The purpose of this work is the modeling of the wine preferences by physicochemical properties. Wine quality is a key element within this context. Certification prevents the illegal adulteration of wines and assures quality for the wine market. Quality evaluation is often part of the certification process and can be used to improve wine making and to stratify wines such as premium brands (useful for setting prices). Such model is useful to support the oenologist wine tasting evaluations, improve and speed-up the wine production.
According to the oenology theory, the quality of wine is dependent on Climate & Weather, Temperature and Sunlight, Growing Practices and Winemaking Practices. However, this dataset provides only the physicochemical properties of wine samples, without the environmental conditions or growing/winemaking procedures. Also according to the oenology theory, a wine can be described by the columns below:
- Type (Red, White, Rosé, Sparkling and Fortified (Dessert)): The different types of wine.
- Fixed Acidity (1000 mg/L - 4000 mg/L): Organic acids of wines whose volatilities are so low that cannot be separated from wine by distillation
- Volatile Acidity (0 - 1700 mg/L): A measure of the low molecular weight fatty acids in wine and is generally perceived as the odour of vinegar
- Citric Acid (< 500 mg/L): Citric acid is used as supplement during the fermentation process to boost the acidity of wine
- Residual Sugar (> 35 mg/L): Natural grape sugars leftover in a wine after the alcoholic fermentation
- Chlorides (< 606 mg/L): A major contributor to saltiness in wine
- Free sulfur Dioxide (< 350 mg/L): A wine additive that preserves wine's freshness and fruit characters by virtue of antioxidant, antimicrobial and anti-enzymatic properties
-
Total Sulfur Dioxide (< 350 mg/L): The portion of
$SO_{2}$ that is free in the wine plus the portion that is bound to other chemicals such as aldehydes, pigments, or sugars -
Density (1080 - 1090 mg/
$cm^3$ ): The density of the wine - pH (2.5 pH - 4.5 pH): A scale used to specify the acidity of the wine
- Sulfites (5 mg/l - 2000 mg/L): chemical by-products created during the fermentation process, also used in food preservation
- Alcohol (5.5% - 25%): The total concentration of alchohol
The quality of each sample is a numerical value between 0.0 and 10.0. The quality is dependent by the values of each of the features that are decrribed above. The goal of this project is:
- Build a Regression model to accurately predict the quality score of a given wine sample, based on its physicochemical properties.
- Analyze the regression model and interpret its decision making system.
A Neural Network was built with 0.41 mean absolute error, after it was tuned using Optuna package. More specifically, a
neural network with 3 Dense Layers (200, 245, 245) and 1 output unit was built. The GELU activation function was added at
the output of each hidden layer. The model was trained using a Google's Yogi optimizer, which is an alternative version of Adam
optimizer. Finally, both the Mean Absolute Error (MAE) & (Mean Absolute Percentage Error) have been used in order to monitor the
validation loss of the model.
The first 10 predictions are shown in the table below.
| Target Quality | Model Prediction | ERROR |
|---|---|---|
| 6 | 6 | 0 |
| 6 | 6 | 0 |
| 5 | 4 | 1 |
| 6 | 5 | 1 |
| 5 | 5 | 0 |
| 7 | 6 | 1 |
| 5 | 5 | 0 |
| 4 | 5 | 1 |
| 6 | 6 | 0 |
| 5 | 5 | 0 |
More predictions can be found on the csv file: https://github.com/kochlisGit/Wine-Quality-Analysis/blob/main/predictions.csv
An XGBoost regressor model was trained in order to better understand the physicochemical properties of the both white and red wine. The SHAP (SHapley Additive exPlanations) approach was used for the feature importance analysis, which is a game theoretic approach of explaining the output of the regressor model.
Most of the results of this analysis confirm the oenological theory. For example:
- The quality of the wine is not affected by the type of the wine (White/Red)
- An increase in the alcohol, which is one of the most important factors of wine production tends to result in a higher quality wine
- The rankings are different within each wine type. For instance, the citric acid and residual sugar levels are more important in white wine, where the equilibrium between the freshness and sweet taste is more appreciated.
- Volatile Acidity has a negative impact on both white and red types of wine, since higher volatile acidity values could indicate wine spoilage. Then the volatile acidity is very high, winemakers usually use Reverse Osmosis (RO) to lower the acetic acid concentration
- In the red wine, the suplhates seems to be 2 times more important than in white wine and it is very important in both types of wine. An increase in sulfites might be related to the fermenting nutrition, which is very important to improve the wine aroma. Additionally, given that a winemaker has very little control over the wine’s storage conditions from the time the wine leaves the winery until it is consumed, it is little wonder that SO2 is so widely used to help guarantee that the bottle of wine you open will be fresh and clean, and taste as the winemaker intended
Demo Link: https://colab.research.google.com/drive/174KThfdovgG-9Ypvm40vV1xbvwy-JqsQ