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Advanced Analytics with R and Tableau

You're reading from   Advanced Analytics with R and Tableau Advanced analytics using data classification, unsupervised learning and data visualization

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781786460110
Length 178 pages
Edition 1st Edition
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Authors (3):
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Roberto Rösler Roberto Rösler
Author Profile Icon Roberto Rösler
Roberto Rösler
Ruben Oliva Ramos Ruben Oliva Ramos
Author Profile Icon Ruben Oliva Ramos
Ruben Oliva Ramos
Jen Stirrup Jen Stirrup
Author Profile Icon Jen Stirrup
Jen Stirrup
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Table of Contents (10) Chapters Close

Preface 1. Advanced Analytics with R and Tableau FREE CHAPTER 2. The Power of R 3. A Methodology for Advanced Analytics Using Tableau and R 4. Prediction with R and Tableau Using Regression 5. Classifying Data with Tableau 6. Advanced Analytics Using Clustering 7. Advanced Analytics with Unsupervised Learning 8. Interpreting Your Results for Your Audience Index

Neural network in R


Let's load up the libraries that we need. We are going to use the neuralnet package. The neuralnet package is a flexible package that is created for the training of neural networks using the backpropagation method. We discussed the backpropagation method previously in this chapter.

Let's install the package using the following command:

install.packages("neuralnet")

Now, let's load the library:

library(neuralnet)

We need to load up some data. We will use the iris quality dataset from the UCI website, which is installed along with your R installation. You can check that you have it, by typing in iris at the Command Prompt. You should get 150 rows of data.

If not, then download the data from the UCI website, and rename the file to iris.csv. Then, use the Import Dataset button on RStudio to import the data.

Now, let's assign the iris data to the data command. Now, let's look at the data to see if it is loaded correctly. It's enough to look at the first few rows of data, and...

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