... Preface ... 17

... Objective ... 17

... Target Audience ... 18

... Structure of This Book ... 19

I ... Getting Started ... 21

1 ... An Introduction to Predictive Analytics ... 23

1.1 ... The Importance of Predictive Analysis ... 24

1.2 ... Predictive Analysis: Prescriptive and Exploratory ... 26

1.3 ... Preparing for a Successful Predictive Analysis Project ... 32

1.4 ... Industry Use Cases ... 37

1.5 ... Summary ... 39

2 ... What Is SAP Predictive Analytics? ... 41

2.1 ... Building Predictive Models with SAP Predictive Analytics ... 41

2.2 ... Automated Analytics and Expert Analytics ... 42

2.3 ... Mass Production of Predictive Models with the Predictive Factory ... 43

2.4 ... Data Preparation ... 45

2.5 ... Additional SAP Predictive Analytics Capabilities ... 47

2.6 ... Summary ... 48

3 ... Installing SAP Predictive Analytics ... 49

3.1 ... Recommended Deployments ... 50

3.2 ... Installing the SAP Predictive Analytics Server ... 51

3.3 ... Installing the SAP Predictive Analytics Client ... 57

3.4 ... Installing the Predictive Factory ... 61

3.5 ... Installing SAP Predictive Analytics Desktop ... 71

3.6 ... SAP HANA Installation Steps ... 74

3.7 ... Summary ... 75

4 ... Planning a Predictive Analytics Project ... 77

4.1 ... Introduction to the CRISP-DM Methodology ... 78

4.2 ... Running a Project ... 81

4.3 ... Summary ... 100

II ... The Predictive Factory ... 101

5 ... Predictive Factory ... 103

5.1 ... Predictive Factory: End-to-End Modeling ... 103

5.2 ... Creating a Project ... 105

5.3 ... External Executables ... 107

5.4 ... Variable Statistics ... 110

5.5 ... Summary ... 110

6 ... Automated Predictive Classification Models ... 111

6.1 ... Introducing Classification Models ... 112

6.2 ... Creating an Automated Classification Model ... 115

6.3 ... Understanding and Improving an Automated Classification Model ... 123

6.4 ... Applying an Automated Classification Model ... 139

6.5 ... The Data Science behind Automated Predictive Classification Models ... 149

6.6 ... Summary ... 172

7 ... Automated Predictive Regression Models ... 173

7.1 ... Introducing Regression Models ... 173

7.2 ... Creating an Automated Regression Model ... 174

7.3 ... Understanding and Improving an Automated Regression Model ... 179

7.4 ... Applying an Automated Regression Model ... 184

7.5 ... Summary ... 191

8 ... Automated Predictive Time Series Forecasting Models ... 193

8.1 ... Creating and Understanding Time Series Forecast Models ... 194

8.2 ... Mass Producing Time Series Forecasts ... 214

8.3 ... Productizing the Forecast Model ... 221

8.4 ... The Data Science behind Automated Time Series Forecasting Models ... 227

8.5 ... Summary ... 231

9 ... Massive Predictive Analytics ... 233

9.1 ... Deploying Predictive Models in Batch Mode ... 234

9.2 ... Model Quality and Deviation ... 241

9.3 ... Automatically Retraining Models ... 244

9.4 ... Scheduling and Combining Massive Tasks ... 250

9.5 ... Deploying Expert Analytics Models ... 253

9.6 ... Summary ... 256

III ... Automated Analytics ... 257

10 ... Automated Analytics User Interface ... 259

10.1 ... When to Use Automated Analytics ... 259

10.2 ... Navigating the User Interface ... 260

10.3 ... Exploring the Automated Analytics Modules ... 264

10.4 ... Summary ... 269

11 ... Automated Predictive Clustering Models ... 271

11.1 ... The Clustering Approach of Automated Analytics ... 272

11.2 ... Creating a Clustering Model ... 273

11.3 ... Supervised and Unsupervised Clustering ... 297

11.4 ... The Data Science behind Automated Clustering Models ... 299

11.5 ... Summary ... 300

12 ... Social Network Analysis ... 301

12.1 ... Terminology of Social Network Analysis ... 302

12.2 ... Automated Functionalities of Social Network Analysis ... 303

12.3 ... Creating a Social