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Mastering Machine Learning with R

You're reading from   Mastering Machine Learning with R Advanced machine learning techniques for building smart applications with R 3.5

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Product type Paperback
Published in Jan 2019
Publisher
ISBN-13 9781789618006
Length 354 pages
Edition 3rd Edition
Languages
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Author (1):
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Cory Lesmeister Cory Lesmeister
Author Profile Icon Cory Lesmeister
Cory Lesmeister
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Table of Contents (16) Chapters Close

Preface 1. Preparing and Understanding Data 2. Linear Regression FREE CHAPTER 3. Logistic Regression 4. Advanced Feature Selection in Linear Models 5. K-Nearest Neighbors and Support Vector Machines 6. Tree-Based Classification 7. Neural Networks and Deep Learning 8. Creating Ensembles and Multiclass Methods 9. Cluster Analysis 10. Principal Component Analysis 11. Association Analysis 12. Time Series and Causality 13. Text Mining 14. Creating a Package 15. Other Books You May Enjoy

Data understanding and preparation

Let's start with installing the R packages needed for this chapter, if you have not done so already:

> library(magrittr)

> install.packages("cluster")

> install.packages("dendextend")

> install.packages("ggthemes")

> install.packages("HDclassif")

> install.packages("NbClust")

> install.packages("tidyverse")

> options(scipen=999)

The dataset is in the HDclassif package. Load the data and examine the structure with the str() function:

> library(HDclassif)

> data(wine)

> str(wine)
'data.frame': 178 obs. of 14 variables:
$ class: int 1 1 1 1 1 1 1 1 1 1 ...
$ V1 : num 14.2 13.2 13.2 14.4 13.2 ...
$ V2 : num 1.71 1.78 2.36 1.95 2.59 1.76 1.87 2.15 1.64 1.35 ...
$ V3 : num 2.43 2.14 2.67 2.5 2.87 2.45 2.45 2.61 2.17 2.27 ...
$ V4 : num 15.6 11.2 18...
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