ziedbha / ml-game-balance

Using ML to understand game balance in competitive eSports

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Authors: Zied Ben-Hadj Alouane, William Corse, Ayya Elzarka, Sadat Shaik

Structure
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This directory three items: The README.txt, the Classifiers Folder, and the Data Cleaning folder. In the Classifiers folder contains all of our classifier models (SVM, Neural Network, Random Forest, Logistic Regression), and in Data Cleaning it contains both notebooks that handle the preprocessing for the Casual and the Professional Datasets.

HOW TO USE
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The general process for using this submission is as follows:

1. Download both the professional and casual datasets from the following links:
Casual: https://www.kaggle.com/paololol/league-of-legends-ranked-matches
Professional: https://www.kaggle.com/chuckephron/leagueoflegends
If you do not want to download from these websites, we've also included it in the code submission folder.
2. Run both "Casual Data Cleaning" and "Professional Data Cleaning" notebooks to generate the train
and test files for both datasets (instructions on how to do so are detailed in the individual notebooks).
3. Place the files on google drive (so they can be accessed). Alternatively, upload them to google colab individually, and if you would like the path for any of these files you can right click "Copy Path" in
Google Colab.
4. Follow the instructions for SVM, Neural Networks, Logistic Regression, and Random Forests to run
the respective models.

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Using ML to understand game balance in competitive eSports