Andrew Gendreau, Olga Rosenbaum, Anthony Taylor, Kenneth Wong, Karl Dusen

Transkript

1 Andrew Gendreau, Olga Rosenbaum, Anthony Taylor, Kenneth Wong, Karl Dusen

2 The Process Goal Definition Data Collection Data Preprocessing EDA Choice of Variables Choice of Method(s) Performance Evaluation Model Selection Model Deployment Objective: By using chemical composition to predict quality, we could provide wineries a way to: Test their wines in advance and know which ones they should push to be reviewed Effectively price their wines before having them reviewed

3 Goal Definition Predict whether a wine will be reviewed as being high quality by examining the physiochemical components of each wine Determine which chemical variables are the true drivers of their rating Determine whether there are differences between red and white wines and how this can be leveraged to improve prediction

4 Data Collection Data Preprocessing Collection: Two datasets related to red and white variants of the Portuguese "Vinho Verde" wine White Wines and 1599 Red Wines examined Input Variables (11): 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) Preprocessing No Missing Data or Outliers Field created to make categorical variable for high quality High Quality defined as quality >= 7

5 Separated significant variables from insignificant variables Red: alcohol, sulphates, volatile acidity, citric acid White: alcohol, residual sugars, density Looked for interactions between significant variables to further simplify model Red: no interactions White: density highly correlated with both alcohol and residual sugars

6 Filter Settings - chlorides: (0.01 <= chlorides <= 0.17) without empty values - free sulfur dioxide: (1.00 <= free sulfur dioxide <= ) without empty values - density: (0.99 <= density <= 1.00) without empty values

7 Filter Settings - chlorides: (0.01 <= chlorides <= 0.17) without empty values - free sulfur dioxide: (1.00 <= free sulfur dioxide <= ) without empty values - density: (0.99 <= density <= 1.00) without empty values

8 Filter Settings - chlorides: (0.01 <= chlorides <= 0.17) without empty values - free sulfur dioxide: (1.00 <= free sulfur dioxide <= ) without empty values - density: (0.99 <= density <= 1.00) without empty values

9 Filter Settings - chlorides: (0.01 <= chlorides <= 0.17) without empty values - free sulfur dioxide: (1.00 <= free sulfur dioxide <= ) without empty values - density: (0.99 <= density <= 1.00) without empty values

10 Filter Settings - chlorides: (0.01 <= chlorides <= 0.17) without empty values - free sulfur dioxide: (1.00 <= free sulfur dioxide <= ) without empty values - density: (0.99 <= density <= 1.00) without empty values

11 Filter Settings - chlorides: (0.01 <= chlorides <= 0.17) without empty values - free sulfur dioxide: (1.00 <= free sulfur dioxide <= ) without empty values - density: (0.99 <= density <= 1.00) without empty values

12 Filter Settings - chlorides: (0.01 <= chlorides <= 0.17) without empty values - free sulfur dioxide: (1.00 <= free sulfur dioxide <= ) without empty values - density: (0.99 <= density <= 1.00) without empty values

13 Choice of methods K-Nearest Neighbors Classification Trees Logistic Regression Performed on red, white, and combined data sets

14 Performance Evaluation Overall error of the test set Classification Confusion Matrix White wines Model Parameters Overall error Classification Tree 50 items in the node % knn K= % Logistic Regression Red wines Model alcohol, total sugar % Parameters Overall error Classification Tree 50 items in the node % knn K= % Logistic Regression alcohol, sulphates volatile acidity, citric acid %

15 Model Dataset Model Parameters Test Error % Classification Tree Combined Number of items in the nodes Classification Tree Combined Number of items in the nodes Classification Tree Red Number of items in the nodes Classification Tree White Number of items in the nodes Classification Tree White Number of items in the nodes knn Combined K= knn Red K=15, 11 variables knn White K=1, 11 variables Logistic Regression Combined 9 variables Logistic Regression Red 4 variables (alcohol, sulfates, volatile acidity, total sulfur dioxide) Logistic Regression White 8 variables Logistic Regression White 2 variables (alcohol, total sugar) Logistic Regression Red 4 variables (alcohol, sulphates, volatile acidity, citric acid) KNN Red K=5, 4 variables KNN White K=13, 2 variables 20.7 Logistic Regression Red 11 variables 11.29

16 Lift Chart Comparisons and Confusion Matrices Cumulative Lift chart (test data) 4 Var Red Wine # cases Cumulativ e high quality when sorted using predicte Red Wine Confusion Matrix Classification Confusion Matrix Predicted Class Actual Class Cumulative Lift chart (test data) All Var Red Wine # cases Cumulativ e high quality when sorted using predicte Cumulative Lift chart (test data) 2 Var White Wine # cases Cumulativ e high quality when sorted using predicte White Wine Confusion Matrix Classification Confusion Matrix Predicted Class Actual Class Cumulative Lift chart (test data) Model # cases Cumulativ e high quality when sorted using predicte

17 Model Selection Logistic Regression Examined Lift Charts Compared lowest error models with models that included our selected variables Minimize overall error while maintaining a simple model Separate models for Red and White wines 4 variable model for Red Wines 2 variable model for White Wines* Confusion Matrix Results Lowest overall error for test data Model optimization for false positives (more costly of the two errors) Achieve low Type 1 errors *If economically viable and mathematically capable, use KNN with the full set of 11 variables and K=1

18 Model Deployment Recommendations Wine Making* Red Wines Increase alcohol, sulfate and citric acid levels Decrease volatile acidity levels White Wines Increase alcohol levels Decrease residual sugar levels Wine Marketing and Promotion Use models to predetermine if wine is high quality Send all high quality wines to be rated Promote, market and distribute wines that are predicted high quality Do not invest money in wines that are not predicted high quality due to the extremely low Type 1 error rate *Make adjustments where possible in the wine making process

19