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Francesco Tisiot Brendan Tierney Predicting good wine in the database with oracle machine learning @Ftisiot @BrendanTierney Rittman Mead Oralytics

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Francesco Tisiot Rittman Mead @Ftisiot Brendan Tierney ORALYTICS @Brendantierney

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Data Engineering Analytics Data Science www.rittmanmead.com info@rittmanmead.com @rittmanmead

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Can I have a glass of Wine? Yes Sir, what kind of wine? tell me what you have there! @Ftisiot @BrendanTierney

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Can I have a glass of Wine? Yes Sir, what kind of wine? Red Wine! @Ftisiot @BrendanTierney

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Can I have a glass of Wine? Yes, Do you like this Nice English pinot noir?? hell no! @Ftisiot @BrendanTierney

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Can I have a glass of Wine? Yes, Do you like this Nice croatian cabernet?? Let me check…. 95% Yes! @Ftisiot @BrendanTierney

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Dataset https://www.kaggle.com/zynicide/wine-reviews @Ftisiot @BrendanTierney

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The Data @Ftisiot @BrendanTierney

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Bad Good wine score @Ftisiot @BrendanTierney

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wine description @Ftisiot @BrendanTierney

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wine SAMPLES by country @Ftisiot @BrendanTierney

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wine price by country @Ftisiot @BrendanTierney

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good/bad wine by price @Ftisiot @BrendanTierney

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From insights to predictions @Ftisiot @BrendanTierney

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CLASSIFICATION Naïve Bayes Logistic Regression (GLM) Decision Tree Random Forest Neural Network Support Vector Machine Explicit Semantic Analysis CLUSTERING Hierarchical K-Means Hierarchical O-Cluster Expectation Maximization (EM) ANOMALY DETECTION One-Class SVM
 TIME SERIES Forecasting - Exponential Smoothing Includes popular models 
 e.g. Holt-Winters with trends, 
 seasonality, irregularity, missing data REGRESSION Linear Model Generalized Linear Model Support Vector Machine (SVM) Stepwise Linear regression Neural Network LASSO ATTRIBUTE IMPORTANCE Minimum Description Length Principal Comp Analysis (PCA) Unsupervised Pair-wise KL Div CUR decomposition for row & AI ASSOCIATION RULES A priori/ market basket PREDICTIVE QUERIES Predict, cluster, detect, features SQL ANALYTICS SQL Windows
 SQL Patterns
 SQL Aggregates •Includes support for Partitioned Models, Transactional, Unstructured, Geo-spatial, Graph data. etc, Oracle Machine Learning Algorithms FEATURE EXTRACTION Principal Comp Analysis (PCA) Non-negative Matrix Factorization Singular Value Decomposition (SVD) Explicit Semantic Analysis (ESA) TEXT MINING SUPPORT Algorithms support text Tokenization and theme extraction Explicit Semantic Analysis (ESA) for document similarity STATISTICAL FUNCTIONS Basic statistics: min, max, 
 median, stdev, t-test, F-test, Pearson’s, Chi-Sq, ANOVA, etc. R PACKAGES Third-party R Packages 
 through Embedded Execution Spark MLlib algorithm integration MODEL DEPLOYMENT SQL—1st Class Objects Oracle RESTful API (ORDS) OML Microservices (for Apps) X1 X2 A1 A2 A3 A4 A5 A6 A7 Copyright © 2019 Oracle and/or its affiliates.

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STATISTICAL FUNCTIONS Descriptive statistics (e.g. median, stdev, mode, sum, etc.)
 Hypothesis testing 
 (t-test, F-test, Kolmogorov-Smirnov test, Mann Whitney test, Wilcoxon Signed Ranks test
 Correlations analysis 
 (parametric and nonparametric e.g. 
 Pearson’s test for correlation, Spearman's rho coefficient, Kendall's tau-b correlation coefficient)
 Ranking functions
 Cross Tabulations with Chi-square statistics
 Linear regression
 ANOVA (Analysis of variance) Test Distribution fit 
 (e.g., Normal distribution test, 
 Binomial test, Weibull test, 
 Uniform test, Exponential test, 
 Poisson test) Statistical Aggregates 
 (min, max, mean, median, stdev, 
 mode, quantiles, plus x sigma, 
 minus x sigma, top n outliers, 
 bottom n outliers) Statistical Functions and Analytical SQL ANALYTICAL SQL SQL Windows
 SQL Aggregate functions LAG/LEAD functions SQL for Pattern Matching
 Additional approximate query processing: APPROX_COUNT, APPROX_SUM, APPROX_RANK Regular Expressions Copyright © 2019 Oracle and/or its affiliates.

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Oracle Machine Learning Multiple Languages UIs Supported for End Users & Apps Development Application Developers DBAs R & Python Data Scientists “Citizen” Data Scientists Notebook Users & DS Teams Coming soon

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BEGIN DBMS_DATA_MINING.CREATE_MODEL( model_name => 'Wine_CLASS_MODEL', mining_function => dbms_data_mining.classification, data_table_name => ''Wine_TRAIN_DATA', case_id_column_name => 'ID', target_column_name => 'POINTS_BIN', settings_table_name => 'Wine_build_settings'); END; / CREATE ML MODEL BEGIN DBMS_DATA_MINING.CREATE_MODEL( model_name => 'Wine_CLASS_MODEL', mining_function => dbms_data_mining.classification, data_table_name => ''Wine_TRAIN_DATA', case_id_column_name => 'ID', target_column_name => 'POINTS_BIN', settings_table_name => 'Wine_build_settings'); END; / BEGIN DBMS_DATA_MINING.CREATE_MODEL( model_name => 'Wine_CLASS_MODEL', mining_function => dbms_data_mining.classification, data_table_name => ''Wine_TRAIN_DATA', case_id_column_name => 'ID', target_column_name => 'POINTS_BIN', settings_table_name => 'Wine_build_settings'); END; / BEGIN DBMS_DATA_MINING.CREATE_MODEL( model_name => 'Wine_CLASS_MODEL', mining_function => dbms_data_mining.classification, data_table_name => ''Wine_TRAIN_DATA', case_id_column_name => 'ID', target_column_name => 'POINTS_BIN', settings_table_name => 'Wine_build_settings'); END; / BEGIN DBMS_DATA_MINING.CREATE_MODEL( model_name => 'Wine_CLASS_MODEL', mining_function => dbms_data_mining.classification, data_table_name => ''Wine_TRAIN_DATA', case_id_column_name => 'ID', target_column_name => 'POINTS_BIN', settings_table_name => 'Wine_build_settings'); END; / BEGIN DBMS_DATA_MINING.CREATE_MODEL( model_name => 'Wine_CLASS_MODEL', mining_function => dbms_data_mining.classification, data_table_name => ''Wine_TRAIN_DATA', case_id_column_name => 'ID', target_column_name => 'POINTS_BIN', settings_table_name => 'Wine_build_settings'); END; / BEGIN DBMS_DATA_MINING.CREATE_MODEL( model_name => 'Wine_CLASS_MODEL', mining_function => dbms_data_mining.classification, data_table_name => ''Wine_TRAIN_DATA', case_id_column_name => 'ID', target_column_name => 'POINTS_BIN', settings_table_name => 'Wine_build_settings'); END; / @Ftisiot @BrendanTierney

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apply ML MODEL SELECT PREDICTION_PROBABILITY( Wine_CLASS_MODEL, 'GT_90_POINTS' USING 25 as PRICE, ‘MALBEC' as VARIETY, ‘SPAIN' as COUNTRY ) FROM dual; SELECT PREDICTION_PROBABILITY( Wine_CLASS_MODEL, 'GT_90_POINTS' USING 25 as PRICE, ‘MALBEC' as VARIETY, ‘SPAIN' as COUNTRY ) FROM dual; SELECT PREDICTION_PROBABILITY( Wine_CLASS_MODEL, 'GT_90_POINTS' USING 25 as PRICE, ‘MALBEC' as VARIETY, ‘SPAIN' as COUNTRY ) FROM dual; SELECT PREDICTION_PROBABILITY( Wine_CLASS_MODEL, 'GT_90_POINTS' USING 25 as PRICE, ‘MALBEC' as VARIETY, ‘SPAIN' as COUNTRY ) FROM dual; @Ftisiot @BrendanTierney

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Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | Confidential – Oracle Internal/Restricted/Highly Restricted 22

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Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | Confidential – Oracle Internal/Restricted/Highly Restricted 24

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Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | Confidential – Oracle Internal/Restricted/Highly Restricted 25

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Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | Confidential – Oracle Internal/Restricted/Highly Restricted 26

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Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | Confidential – Oracle Internal/Restricted/Highly Restricted 27

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Optional: Include Wine Reviews

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Optional: Include Wine Reviews

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Oracle APEX @Ftisiot @BrendanTierney

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Oracle APEX @Ftisiot @BrendanTierney

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Oracle APEX @Ftisiot @BrendanTierney

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@Ftisiot @BrendanTierney

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@Ftisiot @BrendanTierney

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@Ftisiot @BrendanTierney

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@Ftisiot @BrendanTierney

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@Ftisiot @BrendanTierney

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@Ftisiot @BrendanTierney

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ML Model Deployment via ORDS REST API Launch Developmentà APEX @Ftisiot @BrendanTierney

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ML Model Deployment via ORDS REST API @Ftisiot @BrendanTierney

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ML Model Deployment via ORDS REST API Launch RESTful Services @Ftisiot @BrendanTierney

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ML Model Deployment via ORDS REST API @Ftisiot @BrendanTierney

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ML Model Deployment via ORDS REST API Schema enables for ORDS RESTful Services @Ftisiot @BrendanTierney

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ML Model Deployment via ORDS REST API Helpful example templates provided @Ftisiot @BrendanTierney

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ML Model Deployment via ORDS REST API Helpful example templates provided Build your own custom API For What-IF ML predictions / Scoring – micro-services @Ftisiot @BrendanTierney

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ML Model Deployment via ORDS REST API RESTful API for calling 
 OML model to make predictions @Ftisiot @BrendanTierney

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Real-time Wine Recommendation App + OpenDiningTable App Copyright © 2019 Oracle and/or its affiliates. @Ftisiot @BrendanTierney

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ML in database Summary analytics security governance prod deployment knowledge sharing data exploration visualisations storytelling easy reuse @Ftisiot @BrendanTierney

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What about the money? FREE! @Ftisiot @BrendanTierney

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Francesco Tisiot Brendan Tierney Predicting good wine in the database with oracle machine learning @Ftisiot @BrendanTierney Rittman Mead Oralytics