AIML

This Project looks into the white wine quality dataset in predicting through 2 / models by Classification and Regression .

Details on Dataset:

The dataset is based on white variants of the Portuguese "Vinho Verde" wine. It can be viewed as a classification or regression task. The classes are ordered and not balanced (e.g. there is much more normal wines than excellent or poor ones). Outlier detection algorithms could be used to detect the few excellent or poor wines.

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).

Number of Instances: white wine - 4898.

Number of Attributes: 11 + 2 output attribute

✨✨ Attribute information:

🔤Input variables (derived from 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)

13 - taste (Mediocre or Excellent)

Missing Attribute Values: None ^_^