Predicting Good Wine in the Database with Oracle Machine Learning

How To use Oracle Machine Learning in the Oracle Database to predict Good and Bad Wine.
Contains also:
- Oracle Analytics Cloud Analysis
- Oracle APEX
- Oracle REST APIs

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