# Index

## A

- Abstract Data Type (ADT) / Graph implementations
- actual values
- comparing, with predicted results / Comparing actual values with predicted results

- acyclic graph / Terminology and representations
- Adult UCI data
- reference / Prerequisites

- Analysis of variance (ANOVA) / Describing Clusters – Models Tab
- assignment operators / Assignment operators
- Asterix
- about / Working with dirty data

- Azure ML / Next steps

## B

- backpropagation / Different types of neural networks, Backpropagation and Feedforward neural networks
- basic list / Lists
- Bayesian methods / Bayesian methods
- Bayesian probability
- implementing / Bayesian Theory

- business understanding / Business understanding

## C

- Calculation Editor / Replicating our results using R and Tableau together
- cluster
- saving, constraints / Constraints on saving Clusters

- clustering
- about / What is Clustering?
- in Tableau / Clustering in Tableau, How to do Clustering in Tableau, How Clustering Works in Tableau
- results, interpreting / Interpreting your results
- without k-means / Clustering without using k-means

- clustering algorithm / The clustering algorithm
- clustering example
- in Tableau / Clustering example in Tableau

- cluster results
- Tableau group, creating from / Creating a Tableau group from cluster results

- clusters
- finding, in data / Finding clusters in data
- Analytics pane / Why can't I drag my Clusters to the Analytics pane?
- creating / Creating Clusters
- defining / Describing Clusters – Summary tab
- testing / Testing your Clustering

- coefficients / Coefficients
- confusion matrix
- building / Confusion matrix

- control structures, R
- about / Control structures in R
- assignment operators / Assignment operators
- logical operators / Logical operators

- correlation / Interpreting the results
- CRISP-DM methodology
- about / CRISP-DM
- business understanding / Business understanding/data understanding
- data understanding / Business understanding/data understanding
- data preparation / CRISP-DM model — data preparation
- modeling phase / CRISP-DM — modeling phase
- evaluation / CRISP-DM — evaluation
- deployment / CRISP-DM — deployment
- process restarted / CRISP-DM — process restarted
- summary / CRISP-DM summary

- Cross Industry Standard Process for Data Mining (CRISP-DM) / Backpropagation and Feedforward neural networks
- cycle / Terminology and representations

## D

- data
- summarizing, with dplyr / Summarizing the data with dplyr
- relationships, investigating in / Investigating relationships in the data
- describing / Describing the data
- clusters, finding in / Finding clusters in data

- data analysis
- sharing, Tableau used / Sharing our data analysis using Tableau

- data exploration / Data exploration
- data frames
- about / Data frames
- structure / Data frames
- example / Data frames

- data preparation / Data preparation
- datasets, University of California Irvine Data repository
- reference / Business understanding

- data structures, R
- data understanding / Understanding the data
- decision function
- about / Decision system-based Bayesian

- decision maker (DM)
- about / Decision system-based Bayesian

- decision system
- about / Introduction to decision system and machine learning
- building, steps / Decision system-based fuzzy logic

- decision system, and IoT project
- integrating / Integrating a decision system and IoT project

- decision system-based Bayesian
- about / Decision system-based Bayesian

- decision system-based fuzzy logic / Decision system-based fuzzy logic
- decision system-based IoT
- bulding / Building your own decision system-based IoT
- wiring / Wiring
- program, writing / Writing the program
- testing / Testing

- decision tree
- results, analyzing / Analyzing the results of the decision tree

- decision trees
- classification trees / Decision trees in Tableau using R
- regression trees / Decision trees in Tableau using R

- degrees of freedom / Describing Clusters – Models Tab
- descriptive statistics
- about / Introduction to R

- directed acyclic graph (DAG) / Terminology and representations
- directed graph / Terminology and representations
- directed graphs / Terminology and representations
- dirty data
- working with / Working with dirty data

- disaggregating data
- reference / Constraints on saving Clusters

- dplyr
- about / Introduction to dplyr
- data, summarizing with / Summarizing the data with dplyr

## E

- edges / Terminology and representations
- error / What do the terms mean?

## F

- F-statistic / Describing Clusters – Models Tab
- factor / Factors
- feature / Building our multiple regression model
- Feedforward Neural Network / Different types of neural networks, Backpropagation and Feedforward neural networks
- fitted
- versus residuals / Investigating relationships in the data

- for loops / For loops
- functions
- about / Functions
- creating / Creating your own function

- fuzzy logic / Fuzzy logic
- fuzzy logic, for decision system
- enhancement / Enhancement

## G

- graph functions
- num_vert / Graph implementations
- num_edge / Graph implementations
- weightEdge / Graph implementations
- assignEdge / Graph implementations
- deleteEdge / Graph implementations
- firstVertex / Graph implementations
- nextVertex / Graph implementations
- isEdge / Graph implementations
- getMark / Graph implementations
- initMark / Graph implementations

- graphs / Graphs, Terminology and representations
- implementing / Graph implementations

## H

- hierarchical clustering / Hierarchical modeling

## I

- inferential statistics
- about / Introduction to R

- iris dataset
- reference / Using Tableau to evaluate data

## K

- k-means clustering / Clustering in Tableau
- working / How does k-means work?

## L

- leverage
- versus residuals / Investigating relationships in the data

- Lift curves / Evaluating a neural network model
- lift scores / Lift scores
- lists
- about / Lists

- Lloyd algorithm / The clustering algorithm
- lm()
- used, for conducting simple linear regression / Using lm() to conduct a simple linear regression

- logical operators / Logical operators
- low p-value / Understanding the performance of the result

## M

- machine learning
- matrices / Matrices
- Maximum Likelihood Estimate (MLE) / Bayesian methods
- model deployment / Model deployment
- modeling
- in R / Modeling in R

- Model Sum of Squares / Describing Clusters – Models Tab
- multiple regression
- multiple regression model
- building / Building our multiple regression model

## N

- named list / Lists
- neural network, in R
- about / Neural network in R

- neural network model
- evaluating / Evaluating a neural network model

- Neural network performance measures
- Receiver Operating Characteristic curve / Receiver Operating Characteristic curve
- Precision and Recall curve / Precision and Recall curve
- lift scores / Lift scores

- neural network results
- visualizing / Visualizing neural network results

- neural networks
- about / What are neural networks?
- layers / What are neural networks?
- types / Different types of neural networks
- structure / Different types of neural networks

- normal Q-Q / Investigating relationships in the data

## O

- Ordinary Least Squares (OLS) / Using lm() to conduct a simple linear regression

## P

- p-value / Understanding the performance of the result, Describing Clusters – Models Tab
- Pearson's correlation coefficient / Investigating relationships in the data
- Pearson's R / Investigating relationships in the data
- perceptron / Different types of neural networks
- populated data frame
- example / Data frames

- Precision/Recall curves / Evaluating a neural network model
- predicted results
- actual values, comparing with / Comparing actual values with predicted results

- predictor / Understanding the performance of the result

## R

- R
- installing, for Windows / Installing R for Windows
- decision trees, in Tableau / Decision trees in Tableau using R

- R, from CRAN website
- download link / Installing R for Windows

- R, with Tableau
- results, replicating / Replicating our results using R and Tableau together

- Random Forest classifier / Decision trees in Tableau using R
- Receiver Operator Characteristic (ROC) / Evaluating a neural network model
- Receiver Operator Characteristic (ROC) curve / Receiver Operating Characteristic curve
- regression
- about / Getting started with regression
- simple linear regression / Simple linear regression
- multiple regression / Getting started with multiple regression?
- business question, solving / Solving the business question

- relationships
- investigating, in data / Investigating relationships in the data

- Relevant Analysis of variance statistics, Tableau clustering
- F-statistic / Describing Clusters – Models Tab
- p-value / Describing Clusters – Models Tab
- Model Sum of Squares / Describing Clusters – Models Tab
- degrees of freedom / Describing Clusters – Models Tab
- Error Sum of Squares / Describing Clusters – Models Tab

- residuals
- versus leverage / Investigating relationships in the data
- versus fitted / Investigating relationships in the data

- residual standard error / Residual standard error
- RGui / Installing R for Windows
- R language
- about / Introduction to R

- Root Mean Square Deviation / What do the terms mean?
- Root Mean Square Error / What do the terms mean?
- R programming
- core essentials / Core essentials of R programming
- data structures / Data structures in R
- control structures / Control structures in R

- Rserve
- Tableau connectivity / Tableau and R connectivity using Rserve
- R connectivity / Tableau and R connectivity using Rserve
- installing / Installing Rserve

- Rserve connection
- configuring / Configuring an Rserve Connection

- RStudio
- about / RStudio
- installation prerequisites / Prerequisites for RStudio installation
- download link / Prerequisites for RStudio installation

- R website
- reference / Installing R for Windows

## S

- scale location / Investigating relationships in the data
- scaling / Scaling
- scikit-fuzzy
- about / Fuzzy logic
- reference / Fuzzy logic

- scripting
- testing / Testing the scripting

- scripts
- implementing / Implementing the scripts for the book

- simple decision system-based Bayesian theory
- simple linear regression
- about / Simple linear regression
- conducting, lm() used / Using lm() to conduct a simple linear regression

- StackOverflow
- reference / Assignment operators

- statistics / Interpreting the results
- for clustering / Statistics for Clustering

- supervised learning / Building our multiple regression model

## T

- Tableau
- R, improving / Making R run more efficiently in Tableau
- used, for sharing data analysis / Sharing our data analysis using Tableau
- data, modeling / Modeling and evaluating data in Tableau
- data, evaluating / Modeling and evaluating data in Tableau, Using Tableau to evaluate data

- Tableau group
- creating, from cluster results / Creating a Tableau group from cluster results

- Tapply / For loops
- TDSP process
- about / Team Data Science Process
- business understanding / Business understanding
- data acquisition / Data acquisition and understanding
- data understanding / Data acquisition and understanding
- modeling phase / Modeling
- deployment phase / Deployment
- summary / TDSP Summary

- training material, GitHub
- reference / Implementing the scripts for the book

## U

- undirected graphs / Terminology and representations

## V

- variables
- about / Variables
- creating / Creating variables
- working with / Working with variables

- vector / Vector
- vectorization / For loops and vectorization in R, For loops
- vertices / Terminology and representations

## W

- WDI package
- reference / Summarizing the data with dplyr

- Windows
- R, installing for / Installing R for Windows

- World Development Indicators (WDI) / Summarizing the data with dplyr