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R Data Analysis Projects

You're reading from R Data Analysis Projects Build end to end analytics systems to get deeper insights from your data

Product type Paperback

Published in Nov 2017

Publisher Packt

ISBN-13 9781788621878

Length 366 pages

Edition 1st Edition

Languages

R

Tools

ggplot

Concepts

Data Analysis

Author (1):

Gopi Subramanian

View More author details

Table of Contents (9) Chapters

Preface

1. Association Rule Mining FREE CHAPTER

2. Fuzzy Logic Induced Content-Based Recommendation

3. Collaborative Filtering

4. Taming Time Series Data Using Deep Neural Networks

5. Twitter Text Sentiment Classification Using Kernel Density Estimates

6. Record Linkage - Stochastic and Machine Learning Approaches

7. Streaming Data Clustering Analysis in R

8. Analyze and Understand Networks Using R

Data preparation

Our data preparation task involves taking the transactions data and converting it to a form where we have product pairs and their transaction frequency. Transaction frequency is the number of transactions in which both the products have appeared. We will use these product pairs to build our graph. The vertices of our graph are the products. For every product pair, an edge is drawn in the graph between the corresponding product vertices. The weight of the edge is the transaction frequency.

We will use the arules package version 1.5-0 to help us perform this data preparation task:

> library(arules)
> transactions.obj <- read.transactions(file = 'data.csv', format = "single", 
+                                       sep = ",",
+                                       cols = c("order_id", "product_id"), 
+                                       rm.duplicates = FALSE,
+                                       quote = "", skip = 0,
+                                       encoding...

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Authors (1)

Gopi Subramanian

Gopi Subramanian

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.

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