... 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 leírás elrejtése