These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines.

Total wine and more products dataset extracted by crawl feeds team using in-house tools. Tracking all the data points present in the individual product page. Data format: CSV

Red Wine Dataset 🍷

This dataset contains the red wine dataset found here. See also this example of a Scikit-Learn model trained on this dataset.

Data for Wine Statistical Releases is derived directly from the Report of Wine Premises Operations Form 5120.17. This form must be filed with TTB 15 days after the close of the period. The Wine Statistical Release report is generated approximately 45 days after the due date.

Dataset

This dataset was created by Amisha0528

Released under Apache 2.0

Contents

Dataset

This dataset was created by Rini Christy

Contents

Wine has been popular with the public for centuries; in the market, there are a variety of wines to choose from. Among all, Bordeaux, France, is considered as the most famous wine region in the world. In this paper, we try to understand Bordeaux wines made in the 21st century through Wineinformatics study. We developed and studied two datasets: the first dataset is all the Bordeaux wine from 2000 to 2016; and the second one is all wines listed in a famous collection of Bordeaux wines, 1855 Bordeaux Wine Official Classification, from 2000 to 2016. A total of 14,349 wine reviews are collected in the first dataset, and 1359 wine reviews in the second dataset. In order to understand the relation between wine quality and characteristics, Naïve Bayes classifier is applied to predict the qualities (90+/89−) of wines. Support Vector Machine (SVM) classifier is also applied as a comparison. In the first dataset, SVM classifier achieves the best accuracy of 86.97%; in the second dataset, Naïve Bayes classifier achieves the best accuracy of 84.62%. Precision, recall, and f-score are also used as our measures to describe the performance of our models. Meaningful features associate with high quality 21 century Bordeaux wines are able to be presented through this research paper.

Dataset

This dataset was created by Baijanti Dalal

Released under CC0: Public Domain

Contents

Analysis of ‘Wine Dataset for Clustering’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/harrywang/wine-dataset-for-clustering on 28 January 2022.

--- Dataset description provided by original source is as follows ---

This dataset is adapted from the Wine Data Set from https://archive.ics.uci.edu/ml/datasets/wine by removing the information about the types of wine for unsupervised learning.

The following descriptions are adapted from the UCI webpage:

These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines.

The attributes are:

• Alcohol
• Malic acid
• Ash
• Alcalinity of ash
• Magnesium
• Total phenols
• Flavanoids
• Nonflavanoid phenols
• Proanthocyanins
• Color intensity
• Hue
• OD280/OD315 of diluted wines
• Proline

--- Original source retains full ownership of the source dataset ---

Dataset

This dataset was created by Mahmoud Mamdouh

Released under Apache 2.0

Contents

Dataset Card for wine-labels

** The original COCO dataset is stored at dataset.tar.gz**

  Dataset Summary

wine-labels

  Supported Tasks and Leaderboards

object-detection: The dataset can be used to train a model for Object Detection.

  Languages

English

  Dataset Structure







  Data Instances

A data point comprises an image and its object annotations. { 'image_id': 15, 'image': <PIL.JpegImagePlugin.JpegImageFile… See the full description on the dataset page: https://huggingface.co/datasets/Francesco/wine-labels.

Unlock the power of data with our comprehensive Total Wine Alcohol Products Dataset. Featuring detailed information on a wide range of wines, spirits, and beers, this dataset is perfect for data analysis, market research, and enhancing your product database.

Access in-depth product details, reviews, ratings, and more.

Download now to explore extensive alcohol product insights and trends.

Information on wine pricing and attributes.

Dataset

This dataset was created by Jaya Shree Biswas

Released under CC0: Public Domain

Contents

In 2023, there were just 71 large-scale wineries in the United States. Large wineries, such as these, produce more than 500,000 cases of wine per year. In contrast, there were over 5,700 limited production wineries, which produce less than one thousand yearly cases. The total number of U.S. wineries amounted to over 11,000.

Wine import and export in the U.S.

The United States exported over 490 million U.S. dollars’ worth of wine to Canada in 2022, making the country the largest importer of U.S. wine that year. In terms of U.S. wine imports, the vast majority of wine the United States imported in 2022, came from both France and Italy.

Sparkling wine trade

Sparkling wine comes in many varieties, such as French champagne, Italian prosecco, and Catalonian cava. In 2022, the United States exported over three million liters of sparkling wine. This was another decline after years of shrinking exports. At over 363 million liters, the country’s import volume was significantly larger.

Wine trade with EU countries (EU imports and exports)

codesignal/wine-quality dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset includes all the data used to establish and test a dictionary of valenced descriptive sensory terms used to characterize red wines. It is stored as an XLSX file including five tabs. Tab "Subjects": informations about the consumers. Tab "Valence": consumers' scores (on a 5-point Likert scale) for their expected liking for red wines presenting specific sensory attributes. Tab "Dataset A" includes Free-Comment (FC) tasting descriptions and liking data from 106 French consumers for four red PDO (protected denomination of origin) wines: two Bordeaux (W1 and W2) and two Rioja (W3 and W4). Tab "Dataset B" includes FC tasting descriptions and liking data from 60 consumers for four different red wines: one Bordeaux (Bor), one Beaujolais (Gam), one Languedoc (Lan) and one Val de Loire (Val). Tab "Dataset C" includes check-all-that-apply tasting descriptions and liking data collected from 60 consumers (different from dataset B) on the same products.

Analysis of ‘Wine Rating & Price’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/budnyak/wine-rating-and-price on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

I was looking for educational wine dataset with understandable features and suitable for creating ML model for my first DS project. I couldn't find anything relevant, so decided to scrap data from Vivino.com

Content

Data contains 4 files for each winestyle: red, white, rose and sparkling. Also there is a file with wine varieties for further analysis. Files has 8 columns with quite obvious names, but maybe I should add that NumberOfRatings is the number of people who rated this wine.

Inspiration

Analyzing data presented on Vivino.com, I noticed that there are no bottles that have less than 25 ratings, apparently because the company considers the rating of such wines is not accurate enough. So, I had an idea to perfom ML model for predicting the rating of bottles with a small number of ratings. I realised this idea in my project, public notebook with which I also upload here.

As it turned out, the problem of reviews distribution is exists in many spheres. It consists in the fact that customers are often afraid of choosing a product or service that no one has ever bought before. Due to this, many businesses lose large amounts of money on the unnormal distribution of customers by product. My idea is to create a model for any such business that predicts the rating based on other features, it can help to increase the demand for new, but promising products.

--- Original source retains full ownership of the source dataset ---