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by Dipanjan Sarkar, Raghav Bali
R Machine Learning By Example
R Machine Learning By Example
Table of Contents
R Machine Learning By Example
Credits
About the Authors
About the Reviewer
www.PacktPub.com
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Why subscribe?
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Downloading the color images of this book
Errata
Piracy
Questions
1. Getting Started with R and Machine Learning
Delving into the basics of R
Using R as a scientific calculator
Operating on vectors
Special values
Data structures in R
Vectors
Creating vectors
Indexing and naming vectors
Arrays and matrices
Creating arrays and matrices
Names and dimensions
Matrix operations
Lists
Creating and indexing lists
Combining and converting lists
Data frames
Creating data frames
Operating on data frames
Working with functions
Built-in functions
User-defined functions
Passing functions as arguments
Controlling code flow
Working with if, if-else, and ifelse
Working with switch
Loops
Advanced constructs
lapply and sapply
apply
tapply
mapply
Next steps with R
Getting help
Handling packages
Machine learning basics
Machine learning – what does it really mean?
Machine learning – how is it used in the world?
Types of machine learning algorithms
Supervised machine learning algorithms
Unsupervised machine learning algorithms
Popular machine learning packages in R
Summary
2. Let's Help Machines Learn
Understanding machine learning
Algorithms in machine learning
Perceptron
Families of algorithms
Supervised learning algorithms
Linear regression
K-Nearest Neighbors (KNN)
Collecting and exploring data
Normalizing data
Creating training and test data sets
Learning from data/training the model
Evaluating the model
Unsupervised learning algorithms
Apriori algorithm
K-Means
Summary
3. Predicting Customer Shopping Trends with Market Basket Analysis
Detecting and predicting trends
Market basket analysis
What does market basket analysis actually mean?
Core concepts and definitions
Techniques used for analysis
Making data driven decisions
Evaluating a product contingency matrix
Getting the data
Analyzing and visualizing the data
Global recommendations
Advanced contingency matrices
Frequent itemset generation
Getting started
Data retrieval and transformation
Building an itemset association matrix
Creating a frequent itemsets generation workflow
Detecting shopping trends
Association rule mining
Loading dependencies and data
Exploratory analysis
Detecting and predicting shopping trends
Visualizing association rules
Summary
4. Building a Product Recommendation System
Understanding recommendation systems
Issues with recommendation systems
Collaborative filters
Core concepts and definitions
The collaborative filtering algorithm
Predictions
Recommendations
Similarity
Building a recommender engine
Matrix factorization
Implementation
Result interpretation
Production ready recommender engines
Extract, transform, and analyze
Model preparation and prediction
Model evaluation
Summary
5. Credit Risk Detection and Prediction – Descriptive Analytics
Types of analytics
Our next challenge
What is credit risk?
Getting the data
Data preprocessing
Dealing with missing values
Datatype conversions
Data analysis and transformation
Building analysis utilities
Analyzing the dataset
Saving the transformed dataset
Next steps
Feature sets
Machine learning algorithms
Summary
6. Credit Risk Detection and Prediction – Predictive Analytics
Predictive analytics
How to predict credit risk
Important concepts in predictive modeling
Preparing the data
Building predictive models
Evaluating predictive models
Getting the data
Data preprocessing
Feature selection
Modeling using logistic regression
Modeling using support vector machines
Modeling using decision trees
Modeling using random forests
Modeling using neural networks
Model comparison and selection
Summary
7. Social Media Analysis – Analyzing Twitter Data
Social networks (Twitter)
Data mining @social networks
Mining social network data
Data and visualization
Word clouds
Treemaps
Pixel-oriented maps
Other visualizations
Getting started with Twitter APIs
Overview
Registering the application
Connect/authenticate
Extracting sample tweets
Twitter data mining
Frequent words and associations
Popular devices
Hierarchical clustering
Topic modeling
Challenges with social network data mining
References
Summary
8. Sentiment Analysis of Twitter Data
Understanding Sentiment Analysis
Key concepts of sentiment analysis
Subjectivity
Sentiment polarity
Opinion summarization
Feature extraction
Approaches
Applications
Challenges
Sentiment analysis upon Tweets
Polarity analysis
Classification-based algorithms
Labeled dataset
Support Vector Machines
Ensemble methods
Boosting
Cross-validation
Summary
Index
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Prev
Previous Chapter
Summary
Index
A
active-user /
Result interpretation
Adaptive Boosting (AdaBoost) /
Boosting
algorithms
family /
Families of algorithms
supervised learning algorithms /
Supervised learning algorithms
linear regression /
Linear regression
K-Nearest Neighbors (KNN) /
K-Nearest Neighbors (KNN)
analytics
about /
Types of analytics
descriptive /
Types of analytics
diagnostic /
Types of analytics
predictive /
Types of analytics
prescriptive /
Types of analytics
apply function /
apply
Apriori algorithm /
Apriori algorithm
Area under curve (AUC) /
Evaluating predictive models
arrays and matrices
about /
Arrays and matrices
creating /
Creating arrays and matrices
Association for Computational Linguistics (ACL) /
Feature extraction
association rule mining
about /
Association rule mining
dependencies and data, loading /
Loading dependencies and data
exploratory analysis /
Exploratory analysis
shopping trends, detecting /
Detecting and predicting shopping trends
shopping trends, predicting /
Detecting and predicting shopping trends
association rules, visualizing /
Visualizing association rules
B
blogs /
References
boosting /
Boosting
C
CART /
Modeling using decision trees
code flow
controlling /
Controlling code flow
if /
Working with if, if-else, and ifelse
if-else /
Working with if, if-else, and ifelse
ifelse /
Working with if, if-else, and ifelse
switch /
Working with switch
loops /
Loops
collaborative filters
about /
Collaborative filters
core concepts /
Core concepts and definitions
user /
Core concepts and definitions
item /
Core concepts and definitions
rating /
Core concepts and definitions
ratings matrix /
Core concepts and definitions
sparse matrix /
Core concepts and definitions
algorithm /
The collaborative filtering algorithm
predictions /
Predictions
recommendations /
Recommendations
similarity measure /
Similarity
Comprehensive R Archive Network (CRAN) /
Advanced contingency matrices
constructs
about /
Advanced constructs
lapply and sapply /
lapply and sapply
apply function /
apply
tapply function /
tapply
mapply function /
mapply
content-based recommender engines /
Understanding recommendation systems
cosine similarity measure /
Similarity
credit risk
about /
What is credit risk?
predicting /
How to predict credit risk
cross-validation /
Cross-validation
crossTable /
Techniques used for analysis
D
D3
URL /
References
data
getting /
Getting the data
,
Predictive analytics
,
Getting the data
preprocessing /
Predictive analytics
reprocessing /
Data preprocessing
about /
Data and visualization
data analysis
and transformation /
Data analysis and transformation
utilities, building /
Building analysis utilities
dataset, analyzing /
Analyzing the dataset
transformed dataset, saving /
Saving the transformed dataset
data frames
about /
Data frames
creating /
Creating data frames
operating on /
Operating on data frames
data mining
about /
Data mining @social networks
social network data, mining /
Mining social network data
data preprocessing
about /
Data preprocessing
missing values, dealing with /
Dealing with missing values
datatype conversions /
Datatype conversions
numeric variables /
Datatype conversions
categorical variables /
Datatype conversions
dataset
URL /
Getting the data
preparing /
Predictive analytics
decision trees
used, for modeling /
Modeling using decision trees
dendrogram /
Hierarchical clustering
descriptive analytics /
Types of analytics
,
Our next challenge
diagnostic analytics /
Types of analytics
E
Empirical Methods in Natural Language Processing (EMNLP) /
Feature extraction
ensemble methods /
Ensemble methods
epoch /
Implementation
error /
Matrix factorization
Euclidean norms /
Similarity
F
fall-out /
Evaluating predictive models
False Negative (FN) /
Evaluating predictive models
false negative rate /
Evaluating predictive models
False Positive (FP) /
Evaluating predictive models
false positive rate /
Evaluating predictive models
feature
selecting /
Predictive analytics
,
Feature selection
feature sets
about /
Next steps
,
Feature sets
frequent itemset generation technique
about /
Frequent itemset generation
,
Getting started
data retrieval and transformation /
Data retrieval and transformation
itemset association matrix, building /
Building an itemset association matrix
workflow, creating /
Creating a frequent itemsets generation workflow
shopping trends, detecting /
Detecting shopping trends
Frequent Itemsets /
Apriori algorithm
functions
working with /
Working with functions
built-in functions /
Built-in functions
user-defined functions /
User-defined functions
passing, as arguments /
Passing functions as arguments
G
ggvis
URL /
Collecting and exploring data
gradient descent /
Matrix factorization
Graphical User Interface (GUI) /
Delving into the basics of R
H
holdout method /
Cross-validation
hybrid recommender engines /
Understanding recommendation systems
I
Integrated Development Environment (IDE) /
Delving into the basics of R
International Space Station (ISS) /
Topic modeling
Item-Based Collaborative Filtering (IBCF) /
Model preparation and prediction
item-based collaborative filters /
Understanding recommendation systems
K
K-fold cross validation method /
Cross-validation
K-Means /
K-Means
K-Nearest Neighbors (KNN)
about /
K-Nearest Neighbors (KNN)
data, exploring /
Collecting and exploring data
data, collecting /
Collecting and exploring data
data, normalizing /
Normalizing data
training and test data sets, creating /
Creating training and test data sets
data/training model /
Learning from data/training the model
model, evaluating /
Evaluating the model
kNN collaborative filtering /
The collaborative filtering algorithm
Knowledge Discovery from Data (KDD) /
Data mining @social networks
Knowledge Mining /
Data mining @social networks
L
labeled tweets
URL /
Labeled dataset
lapply function /
lapply and sapply
left-hand side (LHS) /
Core concepts and definitions
linear regression /
Linear regression
lists
about /
Lists
creating /
Creating and indexing lists
indexing /
Creating and indexing lists
combining /
Combining and converting lists
converting /
Combining and converting lists
logistic regression
used, for modeling /
Modeling using logistic regression
M
machine learning
about /
Machine learning basics
,
Machine learning – what does it really mean?
,
Understanding machine learning
uses /
Machine learning – how is it used in the world?
algorithms, types /
Types of machine learning algorithms
algorithms, supervised /
Supervised machine learning algorithms
packages /
Popular machine learning packages in R
algorithms /
Algorithms in machine learning
perceptron /
Perceptron
machine learning algorithms
types /
Types of machine learning algorithms
supervised /
Supervised machine learning algorithms
unsupervised /
Unsupervised machine learning algorithms
about /
Machine learning algorithms
linear classification algorithms /
Machine learning algorithms
decision trees /
Machine learning algorithms
ensemble learning methods /
Machine learning algorithms
boosting algorithms /
Machine learning algorithms
neural networks /
Machine learning algorithms
mapply function /
mapply
market basket analysis
about /
Detecting and predicting trends
,
Market basket analysis
,
What does market basket analysis actually mean?
core concepts /
Core concepts and definitions
techniques /
Techniques used for analysis
data driven decisions, making /
Making data driven decisions
matrix factorization /
Building a recommender engine
,
Matrix factorization
matrix operations
about /
Matrix operations
mean absolute error (MAE) /
Model evaluation
mean squared error (MSE) /
Model evaluation
miss rate /
Evaluating predictive models
model
evaluating /
Predictive analytics
tuning /
Predictive analytics
deployment /
Predictive analytics
modeling
logistic regression used /
Modeling using logistic regression
support vector machines used /
Modeling using support vector machines
decision trees used /
Modeling using decision trees
random forests used /
Modeling using random forests
neural networks used /
Modeling using neural networks
comparision /
Model comparison and selection
selecting /
Model comparison and selection
Multivariate Adaptive Regression Splines (MARS) /
Linear regression
N
n-Grams /
Feature extraction
URL /
Feature extraction
names and dimensions
about /
Names and dimensions
natural language processing (NLP) problem /
Challenges
negation /
Feature extraction
negative predictive value /
Evaluating predictive models
neighbour-based collaborative filtering /
The collaborative filtering algorithm
neural networks
used, for modeling /
Modeling using neural networks
NPV /
Modeling using logistic regression
O
Open Authentication (OAuth) protocol /
Registering the application
out of bag error (OOBE) /
Modeling using random forests
P
Parts of Speech (POS) /
Feature extraction
Pearson correlation /
Similarity
perceptron /
Perceptron
pixel-oriented maps /
Pixel-oriented maps
Polarity /
Polarity analysis
positive predictive value /
Evaluating predictive models
precision /
Evaluating predictive models
prediction operation /
Core concepts and definitions
predictive analytics /
Types of analytics
,
Our next challenge
about /
Predictive analytics
predictive modeling
about /
Predictive analytics
,
Important concepts in predictive modeling
data, preparing /
Preparing the data
datasets /
Preparing the data
data, observations /
Preparing the data
data, features /
Preparing the data
data, transformation /
Preparing the data
training data /
Preparing the data
data, training /
Preparing the data
data, testing /
Preparing the data
predictive models, building /
Building predictive models
predictive models, evaluating /
Evaluating predictive models
predictive models, building
about /
Building predictive models
model training /
Building predictive models
predictive model /
Building predictive models
model, selecting /
Building predictive models
hyperparameter optimization /
Building predictive models
cross validation /
Building predictive models
predictive models, evaluating
about /
Evaluating predictive models
prediction values /
Evaluating predictive models
confusion matrix /
Evaluating predictive models
prescriptive analytics /
Types of analytics
Principal Component Analysis (PCA) /
Model preparation and prediction
product contingency matrix
evaluating /
Evaluating a product contingency matrix
data, getting /
Getting the data
data, analyzing /
Analyzing and visualizing the data
data, visualizing /
Analyzing and visualizing the data
global recommendations /
Global recommendations
advanced /
Advanced contingency matrices
R
R
basics /
Delving into the basics of R
using, as scientific calculator /
Using R as a scientific calculator
vectors, operating on /
Operating on vectors
special values /
Special values
data structures /
Data structures in R
next steps /
Next steps with R
help /
Getting help
packages, handling /
Handling packages
radial basis function (RBF) /
Modeling using support vector machines
radial bias kernel (rbf) /
Support Vector Machines
random forests
used, for modeling /
Modeling using random forests
rate of descent /
Matrix factorization
ratings matrix /
Core concepts and definitions
Read-Evaluate-Print Loop (REPL) /
Delving into the basics of R
Receiver Operator Characteristic (ROC) curve /
Evaluating predictive models
recommendation systems
offline-recommender engines /
Understanding recommendation systems
online-recommender engines /
Understanding recommendation systems
types /
Understanding recommendation systems
issues /
Issues with recommendation systems
recommendation systems, issues
sparsity problem /
Issues with recommendation systems
cold start problem /
Issues with recommendation systems
recommendation systems, product ready
about /
Production ready recommender engines
recommendation systems, types
user-based recommender engines /
Understanding recommendation systems
content-based recommender engines /
Understanding recommendation systems
hybrid recommender engines /
Understanding recommendation systems
recommender engine
building /
Building a recommender engine
matrix factorization /
Matrix factorization
implementing /
Implementation
result interpretation /
Result interpretation
recommender engines
production ready /
Production ready recommender engines
extract, transform, and analyze /
Extract, transform, and analyze
model, preparation and prediction /
Model preparation and prediction
model evaluation /
Model evaluation
recommenderlab /
Production ready recommender engines
recommend operation /
Core concepts and definitions
references /
References
regularization /
Matrix factorization
right-hand side (RHS) /
Core concepts and definitions
root mean squared error (RMSE) /
Model evaluation
Root Mean Square Error/RMSE) /
Cross-validation
rotational estimation /
Cross-validation
RTextTools /
Cross-validation
S
sensitivity /
Evaluating predictive models
sentiment analysis
about /
Understanding Sentiment Analysis
key concepts /
Key concepts of sentiment analysis
subjectivity /
Subjectivity
sentiment polarity /
Sentiment polarity
opinion summarization /
Opinion summarization
feature extraction /
Feature extraction
approaches /
Approaches
applications /
Applications
challenges /
Challenges
upon Tweets /
Sentiment analysis upon Tweets
polarity analysis /
Polarity analysis
classification-based algorithms /
Classification-based algorithms
labeled dataset /
Labeled dataset
Support Vector Machines (SVM) /
Support Vector Machines
ensemble methods /
Ensemble methods
boosting /
Boosting
cross-validation /
Cross-validation
sentiment analysis, abstraction
document level /
Approaches
sentence level /
Approaches
social network data mining
challenges /
Challenges with social network data mining
social networks
about /
Social networks (Twitter)
sparse matrix /
Core concepts and definitions
Spearman rank correlation /
Similarity
specificity /
Evaluating predictive models
squared error /
Matrix factorization
supervised learning algorithms
about /
Supervised learning algorithms
problems /
Supervised learning algorithms
regression based machine learning /
Supervised learning algorithms
classification based machine learning /
Supervised learning algorithms
supervised machine learning algorithms
about /
Supervised machine learning algorithms
regression algorithms /
Supervised machine learning algorithms
support vector machines
used, for modeling /
Modeling using support vector machines
Support Vector Machines (SVM) /
Supervised learning algorithms
,
Approaches
,
Support Vector Machines
T
Tableau specific
URL /
References
tag clouds /
Word clouds
tapply function /
tapply
Term Frequency-Inverse Document Frequency (tf-idf) /
Feature extraction
Treemaps /
Treemaps
trends
detecting /
Detecting and predicting trends
predicting /
Detecting and predicting trends
True Negative (TN) /
Evaluating predictive models
true negative rate /
Evaluating predictive models
True Positive (TP) /
Evaluating predictive models
true positive rate /
Evaluating predictive models
Twitter
about /
Social networks (Twitter)
Best Practices, URL /
Overview
Twitter APIs
about /
Getting started with Twitter APIs
,
Overview
URL /
Overview
application, registering /
Registering the application
connect/authenticate /
Connect/authenticate
sample tweets, extracting /
Extracting sample tweets
Twitter data mining
about /
Twitter data mining
frequent words and associations /
Frequent words and associations
popular devices /
Popular devices
hierarchical clustering revisited /
Hierarchical clustering
topic modeling /
Topic modeling
U
unsupervised learning algorithms
about /
Unsupervised learning algorithms
association rule based machine learning /
Unsupervised learning algorithms
clustering based machine learning /
Unsupervised learning algorithms
Apriori algorithm /
Apriori algorithm
K-Means /
K-Means
unsupervised machine learning algorithms
about /
Unsupervised machine learning algorithms
clustering algorithms /
Unsupervised machine learning algorithms
associate rule learning algorithms /
Unsupervised machine learning algorithms
user-based recommender engines /
Understanding recommendation systems
user-user collaborative filtering /
The collaborative filtering algorithm
V
vectors
about /
Vectors
creating /
Creating vectors
indexing /
Indexing and naming vectors
naming /
Indexing and naming vectors
visualizations /
Other visualizations
W
weighted average /
Predictions
word clouds /
Word clouds
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