https://github.com/estaheri7/dotawinpredictor

This project aims to predict the outcome of Dota 2 matches based on the heroes picked by both the Radiant and Dire teams
https://github.com/estaheri7/dotawinpredictor

data-science dota2 machine-learning numpy pandas python random-forest sklearn support-vector-machines

Last synced: about 1 month ago
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This project aims to predict the outcome of Dota 2 matches based on the heroes picked by both the Radiant and Dire teams

• Host: GitHub
• URL: https://github.com/estaheri7/dotawinpredictor
• Owner: Estaheri7
• Created: 2024-08-18T15:56:53.000Z (3 months ago)
• Default Branch: master
• Last Pushed: 2024-08-21T09:17:36.000Z (3 months ago)
• Last Synced: 2024-10-10T08:06:45.435Z (about 1 month ago)
• Topics: data-science, dota2, machine-learning, numpy, pandas, python, random-forest, sklearn, support-vector-machines
• Language: Jupyter Notebook
• Homepage:
• Size: 376 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Dota 2 Match Outcome Prediction

## Overview

This project aims to predict the outcome of [Dota 2](https://www.dota2.com/home) matches based on the heroes picked by both the Radiant and Dire teams. Using machine learning techniques, we train a model to determine the likelihood of either team winning given the heroes selected during the drafting phase.

## Project Structure

- **Data Collection**: We collect match data, including the heroes picked by both teams and the match outcome, using the [OpenDota API](https://docs.opendota.com).

- **Data Preprocessing**: The hero IDs for both teams are encoded using one-hot encoding to create a feature set for machine learning models.

- **Model Training**: A machine learning model is trained on the preprocessed data to predict the match outcome.

- **Prediction**: The trained model is used to predict the outcome of new matches based on the heroes picked.

## Data Directory

  

- **data/**

  - `matches.csv`: Collected match data including match IDs, hero picks, and match outcomes.

  - `heroes.csv`: List of Dota 2 heroes with their corresponding IDs.

## Notes

- The OpenDota API may return the same match details if queried too frequently. It is recommended to space out API requests to avoid collecting duplicate data.

- The model's performance may vary depending on the amount and quality of data collected. Experimenting with different machine learning algorithms and hyperparameters is encouraged.

## Future Work

- **Feature Engineering**: Explore additional features such as player rankings, hero synergy, and counter-picks to improve model accuracy.

- **Model Optimization**: Experiment with different machine learning algorithms and techniques like cross-validation, hyperparameter tuning, and ensemble methods.

- **Real-time Prediction**: Integrate the model with live match data to provide real-time win probability predictions during ongoing matches.