# FeatureBridge
[](https://github.com/iNetanel/featurebridge)
[](https://opensource.org/licenses/MIT)
"Author Website": "https://inetanel.com",
"Documentation": "https://github.com/iNetanel/featurebridge/wiki",
"Source Code": "https://github.com/iNetanel/featurebridge",
"Issue Tracker": "https://github.com/iNetanel/featurebridge/issues",
- **Project Name:** FeatureBridge
- **Author:** Netanel Eliav
- **Author Website:** https://inetanel.com
- **Author Email:** inetanel@me.com
- **License:** MIT License
- **Documentation:** https://github.com/iNetanel/featurebridge/wiki
- **Issue Tracker:** https://github.com/iNetanel/featurebridge/issues
## Overview
FeatureBridge is a revolutionary adaptive modeling for machine learning applications, particularly in the realm of Artificial Intelligence. It tackles a common challenge in AI projects: handling missing features in real-world scenarios. Machine learning models are often trained on specific features, but when deployed, users may not have access to all those features for predictions. FeatureBridge bridges this gap by enabling models to intelligently predict and fill in missing features, similar to how humans handle incomplete data. This ensures that AI models can seamlessly manage missing data and features while providing accurate predictions.
### Key Features
- **Missing Feature Prediction:** FeatureBridge empowers AI models to predict and fill in missing features based on the available data.
- **Feature Selection:** You can select relevant features using configurable thresholds for importance, correlation, and accuracy.
- **Adaptive Modeling:** Utilize machine learning models to predict missing features, maintaining high prediction accuracy even with incomplete data.
- **Custom Accuracy Logic:** Define your own accuracy calculation logic to fine-tune feature selection.
- **Feature Distribution Handling:** Automatically determine the best method for handling feature distribution based on data characteristics.
- **Dependency Management:** Identify mandatory, deviation, and leveled features to optimize AI model performance.
## Usage
With FeatureBridge, integrating this powerful tool into your AI and machine learning pipelines is easy. Fit the class to your data, and let it handle missing features intelligently. Detailed comments and comprehensive documentation are provided for straightforward implementation.
## Getting Started
Follow these steps to get started with FeatureBridge:
1. Clone this repository:
```shell
git clone https://github.com/iNetanel/featurebridge.git
pip install -r requirements.txt
#OR
pip install featurebridge
## Dependencies
- NumPy
- pandas
- matplotlib
## Contribution
Contributions and feedback are highly encouraged. You can open issues, submit pull requests for enhancements or bug fixes, and be part of the AI community that advances FeatureBridge.
## License
This project is licensed under the MIT License. See the LICENSE file for details.
## Disclaimer
This code is provided as-is, without any warranties or guarantees. Please use it responsibly and review the documentation for usage instructions and best practices.
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