Sumário Itens Encontrados: 131roductionChapter 1: Getting Started With Oracle Advanced AnalyticsData Science Business Understanding Data Understanding Data Preparation Modeling Evaluation Deployment Machine Learning Supervised LearningUnsupervised LearningGetting Started with Oracle Advanced Analytics Oracle Data MinerR Technologies in Oracle Oracle R EnterpriseAdvantages of using Oracle R EnterpriseAnalytical SQL and PLSQL Functions Package DBMS_STATS_FUNCSQL FunctionsChapter 2: Installation and Hello WorldBooting Up Oracle Data Miner Installation Prerequisites Installation Welcome to the WorldâOracle Data Miner SQL Developer Components for ODM ODM Data DictionaryBoot Up Oracle R EnterprisePrerequisitesOracle R DistributionOracle R Enterprise Server InstallationOracle R Enterprise Client InstallationInstall Supporting Packages for the ORE Client Welcome to the World of Oracle R EnterpriseORE Data DictionaryChapter 3: Clustering MethodsClustering ApproachesThe k-means Algorithmk-means in Oracle Advanced Analytics Clustering Rules Evaluation MetricsParameters to Tune k-means ClusteringCreating a Cluster Model in Oracle Advanced AnalyticsClustering using SQL and PLSQL Case StudyâCustomer Segmentation RFM SegmentationâData PreparationRFM SegmentationâDATA Modeling Need-Based SegmentationâData Preparation Need-Based SegmentationâData ModelingResult EvaluationDeploymentâStoring the Results Back to the DatabaseAssigning Segments to New CustomersChapter 4: Association Rules Introduction to Association RulesTerminologies Associated with Association RulesWorking of an Apriori AlgorithmIdentify Interesting Rules Algorithm Settings Model Settings Association Rules Using SQL and PLSQLCreating the Association Rules Model Using Oracle R Enterprise Creating the Association Model Using SQL Developer Case StudyâMarket Basket AnalysisData ModelingHigh-Level Technical OverviewExecutionChapter 5: Regression Analysis Understanding RelationshipsRegression AnalysisWorking of OLS RegressionAssumptions of OLS OLS Regression in Oracle Advanced AnalyticsGLM Regression Ridge Regression Parameters to Tune the GLM Model GLM and Ridge Regression in Oracle Advanced AnalyticsGLM Regression Using SQL and PLSQL Creating GLM Regression Using Oracle R EnterpriseCreating GLM and Ridge Regression from SQL Developer Guidelines for Regression Modeling Case Study: Sales ForecastingChapter 6: Classification MethodsOverview of Classification TechniquesLogistic RegressionNaïve BayesCreating a Naïve Bayes Model Using SQL and PLSQL Creating a Naïve Bayes Classifier Using Oracle R EnterpriseAssessing the Model Quality for ClassifiersDecision Trees Entropy and Information Gain Information Gain Pruning Methods/Early Stopping Criteria for Decision Trees Parameters to Tune a Decision Tree Model Decision Tree Modeling Using SQL and PLSQL Decision Tree Modeling Using Oracle R Enterprise SVMParameters to Tune the SVM Model SVM Using SQL and PLSQLSVM Using Oracle R EnterpriseA Few Other Important PLSQL APIs for Classifiers Choosing a ClassifierCase Study: Customer Churn PredictionSelecting the Best ClassifierResults Interpretation Chapter 7: Advanced Topics Overview of Neural NetworksFunction of a Hidden Layer NeuronParameters for Neural NetworkNeural Network Using Oracle Advanced Analytics Overview of Anomaly DetectionOne-Class SVMAnomaly Detection Using Oracle Advanced AnalyticsOverview of Predictive Analytics in Oracle Advanced AnalyticsPredictive Analytics Using SQL Developer GUI DBMS_PREDICTIVE_ANALYTICSEXPLAINPREDICTPROFILEOverview of Product Recommendation EngineProduct Recommendation Engine Using Oracle Advanced Analytics Overview of Random ForestRandom Forest Using Oracle Advanced AnalyticsChapter 8: Solutions DeploymentExport and Import an ODM WorkflowDeploy Option for Data Miner Workflows in SQL DeveloperImport and Export Data Mining Models EXPORT_MODEL IMPORT_MODEL Renaming ModelsDrop ModelsPredictive Model Markup Language (PMML)Importing and Exporting PMML models into Oracle Advanced Analytics