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⚔️ One Piece Match Predictor

A machine learning-powered web application that predicts fight outcomes between One Piece characters using their combat statistics and abilities.

One Piece Banner

🌟 Features

  • ⚔️ Fight Prediction: Predict outcomes between any 2 One Piece characters in the top 100 One Piece Popularity Poll in 2021
  • 🤖 ML-Powered: Uses Support Vector Machine with 96.26% accuracy
  • 📊 Character Database: 100+ One Piece characters with detailed combat stats
  • 🎨 Interactive UI: Streamlit web interface
  • 🚀 Real-time API: FastAPI backend deployed on Railway
  • 📱 Responsive Design: Works on desktop and mobile devices

🎯 Live Demo

🏗️ Architecture

┌─────────────────┐    HTTP    ┌──────────────────┐    ML Model    ┌─────────────────┐
│   Streamlit     │ ◄────────► │   FastAPI        │ ◄─────────────► │   SVM Model     │
│   Frontend      │            │   Backend        │                │   + Scaler      │
│   (UI/UX)       │            │   (API)          │                │   + Encoder     │
└─────────────────┘            └──────────────────┘                └─────────────────┘
      │                               │                                     │
      │                               │                                     │
   Streamlit                       Railway                              Model Files
   Cloud                          Deployment                           (.pkl format)

🛠️ Tech Stack

Frontend

  • Streamlit - Interactive web interface
  • Pandas - Data manipulation
  • Requests - API communication
  • PIL - Image processing

Backend

  • FastAPI - Modern Python web framework
  • Uvicorn - Asynchronous Server Gateway Interface server
  • Pydantic - Data validation
  • CORS Middleware - Cross-origin requests

Machine Learning

  • Scikit-learn - SVM model and preprocessing
  • NumPy - Numerical computations
  • Pandas - Data analysis
  • Joblib - Model serialization

Deployment

  • Railway - Backend API hosting
  • Streamlit Cloud - Frontend hosting
  • GitHub - Version control and CI/CD

🚀 Quick Start

Option 1: Use the Live App (Recommended)

Simply visit the live web app and start predicting fights!

Option 2: Local Development

Prerequisites

  • Python 3.8+
  • Git

Installation

  1. Clone the repository

    git clone https://github.com/yourusername/one-piece-match-predictors.git
cd one-piece-match-predictors

  2. Create a virtual environment

    python -m venv venv

# Windows
venv\Scripts\activate

# macOS/Linux
source venv/bin/activate

  3. Install dependencies

    # Full development environment
pip install -r requirements.txt

Running Locally

  1. Start the FastAPI backend

    python -m uvicorn src.api.main:app --reload --port 8000

  2. Start the Streamlit frontend (in a new terminal)

    streamlit run src/frontend/streamlit_app.py

  3. Open your browser

📁 Project Structure

one-piece-match-predictors/
├── 📁 src/
│   ├── 📁 api/
│   │   └── 📄 main.py              # FastAPI application
│   ├── 📁 frontend/
│   │   └── 📄 streamlit_app.py     # Streamlit web interface
│   ├── 📁 models/
│   │   ├── 📄 svm_fight_predictor.pkl    # Trained ML model
│   │   ├── 📄 feature_scaler.pkl         # Feature scaler
│   │   └── 📄 label_encoder.pkl          # Label encoder
│   ├── 📁 data/
│   │   └── 📁 processed/
│   │       ├── 📄 character_data_cleaned.csv # Character database
│   │       └── 📄 fight_data_cleaned.csv     # Simulated fights database
│   └── 📁 scraping/
│       └── 📄 scraper.py           # Data collection scripts
├── 📄 requirements.txt             # Full dependencies
├── 📄 railway.toml                 # Railway deployment config
├── 📄 .dockerignore               # Docker ignore patterns
├── 📄 .gitignore                  # Git ignore patterns
└── 📄 README.md                   # This file

🤖 Machine Learning Model

Model Details

  • Algorithm: Support Vector Machine (SVM)
  • Accuracy: 96.26% on test data
  • Features: 12 engineered features
  • Classes: Victory, Loss, Draw

Feature Engineering

The model uses difference-based features between fighters:

  • Base Stats Differences (10 features):
    • Reaction Speed, Stamina, Strength, Offense, Defense
    • Combat Skills, Battle IQ, Armament Haki, Observation Haki, Experience
  • Conqueror's Haki Features (2 features):
    • Presence indicator and difference impact

Training Data

  • 100+ One Piece characters with detailed combat statistics
  • Dataset across victory/loss/draw outcomes
  • Feature scaling using StandardScaler
  • Label encoding for categorical outcomes

🔗 API Documentation

Endpoints

Health Check

GET /health

Returns API status and model availability.

Fight Prediction

POST /predict

Predicts fight outcome between two characters.

Request Body:

{
  "fighter_1": {
    "reaction_speed": 85,
    "stamina": 90,
    "strength": 95,
    "offense": 88,
    "defense": 82,
    "combat_skills": 92,
    "battle_iq": 85,
    "armament_haki": 80,
    "observation_haki": 75,
    "conqueror_haki": 90,
    "experience": 88
  },
  "fighter_2": {
    "reaction_speed": 78,
    "stamina": 85,
    "strength": 82,
    "offense": 80,
    "defense": 88,
    "combat_skills": 85,
    "battle_iq": 90,
    "armament_haki": 85,
    "observation_haki": 88,
    "conqueror_haki": 0,
    "experience": 85
  }
}

Response:

{
  "prediction": "victory",
  "confidence": 0.87,
  "probabilities": {
    "victory": 0.87,
    "loss": 0.10,
    "draw": 0.03
  },
  "fighter_1_advantage": {
    "reaction_speed": 7,
    "stamina": 5,
    "strength": 13,
    ...
  },
  "summary": "Fighter 1 wins with 87.0% confidence"
}

Model Information

GET /model/info

Returns model metadata and performance metrics.

Example Request

GET /example

Returns example fighter stats for testing.

🚀 Deployment

Backend (Railway)

The FastAPI backend is automatically deployed to Railway on every push to the main branch.

Environment Variables:

  • PORT: Automatically set by Railway
  • NIXPACKS_PYTHON_VERSION: Set to 3.11

Frontend (Streamlit Cloud)

The Streamlit frontend is deployed on Streamlit Cloud and automatically updates from GitHub.

CI/CD Pipeline

GitHub Push → Railway Build → Health Check → Live Deployment ✅
            → Streamlit Cloud Build → Live Frontend ✅

📊 Character Database

The application includes 100+ One Piece characters with stats including:

  • Physical Stats: Reaction Speed, Stamina, Strength
  • Combat Stats: Offense, Defense, Combat Skills, Battle IQ
  • Haki Abilities: Armament, Observation, Conqueror's Haki
  • Experience: Overall battle experience

Popular characters include:

  • Monkey D. Luffy, Roronoa Zoro, Sanji
  • Kaido, Big Mom, Whitebeard
  • Admiral Akainu, Kizaru, Aokiji
  • And many more!

🤝 Contributing

Contributions are welcome! Here's how to get started:

  1. Fork the repository
  2. Create a feature branch 
    git checkout -b feature/amazing-feature
  3. Make your changes
  4. Add tests (if applicable)
  5. Commit your changes 
    git commit -m "Add amazing feature"
  6. Push to your branch 
    git push origin feature/amazing-feature
  7. Open a Pull Request

Areas for Contribution

  • 🎨 UI/UX improvements
  • 📊 Additional character data
  • 🤖 Model enhancements
  • 📝 Documentation

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

  • Eiichiro Oda - Creator of One Piece
  • One Piece Community - For character data and inspiration

🏴‍☠️ Made with ❤️ for the One Piece community! 🏴‍☠️

"I'm gonna be the King of the Pirates!" - Monkey D. Luffy