Kaggle PUBG Finish Placement Prediction

Competition website: https://www.kaggle.com/c/pubg-finish-placement-prediction

Contents

• Kaggle PUBG Finish Placement Prediction
  • Contents
  • Introduction
    • Competition Description
    • Competition Evaluation
  • Installation
  • Usage
    • Jupyter Notebook
    • Google Colab
    • Kaggle Kernels
  • Exploratory Data Analysis
  • Model Deployment
  • Contributing
  • License

Introduction

My second competition, a solo one. :grinning:

Competition Description

Details

So, where we droppin' boys and girls?

Battle Royale-style video games have taken the world by storm. 100 players are dropped onto an island empty-handed and must explore, scavenge, and eliminate other players until only one is left standing, all while the play zone continues to shrink.

PlayerUnknown's BattleGrounds (PUBG) has enjoyed massive popularity. With over 50 million copies sold, it's the fifth best selling game of all time, and has millions of active monthly players.

The team at PUBG has made official game data available for the public to explore and scavenge outside of "The Blue Circle." This competition is not an official or affiliated PUBG site - Kaggle collected data made possible through the PUBG Developer API.

You are given over 65,000 games' worth of anonymized player data, split into training and testing sets, and asked to predict final placement from final in-game stats and initial player ratings.

What's the best strategy to win in PUBG? Should you sit in one spot and hide your way into victory, or do you need to be the top shot? Let's let the data do the talking!

from competition website...

Competition Evaluation

Details

Submissions are evaluated on Mean Absolute Error between your predicted winPlacePerc and the observed winPlacePerc.

Submission File For each Id in the test set, you must predict their placement as a percentage (0 for last, 1 for first place) for the winPlacePerc variable. The file should contain a header and have the following format:

Id,winPlacePerc
47734,0
47735,0.5
47736,0
47737,1
etc.

See sample_submission.csv on the data page for a full sample submission.

from competition website...

Installation

• Anaconda

Usage

Few ways to run these notebooks interactively:

Jupyter Notebook

Details

1. Download this repository in a zip file by clicking here or execute this from the terminal:

git clone https://github.com/y33-j3T/Kaggle-PUBG-Finish-Placement-Prediction.git 

2. Download the datasets at competition website - data.
3. Put both downloads in the same directory and unzip them.
4. For cleanliness, create a new folder input and put the datasets (with .csv extensions) into it.
5. Open Anaconda Prompt.
6. Navigate to the repository folder with

cd [path address]

6. Create a virtual environment with

conda create -n [environment name] python=3.7

7. Activate the environment with

activate [environment name]

8. Install the required dependencies with

pip install -r requirements.txt

9. Execute Jupyter Notebook from the command line or terminal with

jupyter notebook

10. Click on files with .ipynb extension on the Jupyter Notebook dasboard and enjoy!
11. When you're done, deactivate the virtual environment with

deactivate

Google Colab

Details

1. Download the datasets at competition website - data.
2. Put all downloads in the same directory and unzip them.
3. For cleanliness, create a new folder input and put the datasets (with .csv extensions) into it.
4. Click on any model you want work with at the Model Deployment section.
5. Copy the link of the page and paste it into the search bar of a new page.
6. Replace https://github.com with colab.research.google.com/github and hit enter.
7. Go to the sidebar, click on Files > Upload.
8. Upload all contents in the input folder.
9. You may now run the notebook.
10. Once you are done, remember to save the notebook somewhere.

Kaggle Kernels

Details

1. Download this repository in a zip file by clicking here or execute this from the terminal:

git clone https://github.com/y33-j3T/Kaggle-PUBG-Finish-Placement-Prediction.git 

2. Unzip your download.
3. Go to competition website - kernels.
4. Click on New Kernel.
5. Select Notebook.
6. Click on File > Upload Notebook.
7. Upload any file with .ipynb extension that you want to work with from your downloaded repository and enjoy!
8. Once you are done, remember to click on Commit.

Note: Depending on how datasets are arranged in your directory, paths contained inside read_csv() might need to be changed accordingly.

Exploratory Data Analysis

• work in progress

Model Deployment

• Random Forest model
• XGBoost model
• Ensemble model

Contributing

Please refer to CONTRIBUTE.md for details. 😍

License

Kaggle PUBG Finish Placement Prediction is released under the MIT License.