This is a Classroom Challenge forked from WINEQ.
🛠 Contribute : Found a typo? Or any other change in the description that you would like to see? Please consider sending us a pull request in the public repo of the challenge here
Wine a little, laugh a lot and enjoy this challenge. The secret to a quality of wine is not only in it's smell ! let’s take a trip to north portugal .
We give you features of white vinho verde wine, you have to
predict the quality of the wine.
Understand with code! Here is
getting started code for you.
The dataset is related to white vinho verde wine of the Portugal. For more details, consult: Web Link or the reference [Cortez et al., 2009]. Due to privacy and logistic issues, only physicochemical (inputs) and sensory (the output) variables are available (e.g. there is no data about grape types, wine brand, wine selling price, etc.) but we provide you with all the attribute related information here.
For simplification, attributes have been stored in csv file which has
12 columns, the
last column is the
quality rating and the rest
11 contain the features of the wine sample.
Following files are available in the
3917samples) This csv file contains the attributes describing the wine along with the wine quality.
980samples) File that will be used for actual evaluation for the leaderboard score but does not have the value denoting the wine quality.
- Prepare a csv containing header as
qualityand predicted value as ratings of the wine quality from
- The file should be named as
- Sample submission format available at
Make your first submission here 🚀 !!
🖊 Evaluation Criteria
During evaluation F1 score will be used to test the efficiency of the model where,
- 💪 Challenge Page: https://www.aicrowd.com/challenges/wineq
- 🗣️ Discussion Forum: https://www.aicrowd.com/challenges/wineq/discussion
- 🏆 Leaderboard: https://www.aicrowd.com/challenges/wineq/leaderboards
- P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis.
- Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009.
- Image Source