Dataset : https://www.kaggle.com/datasets/uciml/red-wine-quality-cortez-et-al-2009
Total No. of entries – 1599 observation
- Fixed.acidity : are non-volatile acids that do not evaporate readily
- Volatile.acidity : are high acetic acid in wine which leads to an unpleasant vinegar taste
- Citric.acid : acts as a preservative to increase acidity (small quantities add freshness and flavour to wines.
- Residual.sugar : is the amount of sugar remaining after fermentation stops. The key is to have a perfect balance between — sweetness and sourness (wines > 45g/litres are sweet)
- Chlorides : the amount of salt in the wine
- Free.sulfur.dioxide : it prevents microbial growth and the oxidation of wine
- Total.sulfur.dioxide : is the amount of free + bound forms of SO2
- PH : the level of acidity
- Sulphates: a wine additive that contributes to SO2 levels and acts as an antimicrobial and antioxidant
- Alcohol: the amount of alcohol in wine
- Quality: the numeric label indicating quality of the wine sample ranging from 3 to 8.
- Density: sweeter wines have a higher density
- Random Forest Classifier
- SVM
In this project we have successfully implemented Wine Quality Prediction using Random Forest Classifier and Support Vector Machine Algorithm. Random forest classifier and the support vector machine predict the quality of wine for a record sample and determine which factor contributes maximum to the prediction respectively. One of the biggest advantages of random forest is its versatility. It can be used for both regression and classification tasks, and it's also easy to view the relative importance it assigns to the input features.Support vector machine works comparably well when there is an understandable margin of dissociation between classes. It is more productive in high dimensional spaces. It is effective in instances where the number of dimensions is larger than the number of specimens.