Wine Quality
Donated on 10/6/2009
Two datasets are included, related to red and white vinho verde wine samples, from the north of Portugal. The goal is to model wine quality based on physicochemical tests (see [Cortez et al., 2009], http://www3.dsi.uminho.pt/pcortez/wine/).
Dataset Characteristics
Multivariate
Subject Area
Business
Associated Tasks
Classification, Regression
Feature Type
Real
# Instances
4898
# Features
11
Dataset Information
Additional Information
The two datasets are related to red and white variants of the Portuguese "Vinho Verde" wine. For more details, consult: http://www.vinhoverde.pt/en/ or the reference [Cortez et al., 2009]. Due to privacy and logistic issues, only physicochemical (inputs) and sensory (the output) variables are available (e.g. there is no data about grape types, wine brand, wine selling price, etc.). These datasets can be viewed as classification or regression tasks. The classes are ordered and not balanced (e.g. there are many more normal wines than excellent or poor ones). Outlier detection algorithms could be used to detect the few excellent or poor wines. Also, we are not sure if all input variables are relevant. So it could be interesting to test feature selection methods.
Has Missing Values?
No
Introductory Paper
By P. Cortez, A. Cerdeira, Fernando Almeida, Telmo Matos, J. Reis. 2009
Published in Decision Support Systems
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| fixed_acidity | Feature | Continuous | no | ||
| volatile_acidity | Feature | Continuous | no | ||
| citric_acid | Feature | Continuous | no | ||
| residual_sugar | Feature | Continuous | no | ||
| chlorides | Feature | Continuous | no | ||
| free_sulfur_dioxide | Feature | Continuous | no | ||
| total_sulfur_dioxide | Feature | Continuous | no | ||
| density | Feature | Continuous | no | ||
| pH | Feature | Continuous | no | ||
| sulphates | Feature | Continuous | no |
0 to 10 of 13
Additional Variable Information
For more information, read [Cortez et al., 2009]. Input variables (based on physicochemical tests): 1 - fixed acidity 2 - volatile acidity 3 - citric acid 4 - residual sugar 5 - chlorides 6 - free sulfur dioxide 7 - total sulfur dioxide 8 - density 9 - pH 10 - sulphates 11 - alcohol Output variable (based on sensory data): 12 - quality (score between 0 and 10)
Dataset Files
| File | Size |
|---|---|
| winequality-white.csv | 258.2 KB |
| winequality-red.csv | 82.2 KB |
| winequality.names | 3.2 KB |
Reviews
There are no reviews for this dataset yet.
pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset wine_quality = fetch_ucirepo(id=186) # data (as pandas dataframes) X = wine_quality.data.features y = wine_quality.data.targets # metadata print(wine_quality.metadata) # variable information print(wine_quality.variables)
Cortez, P., Cerdeira, A., Almeida, F., Matos, T., & Reis, J. (2009). Wine Quality [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C56S3T.
Keywords
Creators
Paulo Cortez
A. Cerdeira
F. Almeida
T. Matos
J. Reis
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.