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.