The Wine Quality Dataset from the UCI Machine Learning Repository is a widely used dataset for classification and regression tasks. It contains information about physicochemical properties of wine samples, which are analyzed to predict wine quality. This dataset focuses on two types of wine: red and white Portuguese “Vinho Verde.”
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Dataset Overview
- Total Instances:
- Red wine: 1,599 samples
- White wine: 4,898 samples
- Features: 12 columns including physicochemical attributes such as:
- Fixed Acidity
- Volatile Acidity
- Citric Acid
- Residual Sugar
- Chlorides
- Free Sulfur Dioxide
- Total Sulfur Dioxide
- Density
- pH
- Sulphates
- Alcohol
- Target Variable:
- Quality (integer score from 0 to 10)
Data Characteristics
- Physicochemical Properties: These continuous variables describe the chemical makeup and physical properties of each wine sample, influencing its taste and quality.
- Quality Scores: These are based on sensory data evaluated by wine tasters, providing a subjective measure of the wine’s excellence.
Applications
The dataset is used to:
- Build machine learning models to classify wines into quality categories (low, medium, high).
- Predict the exact quality score as a regression problem.
- Analyze the relationship between physicochemical properties and quality.
Challenges
- Imbalanced Data: Most wines fall into a limited range of quality scores (5–7), making the prediction task biased.
- Subjectivity in Labels: Quality is determined by human tasters, introducing potential bias and variability.
- Correlated Features: Some features, such as total sulfur dioxide and free sulfur dioxide, are correlated, requiring careful preprocessing.
Preprocessing
- Normalize features to address varying scales.
- Handle class imbalance using techniques like resampling or adjusting weights during training.
Significance
This dataset is valuable for exploring classification, regression, and data preprocessing techniques. It offers insights into how chemical properties influence wine quality, with potential applications in the wine industry for quality assurance and product development.