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 ML Regression algorithms.
You can follow these steps to reproduce the same output:
- Clone the repository
- The repo contains the IPython Notebook for prediction task and the dataset as csv file.
- Run the ipynb to see the results.
- Python
- Pandas
- matplotlib
- numpy
- scikit-learn
The dataset used here is Wine Quality Data set from UCI Machine Learning Repository. The csv file needed "winequality-red.csv" is attached in the repository. 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)