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?
– 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?

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

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

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)

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

Explore: Inputs
• Correlation Analysis
– Some correlation, but not enough to discard inputs
• ods graphics on;
• ods select MatrixPlot;
•
• proc corr data=wino.red PLOTS(MAXPOINTS=100000 ) plots=matrix(histogram nvar=all);
• var quality alcohol ph fixed_acidity density volatile_acidity sulphates citric_acid;
• run;

Explore: Correlation Graphs

Explore: Chi 2 Statistics of Inputs

Explore: Worth of Inputs

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

Modify
• At this stage, no modifications are done

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

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

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:
data wino.xx; set wino.red; if (quality>6) then isgood=1; else isgood = 0;
run;
proc print data = wino.xx; title 'xx';
run;

Explore: Target = isGood

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

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

Assess: Decision Tree Results
• Much simpler Tree

Assess: Decision Tree Results 2
• Leaf Statistics

Assess: Variable Importance
Variable
Name alcohol density
Label volatile_acidity sulphates fixed_acidity citric_acid free_sulfur_dioxide pH chlorides total_sulfur_dioxide residual_sugar
Number of
Splitting
Rules
1
0
0
0
0
1
0
0
0
0
0
Number of
Surrogate
Rules Importance
Validation
Importance
0 1 1
1 0.77055175
0.77055175
1 0.728868987 0.728868987
0 0.671675628 0.477710505
1 0.553719729 0.393817671
1 0.549750361 0.390994569
0
0
0
0
0
0
0
0
0
0
0 NaN
0 NaN
0 NaN
0 NaN
0 NaN
Ratio of
Validation to
Training
Importance
1
0.711222032
1
1
0.711222032
0.711222032
Event Classification Table
Data Role=TRAIN Target=isgood
False
Negative True Negative
53 539
False
Positive
14
True
Positive
34
Data Role=VALIDATE Target=isgood
False
Negative True Negative
43 403
False
Positive
12
True
Positive
21

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

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

Modify: R 2 Filter
Variable Name alcohol chlorides citric_acid
Role
Measurement
Level
INPUT INTERVAL
INPUT INTERVAL
REJECTED INTERVAL density fixed_acidity
INPUT INTERVAL
INPUT INTERVAL free_sulfur_dioxide INPUT INTERVAL pH REJECTED INTERVAL residual_sugar sulphates
REJECTED INTERVAL
INPUT INTERVAL total_sulfur_dioxide REJECTED INTERVAL volatile_acidity INPUT INTERVAL
Reasons for Rejection
Varsel:Small R-square value
Varsel:Small R-square value
Varsel:Small R-square value
Varsel:Small R-square value

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

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

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

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

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.”

Assess: Paper Variable Importance

Overall Project in SAS EM

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