R Data Analysis Projects

361 Pages·2017·9.493 MB·English

by  Gopi Subramanian

R Data Analysis Projects
Build end to end analytics systems to get deeper insights from
your data
Gopi Subramanian
BIRMINGHAM - MUMBAI
R Data Analysis Projects
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First published: November 2017
Production reference: 1151117
Published by Packt Publishing Ltd.
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ISBN 978-1-78862-187-8
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Credits
Author Copy Editors
Gopi Subramanian Safis Editing
Reviewer Project Coordinator
Mark Hodnett Manthan Patel
Commissioning Editor Proofreader
Amey Varangaonkar Safis Editing
Acquisition Editor Indexer
Tushar Gupta Tejal Daruwale Soni
Content Development Editor Graphics
Aaryaman Singh Tania Dutta
Technical Editor Production Coordinator
Dharmendra Yadav Arvindkumar Gupta
About the Author
Gopi Subramanian is a scientist and author with over 18 years of experience in the fields of
data mining and machine learning. During the past decade, he has worked extensively in
data mining and machine learning, solving a variety of business problems.
He has 16 patent applications with the US and Indian patent offices and several
publications to his credit. He is the author of Python Data Science Cookbook by Packt
Publishing.
I would like to thank my parents Vijaya and Mani and sister Geetha for being a great
source of inspiration. My family Anita and Rishi for their support.
About the Reviewer
Mark Hodnett is a data scientist with over 20 years of industry experience. He has worked
in a variety of industries, ranging from website development, retail loyalty, and industrial
systems to accountancy software. He holds a master's in data science and an MBA.
His current role is with AltViz as a senior data scientist. Altviz applies machine learning,
optimization, and intelligent evaluation to companies in many sectors, including retail and
insurance.
I would like to thank the author, Gopi, for giving me the opportunity to collaborate on this
project. I would also like to thank Sharon and Conor for their patience while I worked on
this project.
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Table of Contents
Preface
1
Chapter 1: Association Rule Mining
6
Understanding the recommender systems 7
Transactions 8
Weighted transactions 9
Our web application 9
Retailer use case and data 10
Association rule mining 13
Support and confidence thresholds 25
The cross-selling campaign 30
Leverage 33
Conviction 34
Weighted association rule mining 35
Hyperlink-induced topic search (HITS) 42
Negative association rules 50
Rules visualization 53
Wrapping up 57
Summary 65
Chapter 2: Fuzzy Logic Induced Content-Based Recommendation
66
Introducing content-based recommendation 68
News aggregator use case and data 72
Designing the content-based recommendation engine 77
Building a similarity index 80
Bag-of-words 80
Term frequency 81
Document frequency 81
Inverse document frequency (IDF) 81
TFIDF 82
Why cosine similarity? 85
Searching 86
Polarity scores 89
Jaccard's distance 91
Jaccards distance/index 92
Ranking search results 94
Fuzzy logic 95
Fuzzification 95
Table of Contents
Defining the rules 97
Evaluating the rules 97
Defuzzification 98
Complete R Code 107
Summary 113
Chapter 3: Collaborative Filtering
115
Collaborative filtering 116
Memory-based approach 118
Model-based approach 119
Latent factor approach 120
Recommenderlab package 122
Popular approach 124
Use case and data 126
Designing and implementing collaborative filtering 136
Ratings matrix 137
Normalization 139
Train test split 141
Train model 144
User-based models 149
Item-based models 152
Factor-based models 153
Complete R Code 155
Summary 161
Chapter 4: Taming Time Series Data Using Deep Neural Networks
162
Time series data 164
Non-seasonal time series 165
Seasonal time series 166
Time series as a regression problem 167
Deep neural networks 171
Forward cycle 174
Backward cycle 175
Introduction to the MXNet R package 175
Symbolic programming in MXNet 178
Softmax activation 182
Use case and data 184
Deep networks for time series prediction 186
Training test split 188
Complete R code 202
Summary 209
[ ii ]