https://github.com/blarc/lol-dodge-predictor
This repository contains implementation of the related article that was written as a part of Machine learning course at the Faculty of Computer and Information Science at the University of Ljubljana. In the article I describe how decision trees can be used to predict result of League of Legends game by using statistic data of previous games.
https://github.com/blarc/lol-dodge-predictor
decisiontrees leagueoflegends machinelearning machinelearning-python prediction
Last synced: 8 months ago
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This repository contains implementation of the related article that was written as a part of Machine learning course at the Faculty of Computer and Information Science at the University of Ljubljana. In the article I describe how decision trees can be used to predict result of League of Legends game by using statistic data of previous games.
- Host: GitHub
- URL: https://github.com/blarc/lol-dodge-predictor
- Owner: Blarc
- License: mit
- Created: 2020-11-10T11:04:20.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2021-01-04T14:21:38.000Z (over 5 years ago)
- Last Synced: 2025-10-08T12:02:32.791Z (8 months ago)
- Topics: decisiontrees, leagueoflegends, machinelearning, machinelearning-python, prediction
- Language: Jupyter Notebook
- Homepage:
- Size: 5.86 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
- License: LICENSE
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README
# Using machine learning for predicting League of Legends match outcome
This repository contains the implementation of related article that was written as a part of Machine learning course at
the Faculty of Computer and Information Science at the University of Ljubljana.
## Repository structure
The repository contains the following folders:
- `article` contains the article
- `classifiers` contains implementations of the solutions for the specified problem
- `figs` contains images of plots
- `old` contains old python programs, that were not used in the final implementations
- `prepare_data.py` python program for processing the raw data
- `time_comparison.ipynb` Jupyter Notebook file for comparing classifiers by their learning time and accuracy
## Data
The data set that was used for buliding and testing the models can be accessed [here](https://www.kaggle.com/gyejr95/league-of-legendslol-ranked-games-2020-ver1).
## Reproducing results
To run the Python programs and reproduce the results I got, you will have to download the Kaggle data [set](https://www.kaggle.com/gyejr95/league-of-legendslol-ranked-games-2020-ver1) and extract it to folder `\data\raw_data`.
After you successfully forked this repository and downloaded the data set from Kaggle you will have to install python dependencies listed in `requirements.txt`. You can do this by using pip:
```bash
pip install -r requirements.txt
```