Metadata-Version: 2.4
Name: vs-score
Version: 0.1.0
Summary: A package for volleyball match outcome prediction and statistical analysis.
Author-email: irem <iremozluoglu@gmail.com>
License: MIT License
        
        Copyright (c) 2024 [irem]
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE. 
Project-URL: Homepage, https://github.com/iremozluoglu/vs-score
Keywords: volleyball,prediction,machine learning,fastapi
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Dynamic: license-file

# vs_score

A Python package for volleyball match outcome prediction and statistical analysis.

## Installation

```bash
pip install .
```

## Usage

All main modules are located in the `src/vs_score/` directory.

### Data Preparation & Feature Engineering
Run the following scripts in order to clean your data and generate features:

```bash
python src/vs_score/merge_clean.py
python src/vs_score/feature_engineering.py
python src/vs_score/train_model.py
```

### Running the FastAPI Backend
To start the prediction API:

```bash
python src/vs_score/predict_api.py
```

Then open your browser and go to [http://localhost:8000](http://localhost:8000) to use the web interface.

## License
MIT
