Metadata-Version: 2.4
Name: automl-framework
Version: 0.1.1
Summary: A comprehensive, modular framework for automated machine learning with overfitting detection and mitigation
Home-page: https://github.com/nandarizkika/automl-framework
Author: Nanda Rizkika Ruanawijaya
Author-email: Nanda Rizkika Ruanawijayae <nandarizky52@gmail.com>
Maintainer-email: Nanda Rizkika Ruanawijayae <nandarizky52@gmail.com>
License: MIT License
        
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Project-URL: Homepage, https://github.com/nandarizkika/automl-framework
Project-URL: Documentation, https://github.com/nandarizkika/automl-framework/docs
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Project-URL: Changelog, https://github.com/nandarizkika/automl-framework/blob/main/CHANGELOG.md
Keywords: machine learning,automl,automated machine learning,overfitting,hyperparameter tuning,model selection,scikit-learn
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
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Description-Content-Type: text/markdown
License-File: LICENSE
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# AutoML Framework: Intelligent Machine Learning Automation 🤖🧠

[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![PyPI version](https://img.shields.io/badge/pypi-v0.1.0-blue)](https://pypi.org/project/automl-framework/)
[![Coverage](https://img.shields.io/codecov/c/github/nandarizkika/automl-framework)](https://codecov.io/gh/nandarizkika/automl-framework)

## 🌟 Project Overview

AutoML Framework is a cutting-edge, comprehensive machine learning library designed to simplify and automate the entire machine learning workflow. Our mission is to democratize machine learning by providing an intelligent, easy-to-use solution that handles complex model selection, optimization, and evaluation.

### 🚀 Key Differentiators

- **Intelligent Model Selection**: Automatically tries and evaluates multiple algorithms
- **Advanced Overfitting Detection**: Sophisticated techniques to prevent model overfitting
- **Comprehensive Hyperparameter Optimization**: Finds optimal model configurations
- **Detailed Performance Reporting**: In-depth insights into model performance

## 📦 Installation

### Quick Install
```bash
# Install stable version
pip install automl-framework

# Install latest development version
pip install git+https://github.com/nandarizkika/automl-framework.git
```

### Installation Options
```bash
# Install with all optional dependencies
pip install automl-framework[all]

# For specific use cases
pip install automl-framework[visualization]  # Visualization tools
pip install automl-framework[tuning]         # Advanced hyperparameter tuning
```

## 🧠 Quick Start Examples

### Classification Example
```python
from automl import AutoML
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# Load dataset
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Initialize AutoML
automl = AutoML(problem_type='classification')

# Train and evaluate models
automl.fit(X_train, y_train)
results = automl.evaluate(X_test, y_test)

# Get best model and predictions
best_model = automl.predict(X_test)
leaderboard = automl.get_leaderboard()
print(leaderboard)
```

### Regression Example
```python
from automl import AutoML
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split

# Load dataset
X, y = load_boston(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Initialize AutoML for regression
automl = AutoML(problem_type='regression')
automl.fit(X_train, y_train)
results = automl.evaluate(X_test, y_test)
```

## 🔧 Advanced Features

### Overfitting Detection
```python
# Enable advanced overfitting control
automl.pipeline.set_overfitting_control(
    detection_enabled=True,
    auto_mitigation=True,
    threshold=0.3
)

# Get overfitting assessment
assessment = automl.get_overfitting_assessment()
suggestions = automl.get_improvement_suggestions()
```

### Hyperparameter Tuning
```python
from automl import TuningIntegrator

# Create tuning integrator
tuner = TuningIntegrator(automl)

# Advanced model tuning
summary = tuner.tune_models(
    X_train, y_train,
    search_type='bayesian',
    n_iter=50,
    register_best=True
)
```

## 🌈 Key Features

### 1. Automated Machine Learning
- Automatic algorithm selection
- Intelligent model ranking
- Performance optimization

### 2. Overfitting Management
- Multi-metric overfitting detection
- Automatic and manual mitigation strategies
- Comprehensive model fit assessment

### 3. Hyperparameter Optimization
- Grid Search
- Random Search
- Bayesian Optimization
- Hyperopt Integration

### 4. Comprehensive Reporting
- Detailed performance leaderboards
- Feature importance analysis
- Training logs and insights

## 🧪 Supported Models

### Classification
- Random Forest
- Logistic Regression
- Gradient Boosting
- Support Vector Machines
- Decision Trees
- K-Nearest Neighbors
- Neural Networks

### Regression
- Linear Regression
- Ridge Regression
- Random Forest Regressor
- Gradient Boosting Regressor
- Support Vector Regression

## 📊 Performance Benchmarks

| Dataset | Models | Best Accuracy | Training Time | Overfitting Detected |
|---------|--------|--------------|---------------|----------------------|
| Iris | 5 | 97.8% | 2.3s | None |
| Wine | 7 | 94.4% | 4.1s | 1 model |
| Breast Cancer | 8 | 96.5% | 8.7s | 2 models |

## 🛣️ Roadmap

### v0.2.0
- [ ] Deep Learning Integration
- [ ] Time Series Support
- [ ] Advanced Feature Engineering
- [ ] Enhanced Visualization

### v0.3.0
- [ ] Distributed Training
- [ ] Model Deployment Tools
- [ ] Advanced NLP Support

## 🤝 Contributing

We welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for details.

### Ways to Contribute
- 🐛 Report Bugs
- 💡 Suggest Features
- 📝 Improve Documentation
- 🔧 Submit Pull Requests

## 📄 License

MIT License - See [LICENSE](LICENSE) for details

## 📞 Support

- 📧 Email: nandarizky52@gmail.com
- 🐞 Issues: [GitHub Issues](https://github.com/nandarizkika/automl-framework/issues)
- 💬 Discussions: [GitHub Discussions](https://github.com/nandarizkika/automl-framework/discussions)

## 🌟 Star History

[![Star History Chart](https://api.star-history.com/svg?repos=nandarizkika/automl-framework&type=Date)](https://star-history.com/#nandarizkika/automl-framework&Date)

---

<div align="center">
🚀 Empowering Machine Learning for Everyone 🚀

**AutoML Framework: Where Intelligence Meets Automation**
</div>
