# Density Aware SMOTE
A Python package implementing **Density Aware SMOTE**, developed in the [**Improved-Oversampling-Density-Aware-Smote** repository](https://github.com/nbeeeel/Improved-Oversampling-Density-Aware-Smote).
This method enhances SMOTE by generating synthetic samples based on **local data density**, reducing oversampling in sparse regions and preserving valuable patterns in dense areas.
---
## Features
- Density-aware oversampling tuned for local distribution
- Flexible sampling strategy: `auto`, ratio float, or custom class counts
- Custom neighbor selection: `random`, `nearest`, `farthest`
- Seamless integration with scikit-learn pipelines
- Built-in visuals: class distribution, synthetic samples, decision boundaries
---
## Installation
```bash
pip install density-aware-smote
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"description": "# Density Aware SMOTE\r\n\r\n\r\n\r\n\r\n\r\nA Python package implementing **Density Aware SMOTE**, developed in the [**Improved-Oversampling-Density-Aware-Smote** repository](https://github.com/nbeeeel/Improved-Oversampling-Density-Aware-Smote). \r\nThis method enhances SMOTE by generating synthetic samples based on **local data density**, reducing oversampling in sparse regions and preserving valuable patterns in dense areas.\r\n\r\n---\r\n\r\n## Features\r\n- Density-aware oversampling tuned for local distribution \r\n- Flexible sampling strategy: `auto`, ratio float, or custom class counts \r\n- Custom neighbor selection: `random`, `nearest`, `farthest` \r\n- Seamless integration with scikit-learn pipelines \r\n- Built-in visuals: class distribution, synthetic samples, decision boundaries \r\n\r\n---\r\n\r\n## Installation\r\n\r\n```bash\r\npip install density-aware-smote\r\n",
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