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Balanced Wine Quality Classification Data
Data Science and Analytics
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About
The Balanced Wine Quality dataset offers insights into the various physiochemical properties that influence wine quality ratings. This dataset is a valuable resource for developing predictive models and performing in-depth data analysis related to wine characteristics.
Brief Description
This dataset focuses on predicting wine quality based on its chemical attributes. It has been meticulously processed using cGAN to address initial data imbalance, ensuring that each quality class is equally represented. The data is suitable for tasks involving classification of wine quality, ranging from poor to excellent.
Columns
- fixed_acidity (float64): Represents the quantity of fixed acids in the wine, which is typically a blend of tartaric, malic, and citric acids.
- volatile_acidity (float64): Indicates the amount of volatile acids present, with acetic acid being the primary component.
- citric_acid (float64): Shows the quantity of citric acid in the wine, contributing to its overall acidity profile.
- residual_sugar (float64): Details the amount of sugar that remains after the fermentation process.
- chlorides (float64): Refers to the concentration of chlorides, which can signify the presence of salt.
- free_sulfur_dioxide (float64): Measures the free sulfur dioxide in the wine, commonly used as a preservative.
- total_sulfur_dioxide (float64): Encompasses the overall amount of sulfur dioxide, including both bound and free forms.
- density (float64): The density of the wine, which is often linked to its alcohol and sugar content.
- pH (float64): The pH level of the wine, providing an indication of its acidity or alkalinity.
- sulphates (float64): The amount of sulphates in the wine, which plays a role in its taste and preservation.
- alcohol (float64): The percentage of alcohol content in the wine.
- quality (int64): The perceived quality of the wine, scored on a scale from 3 to 9, where higher numbers denote better quality.
Distribution
The dataset contains 21,000 individual records across 12 distinct variables. It has been balanced such that each quality class (from 3 to 9) contains 3,000 instances. All records are valid, with no mismatched or missing values reported across any of the columns. The data types are primarily float64, with the 'quality' variable being int64.
Usage
This dataset is designed for a variety of analytical and machine learning tasks:
- Exploratory Data Analysis (EDA): Ideal for analysing key features, distribution patterns, and identifying relationships among factors that influence wine quality.
- Multi Classification: Suitable for building predictive models to classify the wine quality variable (ratings 3 through 9).
- Binary Classification: Can be used to create models that classify wine into binary categories, such as 'good' or 'bad', by setting a specific quality threshold (e.g., quality 6 or above).
Coverage
The sources do not provide specific details regarding the geographic origin, time range of data collection, or demographic scope of this dataset.
License
CC0: Public Domain
Who Can Use It
- Data Scientists and Machine Learning Engineers: For building and evaluating predictive models for wine quality classification.
- Wine Industry Professionals: To understand the chemical determinants of wine quality and potentially inform production processes.
- Researchers and Academics: For studies into food science, chemistry, and sensory analysis related to beverages.
- Students: As an educational resource for learning about data processing, classification algorithms, and real-world data analysis.
Dataset Name Suggestions
- Balanced Wine Quality Classification Data
- Enhanced Wine Attribute Dataset
- Predictive Wine Quality Data
- C-GAN Balanced Wine Data
- Wine Chemistry and Quality Dataset
Attributes
Original Data Source:Balanced Wine Quality Classification Data
QUALITY