Wine Quality Dataset

Wine Quality
Dataset

Portuguese "Vinho Verde" wine quality dataset, containing 6,497 red and white wine samples, 11 physicochemical indicators 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 Samples
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12
Feature Dimensions
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2
Wine Types
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CC BY 4.0
Open License Agreement

Dataset Highlights

A classic machine learning dataset, suitable for a wide range of analysis scenarios from beginner to advanced

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Real-World Data

The data comes from actual wine samples from Portugal's Vinho Verde region, quality-rated by professional tasters, with real industrial reference value.

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Two Datasets

Includes two independent subsets: red wine (1,599 records) and white wine (4,898 records), which can be modeled separately or analyzed together with great flexibility.

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Suitable for Both Regression and Classification

Quality scores are continuous integers from 0-10, allowing them to be used as regression targets for precise score prediction or converted into binary or multiclass tasks.

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Feature Engineering Friendly

The 11 physicochemical indicators have rich correlations and nonlinear relationships, making the dataset ideal for practicing techniques such as feature selection, dimensionality reduction, and feature combinations.

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Well Documented

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

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Scientific Source

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

Use Cases

From classroom teaching to professional research, it delivers value

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Regression Analysis

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

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Classification Modeling

Convert quality scores into "high/medium/low" categories and train classifiers such as SVM, decision trees, and neural networks

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Feature Selection

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

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Data Visualization

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

Machine Learning Basics Kaggle Competitions Statistical Modeling Teaching Demonstrations Benchmarking

Data Preview

Below are 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

Get Started Quickly in 3 Steps

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

01

Browse the Dataset

View dataset details on the Ace Data Cloud platform to learn about metadata such as field descriptions, sample size, and license agreement.

02

Download Data After Purchase

After purchase, download separate red and white wine ZIP delivery packages, which retain the original CSV files (84 KB / 258 KB), source attribution, and CC BY 4.0 license text.

03

Load and Analyze

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

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Start Exploring Wine Quality Data

A classic dataset with previewable license information and data samples, available for download after purchase. Whether you are a machine learning beginner or an experienced data scientist, this dataset is worth trying.

View US$1.49 Package