The dataset contains different chemical information about wine. It has 4898 instances with 14 variables each. The dataset is good for classification and regression tasks. The model can be used to predict wine quality.
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.
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Data Type
Multivariate
Default Task
Classification, Regression
Attribute Type
Real
Published Year
2009
Area of Dataset
Business
Missing Values
No
No. of Instances
4898
No. of Attributes
12
| Data Type | Multivariate | Default Task | Classification, Regression |
|---|---|---|---|
| Attribute Type | Real | Published Year | 2009 |
| Area of Dataset | Business | Missing Values | No |
| No. of Instances | 4898 | No. of Attributes | 12 |
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