Metadata-Version: 2.1
Name: eazyml-segmentation
Version: 0.0.9
Summary: EazyML provides a suite of APIs for identifying the Urbanicity of zip codes based on population density
Home-page: https://eazyml.com/
Author: EazyML
Author-email: admin@ipsoftlabs.com
Project-URL: Documentation, https://docs.eazyml.com/
Project-URL: Homepage, https://eazyml.com/
Project-URL: Contact Us, https://eazyml.com/trust-in-ai
Project-URL: eazyml-automl, https://pypi.org/project/eazyml-automl/
Project-URL: eazyml-counterfactual, https://pypi.org/project/eazyml-counterfactual/
Project-URL: eazyml-xai, https://pypi.org/project/eazyml-xai/
Project-URL: eazyml-xai-image, https://pypi.org/project/eazyml-xai-image/
Project-URL: eazyml-insight, https://pypi.org/project/eazyml-insight/
Project-URL: eazyml-data-quality, https://pypi.org/project/eazyml-data-quality/
Keywords: auto-ml,automl,machine-learning,model-training,hyperparameter-tuning,feature-selection,cross-validation,confidence-score,ml-api,model-evaluation
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: Other/Proprietary License
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: System Administrators
Classifier: Intended Audience :: Information Technology
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: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
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## EazyML Responsible-AI: Data Quality Assessment
![Python](https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10%20%7C%203.11%20%7C%203.12-blue)  ![PyPI package](https://img.shields.io/badge/pypi%20package-0.0.43-brightgreen) ![Code Style](https://img.shields.io/badge/code%20style-black-black)

![EazyML](https://github.com/EazyML/eazyml-docs/raw/refs/heads/master/EazyML_logo.png)

## Overview
`eazyml-segmentation` is a python utility designed to segment number of zip codes into their Urbanicity as Rural, Urban and Semi-Urban

## Installation
To use the Segmentation , ensure you have Python installed on your system.
### User installation
The easiest way to install data quality is using pip:
```bash
pip install -U eazyml-segmentation
```
### Dependencies
This package requires:
- pandas
- numpy
- cryptography

## Usage
Here's an example of how you can use the APIs from this package.

#### Imports
```python
from eazyml_segmentation import ez_segmentation
```

#### Initialize and Read Data
```
# Initialize the EazyML automl library.
_ = ez_init()

# Define ZIP data (Replace with the correct data path).
zip_data_path = "path_to_your_zip_data.csv"

# Define Thresholds.
thresholds = [1.8,2.2]

```

#### Perform Segmentation
```

# Call the EazyML APIs to perform segmentation 
seg_response = ez_segmentation(zip_data_path, thresholds)

```
You can find more information in the [documentation](https://eazyml.readthedocs.io/en/latest/packages/eazyml_dq.html).


## Useful links, other packages from EazyML family
- [Documentation](https://docs.eazyml.com)
- [Homepage](https://eazyml.com)
- If you have questions or would like to discuss a use case, please contact us [here](https://eazyml.com/trust-in-ai)
- Here are the other packages from EazyML suite:

    - [eazyml-automl](https://pypi.org/project/eazyml-automl/): eazyml-automl provides a suite of APIs for training, optimizing and validating machine learning models with built-in AutoML capabilities, hyperparameter tuning, and cross-validation.
    - [eazyml-data-quality](https://pypi.org/project/eazyml-data-quality/): eazyml-data-quality provides APIs for comprehensive data quality assessment, including bias detection, outlier identification, and drift analysis for both data and models.
    - [eazyml-counterfactual](https://pypi.org/project/eazyml-counterfactual/): eazyml-counterfactual provides APIs for optimal prescriptive analytics, counterfactual explanations, and actionable insights to optimize predictive outcomes to align with your objectives.
    - [eazyml-insight](https://pypi.org/project/eazyml-insight/): eazyml-insight provides APIs to discover patterns, generate insights, and mine rules from your datasets.
    - [eazyml-xai](https://pypi.org/project/eazyml-xai/): eazyml-xai provides APIs for explainable AI (XAI), offering human-readable explanations, feature importance, and predictive reasoning.
    - [eazyml-xai-image](https://pypi.org/project/eazyml-xai-image/): eazyml-xai-image provides APIs for image explainable AI (XAI).

## License
This project is licensed under the [Proprietary License](https://github.com/EazyML/eazyml-docs/blob/master/LICENSE).

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