https://github.com/walidalsafadi/red-wine-quality

Practice dataset for regression or classification modelling
https://github.com/walidalsafadi/red-wine-quality

data-cleaning data-science dicision-tree exploratory-data-analysis machine-learning random-forest wine-quality

Last synced: over 1 year ago
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Practice dataset for regression or classification modelling

• Host: GitHub
• URL: https://github.com/walidalsafadi/red-wine-quality
• Owner: WalidAlsafadi
• License: mit
• Created: 2023-09-20T20:57:18.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2024-08-01T12:01:21.000Z (almost 2 years ago)
• Last Synced: 2025-01-22T17:16:16.244Z (over 1 year ago)
• Topics: data-cleaning, data-science, dicision-tree, exploratory-data-analysis, machine-learning, random-forest, wine-quality
• Language: Jupyter Notebook
• Homepage: https://www.kaggle.com/code/walidkw/red-wine-quality-eda-classification-90
• Size: 9.48 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Red Wine Quality

![Wine](https://i.pinimg.com/1200x/48/a1/c3/48a1c3c3a2c198a7773500c5583ffc9f.jpg)

* Kindly review my Kaggle notebook for accessing the interactive plots.

https://www.kaggle.com/code/walidkw/red-wine-quality-eda-classification-90

# About Dataset:

### Context

The two datasets are related to red and white variants of the Portuguese "Vinho Verde" wine. For more details, consult 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 much more normal wines than excellent or poor ones).

### Content

The dFor 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)atasets consists of several medical predictor variables and one target variable, Outcome. Predictor variables includes the number of pregnancies the patient has had, their BMI, insulin level, age, and so on.

### Inspiration

Use machine learning to determine which physiochemical properties make a wine 'good'!

# Requirements:

- Perform Exploratory Data Analysis

- Data Cleaning

- Plot relationship between variables.

- implement machine learning models.

- Perform Cross Validation, Feature Selection and Grid Search