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Knowledge Discovery in Databases

Knowledge Discovery in Databases. MIS 637 Professor Mahmoud Daneshmand Fall 2012 Final Project: Red Wine Recipe Data Mining By Jorge Madrazo. Profound Questions. What basic properties are the formula for a good wine?

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Knowledge Discovery in Databases

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  1. Knowledge Discovery in Databases MIS 637 Professor Mahmoud Daneshmand Fall 2012 Final Project: Red Wine Recipe Data Mining By Jorge Madrazo

  2. Profound Questions • What basic properties are the formula for a good wine? • Wine making is believed to be an art. But is there a formula for a quality wine? • There was a paper on “Modeling wine preferences by Data Mining” submitted by the provider of the data set. How do my results compare with the paper’s?

  3. Procedure • Follow a data mining process • Use SAS and SAS Enterprise Miner to execute the process • SAS Enterprise Miner tool is modeled on the SAS Institute defined data mining process of SEMMA – Sample, Explore, Modify, Model, Assess • SEMMA is similar to the CRISP DM process

  4. Sample • 1,599 records • Set up a data partition • Training 40% • Validation 30% • Test 30%

  5. Explore: Data Background • Data source • UCI Machine Learning Repository. • Wine Quality Data Set. • There are a red and white wine data set. I focused on the red wine set only. • There are 11 input variables and one target variable. • fixed acidity • volatile acidity • citric acid • residual sugar • chlorides • free sulfur dioxide • total sulfur dioxide • density • pH • sulphates • alcohol • Output variable (based on sensory data): quality (score between 0 and 10)

  6. Explore: Target=Quality • Quality • People gave a quality assessment of different wines on a scale of 0-10. Actual range 3-8. • An ordinal target

  7. Explore: Inputs • Correlation Analysis • Some correlation, but not enough to discard inputs • ods graphics on; • ods select MatrixPlot; • proccorr data=wino.red PLOTS(MAXPOINTS=100000 ) • plots=matrix(histogram nvar=all); • var quality alcohol phfixed_acidity density volatile_aciditysulphatescitric_acid; • run;

  8. Explore: Correlation Graphs

  9. Explore: Chi2 Statistics of Inputs

  10. Explore: Worth of Inputs

  11. Explore: Worth Graph • The Worth Tracks closely with the Chi Statistic

  12. Modify • At this stage, no modifications are done

  13. Model: Selection • Because I want to list the important elements in what is considered a quality wine, I choose a Decision Tree • Configuration • The Splitting Rule is Entropy • Maximum Branch is set to 5 • Therefore a C4.5 type of algorithm is being implemented

  14. Assess: Initial Results • A Bushy Tree using. The Resulting tree is too intricate for simple recommendation. • Over 20 Leaf nodes.

  15. Modify: Target • Change the target so that it becomes a binary. • New variable in the model called isGood. Any rating over 6 is categorized as isGood. • SAS Code: datawino.xx; set wino.red; if (quality>6) then isgood=1; else isgood = 0; run; procprint data = wino.xx; title 'xx'; run;

  16. Explore: Target = isGood

  17. Model Strategy for isGood • Model with Decision Tree to hope for more descriptive results. • Also model with Neural Network to aid in assessment and do comparison

  18. Model: Decision Tree • ProbF splitting criteria at Significance Level .2 • Maximum Branch size = 5

  19. Assess: Decision Tree Results • Much simpler Tree

  20. Assess: Decision Tree Results 2 • Leaf Statistics

  21. Assess: Variable Importance

  22. Model: Neural Network • Positive – better at predicting • Negative – hard to interpret the model • Configured with 3 Hidden Nodes

  23. Modify: Input Variables to NN • Because of the complexity of the NN, it is recommended to prune variables prior to running the network.

  24. Modify: R2 Filter

  25. Model: NN • Specify 3 Hidden Units in the Hidden Layer

  26. Assess: NN Results • Hard to interpret results to formulate a recipe The NEURAL Procedure Optimization Results Parameter Estimates Gradient Objective N Parameter Estimate Function 1 alcohol_H11 3.679818 -0.001411 2 chlorides_H11 0.520190 -0.000479 3 density_H11 -2.171623 0.000883 4 fixed_acidity_H11 -0.055929 0.000179 5 free_sulfur_dioxide_H11 0.403412 0.000139 6 sulphates_H11 -4.954290 -0.000224 7 volatile_acidity_H11 2.686209 0.000205 8 alcohol_H12 -0.313005 0.001209 9 chlorides_H12 0.200973 0.000759

  27. Assess: Comparative Results • Receiver Operating Characteristics (ROC) Chart for NN vs Decision Tree

  28. Assess: Comparative Results • Cumulative Lift for NN vs Decision Tree

  29. Assess: Comparison with Reference Paper • Used R-Miner • Support Vector Machine (SVM) and Neural Network used • He applied techniques to extract relative importance of variables • He attempted to predict every quality level • He noted the importance of alcohol and sulphates. “An increase in sulphates might be related to the fermenting nutrition, which is very important to improve the wine aroma.”

  30. Assess: Paper Variable Importance

  31. Overall Project in SAS EM

  32. References • UCI Machine Learning Repository http://archive.ics.uci.edu/ml/datasets/Wine • P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties.In Decision Support Systems, Elsevier, 47(4):547-553, 2009. • Modeling wine preferences by data mining from physicochemical properties, Paulo Cortez et. al http://www3.dsi.uminho.pt/pcortez/wine5.pdf

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