Assignment 1: Completed #classroom
2970
186
83
3492

## 🕵️ 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.

## 💾 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.

## 📁 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.

## 🚀 Submission

• Submit a python file named as `submission.py`.
• Sample submission is available as `submission.py` in the `resources` section.
• Make sure it contains a class called `Submission` containing a method called `predict` which generates a `submission.csv` containing the predictions for the test set.

Make your first submission here 🚀 !!

## 🖊 Evaluation Criteria

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

$F1 = 2 * \frac{precision*recall}{precision+recall}$

## 📚 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