Red wine quality dataset which has one table with 12 columns and 1599 rows.
In this project, I have used some attributes to predict the quality of the wine. I have used Linear regression and Decision tree regressor to predict the exact quality values,and found out the Root Mean Squared Errors of both the models to know which one works best. Then, I have used Random Forest classifier to predict whether the wine is good, bad or medium, and found out the accuracy of the model.
Data source: Kaggle
Data Name: Red Wine Quality
Data URL: https://www.kaggle.com/uciml/red-wine-quality-cortez-et-al-2009/notebooks
File type: csv file
There are 11 input variables namely,
'fixed acidity' , 'volatile acidity' , 'citric acid' , 'residual sugar' , 'chlorides' , 'free sulfur dioxide' , 'total sulfur dioxide' , 'density' , 'pH' , 'sulphates' , 'alcohol'
There is one output variable for the regression model namely,
'quality'
There is one output variable for the classification model namely, which I have added later on.
All the columns are useful for the prediction model.