https://github.com/codedby-mozz/lol-match-outcome-predictor
This repository contains a Jupyter Notebook that demonstrates the development, training, and evaluation of a binary classification model using machine learning techniques.
https://github.com/codedby-mozz/lol-match-outcome-predictor
machine-learning python pytorch
Last synced: 9 months ago
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This repository contains a Jupyter Notebook that demonstrates the development, training, and evaluation of a binary classification model using machine learning techniques.
- Host: GitHub
- URL: https://github.com/codedby-mozz/lol-match-outcome-predictor
- Owner: codedby-mozz
- License: mit
- Created: 2025-04-16T11:43:35.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-05-06T14:19:35.000Z (9 months ago)
- Last Synced: 2025-05-06T15:40:49.164Z (9 months ago)
- Topics: machine-learning, python, pytorch
- Language: Jupyter Notebook
- Homepage:
- Size: 205 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# LoL-match-outcome-predictor: A Binary Classification Model for league_of_legends_data_large.csv
## Overview
This repository contains a Jupyter Notebook that demonstrates the development, training, and evaluation of a binary classification model using machine learning techniques. The model was developed as part of the final project for the online course PyTorch Basics for Machine Learning. The primary objective of this project was to apply the machine learning techniques I learned throughout the course to a real-world dataset, using PyTorch to predict the outcomes of League of Legends (LoL) matches based on historical data.
## Files
- LoL_Match_Outcome_Predictor.ipynb: Jupyter notebook with all code, explanations, and visualizations.
- league_of_legends_data_large.csv: The used dataset
- requirements.txt: List of required libraries and dependencies for running the project.
- LICENSE: Open-source license.
## How to Use
1. Clone the repository:
```bash
git clone https://github.com/codedby-mozz/LoL-match-predictor.git
```
2. Install dependencies
```bash
pip install -r requirements.txt
```
or run the setup code in the Jupyter notebook.
3. Open the notebook and run the cells to see the results, modify the model, and tune parameters as needed.
The notebook includes detailed explanations and visualizations of the model's performance, including:
- Confusion Matrix
- ROC Curve
- Classification Report
## Requirements
- Python 3.x
- torch
- sklearn
- matplotlib
- pandas
- numpy
## License
[MIT](https://choosealicense.com/licenses/mit/)