Dark Mode
Portuguese Vinho Verde White Wine Physicochemical Data
Data Science and Analytics
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
The Vinho Verde Wine Quality Dataset comprises a detailed collection of physicochemical and sensory attributes pertaining to white variants of the Portuguese "Vinho Verde" wine. Each record represents a distinct wine sample, providing a robust foundation for predictive analytics. The primary objective of this data is to facilitate the prediction of wine quality based on objective chemical tests, making it highly suitable for classification and regression tasks, as well as feature selection and outlier detection in machine learning workflows.
Columns
- fixed acidity: Represents the amount of non-volatile acids present in the wine.
- volatile acidity: Indicates the amount of volatile acids in the wine.
- citric acid: Measures the amount of citric acid added to or naturally present in the wine.
- residual sugar: Quantifies the amount of sugar remaining after the fermentation process.
- chlorides: Denotes the amount of salt present in the wine.
- free sulfur dioxide: Measures the amount of free sulfur dioxide, which prevents microbial growth and oxidation.
- total sulfur dioxide: Represents the total amount of sulfur dioxide (free and bound forms).
- density: The density of the wine sample.
- pH: Describes the pH level, indicating the acidity or alkalinity of the wine.
- sulphates: Measures the amount of sulphates, a wine additive which can contribute to sulfur dioxide levels.
- alcohol: The percentage of alcohol content in the wine.
- quality: The target variable, consisting of a sensory score between 0 and 10.
Distribution
- Format: CSV (white_wine_quality.csv)
- Size: 264.43 kB
- Structure: 12 columns
- Records: 4,898 valid rows (part of a larger collection of over 6,000 red and white wine records).
Usage
- Predictive Modelling: Training classification or regression models to predict wine quality scores based on chemical inputs.
- Oenological Research: Analysing correlations between chemical properties (like acidity or sugar) and sensory quality ratings.
- Feature Selection: Identifying which physicochemical properties have the most significant impact on wine quality.
- Outlier Detection: Detecting anomalies in wine production batches.
- Algorithm Testing: Benchmarking machine learning algorithms on a clean, structured dataset.
Coverage
- Geographic Scope: Portugal (specifically the Vinho Verde region).
- Product Scope: White wine variants.
- Data Validity: 100% valid records across all columns with 0% missing or mismatched data.
License
Attribution 4.0 International (CC BY 4.0)
Who Can Use It
- Data Scientists: For practicing regression and classification techniques.
- Oenologists & Winemakers: For understanding chemical drivers of perceived quality.
- Machine Learning Students: As a standard dataset for testing algorithms.
- Food & Beverage Analysts: For market quality analysis.
Dataset Name Suggestions
- Portuguese Vinho Verde White Wine Physicochemical Data
- White Wine Quality Attributes & Sensory Scores
- Vinho Verde Chemical Properties Analysis Set
- Predictive Wine Quality Dataset (White Variant)
Attributes
Original Data Source:Portuguese Vinho Verde White Wine Physicochemical Data
QUALITYLoading...