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
- 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
- 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