Book Image

Learning Predictive Analytics with R

By : Eric Mayor
Book Image

Learning Predictive Analytics with R

By: Eric Mayor

Overview of this book

This book is packed with easy-to-follow guidelines that explain the workings of the many key data mining tools of R, which are used to discover knowledge from your data. You will learn how to perform key predictive analytics tasks using R, such as train and test predictive models for classification and regression tasks, score new data sets and so on. All chapters will guide you in acquiring the skills in a practical way. Most chapters also include a theoretical introduction that will sharpen your understanding of the subject matter and invite you to go further. The book familiarizes you with the most common data mining tools of R, such as k-means, hierarchical regression, linear regression, association rules, principal component analysis, multilevel modeling, k-NN, Naïve Bayes, decision trees, and text mining. It also provides a description of visualization techniques using the basic visualization tools of R as well as lattice for visualizing patterns in data organized in groups. This book is invaluable for anyone fascinated by the data mining opportunities offered by GNU R and its packages.

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Learning Predictive Analytics with R
Eric Mayor
Sep, 2015 | 332 pages
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Table of Contents (23 chapters)
Learning Predictive Analytics with R
Learning Predictive Analytics with R
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Setting GNU R for Predictive Analytics
Setting GNU R for Predictive Analytics
Installing GNU R
The R graphic user interface
The menu bar of the R console
Packages
Summary
2
Visualizing and Manipulating Data Using R
Visualizing and Manipulating Data Using R
The roulette case
Histograms and bar plots
Scatterplots
Boxplots
Line plots
Application – Outlier detection
Formatting plots
Summary
3
Data Visualization with Lattice
Data Visualization with Lattice
Loading and discovering the lattice package
Discovering multipanel conditioning with xyplot()
Discovering other lattice plots
Updating graphics
Case study – exploring cancer-related deaths in the US
Summary
4
Cluster Analysis
Cluster Analysis
Distance measures
Learning by doing – partition clustering with kmeans()
Using k-means with public datasets
Summary
5
Agglomerative Clustering Using hclust()
Agglomerative Clustering Using hclust()
The inner working of agglomerative clustering
Agglomerative clustering with hclust()
Summary
6
Dimensionality Reduction with Principal Component Analysis
Dimensionality Reduction with Principal Component Analysis
The inner working of Principal Component Analysis
Learning PCA in R
Summary
7
Exploring Association Rules with Apriori
Exploring Association Rules with Apriori
Apriori – basic concepts
The inner working of apriori
Analyzing data with apriori in R
Summary
8
Probability Distributions, Covariance, and Correlation
Probability Distributions, Covariance, and Correlation
Probability distributions
Covariance and correlation
Summary
9
Linear Regression
Linear Regression
Understanding simple regression
Working with multiple regression
Analyzing data in R: correlation and regression
Robust regression
Bootstrapping
Summary
10
Classification with k-Nearest Neighbors and Naïve Bayes
Classification with k-Nearest Neighbors and Naïve Bayes
Understanding k-NN
Working with k-NN in R
Understanding Naïve Bayes
Working with Naïve Bayes in R
Computing the performance of classification
Summary
11
Classification Trees
Classification Trees
Understanding decision trees
ID3
C4.5
C5.0
Classification and regression trees and random forest
Conditional inference trees and forests
Installing the packages containing the required functions
Performing the analyses in R
Caret – a unified framework for classification
Summary
12
Multilevel Analyses
Multilevel Analyses
Nested data
Multilevel regression
Multilevel modeling in R
Predictions using multilevel models
Summary
13
Text Analytics with R
Text Analytics with R
An introduction to text analytics
Loading the corpus
Data preparation
Creating the training and testing data frames
Classification of the reviews
Mining the news with R
Summary
14
Cross-validation and Bootstrapping Using Caret and Exporting Predictive Models Using PMML
Cross-validation and Bootstrapping Using Caret and Exporting Predictive Models Using PMML
Cross-validation and bootstrapping of predictive models using the caret package
Exporting models using PMML
Summary
Exercises and Solutions
Exercises and Solutions
Exercises
Solutions
Further Reading and References
Further Reading and References
Preface
Chapter 1 – Setting GNU R for Predictive Modeling
Chapter 2 – Visualizing and Manipulating Data Using R
Chapter 3 – Data Visualization with Lattice
Chapter 4 – Cluster Analysis
Chapter 5 – Agglomerative Clustering Using hclust()
Chapter 6 – Dimensionality Reduction with Principal Component Analysis
Chapter 7 – Exploring Association Rules with Apriori
Chapter 8 – Probability Distributions, Covariance, and Correlation
Chapter 9 – Linear Regression
Chapter 10 – Classification with k-Nearest Neighbors and Naïve Bayes
Chapter 11 – Classification Trees
Chapter 12 – Multilevel Analyses
Chapter 13 – Text Analytics with R
Chapter 14 – Cross-validation and Bootstrapping Using Caret and Exporting Predictive Models Using PMML
Index
Index

The inner working of Principal Component Analysis

Principal Component Analysis aims at finding the dimensions (principal component) that explain most of the variance in a dataset. Once these components are found, a principal component score is computed for each row and each principal component. Remember the example of the questionnaire data we discussed in the preceding section. These scores can be understood as summaries (combinations) of the attributes that compose the data frame.

PCA produces the principal components by computing the eigenvalues of the covariance matrix of a dataset. There is one eigenvalue for each row in the covariance matrix. The computation of eigenvectors is also required to compute the principal component scores. The eigenvalues and eigenvectors are computed using the following equation, where A is the covariance matrix of interest, I is the identity matrix, k is a positive integer, λ is the eigenvalue and v is the eigenvector:

What is important to understand for...

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