Predicting Good Wine in the Database with Oracle Machine Learning

Transcript

1. Francesco Tisiot Brendan Tierney Predicting good wine in the database

   with oracle machine learning @Ftisiot @BrendanTierney Rittman Mead Oralytics

2. Francesco Tisiot Rittman Mead @Ftisiot Brendan Tierney ORALYTICS @Brendantierney

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

4. Can I have a glass of Wine? Yes Sir, what

   kind of wine? tell me what you have there! @Ftisiot @BrendanTierney

5. Can I have a glass of Wine? Yes Sir, what

   kind of wine? Red Wine! @Ftisiot @BrendanTierney

6. Can I have a glass of Wine? Yes, Do you

   like this Nice English pinot noir?? hell no! @Ftisiot @BrendanTierney

7. Can I have a glass of Wine? Yes, Do you

   like this Nice croatian cabernet?? Let me check…. 95% Yes! @Ftisiot @BrendanTierney

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

9. The Data @Ftisiot @BrendanTierney

10. Bad Good wine score @Ftisiot @BrendanTierney

11. wine description @Ftisiot @BrendanTierney

12. wine SAMPLES by country @Ftisiot @BrendanTierney

13. wine price by country @Ftisiot @BrendanTierney

14. good/bad wine by price @Ftisiot @BrendanTierney

15. From insights to predictions @Ftisiot @BrendanTierney

16. 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.

17. 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.

18. 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

19. 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

20. 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
21. None

22. Copyright © 2018, Oracle and/or its affiliates. All rights reserved.

    | Confidential – Oracle Internal/Restricted/Highly Restricted 22
23. None

24. Copyright © 2018, Oracle and/or its affiliates. All rights reserved.

    | Confidential – Oracle Internal/Restricted/Highly Restricted 24

25. Copyright © 2018, Oracle and/or its affiliates. All rights reserved.

    | Confidential – Oracle Internal/Restricted/Highly Restricted 25

26. Copyright © 2018, Oracle and/or its affiliates. All rights reserved.

    | Confidential – Oracle Internal/Restricted/Highly Restricted 26

27. Copyright © 2018, Oracle and/or its affiliates. All rights reserved.

    | Confidential – Oracle Internal/Restricted/Highly Restricted 27

28. Optional: Include Wine Reviews

29. Optional: Include Wine Reviews

30. Oracle APEX @Ftisiot @BrendanTierney

31. Oracle APEX @Ftisiot @BrendanTierney

32. Oracle APEX @Ftisiot @BrendanTierney

33. @Ftisiot @BrendanTierney

34. @Ftisiot @BrendanTierney

35. @Ftisiot @BrendanTierney

36. @Ftisiot @BrendanTierney

37. @Ftisiot @BrendanTierney

38. @Ftisiot @BrendanTierney

39. ML Model Deployment via ORDS REST API Launch Developmentà APEX

    @Ftisiot @BrendanTierney
40. None

41. ML Model Deployment via ORDS REST API @Ftisiot @BrendanTierney

42. ML Model Deployment via ORDS REST API Launch RESTful Services

    @Ftisiot @BrendanTierney

43. ML Model Deployment via ORDS REST API @Ftisiot @BrendanTierney

44. ML Model Deployment via ORDS REST API Schema enables for

    ORDS RESTful Services @Ftisiot @BrendanTierney

45. ML Model Deployment via ORDS REST API Helpful example templates

    provided @Ftisiot @BrendanTierney

46. 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

47. ML Model Deployment via ORDS REST API RESTful API for

    calling 
 OML model to make predictions @Ftisiot @BrendanTierney

48. Real-time Wine Recommendation App + OpenDiningTable App Copyright © 2019

    Oracle and/or its affiliates. @Ftisiot @BrendanTierney

49. ML in database Summary analytics security governance prod deployment knowledge

    sharing data exploration visualisations storytelling easy reuse @Ftisiot @BrendanTierney

50. What about the money? FREE! @Ftisiot @BrendanTierney

51. Francesco Tisiot Brendan Tierney Predicting good wine in the database

    with oracle machine learning @Ftisiot @BrendanTierney Rittman Mead Oralytics