6,497 labeled wine samples across two categories (red and white) characterized by their physicochemical properties. The data includes 12 chemical attributes such as acidity, sugar, and alcohol levels used to distinguish between wine types.
Use Cases
- Train a binary classification model to predict the 'type' of wine using chemical composition features
- Analyze the correlation between 'chlorides' and 'density' to identify patterns specific to red or white wines
- Perform feature selection to determine which attributes, such as 'volatile acidity' or 'pH', are most predictive of wine category
Strengths
- 6,497 total observations combining 1,599 red and 4,898 white wine samples
- 12 physicochemical features including 'fixed acidity', 'volatile acidity', and 'citric acid'
- Includes chemical measurements for 'free sulfur dioxide', 'total sulfur dioxide', and 'alcohol' percentage