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.

Sure! Here's the updated Kaggle dataset description with your data visualization work included:

📊 Wine Quality - Red Wine Dataset

This dataset contains physicochemical attributes of red variants of Portuguese "Vinho Verde" wine, along with their quality score (rated between 0 to 10). The goal is to predict wine quality using various classification models based on the chemical properties of the wine.

🧪 Features Overview (12 columns):

• fixed acidity: most acids involved with wine are fixed/nonvolatile
• volatile acidity: amount of acetic acid (can affect taste)
• citric acid: adds freshness and flavor
• residual sugar: sugar left after fermentation
• chlorides: salt content
• free sulfur dioxide: protects wine from microbes
• total sulfur dioxide: total SO₂ content
• density: wine density
• pH: acidity level
• sulphates: preservative and antimicrobial
• alcohol: alcohol percentage
• quality (target): wine quality score (0–10)

🤖 Model Performance Summary:

Multiple machine learning models were trained to predict wine quality. The following accuracy scores were observed:

📈 Data Visualization:

A comparison plot of model performance was created to visually represent the accuracy of each algorithm. This helps in understanding which models generalized well and which ones may have overfit to the training data.

📁 File Info:

• Filename: winequality-red.csv
• Size: ~100 KB
• Rows: 1,599
• Columns: 12

📌 Ideal For:

• Classification model evaluation
• Feature correlation analysis
• EDA and visualization
• ML model tuning and comparison

part of the dataset supplied in https://www.kaggle.com/datasets/uciml/red-wine-quality-cortez-et-al-2009 https://archive.ics.uci.edu/ml/datasets/wine+quality

Feature introduction:

Fixed acidity: acids are major wine properties and contribute greatly to the wine’s taste. Usually, the total acidity is divided into two groups: the volatile acids and the nonvolatile or fixed acids. Among the fixed acids that you can find in wines are the following: tartaric, malic, citric, and succinic. This variable is expressed in g(tartaricacidtartaricacid)/dm3dm3 in the data sets.

Volatile acidity: the volatile acidity is basically the process of wine turning into vinegar. In the U.S, the legal limits of Volatile Acidity are 1.2 g/L for red table wine and 1.1 g/L for white table wine. In these data sets, the volatile acidity is expressed in g(aceticacidaceticacid)/dm3dm3.

Citric acid is one of the fixed acids that you’ll find in wines. It’s expressed in g/dm3dm3 in the two data sets. Residual sugar typically refers to the sugar remaining after fermentation stops, or is stopped. It’s expressed in g/dm3dm3 in the red and white data.

Chlorides can be a major contributor to saltiness in wine. Here, you’ll see that it’s expressed in g(sodiumchloridesodiumchloride)/dm3dm3.

Free sulfur dioxide: the part of the sulphur dioxide that is added to a wine and that is lost into it is said to be bound, while the active part is said to be free. Winemaker will always try to get the highest proportion of free sulphur to bind. This variables is expressed in mg/dm3dm3 in the data.

Total sulfur dioxide is the sum of the bound and the free sulfur dioxide (SO2). Here, it’s expressed in mg/dm3dm3. There are legal limits for sulfur levels in wines: in the EU, red wines can only have 160mg/L, while white and rose wines can have about 210mg/L. Sweet wines are allowed to have 400mg/L. For the US, the legal limits are set at 350mg/L and for Australia, this is 250mg/L.

Density is generally used as a measure of the conversion of sugar to alcohol. Here, it’s expressed in g/cm3cm3. pH or the potential of hydrogen is a numeric scale to specify the acidity or basicity the wine. As you might know, solutions with a pH less than 7 are acidic, while solutions with a pH greater than 7 are basic. With a pH of 7, pure water is neutral. Most wines have a pH between 2.9 and 3.9 and are therefore acidic.

Sulphates are to wine as gluten is to food. You might already know sulphites from the headaches that they can cause. They are a regular part of the winemaking around the world and are considered necessary. In this case, they are expressed in g(potassiumsulphatepotassiumsulphate)/dm3dm3.

Alcohol: wine is an alcoholic beverage and as you know, the percentage of alcohol can vary from wine to wine. It shouldn’t surprised that this variable is inclued in the data sets, where it’s expressed in % vol.

Quality: wine experts graded the wine quality between 0 (very bad) and 10 (very excellent). The eventual number is the median of at least three evaluations made by those same wine experts.

Dataset

This dataset was created by HamnaKhalid

Contents

Two datasets are included, related to red and white vinho verde wine samples, from the north of Portugal. The goal is to model wine quality based on physicochemical tests (see [Cortez et al., 2009], http://www3.dsi.uminho.pt/pcortez/wine/).

Analysis of ‘Wine Quality’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/danielpanizzo/wine-quality on 30 September 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 ---

The Wine Quality data combines two benchmark data sets from UCI related to red and white wines.

Dataset

This dataset was created by Anna Korotysheva

Contents

A random forest model trained on part of the dataset supplied in https://www.kaggle.com/datasets/uciml/red-wine-quality-cortez-et-al-2009 https://archive.ics.uci.edu/ml/datasets/wine+quality

Dataset

This dataset was created by kiran kuyate

Contents

Dataset

This dataset was created by Tasnim Niger

Released under Database: Open Database, Contents: Database Contents

Contents

This is an enhanced version of the Red Wine Quality dataset.

Modifications and additions include: - ✅ A binary column is_high_quality (1 if quality ≥ 6, else 0) - ✅ A calculated column total_acidity (sum of fixed, volatile, and citric acid) - ✅ A new column user_comment with static text - ✅ One synthetic custom data row manually added

Perfect for experimenting with binary classification, feature engineering, and data enrichment.

China Retail Price: 36 City Avg: Dry Red Wine: 12 Degree: 750ml: High-quality data was reported at 287.690 RMB/Bottle in Mar 2025. This records an increase from the previous number of 286.150 RMB/Bottle for Feb 2025. China Retail Price: 36 City Avg: Dry Red Wine: 12 Degree: 750ml: High-quality data is updated monthly, averaging 360.000 RMB/Bottle from Jan 2012 (Median) to Mar 2025, with 159 observations. The data reached an all-time high of 630.000 RMB/Bottle in Apr 2012 and a record low of 280.970 RMB/Bottle in Nov 2023. China Retail Price: 36 City Avg: Dry Red Wine: 12 Degree: 750ml: High-quality data remains active status in CEIC and is reported by Price Monitoring Center, NDRC. The data is categorized under China Premium Database’s Price – Table CN.PA: Price Monitoring Center, NDRC: 36 City Monthly Avg: Retail Price: Consumer Goods.

1) Data Introduction • The Wine_Quality_Data Dataset is a structured dataset that includes various chemical properties of wine such as acidity, sugar content, pH level, and alcohol concentration, along with a quality score (ranging from 3 to 9) and color information (red or white).

2) Data Utilization (1) Characteristics of the Wine_Quality_Data Dataset: • This dataset is designed for developing models that assess and classify wine quality, making it suitable for analyzing chemical composition and solving classification problems related to product quality. • Each sample contains chemical measurements of the wine (e.g., acidity, sugar, pH, alcohol), and the quality column provides a multi-class label representing the wine's quality score on a scale from 3 to 9.

(2) Applications of the Wine_Quality_Data Dataset: • Wine quality prediction model training: The dataset can be used to train classification or regression models that predict wine quality scores based on various chemical attributes.

Chocolate Red Wine Market Outlook

The global market size for Chocolate Red Wine was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 2.7 billion by 2032, growing at a compound annual growth rate (CAGR) of about 9.5% during the forecast period. The increasing popularity of innovative and exotic flavor combinations, rising disposable incomes, and growing consumer interest in premium and artisanal beverages are key factors driving the market growth.

One of the primary growth factors for the Chocolate Red Wine market is the rising consumer preference for unique and luxurious food and beverage experiences. Chocolate red wine, with its rich flavor profile that combines the indulgence of chocolate with the sophistication of red wine, appeals to a growing segment of consumers who seek novel and high-quality products. This trend is particularly strong among millennials and young professionals who are willing to experiment with their tastes and spend more on premium products. Additionally, the increasing penetration of e-commerce platforms has made it easier for consumers to access a wide variety of chocolate red wines, further driving market growth.

Another significant growth factor is the health benefits associated with moderate consumption of red wine and dark chocolate. Both components are rich in antioxidants, such as flavonoids and resveratrol, which have been shown to improve heart health, reduce inflammation, and provide other health benefits. As health-conscious consumers become more aware of these benefits, the demand for products that combine these two ingredients is expected to rise. This health and wellness trend is influencing product development and marketing strategies, with manufacturers highlighting the nutritional advantages of their offerings.

The market growth is also supported by the increasing trend of premiumization in the beverage industry. Consumers are increasingly willing to pay a premium for high-quality, artisanal products that offer unique sensory experiences. Chocolate red wine fits perfectly into this trend, as it is often marketed as a luxury item that can be enjoyed on special occasions or as a gift. This premium positioning allows manufacturers to command higher prices and achieve better profit margins, further incentivizing investment in this segment.

The packaging of Chocolate Red Wine plays a pivotal role in its market appeal, and Red Wine Glass Bottles are a classic choice that exudes elegance and sophistication. These bottles not only preserve the rich flavors and aromas of the wine but also enhance its visual appeal, making them a preferred option for consumers seeking a premium experience. The use of glass bottles also aligns with consumer preferences for traditional packaging that conveys quality and authenticity. As the market continues to grow, manufacturers are exploring innovative designs and labeling to make their products stand out on the shelves, while still maintaining the timeless allure of Red Wine Glass Bottles.

Regionally, North America holds a significant share of the Chocolate Red Wine market, driven by high consumer awareness and disposable income levels. Europe is another key market, with countries like France, Italy, and Spain having a long tradition of wine consumption and a growing appreciation for innovative wine products. The Asia Pacific region is expected to exhibit the highest growth rate during the forecast period, fueled by increasing urbanization, rising middle-class incomes, and a growing interest in Western lifestyle products. Latin America and the Middle East & Africa are also emerging markets with significant growth potential due to improving economic conditions and expanding distribution networks.

Product Type Analysis

The Chocolate Red Wine market is segmented by product type into Dark Chocolate Red Wine, Milk Chocolate Red Wine, and White Chocolate Red Wine. Each of these product types caters to different consumer preferences and occasions, contributing to the overall market diversity and growth.

Dark Chocolate Red Wine is expected to dominate the market due to its rich and intense flavor profile, which appeals to connoisseurs and those looking for a sophisticated beverage experience. Dark chocolate is known for its higher cocoa content and lower sugar levels, making it a popular choice among health-conscious consumers. The combination of dark chocolate and red wine not only offers a unique

Context

The two datasets are related to red and white variants of the Portuguese "Vinho Verde" wine. For more details, refer to [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 many 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.

Content

For more information, read [Cortez et al., 2009]. 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): 12 - quality (score between 0 and 10)

Acknowledgements

This dataset is also available from the UCI machine learning repository, https://archive.ics.uci.edu/ml/datasets/wine+quality, to get both the dataset i.e. red and white vinho verde wine samples, from the north of Portugal, please visit the above link.

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, 2009.

Inspiration

We kagglers can apply several machine-learning algorithms to determine which physiochemical properties make a wine 'good'!

Relevant papers

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, 2009.

The global dry red wine market is a substantial and dynamic sector, exhibiting consistent growth driven by several key factors. Increased consumer preference for healthier alcoholic beverages, coupled with the rising disposable incomes in emerging economies, fuels demand. The growing popularity of wine as a sophisticated yet accessible beverage, particularly among millennials and Gen Z, further contributes to market expansion. Furthermore, innovative marketing strategies by leading wine producers, including targeted campaigns highlighting specific health benefits (within reasonable limits) and diverse flavor profiles, are effectively broadening the consumer base. The market is segmented by region (North America, Europe, Asia-Pacific, etc.), grape varietal (Cabernet Sauvignon, Merlot, Pinot Noir, etc.), and price point (premium, mid-range, budget). Competitive dynamics are intense, with established international players and regional wineries vying for market share through product diversification, strategic acquisitions, and brand building. While fluctuating grape prices and potential trade restrictions pose challenges, the overall market outlook remains positive, projected to maintain a healthy Compound Annual Growth Rate (CAGR) throughout the forecast period. Despite these positive trends, the market faces certain restraints. The impact of climate change on grape yields and wine quality presents a significant risk. Increasing regulatory pressures related to alcohol consumption and health concerns may also influence market growth. Furthermore, economic downturns in major consumer markets could lead to decreased discretionary spending, affecting demand for premium wine products. However, the market's resilience is underpinned by the strong and enduring appeal of dry red wine, its versatility in culinary pairings, and continuous innovation in production techniques and marketing strategies. The market is expected to witness increased consolidation, with larger players potentially acquiring smaller wineries to gain market share and achieve greater economies of scale. This could lead to both opportunities and challenges for smaller, independent producers, who may need to focus on niche markets or build stronger brand identities to maintain competitiveness.

Discover some of the best red wine brands in the world, known for their exceptional quality and taste. From Château Margaux in France to Penfolds in Australia, explore the prestigious wines that have garnered worldwide acclaim.

The global market for anti-counterfeiting technology in the red wine industry is projected to reach a value of $XXX million by 2033, expanding at a CAGR of XX% from 2025 to 2033. The escalating prevalence of counterfeit wine products, coupled with rising consumer awareness and concerns over the quality and authenticity of red wine, are major drivers fueling market growth. Additionally, advancements in anti-counterfeiting technologies, such as the integration of digital solutions, including blockchain and radio frequency identification (RFID) tags, are further bolstering market prospects. Key trends influencing the anti-counterfeiting technology market for red wine include the increasing demand for tamper-proof packaging and advanced security measures, as well as the stringent regulations implemented by various governments to combat counterfeit products. The rising adoption of anti-counterfeiting solutions by small and medium-sized wine producers, along with the growing popularity of e-commerce channels for wine purchases, are expected to present significant growth opportunities for the market. However, challenges related to the cost of implementation and concerns over the privacy and security of consumer data may hinder market growth to some extent. Nevertheless, the long-term outlook for the anti-counterfeiting technology market for red wine remains positive, driven by the increasing demand for authenticity and quality assurance in the global wine industry.