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https://github.com/shivang1179793/wine-quality-prediction


https://github.com/shivang1179793/wine-quality-prediction

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# Problem Statement
Machine Learning model to predict the quality of wine using linear regression

# Dataset
The dataset consists of 12 attributes of different wines. Each row represents a wine sample, and the columns represent different features of the wine. Here's a breakdown of the attributes in the dataset:

1. Fixed acidity: It represents the non-volatile acids in wine, which do not evaporate easily.
2. Volatile acidity: This attribute refers to the amount of acetic acid in wine, which can contribute to a vinegar-like taste.
3. Citric acid: It measures the presence of citric acid in wine, which can add freshness and flavor.
4. Residual sugar: This attribute represents the amount of sugar remaining in the wine after fermentation is complete.
5. Chlorides: It quantifies the amount of salt in the wine.
6. Free sulfur dioxide: This attribute measures the presence of sulfur dioxide (SO2) in free form in wine, which acts as an antioxidant and preservative.
7. Total sulfur dioxide: It indicates the total amount of sulfur dioxide (free and bound forms) in wine.
8. Density: This attribute represents the density of the wine, which is usually correlated with the alcohol content.
9. pH: It quantifies the acidity level of the wine on a scale from 0 to 14, with lower values indicating higher acidity.
10. Sulphates: This attribute measures the amount of sulfur compounds in wine, which can contribute to its overall flavor and aroma.
11. Alcohol: It indicates the alcohol content of the wine by volume.
12. Quality: This attribute represents the sensory quality of the wine, typically rated on a scale from 0 to 10.