Anasse Bari, Ph.D. is data science expert and a university professor who has many years of predictive modeling and data analytics experience.

Mohamed Chaouchi is a veteran software engineer who has conducted extensive research using data mining methods.

Tommy Jung is a software engineer with expertise in enterprise web applications and analytics.

INTRODUCTION 1

PART 1: GETTING STARTED WITH PREDICTIVE ANALYTICS 5

CHAPTER 1: Entering the Arena 7

Exploring Predictive Analytics 7

Mining data 8

Highlighting the model 9

Adding Business Value 10

Endless opportunities 11

Empowering your organization 12

Starting a Predictive Analytic Project 13

Business knowledge 14

Data-science team and technology 15

The Data 16

Ongoing Predictive Analytics 17

Forming Your Predictive Analytics Team 18

Hiring experienced practitioners 18

Demonstrating commitment and curiosity 19

Surveying the Marketplace 19

Responding to big data 20

Working with big data 20

CHAPTER 2: Predictive Analytics in the Wild 23

Online Marketing and Retail 25

Recommender systems 25

Personalized shopping on the Internet 26

Implementing a Recommender System 28

Collaborative filtering 28

Content-based filtering 36

Hybrid recommender systems 39

Target Marketing 41

Targeting using predictive modeling 42

Uplift modeling 43

Personalization 46

Online customer experience 46

Retargeting 47

Implementation 47

Optimizing using personalization 48

Similarities of Personalization and Recommendations 48

Content and Text Analytics 50

CHAPTER 3: Exploring Your Data Types and Associated Techniques 51

Recognizing Your Data Types 52

Structured and unstructured data 52

Static and streamed data 56

Identifying Data Categories 58

Attitudinal data 59

Behavioral data 60

Demographic data 61

Generating Predictive Analytics 61

Data-driven analytics 62

User-driven analytics 64

Connecting to Related Disciplines 65

Statistics 65

Data mining 66

Machine learning 67

CHAPTER 4: Complexities of Data 69

Finding Value in Your Data 70

Delving into your data 70

Data validity 70

Data variety 71

Constantly Changing Data 72

Data velocity 72

High volume of data 73

Complexities in Searching Your Data 73

Keyword-based search 74

Semantic-based search 74

Contextual search 76

Differentiating Business Intelligence from Big-Data Analytics 79

Exploration of Raw Data 80

Identifying data attributes 80

Exploring common data visualizations 81

Tabular visualizations 81

Word clouds 82

Flocking birds as a novel data representation 83

Graph charts 85

Common visualizations 87

PART 2: INCORPORATING ALGORITHMS IN YOUR MODELS 89

CHAPTER 5: Applying Models 91

Modeling Data 92

Models and simulation 92

Categorizing models 94

Describing and summarizing data 96

Making better business decisions 97

Healthcare Analytics Case Studies 97

Google Flu Trends 97

Cancer survivability predictors 99

Social and Marketing Analytics Case Studies 101

Target store predicts pregnant women 101

Twitter-based predictors of earthquakes 102

Twitter-based predictors of political campaign outcomes 103

Tweets as predictors for the stock market 105

Predicting variation of stock prices from news articles 106

Analyzing New York City's bicycle usage 107

Predictions and responses 110

Data compression 111

Prognostics and its Relation to Predictive Analytics 112

The Rise of Open Data 113

CHAPTER 6: Identifying Similarities in Data 115

Explaining Data Clustering 116

Converting Raw Data into a Matrix 120

Creating a matrix of terms in documents 120

Term selection 121

Identifying Groups in Your Data 122

K-means clustering algorithm 122

Clustering by nearest neighbors 126

Density-based algorithms 130

Finding Associations in Data Items 132

Applying Biologically Inspired Clustering Techniques 136

Birds flocking: Flock by Leader algorithm 136

Ant colonies 143

CHAPTER 7: Predicting the Future Using Data Classification 147

Explaining Data Classification 149

Introducing Data Classification to Your Business 152

Exploring the Data-Classification Process 154

Using Data Classification to Predict the Future 156

Decision trees 156

Algorithms for Generating Decision Trees 159

Support vector machine 163

Ensemble Methods to Boost Prediction Accuracy 165

Naïve Bayes classification algorithm 166

The Markov Model 172

Linear regression 177

Neural networks 177

Deep Learning 179

PART 3: DEVELOPING A ROADMAP 185

CHAPTER 8: Convincing Your Management to Adopt Predictive Analytics 187

Making the Business Case 188

Gathering Support from Stakeholders 195

Presenting Your Proposal 206

CHAPTER 9: Preparing Data 209

Listing the Business Objectives 210

Processing Your Data 212

Identifying the data 212

Cleaning the data 213

Generating any derived data 215

Reducing the dimensionality of your data 215

Applying principal component analysis 216

Leveraging singular value decomposition 218

Working with Features 219

Structuring Your Data 224

Extracting, transforming and loading your data 225

Keeping the data up to date 226

Outlining testing and test data 226

CHAPTER 10: Building a Predictive Model 229

Getting Started 230

Defining your business objectives 232

Preparing your data 233

Choosing an algorithm 236

Developing and Testing the Model 237

Going Live with the Model 242

CHAPTER 11: Visualization of Analytical Results 245

Visualization as a Predictive Tool 246

Evaluating Your Visualization 249

Visualizing Your Model's Analytical Results 251

Visualizing hidden groupings in your data 251

Visualizing data classification results 252

Visualizing outliers in your data 254

Visualization of Decision Trees 254

Visualizing predictions 256

Novel Visualization in Predictive Analytics 258

Big Data Visualization Tools 262

Tableau 263

Google Charts 263

Plotly 263

Infogram 264

PART 4: PROGRAMMING PREDICTIVE ANALYTICS 265

CHAPTER 12: Creating Basic Prediction Examples 267

Installing the Software Packages 268

Installing Python 268

Installing the machine-learning module 270

Installing the dependencies 274

Preparing the Data 278

Making Predictions Using Classification Algorithms 280

Creating a supervised learning model with SVM 281

Creating a supervised learning model with logistic regression 288

Creating a supervised learning model with random forest 295

Comparing the classification models 297

CHAPTER 13: Creating Basic Examples of Unsupervised Predictions 299

Getting the Sample Dataset 300

Using Clustering Algorithms to Make Predictions 301

Comparing clustering models 301

Creating an unsupervised learning model with K-means 302

Creating an unsupervised learning model with DBSCAN 314

Creating an unsupervised learning model with mean shift 318

CHAPTER 14: Predictive Modeling with R 323

Programming in R 325

Installing R 325

Installing RStudio 326

Getting familiar with the environment 327

Learning just a bit of R 328

Making Predictions Using R 334

Predicting using regression 334

Using classification to predict 345

Classification by random forest 354

CHAPTER 15: Avoiding Analysis Traps 359

Data Challenges 360

Outlining the limitations of the data 361

Dealing with extreme cases (outliers) 364

Data smoothing 367

Curve fitting 371

Keeping the assumptions to a minimum 374

Analysis Challenges 375

PART 5: EXECUTING BIG DATA 381

CHAPTER 16: Targeting Big Data 383

Major Technological Trends in Predictive Analytics 384

Exploring predictive analytics as a service 384

Aggregating distributed data for analysis 385

Real-time data-driven analytics 387

Applying Open-Source Tools to Big Data 388

Apache Hadoop 388

Apache Spark 394

CHAPTER 17: Getting Ready for Enterprise Analytics 399

Analytics as a Service 403

Google Analytics 403

IBM Watson 405

Microsoft Revolution R Enterprise 405

Preparing for a Proof-of-Value of Predictive Analytics Prototype 406

Prototyping for predictive analytics 406

Testing your predictive analytics model 409

PART 6: THE PART OF TENS 411

CHAPTER 18: Ten Reasons to Implement

Predictive Analytics 413

CHAPTER 19: Ten Steps to Build a Predictive Analytic Model 423

INDEX 433