Applied Predictive Analytics Principles and Techniques for the Professional Data Analyst

Learn the art and science of predictive analytics — techniques that get results

Predictive analytics is what translates big data into meaningful, usable business information. Written by a leading expert in the field, this guide examines the science of the underlying algorithms as well as the principles and best practices that govern the art of predictive analytics. It clearly explains the theory behind predictive analytics, teaches the methods, principles, and techniques for conducting predictive analytics projects, and offers tips and tricks that are essential for successful predictive modeling. Hands-on examples and case studies are included.

• The ability to successfully apply predictive analytics enables businesses to effectively interpret big data; essential for competition today
• This guide teaches not only the principles of predictive analytics, but also how to apply them to achieve real, pragmatic solutions
• Explains methods, principles, and techniques for conducting predictive analytics projects from start to finish
• Illustrates each technique with hands-on examples and includes as series of in-depth case studies that apply predictive analytics to common business scenarios
• A companion website provides all the data sets used to generate the examples as well as a free trial version of software

Applied Predictive Analytics arms data and business analysts and business managers with the tools they need to interpret and capitalize on big data.

DEAN ABBOTT is President of Abbott Analytics, Inc. (San Diego). He is an internationally recognized data mining and predictive analytics expert with over two decades experience in fraud detection, risk modeling, text mining, personality assessment, planned giving, toxicology, and other applications. He is also Chief Scientist of SmarterRemarketer, a company focusing on behaviorally- and data-driven marketing and web analytics.

Chapter 1 Overview of Predictive Analytics 1

What Is Analytics? 3

What Is Predictive Analytics? 3

Supervised vs. Unsupervised Learning 5

Parametric vs. Non-Parametric Models 6

Business Intelligence 6

Predictive Analytics vs. Business Intelligence 8

Do Predictive Models Just State the Obvious? 9

Similarities between Business Intelligence and Predictive Analytics 9

Predictive Analytics vs. Statistics 10

Statistics and Analytics 11

Predictive Analytics and Statistics Contrasted 12

Predictive Analytics vs. Data Mining 13

Who Uses Predictive Analytics? 13

Challenges in Using Predictive Analytics 14

Obstacles in Management 14

Obstacles with Data 14

Obstacles with Modeling 15

Obstacles in Deployment 16

What Educational Background Is Needed to Become a Predictive Modeler? 16

Chapter 2 Setting Up the Problem 19

Predictive Analytics Processing Steps: CRISP-DM 19

Business Understanding 21

The Three-Legged Stool 22

Business Objectives 23

Defining Data for Predictive Modeling 25

Defining the Columns as Measures 26

Defining the Unit of Analysis 27

Which Unit of Analysis? 28

Defining the Target Variable 29

Temporal Considerations for Target Variable 31

Defining Measures of Success for Predictive Models 32

Success Criteria for Classifi cation 32

Success Criteria for Estimation 33

Other Customized Success Criteria 33

Doing Predictive Modeling Out of Order 34

Building Models First 34

Early Model Deployment 35

Case Study: Recovering Lapsed Donors 35

Overview 36

Business Objectives 36

Data for the Competition 36

The Target Variables 36

Modeling Objectives 37

Model Selection and Evaluation Criteria 38

Model Deployment 39

Case Study: Fraud Detection 39

Overview 39

Business Objectives 39

Data for the Project 40

The Target Variables 40

Modeling Objectives 41

Model Selection and Evaluation Criteria 41

Model Deployment 41

Summary 42

Chapter 3 Data Understanding 43

What the Data Looks Like 44

Single Variable Summaries 44

Mean 45

Standard Deviation 45

The Normal Distribution 45

Uniform Distribution 46

Applying Simple Statistics in Data Understanding 47

Skewness 49

Kurtosis 51

Rank-Ordered Statistics 52

Categorical Variable Assessment 55

Data Visualization in One Dimension 58

Histograms 59

Multiple Variable Summaries 64

Hidden Value in Variable Interactions: Simpson’s Paradox 64

The Combinatorial Explosion of Interactions 65

Correlations 66

Spurious Correlations 66

Back to Correlations 67

Crosstabs 68

Data Visualization, Two or Higher Dimensions 69

Scatterplots 69

Anscombe’s Quartet 71

Scatterplot Matrices 75

Overlaying the Target Variable in Summary 76

Scatterplots in More Than Two Dimensions 78

The Value of Statistical Signifi cance 80

Pulling It All Together into a Data Audit 81

Summary 82

Chapter 4 Data Preparation 83

Variable Cleaning 84

Incorrect Values 84

Consistency in Data Formats 85

Outliers 85

Multidimensional Outliers 89

Missing Values 90

Fixing Missing Data 91

Feature Creation 98

Simple Variable Transformations 98

Fixing Skew 99

Binning Continuous Variables 103

Numeric Variable Scaling 104

Nominal Variable Transformation 107

Ordinal Variable Transformations 108

Date and Time Variable Features 109

ZIP Code Features 110

Which Version of a Variable Is Best? 110

Multidimensional Features 112

Variable Selection Prior to Modeling 117

Sampling 123

Example: Why Normalization Matters for K-Means Clustering 139

Summary 143

Chapter 5 Itemsets and Association Rules 145

Terminology 146

Condition 147

Left-Hand-Side, Antecedent(s) 148

Right-Hand-Side, Consequent, Output, Conclusion 148

Rule (Item Set) 148

Support 149

Antecedent Support 149

Confi dence, Accuracy 150

Lift 150

Parameter Settings 151

How the Data Is Organized 151

Standard Predictive Modeling Data Format 151

Transactional Format 152

Measures of Interesting Rules 154

Deploying Association Rules 156

Variable Selection 157

Interaction Variable Creation 157

Problems with Association Rules 158

Redundant Rules 158

Too Many Rules 158

Too Few Rules 159

Building Classification Rules from Association Rules 159

Summary 161

Chapter 6 Descriptive Modeling 163

Data Preparation Issues with Descriptive Modeling 164

Principal Component Analysis 165

The PCA Algorithm 165

Applying PCA to New Data 169

PCA for Data Interpretation 171

Additional Considerations before Using PCA 172

The Effect of Variable Magnitude on PCA Models 174

Clustering Algorithms 177

The K-Means Algorithm 178

Data Preparation for K-Means 183

Selecting the Number of Clusters 185

The Kohonen SOM Algorithm 192

Visualizing Kohonen Maps 194

Similarities with K-Means 196

Summary 197

Chapter 7 Interpreting Descriptive Models 199

Standard Cluster Model Interpretation 199

Problems with Interpretation Methods 202

Identifying Key Variables in Forming Cluster Models 203

Cluster Prototypes 209

Cluster Outliers 210

Summary 212

Chapter 8 Predictive Modeling 213

Decision Trees 214

The Decision Tree Landscape 215

Building Decision Trees 218

Decision Tree Splitting Metrics 221

Decision Tree Knobs and Options 222

Reweighting Records: Priors 224

Reweighting Records: Misclassifi cation Costs 224

Other Practical Considerations for Decision Trees 229

Logistic Regression 230

Interpreting Logistic Regression Models 233

Other Practical Considerations for Logistic Regression 235

Neural Networks 240

Building Blocks: The Neuron 242

Neural Network Training 244

The Flexibility of Neural Networks 247

Neural Network Settings 249

Neural Network Pruning 251

Interpreting Neural Networks 252

Neural Network Decision Boundaries 253

Other Practical Considerations for Neural Networks 253

K-Nearest Neighbor 254

The k-NN Learning Algorithm 254

Distance Metrics for k-NN 258

Other Practical Considerations for k-NN 259

Naïve Bayes 264

Bayes’ Theorem 264

The Naïve Bayes Classifier 268

Interpreting Naïve Bayes Classifi ers 268

Other Practical Considerations for Naïve Bayes 269

Regression Models 270

Linear Regression 271

Linear Regression Assumptions 274

Variable Selection in Linear Regression 276

Interpreting Linear Regression Models 278

Using Linear Regression for Classification 279

Other Regression Algorithms 280

Summary 281

Chapter 9 Assessing Predictive Models 283

Batch Approach to Model Assessment 284

Percent Correct Classifi cation 284

Rank-Ordered Approach to Model Assessment 293

Assessing Regression Models 301

Summary 304

Chapter 10 Model Ensembles 307

Motivation for Ensembles 307

The Wisdom of Crowds 308

Bias Variance Tradeoff 309

Bagging 311

Boosting 316

Improvements to Bagging and Boosting 320

Random Forests 320

Stochastic Gradient Boosting 321

Heterogeneous Ensembles 321

Model Ensembles and Occam’s Razor 323

Interpreting Model Ensembles 323

Summary 326

Chapter 11 Text Mining 327

Motivation for Text Mining 328

A Predictive Modeling Approach to Text Mining 329

Structured vs. Unstructured Data 329

Why Text Mining Is Hard 330

Text Mining Applications 332

Data Sources for Text Mining 333

Data Preparation Steps 333

POS Tagging 333

Tokens 336

Stop Word and Punctuation Filters 336

Character Length and Number Filters 337

Stemming 337

Dictionaries 338

The Sentiment Polarity Movie Data Set 339

Text Mining Features 340

Term Frequency 341

Inverse Document Frequency 344

TF-IDF 344

Cosine Similarity 346

Multi-Word Features: N-Grams 346

Reducing Keyword Features 347

Grouping Terms 347

Modeling with Text Mining Features 347

Regular Expressions 349

Uses of Regular Expressions in Text Mining 351

Summary 352

Chapter 12 Model Deployment 353

General Deployment Considerations 354

Deployment Steps 355

Summary 375

Chapter 13 Case Studies 377

Survey Analysis Case Study: Overview 377

Business Understanding: Defining the Problem 378

Data Understanding 380

Data Preparation 381

Modeling 385

Deployment: “What-If” Analysis 391

Revisit Models 392

Deployment 401

Summary and Conclusions 401

Help Desk Case Study 402

Data Understanding: Defining the Data 403

Data Preparation 403

Modeling 405

Revisit Business Understanding 407

Deployment 409

Summary and Conclusions 411

Index 413

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