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Synthetic Wine Quality Classification Dataset
Synthetic Data Generation
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About
A synthetic dataset created specifically for classification tasks related to wine quality. The data includes 1,000 samples featuring key chemical attributes such as fixed acidity level, residual sugar content, alcohol percentage, and liquid density. Each sample is clearly labelled with one of three wine quality classes: low, medium, or high. This resource is excellent for developing and testing machine learning models.
Columns (Features)
- fixed_acidity: The level of fixed acidity in the wine sample.
- residual_sugar: The sugar level that remains after the fermentation process.
- alcohol: The alcohol content of the wine, expressed as a percentage.
- density: The measurement of the liquid density of the sample.
- quality_label: The categorical quality class assigned to the wine (low, medium, high).
Distribution
The dataset contains exactly 1,000 valid records and consists of 5 columns. It is typically distributed in a structured format like CSV, ideal for immediate analytical ingestion. There are no missing values recorded across any of the features. The quality labels are reasonably distributed, with medium quality being the most frequently observed class. The data is entirely tabular.
Usage
This data is ideal for training and benchmarking classification algorithms, including Support Vector Machines (SVM), XGBoost, and Logistic Regression. It allows users to explore how specific chemical features influence perceived wine quality and provides a suitable environment for practising machine learning tasks within the context of the food and beverage sector.
Coverage
As this is a synthetic dataset generated for educational and classification modelling purposes, it focuses exclusively on chemical measurements. Therefore, there is no associated geographic scope, temporal range, or demographic information applicable.
License
CC BY-SA 4.0
Who Can Use It
- Machine Learning Engineers: To train, evaluate, and fine-tune classification models designed for quality prediction.
- Data Scientists: For feature engineering exercises and analyzing the impact of chemical composition on quality outcomes.
- Students and Educators: Perfect for introductory projects on supervised classification and handling food science data.
- Researchers: To simulate and study quality control systems in the beverage industry.
Dataset Name Suggestions
Synthetic Wine Quality Classification Dataset
ML Wine Chemical Attributes and Quality
Food Quality Prediction Data (Simulated)
Wine Classification Feature Set
Attributes
Original Data Source: Synthetic Wine Quality Classification Dataset
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