Given a set of features as inputs, the task here is to predict the quality of wine on a scale of 0 - 10. I have solved it as a regression problem using Linear Regression.
The dataset used here is Wine Quality Data set from UCI Machine Learning Repository. I have attached the csv file needed for the regression task - winequality-red.csv The same can also be found here https://archive.ics.uci.edu/ml/datasets/Wine+Quality
Input variables (based on physicochemical tests):
- fixed acidity
- volatile acidity
- citric acid
- residual sugar
- chlorides
- free sulfur dioxide
- total sulfur dioxide
- density
- pH
- sulphates
- alcohol
Output variable (based on sensory data): quality (score between 0 and 10)
- Python
- Pandas
- matplotlib
- numpy
- scikit-learn
- Clone the repository
- pip install -r reqirements.txt
- Run the main.py to see the results.
Correlation between different attributes
RMSE
Coefficient values
