May 2020: Completed #educational Weight: 10.0

# PKHND

Hidden

Poker Hand Recognition

1691
30
30
320

🛠 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 this challenge here.

## 🕵️ Introduction

Lousy at poker faces? To all the wannabe players who failed at poker, we bring you redemption! Just let your model play your hand and you will master an old foe. We will put `5 cards` on your table. `Predict` the `Hand` it makes !!

Understand with code! Here is getting started code for you.😄

## 💾 Dataset

Each record is an example of a hand consisting of `5` playing cards drawn from a standard deck of `52`. Each card is described using `2` attributes its `suit` and its `rank`, this gives a `10` attribute representation of a hand. Corresponding to each hand we also have `1` class attribute that describes the outcome of that `hand`. More details about attributes and types of hands can be found here.

## 📁 Files

The following files can be found in `resources` section.

• `train.csv` - (`1,000,000` samples) File that should be used for training. It contains the feature representation and respective outcomes of different hands.
• `test.csv` - (`25010` samples) File that will be used for testing. Unlike the training file it contains only the feature representation of hands and not their outcomes.

## 🚀 Submission

• Prepare a CSV containing header as `label` and predicted value as digit between `[0...9]` representing one of the `10` possible hands.
• Sample submission format is available in the resources section of the challenge page as sample_submission.csv.

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

• Dataset Created by - Robert Cattral (cattral@gmail.com), Franz Oppacher (oppacher@scs.carleton.ca) Carleton University, Department of Computer Science

• Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.