Chapter 1, Association Rule Mining, builds recommender systems with transaction data. We identify cross-sell and upsell opportunities.

Chapter 2, Fuzzy Logic Induced Content-Based Recommendation, addresses the cold start problem in the recommender system. We handle the ranking problem with multi-similarity metrics using a fuzzy sets approach.

Chapter 3, Collaborative Filtering, introduces different approaches to collaborative filtering for recommender systems.

Chapter 4, Taming Time Series Data Using Deep Neural Networks, introduces MXNet R, a package for deep learning in R. We leverage MXNet to build a deep connected network to predict stock closing prices.

Chapter 5, Twitter Text Sentiment Classification Using Kernel Density Estimates, shows ability to process Twitter data in R. We introduce delta-tfidf, a new metric for sentiment classification. We leverage the kernel density estimate based Naïve Bayes algorithm to classify sentiments.

Chapter 6, Record Linkage - Stochastic and Machine Learning Approaches, covers the problem of master data management and how to solve it in R using the recordLinkage package.

Chapter 7, Streaming Data Clustering Analysis in R, introduces a package stream for handling streaming data in R, and the clustering of streaming data, as well as the online/offline clustering model.

Chapter 8, Analyzing and Understanding Networks Using R, covers the igraph package for performing graph analysis in R. We solve product network analysis problems with graph algorithms.

Base R must be installed. The code in this book was written using R version 3.4.1 (2017-06-30), single candle, on a Mac OS darwin15.6.0. They should be compatible with Linux and Windows operating systems. RStudio Version 0.99.491 was used as an editor to write and compile R code.

If you are looking for a book that takes you all the way through the practical applications of advanced and effective analytics methodologies in R, then this is the book for you. A fundamental understanding of R and the basic concepts of data analysis are all you need to get started with this book.

In this book, you will find a number of text styles 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: "We can include other contexts through the use of the include directive."

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zero.matrix.gup <- mx.nd.zeros(c(3,3), mx.gpu(0))

New terms and important words are shown in bold.

Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "Clicking the Next button moves you to the next screen."

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