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