Synopses & Reviews
Synopsis
Introduction
Chapter 1: Getting Started with Oracle Advanced Analytics
Overview of Data Science and CRISP-DM MethodologyOverview of machine learning and its application in industriesGetting started with Oracle Advanced Analytics- Oracle Data Miner and Oracle R EnterpriseAnalytical SQL and PL/SQL functionsSummary
Chapter 2: Installation and Hello World
Oracle Data Miner InstallationSample Hello World Oracle Data Miner workflowOracle Data Miner components for SQL Developer GUIOracle R Enterprise InstallationSample Hello World program using Oracle RSummaryChapter 3: Clustering Methods
Approaches for cluster analysisK-means algorithm fundamentalsK-means algorithm in Oracle Advanced AnalyticsMetrics for evaluating clustering algorithmsCreate clusters using Oracle SQL and PLSQL API'sCreate clusters using Oracle R EnterpriseCreate clusters using Oracle SQL Developer GUICase Study - Customer SegmentationSummaryChapter 4: Association Rules
Introduction to association rulesTerminologies associated with association rulesApriori algorithm fundamentalsIdentify interesting rulesAssociation rules using Oracle SQL and PLSQL API'sAssociation rules using Oracle R EnterpriseAssociation rules using Oracle SQL Developer GUICase Study - Market Basket AnalysisSummaryChapter 5: Regression Analysis
Understanding RelationshipsIntroduction to Regression AnalysisOLS Regression fundamentalsOLS Regression using Oracle Advanced AnalyticsGLM and Ridge Regression OverviewGLM Regression using Oracle SQL and PLSQL API'sGLM Regression using Oracle R EnterpriseGLM Regression using Oracle SQL Developer GUICase Study - Sales ForecastSummaryChapter 6: Classification Techniques
Overview of classification techniquesLogistics Regression fundamentalsDecision Tree fundamentalsSVM fundamentalsNa ve Bayes fundamentalsClassification using Oracle Advanced AnalyticsClassification using Oracle SQL and PLSQL API'sClassification using Oracle R EnterpriseClassification using Oracle SQL Developer GUICase Study - Customer Churn PredictionSummary
Chapter 7: Advanced Topics
Overview of Neural NetworksNeural Network using Oracle Advanced AnalyticsOverview of Anomaly detectionAnomaly detection using Oracle Advanced AnalyticsOverview of Random ForestRandom Forest using Oracle Advanced AnalyticsOverview of Predictive QueriesPredictive Queries using Oracle Advanced AnalyticsOverview of Product Recommendation EngineProduct Recommendation engine using Oracle Advanced AnalyticsSummaryChapter 8: Solution Deployment
Oracle Data Miner Import and Export functionalityIntroduction to PMMLGenerating PMML from Oracle Advanced Analytics models
Synopsis
Automate the predictive analytics process using Oracle Data Miner and Oracle R Enterprise. This book talks about how both these technologies can provide a framework for in-database predictive analytics. You'll see a unified architecture and embedded workflow to automate various analytics steps such as data preprocessing, model creation, and storing final model output to tables.
You'll take a deep dive into various statistical models commonly used in businesses and how they can be automated for predictive analytics using various SQL, PLSQL, ORE, ODM, and native R packages. You'll get to know various options available in the ODM workflow for driving automation. Also, you'll get an understanding of various ways to integrate ODM packages, ORE, and native R packages using PLSQL for automating the processes.
Data Science Automation Using Oracle Data Miner and Oracle R Enterprise starts with an introduction to business analytics, covering why automation is necessary and the level of complexity in automation at each analytic stage. Then, it focuses on how predictive analytics can be automated by using Oracle Data Miner and Oracle R Enterprise. Also, it explains when and why ODM and ORE are to be used together for automation.
The subsequent chapters detail various statistical processes used for predictive analytics such as calculating attribute importance, clustering methods, regression analysis, classification techniques, ensemble models, and neural networks. In these chapters you will also get to understand the automation processes for each of these statistical processes using ODM and ORE along with their application in a real-life business use case.
What you'll learn
- Discover the functionality of Oracle Data Miner and Oracle R Enterprise
- Gain methods to perform in-database predictive analytics
- Use Oracle's SQL and PLSQL APIs for building analytical solutions
- Acquire knowledge of common and widely-used business statistical analysis techniques
Who this book is for
IT executives, BI architects, Oracle architects and developers, R users and statisticians.