# 🦈 SharkPy
A friendly machine learning framework with shark-themed feedback! SharkPy simplifies the machine learning workflow while making it fun and educational.
## Features
- **Model Battle Arena**: Compare multiple models automatically
- **Smart Reporting**: Generate reports in TXT, PDF, and DOCX formats
- **Interactive Visualization**: Beautiful plots with shark-themed styling
- **Model Explanations**: Clear explanations of model behavior
- **Automated Optimization**: Hyperparameter tuning with Optuna
- **Shapash Integration**: Interactive dashboards for model interpretation
## Quick Start
### Installation
```bash
# Basic installation
pip install sharkpy
# Full installation (includes LightGBM and CatBoost)
pip install sharkpy[full]
# Development installation
pip install sharkpy[dev]
```
### Basic Usage
```python
from sharkpy import Shark
import pandas as pd
# Create a Shark instance
shark = Shark()
# Load your data
data = pd.read_csv('your_data.csv')
# Train a model
shark.learn(
data=data,
target='target_column',
model_choice='random_forest'
)
# Make predictions
predictions = shark.predict(new_data)
# Generate reports
shark.report(export_path='report.pdf', format='pdf')
```
### Model Battle Example
```python
# Define models to compete
models = [
'linear_regression',
'random_forest',
'xgboost',
'lightgbm',
'catboost'
]
# Let them battle!
battle_results = shark.battle(
data=data,
target='target_column',
models=models,
metric='r2'
)
# Get the champion
print(f"Winner: {battle_results['champion']}")
print(f"Score: {battle_results['score']:.4f}")
```
## Supported Models
- Linear Regression
- Logistic Regression
- Random Forest
- Support Vector Machines
- Ridge Regression
- Lasso Regression
- K-Nearest Neighbors
- XGBoost
- LightGBM (with full installation)
- CatBoost (with full installation)
## Reports
SharkPy can generate comprehensive reports in multiple formats:
```python
# Text report
shark.report(export_path='report.txt', format='txt')
# PDF report (requires MS Word or LibreOffice)
shark.report(export_path='report.pdf', format='pdf')
# Word document
shark.report(export_path='report.docx', format='docx')
```
## Visualizations
```python
# Prediction plot
shark.plot(kind="prediction")
# Feature importance
shark.plot(kind="feature_importance")
# Interactive Shapash dashboard
shark.explain_with_shapash()
```
## Model Explanations
```python
# Get friendly explanations of your model
shark.explain()
# Create interactive Shapash dashboard
shark.explain_with_shapash(title_story="My Analysis")
```
## Save & Load Models
```python
# Save your model
shark.save_model(name="my_model")
# Load a saved model
shark.load_model("models/my_model.joblib")
```
## Development
```bash
# Install development dependencies
pip install sharkpy[dev]
# Run tests
pytest
# Format code
black sharkpy/
```
## Documentation
Full documentation is available at [sharkpy.readthedocs.io](https://sharkpy.readthedocs.io/)
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Acknowledgments
- Built with scikit-learn, XGBoost, and other amazing open-source tools
- Inspired by making machine learning more accessible and fun
## Contact
- Author: Ezz Eldin Ahmed
- Email: ezzeldinahmad96@gmail.com
- GitHub: [Ezzio11](https://github.com/Ezzio11)
---
Made with 🦈 by SharkPy Team
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