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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

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781788621878
Length 366 pages
Edition 1st Edition
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Author (1):
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Gopi Subramanian Gopi Subramanian
Author Profile Icon Gopi Subramanian
Gopi Subramanian
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Toc

Table of Contents (9) Chapters Close

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

Introducing content-based recommendation

To understand the inner workings of a content-based recommendation system, let's look at a simple example. We will use the wine dataset from https://archive.ics.uci.edu/ml/datasets/wine. 

This dataset is the result of the chemical analysis of wine grown in the same region in Italy. We have data from three different cultivars (From an assemblage of plants selected for desirable characters, Wikipedia: https://en.wikipedia.org/wiki/Cultivar).

Let's extract the data from UCI machine learning repository:

> library(data.table)
> wine.data <- fread('https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data')
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
0 0 0 0 0 0...
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