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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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Toc

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

Fuzzy logic


One of the famous Python libraries for fuzzy logic is scikit-fuzzy. Several fuzzy logic algorithms have already been implemented on this library. Since scikit-fuzzy is an open source library, you can review the source code at https://github.com/scikit-fuzzy/scikit-fuzzy.

Before you install this library, you should already have installed NumPy and SciPy libraries. You can install scikit-fuzzy using pip, by typing the following command:

$ sudo pip install scikit
-fuzzy

As another option, you can install the scikit-fuzzy library from source code.

Type these commands:

$ git clone https://github.com/scikit-fuzzy/scikit-fuzzy
$ cd scikit-fuzzy/
$ sudo python setup.py install

After completing the installation, you can use scikit-fuzzy. To test how to work with scikit-fuzzy, we will build a fuzzy membership for temperature using the fuzz.trimf() function. You can write the following scripts:

import matplotlib
matplotlib.use('Agg')

import numpy as np
import skfuzzy as fuzz
import matplotlib...
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