Wine Quality Dataset

Table of Contents

• Overview
• Dataset Structure
• Reference
• License

Overview

The Wine Quality dataset is a curated collection of tabular data designed to advance automated classification of wine quality. It provides a robust set of samples for multi-class classification, enabling researchers and students to explore advanced data processing and machine learning models.

The goal is to classify each sample into a wine quality score.

Property	Value
Total samples	3599
Training samples	2000
Test samples	1599
Features	Tabular data
Classes	11 (between 0 and 10)

Dataset Structure

Clone the repository:

git clone [email protected]:AIOZAI/wine_quality_dataset.git

Files

File	Samples	Description
data/train.csv	2000 samples	Training data with features and quality labels.
data/test.csv	1599 samples	Test data for submission - quality column hidden.

Data Format

This file contains the features and ground truth labels for the training samples.

Field	Type	Description
id	integer	Unique identifier for each wine sample.
type	string	Type of wine (red or white).
fixed_acidity	float	Non-volatile acids (g/dm3).
volatile_acidity	float	Volatile acids (g/dm3).
citric_acid	float	Natural acid that adds freshness and flavor (g/dm3).
residual_sugar	float	Remaining sugar after fermentation (g/dm3).
chlorides	float	Salt content (g/dm3).
free_sulfur_dioxide	float	Free SO2 preventing oxidation (mg/dm3).
total_sulfur_dioxide	float	Total SO2 content (mg/dm3).
density	float	Wine density influenced by sugar/alcohol (g/cm3).
pH	float	Acidity level of wine.
sulphates	float	Potassium sulphate level (g/dm3).
alcohol	float	Alcohol content (% by volume).
quality	integer	Target variable: wine quality score (0–10).

Example: train.csv

id	type	fixed_acidity	volatile_acidity	citric_acid	residual_sugar	chlorides	free_sulfur_dioxide	total_sulfur_dioxide	density	pH	sulphates	alcohol	quality
10	white	7.7	0.42	0.31	9.2	0.048	22.0	221.0	0.9969	3.06	0.61	9.2	6
11	red	7.6	0.41	0.24	1.8	0.08	4.0	11.0	0.9962	3.28	0.59	9.5	5

Reference

For more information about the original dataset, please visit the UCI Machine Learning Repository.

License

This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0).