Wine Quality Dataset

Wine Quality
Dataset

The "Vinho Verde" wine quality dataset from Portugal, containing a total of 6,497 samples of red and white wines, 11 physicochemical properties, and quality ratings, widely used for regression, classification, and feature analysis research.

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6,497 samples 12 features CC BY 4.0 license P. Cortez et al. (2009)
Wine Quality Dataset
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6,497
Total number of samples
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12
Feature dimensions
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2
Types of wine
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CC BY 4.0
Open license agreement

Dataset Highlights

Classic machine learning dataset, suitable for various analysis scenarios from beginner to advanced.

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Real World Data

The data comes from actual wine samples from the Vinho Verde region in Portugal, rated for quality by professional tasters, providing real industrial reference value.

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Dual Datasets

Includes two independent subsets of red wine (1,599 entries) and white wine (4,898 entries), allowing for separate modeling or combined analysis, highly flexible.

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Applicable for Regression and Classification

Quality ratings are continuous integers from 0-10, which can be used as regression targets to predict precise scores or transformed into binary or multi-class tasks.

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Feature Engineering Friendly

There are rich correlations and nonlinear relationships among 11 physicochemical indicators, making it very suitable for practicing techniques such as feature selection, dimensionality reduction, and feature combination.

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Comprehensive Documentation

The dataset was published in an academic paper by P. Cortez et al. in 2009, with detailed records of feature meanings, collection methods, and scoring criteria.

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Scientific Source

Originating from the UCI Machine Learning Repository, widely cited in academia and industry, it is a standard choice for machine learning introduction and benchmarking.

Applicable Scenarios

From classroom teaching to professional research, it can provide value

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Regression Analysis

Predict 0-10 quality scores, practice linear regression, random forests, XGBoost, and other regression algorithms

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Classification Modeling

Transform quality scores into "Good/Average/Poor" categories, train SVM, decision trees, neural networks, and other classifiers

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Feature Selection

Analyze the impact weights of 11 physicochemical indicators on quality, practice SHAP, Lasso, mutual information, and other feature importance methods

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Data Visualization

Explore variable distributions, correlation matrices, and dimensionality reduction projections, suitable for EDA and data storytelling practice

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Data Preview

The following are examples of the first few rows of the red wine dataset, with fields separated by semicolons

CSV
"fixed acidity";"volatile acidity";"citric acid";"residual sugar";"chlorides";"free sulfur dioxide";"total sulfur dioxide";"density";"pH";"sulphates";"alcohol";"quality"
7.4;0.70;0.00;1.9;0.076;11;34;0.9978;3.51;0.56;9.4;5
7.8;0.88;0.00;2.6;0.098;25;67;0.9968;3.20;0.68;9.8;5
7.8;0.76;0.04;2.3;0.092;15;54;0.9970;3.26;0.65;9.8;5
11.2;0.28;0.56;1.9;0.075;17;60;0.9980;3.16;0.58;9.8;6
7.4;0.70;0.00;1.9;0.076;11;34;0.9978;3.51;0.56;9.4;5
7.4;0.66;0.00;1.8;0.075;13;40;0.9978;3.51;0.56;9.4;5
7.9;0.60;0.06;1.6;0.069;15;59;0.9964;3.30;0.46;9.4;5
7.3;0.65;0.00;1.2;0.065;15;21;0.9946;3.39;0.47;10.0;7
8.1;0.22;0.43;1.5;0.044;28;129;0.9938;3.22;0.45;11.0;6

3 Steps to Get Started Quickly

From browsing to analysis, you can start your data science project in just a few minutes

01

Browse Datasets

View dataset details on the Ace Data Cloud platform, including field descriptions, sample size, and licensing agreements.

02

Download Data

Download the red wine (84 KB) and white wine (258 KB) CSV files, ready to use without additional cleaning.

03

Load and Analyze

Use pandas.read_csv(sep=";") to load the data and start exploratory analysis, modeling, and visualization.

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Start Exploring Wine Quality Data

A classic dataset with open licensing, available for immediate download. Whether you are a beginner in machine learning or an experienced data scientist, this dataset is worth a try.

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