Two datasets were created, using red and white wine samples. The inputs include objective tests (e.g. PH values) and the output is based on sensory data (median of at least 3 evaluations made by wine experts). Each expert graded the wine quality between 0 (very bad) and 10 (very excellent). Several data mining methods were applied to model these datasets under a regression approach. The support vector machine model achieved the best results. Several metrics were computed: MAD, confusion matrix for a fixed error tolerance (T), etc. Also, we plot the relative importances of the input variables (as measured by a sensitivity analysis procedure).

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.).

Number of Instances: red wine - 1599; white wine - 4898

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):

1. quality (score between 0 and 10)

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('wine_quality', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

Abstract We present WineSensed, a large multimodal wine dataset for studying the relations between visual perception, language, and flavor. The dataset encompasses 897k images of wine labels and 824k reviews of wines curated from the Vivino platform. It has over 350k unique vintages, annotated with year, region, rating, alcohol percentage, price, and grape composition. We obtained fine-grained flavor annotations on a subset by conducting a wine-tasting experiment with 256 participants who were asked to rank wines based on their similarity in flavor, resulting in more than 5k pairwise flavor distances. We propose a low-dimensional concept embedding algorithm that combines human experience with automatic machine similarity kernels. We demonstrate that this shared concept embedding space improves upon separate embedding spaces for coarse flavor classification (alcohol percentage, country, grape, price, rating) and aligns with the intricate human perception of flavor.

Dataset structure See https://github.com/thoranna/learning-to-taste.github.io Read about the background: https://thoranna.github.io/learning_to_taste/
How to download Follow the instructions at https://github.com/thoranna/learning-to-taste.github.io

X-Wines is a consistent wine dataset containing 100,646 instances and 21 million real evaluations carried out by users. Data were collected on the open Web in 2022 and pre-processed for wider free use. They refer to the scale 1–5 ratings carried out over a period of 10 years (2012–2021) for wines produced in 62 different countries.

Here are a few use cases for this project:

1. Wine Tasting events: This model could be used at wine tasting events or wine production facilities to automatically identify and sort glasses based on their contents. Attendees could take a picture for the model to analyze and get details about the wine they are about to taste.

2. Hospitality Industry: Hotels, bars, and restaurants could employ this technology in their establishments to quickly audit their glassware and accurately serve specific types of wine.

3. Inventory Management: Retailers selling various types of wine glasses could use this model to automatically categorize and manage their inventory based on the type of wine the glass is meant to hold.

4. Advanced AI Systems for blind people: This model could be incorporated in AI systems designed for assisting visually impaired individuals in identifying types of wine by processing the image of the glass and providing auditory feedback.

5. Wine Producers and Distributors: Wine producers and distributors could use this technology to ensure the correct presentation of their wines at exhibitions, promotional events, and in advertising materials. They could confirm the correct type of glass is being used for their wine, improving customer experience.

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

California is famous for their wine and they have produced a lot of wine through the years.

In this dataset you will find the acres harvested, yield, amount produced, total price, for each county in California from 1980 to 2020.

This data could be a great way to practice some data visualization techniques. Combine with California rain data to create a machine learning model. Or maybe try to forecast the next 10 years of productions.

130k wine reviews with variety, location, winery, price, and description

Analysis of ‘Red Wine Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/piyushgoyal443/red-wine-dataset on 13 November 2021.

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

Citation Request: This dataset is public available for research. The details are described in [Cortez et al., 2009]. Please include this citation if you plan to use this database:

P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553. ISSN: 0167-9236.

Available at: [@Elsevier] http://dx.doi.org/10.1016/j.dss.2009.05.016 [Pre-press (pdf)] http://www3.dsi.uminho.pt/pcortez/winequality09.pdf [bib] http://www3.dsi.uminho.pt/pcortez/dss09.bib

1. Title: Wine Quality

2. Sources Created by: Paulo Cortez (Univ. Minho), Antonio Cerdeira, Fernando Almeida, Telmo Matos and Jose Reis (CVRVV) @ 2009

3. Past Usage:

   P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553. ISSN: 0167-9236.

   In the above reference, two datasets were created, using red and white wine samples. The inputs include objective tests (e.g. PH values) and the output is based on sensory data (median of at least 3 evaluations made by wine experts). Each expert graded the wine quality between 0 (very bad) and 10 (very excellent). Several data mining methods were applied to model these datasets under a regression approach. The support vector machine model achieved the best results. Several metrics were computed: MAD, confusion matrix for a fixed error tolerance (T), etc. Also, we plot the relative importances of the input variables (as measured by a sensitivity analysis procedure).

4. Relevant 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 munch 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.

5. Number of Instances: red wine - 1599; white wine - 4898.

6. Number of Attributes: 11 + output attribute

   Note: several of the attributes may be correlated, thus it makes sense to apply some sort of feature selection.

7. Attribute information:

   For more information, read [Cortez et al., 2009].

   Input variables (based on physicochemical tests): 1 - fixed acidity (tartaric acid - g / dm^3) 2 - volatile acidity (acetic acid - g / dm^3) 3 - citric acid (g / dm^3) 4 - residual sugar (g / dm^3) 5 - chlorides (sodium chloride - g / dm^3 6 - free sulfur dioxide (mg / dm^3) 7 - total sulfur dioxide (mg / dm^3) 8 - density (g / cm^3) 9 - pH 10 - sulphates (potassium sulphate - g / dm3) 11 - alcohol (% by volume) Output variable (based on sensory data): 12 - quality (score between 0 and 10)

8. Missing Attribute Values: None

9. Description of attributes:

   1 - fixed acidity: most acids involved with wine or fixed or nonvolatile (do not evaporate readily)

   2 - volatile acidity: the amount of acetic acid in wine, which at too high of levels can lead to an unpleasant, vinegar taste

   3 - citric acid: found in small quantities, citric acid can add 'freshness' and flavor to wines

   4 - residual sugar: the amount of sugar remaining after fermentation stops, it's rare to find wines with less than 1 gram/liter and wines with greater than 45 grams/liter are considered sweet

   5 - chlorides: the amount of salt in the wine

   6 - free sulfur dioxide: the free form of SO2 exists in equilibrium between molecular SO2 (as a dissolved gas) and bisulfite ion; it prevents microbial growth and the oxidation of wine

   7 - total sulfur dioxide: amount of free and bound forms of S02; in low concentrations, SO2 is mostly undetectable in wine, but at free SO2 concentrations over 50 ppm, SO2 becomes evident in the nose and taste of wine

   8 - density: the density of water is close to that of water depending on the percent alcohol and sugar content

   9 - pH: describes how acidic or basic a wine is on a scale from 0 (very acidic) to 14 (very basic); most wines are between 3-4 on the pH scale

   10 - sulphates: a wine additive which can contribute to sulfur dioxide gas (S02) levels, wich acts as an antimicrobial and antioxidant

   11 - alcohol: the percent alcohol content of the wine

   Output variable (based on sensory data): 12 - quality (score between 0 and 10)

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

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

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.

About This Data

This is a list of over 2,000 reviews for beer, liquor, and wine sold online provided by Datafiniti's Product Database. The dataset includes address, city, state, business name, business categories, menu data, phone numbers, and more.

Note that this is a sample of a large dataset. The full dataset is available through Datafiniti.

What You Can Do With This Data

You can use this data to discover insights into how consumers review alcoholic beverages. E.g.:

• Which brands have the best reviews?
• Does white wine or red wine get better reviews?
• What words are most commonly associated with each beverage type?

Data Schema

A full schema for the data is available in our support documentation.

About Datafiniti

Datafiniti provides instant access to web data. We compile data from thousands of websites to create standardized databases of business, product, and property information. Learn more.

Interested in the Full Dataset?

Get this data and more by creating a free Datafiniti account or requesting a demo.

In 2022, wine consumption worldwide was estimated to amount to 232 million hectoliters, down from 247 million hectoliters in 2017. Wine is an alcoholic drink commonly made from fermented grapes. Wines not made from grapes involve the fermentation of several other sources such as rice, plum, cherry, pomegranate, and elderberry. The origins of wine can be traced back to 6000 BC from the country of Georgia.

The global wine market

The global wine market is segmented by color; most notably red, rose, and white wine. Product types include still, sparkling, dessert, and fortified wine. Wine is sold and distributed by supermarkets, specialty stores, convenience stores, and online channels. The largest wine-producing regions are located in Italy, Spain, France, the United States, and China. Sparkling wine, champagne more specifically, is seen as status symbol and often consumed at celebratory events. As of 2018, the highest-selling sparkling wine and champagne brand is the Italian-based La Marca. Wineries have started to explore differing forms of packaging such as cans and cartons to create the perception of their product as an everyday drink.

The global wine market is projected to attain a valuation of US$ 2971.13 billion by 2033. Wine producers can expect a CAGR of 5.7% through 2023. The current valuation of the market is US$ 1709.27 billion in 2023.

Attributes	Details
Market Size in 2022	US$ 1623.94 billion
Market Size in 2023	US$ 1709.27 billion
Market Size by 2033	US$ 2971.13 billion
Value CAGR (2023 to 2033)	5.7%

Historical Performance of Wine Market

Attributes	Details
Market Size (2018)	US$ 1300.98 billion
Market Size (2022)	US$ 1623.94 billion
Market (CAGR 2018 to 2022)	2.3%

Category-wise Insights

Segment	Wine Type (Red Wine)
2022 Value Share	41.50%

Segment	Sales Channel (Retail)
2022 Value Share	43.50%

Country-wise Insights

Country	China
2023 Market Share	11.0%

Country	Australia
2023 Market Share	4.80%

Country	Germany
2023 Market Share	9.40%

Country	United States
2023 Market Share	17.50%

Wines: Approved for Circulation: AOC & VDQS: Dept: Paris data was reported at 172.000 hl in Apr 2018. This records a decrease from the previous number of 257.000 hl for Mar 2018. Wines: Approved for Circulation: AOC & VDQS: Dept: Paris data is updated monthly, averaging 86.000 hl from Aug 2002 to Apr 2018, with 188 observations. The data reached an all-time high of 1,295.000 hl in Aug 2005 and a record low of 5.000 hl in Aug 2003. Wines: Approved for Circulation: AOC & VDQS: Dept: Paris data remains active status in CEIC and is reported by General Directorate of Customs and Excise. The data is categorized under Global Database’s France – Table FR.B013: Wine Statistics.

Wecome!

This is a project on training the machine to read and pickup wine label information, specifically there are several class labels I look at from each of the wine labels, in each class, specific class attributes (such as under the wine type different attributes: Cabernet Sauvignion or Riesling or Merlot) can be assigned to provide more detailed information:

(1)Maker/Name of the vineyard or producer (2)Vintage/Year of the wine produced (3)Whether being sustainable or sustainably farmed (4)Whether being organic or not (5)Alcohol level (6)Appellation Quality in terms of common AVA ratings (7)Established Year of the vineyard (8)Whether having any appelation AOC DOC AVA name (9)Whether Country of the origin can be identified (10)Whether type of the wine can be identified (11)Whether there is distinct picture or brand logo (12) Whether there is indication of sweetness level

I hope we all can help train the machine to be better at reading the wine label and be smarter and make more quality inference rather than just reading and picking up information as it which would be just like an OCR

-Yilong Eric Zheng

Wines: Approved for Circulation: AOC & VDQS: Dept: Corse Du Sud data was reported at 3,394.000 hl in Apr 2018. This records an increase from the previous number of 81.000 hl for Mar 2018. Wines: Approved for Circulation: AOC & VDQS: Dept: Corse Du Sud data is updated monthly, averaging 300.000 hl from Aug 2002 to Apr 2018, with 186 observations. The data reached an all-time high of 53,016.000 hl in Dec 2017 and a record low of 7.000 hl in Jan 2018. Wines: Approved for Circulation: AOC & VDQS: Dept: Corse Du Sud data remains active status in CEIC and is reported by General Directorate of Customs and Excise. The data is categorized under Global Database’s France – Table FR.B013: Wine Statistics.

Wines: Approved for Circulation: AOC & VDQS: Dept: Moselle data was reported at 351.000 hl in Apr 2018. This records a decrease from the previous number of 439.000 hl for Mar 2018. Wines: Approved for Circulation: AOC & VDQS: Dept: Moselle data is updated monthly, averaging 490.500 hl from Aug 2002 to Apr 2018, with 188 observations. The data reached an all-time high of 5,427.000 hl in Jun 2015 and a record low of 25.000 hl in Aug 2005. Wines: Approved for Circulation: AOC & VDQS: Dept: Moselle data remains active status in CEIC and is reported by General Directorate of Customs and Excise. The data is categorized under Global Database’s France – Table FR.B013: Wine Statistics.

Wines: Approved for Circulation: AOC & VDQS: Dept: Jura data was reported at 3,010.000 hl in Apr 2018. This records a decrease from the previous number of 3,833.000 hl for Mar 2018. Wines: Approved for Circulation: AOC & VDQS: Dept: Jura data is updated monthly, averaging 5,322.000 hl from Aug 2002 to Apr 2018, with 189 observations. The data reached an all-time high of 16,793.000 hl in Oct 2005 and a record low of 3,010.000 hl in Apr 2018. Wines: Approved for Circulation: AOC & VDQS: Dept: Jura data remains active status in CEIC and is reported by General Directorate of Customs and Excise. The data is categorized under Global Database’s France – Table FR.B013: Wine Statistics.

Report Metric	Details
Forecast Period	2024 to 2031
Base Year	2023
Historic Years	2022 (Customizable to 2016-2021)
Quantitative Units	Revenue in USD Million, Volumes in Units, Pricing in USD
Segments Covered	Nature (Organic and Conventional), Variety (Traditional and Cyser (Mead with apples), Melomel (Mead with other fruits), Pyment (Mead with gapes), and Metheglin (Mead with spices or herbs)), Sales Channel (Modern Trade, Specialty Stores, Convenience Stores, Commercial, Hotels/Restaurants/Bars, and Online Retailers), Product Type (Carbonated (Sparkling), Dry, Semi-Dry, Sweet, and Semi-Sweet)
Countries Covered	U.S., Canada, Mexic, Germany, Sweden, Poland, Denmark, Italy, U.K., France, Spain, Netherlands, Belgium, Switzerland, Turkey, Russia, Rest of Europe in Europe, Japan, China, India, South Korea, New Zealand, Vietnam, Australia, Singapore, Malaysia, Thailand, Indonesia, Philippines, Rest of Asia-Pacific, Brazil, Argentina, Rest of South America as a part of South America, U.A.E, Saudi Arabia, Oman, Qatar, Kuwait, South Africa, and Rest of Middle East and Africa
Market Players Covered	moonshinemeadery (India), The Honey Wine Company (U.S.), Etowah Meadery (U.S.), Schramm's Mead (U.S.), B. Nektar Meadery (U.S.), Wicked Warren's (U.S.), BENT RUN BREWING CO (U.S.), Modern Methods Brewing Company (U.S.), Humble Bee Wines (U.S.), Real Beer Media, Inc. (U.K.), ROSEWOOD ESTATES WINERY & MEADERY.(Canada), Brothers Drake Meadery. (U.S.) Redstone Meadery (U.S.), and Medovina (U.S.) among others
Market Opportunities	Increasing popularity of unique flavor experiences Growing interest in natural and authentic products