature of these methods, this chapter shows how easy it is to make an accurate prediction using these algorithms in R.
Chapter 7, Model Evaluation, reveals some measurements that you can use to evaluate the performance of a fitted model. With these measurements, we can select the optimum model that accurately predicts responses for future subjects.
Chapter 8, Ensemble Learning, introduces how to use the power of ensemble learners to produce better classification and regression results, as compared to a single learner. As an ensemble learner is frequently the winning approach in many data prediction competitions; you should know how to apply ensemble learners to your projects.
Chapter 9, Clustering, explores different types of clustering methods. Clustering can group similar points of data together. In this chapter, we demonstrate how to apply the clustering technique to segment customers and further compare differences between each clustering method.
Chapter 10, Association Analysis and Sequence Mining, exposes you to the common methods used to discover associated items and underlying frequent patterns from transaction data. This chapter is a must read for those of you interested in finding out how researchers discovered the famous association between customers that purchase beer and those who purchase diapers.
Chapter 11, Dimension Reduction, teaches you how to select and extract features from original variables. With this technique, we can remove the effect from redundant features, and reduce the computational cost to avoid overfitting. For a more concrete example, this chapter reveals how to compress and restore an image with the dimension reduction approach.
Chapter 12, Big Data Analysis (R and Hadoop), reveals how you can use RHadoop, which allows R to leverage the scalability of Hadoop, so as to process and analyze big data. We cover all the steps, from setting up the RHadoop environment to actual big data processing and machine learning on big data. Lastly, we explore how to deploy an RHadoop cluster using Amazon EC2.
Appendix A, Resources for R and Machine Learning, will provide you with all the resources for R and machine learning.
Appendix B, Dataset – Survival of Passengers on the Titanic, shows you the dataset for survival of passengers on the Titanic.
To follow the book's examples, you will need a computer with access to the Internet and the ability to install the R environment. You can download R from http://www.cran.r-project.org/. Detailed installation instructions are available in the first chapter.
The examples provided in this book were coded and tested with R Version 3.1.2 on a computer with Microsoft Windows installed on it. These examples should also work with any recent version of R installed on either MAC OSX or a Unix-like OS.
This book is ideal for those of you who want to learn how to use R for machine learning and gain insights from data. Regardless of your level of experience, this book covers the basics of applying R to machine learning through advanced techniques. While it is helpful if you are familiar with basic programming or machine learning concepts, you do not require prior experience to benefit from this book.
In this book, you will find several headings that appear frequently (Getting ready, How to do it, How it works, There's more, and See also).
To give clear instructions on how to complete a recipe, we use these sections as follows:
This section tells you what to expect in the recipe, and describes how to set up any software or any preliminary settings required for the recipe.
This section usually consists of a detailed explanation of what happened in the previous section.
This section consists of additional information about the recipe in order to make the reader more knowledgeable about the recipe.
This book contains a number of styles of text that distinguish between different kinds of information. Here are some examples of these styles, and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "Use the rpart function to build a classification tree model."
A block of code is set as follows:
> churn.rp = rpart(churn ~ ., data=trainset)
Any command-line input or output is written as follows:
$ sudo R CMD INSTALL rmr2_3.3.0.tar.gz
New terms and important words are shown in bold. Words that you see on the screen, in menus or dialog boxes for example, appear in text in the following format: "In R, a missing value is noted with the symbol NA (not available), and an impossible value is NaN (not a number)."
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