R data analysis projects : build end to end analytics systems to get deeper insights from your data / Gopi Subramanian.

Format:

Book

Author/Creator:

Subramanian, Gopi, author.

Language:

English

Subjects (All):

R (Computer program language).

Physical Description:

1 online resource (1 volume) : illustrations

Edition:

1st edition

Other Title:

Build end to end analytics systems to get deeper insights from your data

Place of Publication:

Birmingham, England ; Mumbai, [India] : Packt Publishing, 2017.

System Details:

text file

Summary:

Get valuable insights from your data by building data analysis systems from scratch with R. About This Book A handy guide to take your understanding of data analysis with R to the next level Real-world projects that focus on problems in finance, network analysis, social media, and more From data manipulation to analysis to visualization in R, this book will teach you everything you need to know about building end-to-end data analysis pipelines using R Who This Book Is For If you are looking for a book that takes you all the way through the practical application 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 is all you need to get started with this book. What You Will Learn Build end-to-end predictive analytics systems in R Build an experimental design to gather your own data and conduct analysis Build a recommender system from scratch using different approaches Use and leverage RShiny to build reactive programming applications Build systems for varied domains including market research, network analysis, social media analysis, and more Explore various R Packages such as RShiny, ggplot, recommenderlab, dplyr, and find out how to use them effectively Communicate modeling results using Shiny Dashboards Perform multi-variate time-series analysis prediction, supplemented with sensitivity analysis and risk modeling In Detail R offers a large variety of packages and libraries for fast and accurate data analysis and visualization. As a result, it's one of the most popularly used languages by data scientists and analysts, or anyone who wants to perform data analysis. This book will demonstrate how you can put to use your existing knowledge of data analysis in R to build highly efficient, end-to-end data analysis pipelines without any hassle. You'll start by building a content-based recommendation system, followed by building a project on sentiment analysis with tweets. You'll implement time-series modeling for anomaly detection, and understand cluster analysis of streaming data. You'll work through projects on performing efficient market data research, building recommendation systems, and analyzing networks accurately, all provided with easy to follow codes. With the help of these real-world projects, you'll get a better understanding of the challenges faced when building data analysis pipelines, and see how you can overcome them without comp...

Contents:

Cover

Title Page

Copyright

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Table of Contents

Preface

Chapter 1: Association Rule Mining

Understanding the recommender systems

Transactions

Weighted transactions

Our web application

Retailer use case and data

Association rule mining

Support and confidence thresholds

The cross-selling campaign

Leverage

Conviction

Weighted association rule mining

Hyperlink-induced topic search (HITS)

Negative association rules

Rules visualization

Wrapping up

Summary

Chapter 2: Fuzzy Logic Induced Content-Based Recommendation

Introducing content-based recommendation

News aggregator use case and data

Designing the content-based recommendation engine

Building a similarity index

Bag-of-words

Term frequency

Document frequency

Inverse document frequency (IDF)

TFIDF

Why cosine similarity?

Searching

Polarity scores

Jaccard's distance

Jaccards distance/index

Ranking search results

Fuzzy logic

Fuzzification

Defining the rules

Evaluating the rules

Defuzzification

Complete R Code

Chapter 3: Collaborative Filtering

Collaborative filtering

Memory-based approach

Model-based approach

Latent factor approach

Recommenderlab package

Popular approach

Use case and data

Designing and implementing collaborative filtering

Ratings matrix

Normalization

Train test split

Train model

User-based models

Item-based models

Factor-based models

Chapter 4: Taming Time Series Data Using Deep Neural Networks

Time series data

Non-seasonal time series

Seasonal time series

Time series as a regression problem

Deep neural networks

Forward cycle

Backward cycle.

Introduction to the MXNet R package

Symbolic programming in MXNet

Softmax activation

Deep networks for time series prediction

Training test split

Complete R code

Chapter 5: Twitter Text Sentiment Classification Using Kernel Density Estimates

Kernel density estimation

Twitter text

Sentiment classification

Dictionary methods

Machine learning methods

Our approach

Dictionary based scoring

Text pre-processing

Term-frequeny inverse document frequency (TFIDF)

Delta TFIDF

Building a sentiment classifier

Assembling an RShiny application

Chapter 6: Record Linkage - Stochastic and Machine Learning Approaches

Introducing our use case

Demonstrating the use of RecordLinkage package

Feature generation

String features

Phonetic features

Stochastic record linkage

Expectation maximization method

Weights-based method

Machine learning-based record linkage

Unsupervised learning

Supervised learning

Building an RShiny application

Machine learning method

RShiny application

Chapter 7: Streaming Data Clustering Analysis in R

Streaming data and its challenges

Bounded problems

Drift

Single pass

Real time

Introducing stream clustering

Macro-cluster

Introducing the stream package

Data stream data

DSD as a static simulator

DSD as a simulator with drift

DSD connecting to memory, file, or database

Inflight operation

Can we connect this DSD to an actual data stream?

Data stream task

Speed layer

Batch layer

Reservoir sampling

Chapter 8: Analyze and Understand Networks Using R

Graphs in R.

Degree of a vertex

Strength of a vertex

Adjacency Matrix

More networks in R

Centrality of a vertex

Farness and Closeness of a node

Finding the shortest path between nodes

Random walk on a graph

Data preparation

Product network analysis

Building a RShiny application

The complete R script

Index.

Notes:

Includes index.

Description based on online resource; title from PDF title page (EBC, viewed December 18, 2017).

OCLC:

1017754231