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WINEQ

Predict Wine Quality

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πŸ›  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

πŸ•΅οΈ Introduction

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.πŸ˜„

πŸ’Ύ Dataset

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.

πŸ“ Files

Following files are available in the resources section:

  • train.csv - (3917 samples) This csv file contains the attributes describing the wine along with the wine quality.

  • test.csv - (980 samples) File that will be used for actual evaluation for the leaderboard score but does not have the value denoting the wine quality.

πŸš€ Submission

  • Prepare a csv containing header as quality and predicted value as ratings of the wine quality from [0-10].
  • The file should be named as submission.csv.
  • Sample submission format available at sample_submission.csv.

Make your first submission here πŸš€ !!

πŸ–Š Evaluation Criteria

During evaluation F1 score will be used to test the efficiency of the model where,

πŸ”— Links

πŸ“± Contact

πŸ“š References

  • 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

Participants

Getting Started

Leaderboard

01 andreidore 0.677
02 adithyasunil26 0.564
03 ashivani 0.563
03 jaggbow 0.563
03 darthgera123 0.563

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