## EazyML Modeling
  
`EazyML` is a comprehensive Python package designed to simplify machine learning workflows for data scientists, engineers, and developers. With **AutoML capabilities**, eazyml enables automated feature selection, model training, hyperparameter optimization, and cross-validation, all with minimal code. The package trains multiple models in the background, ranks them by performance metrics, and recommends the best model for your use case.
### Features
- **Global Feature Importance**: Get insights into the most impactful features in your dataset.
- **Confidence Scoring**: Enhance predictive reliability with confidence scores.
`EazyML` is perfect for users looking to streamline the development of robust and efficient machine learning models.
## Installation
### User installation
The easiest way to install eazyml modeling is using pip:
```bash
pip install -U eazyml
```
### Dependencies
Eazyml Augmented Intelligence requires :
- werkzeug,
- unidecode,
- pandas,
- scikit-learn,
- nltk,
- pyyaml,
- requests
## Usage
Initialize and build a predictive model based on the provided dataset and options.
Perform prediction on the given test data based on model options.
```python
from eazyml_augi import ez_init, ez_augi
# Replace 'your_license_key' with your actual EazyML license key
ez_init(license_key="your_license_key")
ez_init_model(
df='train_dataframe'
options={
"model_type": "predictive",
"accelerate": "yes",
"outcome": "target",
"remove_dependent": "no",
"derive_numeric": "yes",
"derive_text": "no",
"phrases": {"*": []},
"text_types": {"*": ["sentiments"]},
"expressions": []
}
)
ez_predict(
test_data ='test_dataframe'
options={
"extra_info": {
},
"model": "Specified model to be used for prediction",
"outcome": "target",
}
)
```
You can find more information in the [documentation](https://eazyml.readthedocs.io/en/latest/packages/eazyml_model.html).
## Useful links and similar projects
- [Documentation](https://docs.eazyml.com)
- [Homepage](https://eazyml.com)
- If you have more questions or want to discuss a specific use case please book an appointment [here](https://eazyml.com/trust-in-ai)
- Here are some other EazyML's packages :
- [eazyml](https://pypi.org/project/eazyml/): Eazyml provides a suite of APIs for training, testing and optimizing machine learning models with built-in AutoML capabilities, hyperparameter tuning, and cross-validation.
- [eazyml-dq](https://pypi.org/project/eazyml-dq/): `eazyml-dq` provides APIs for comprehensive data quality assessment, including bias detection, outlier identification, and data drift analysis.
- [eazyml-cf](https://pypi.org/project/eazyml-cf/): `eazyml-cf` provides APIs for counterfactual explanations, prescriptive analytics, and actionable insights to optimize predictive outcomes.
- [eazyml-augi](https://pypi.org/project/eazyml-augi/): `eazyml-augi` provides APIs to uncover patterns, generate insights, and discover rules from training 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).
---
*Maintained by [EazyML](https://eazyml.com)*
*© 2025 EazyML. All rights reserved.*
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"description": "## EazyML Modeling\n  \n\n\n\n`EazyML` is a comprehensive Python package designed to simplify machine learning workflows for data scientists, engineers, and developers. With **AutoML capabilities**, eazyml enables automated feature selection, model training, hyperparameter optimization, and cross-validation, all with minimal code. The package trains multiple models in the background, ranks them by performance metrics, and recommends the best model for your use case.\n\n### Features\n- **Global Feature Importance**: Get insights into the most impactful features in your dataset.\n- **Confidence Scoring**: Enhance predictive reliability with confidence scores.\n\n`EazyML` is perfect for users looking to streamline the development of robust and efficient machine learning models.\n\n## Installation\n### User installation\nThe easiest way to install eazyml modeling is using pip:\n```bash\npip install -U eazyml\n```\n### Dependencies\nEazyml Augmented Intelligence requires :\n- werkzeug,\n- unidecode,\n- pandas,\n- scikit-learn,\n- nltk,\n- pyyaml,\n- requests\n\n## Usage\nInitialize and build a predictive model based on the provided dataset and options. \nPerform prediction on the given test data based on model options.\n\n```python\nfrom eazyml_augi import ez_init, ez_augi\n# Replace 'your_license_key' with your actual EazyML license key\nez_init(license_key=\"your_license_key\")\n\nez_init_model(\n df='train_dataframe'\n options={\n \"model_type\": \"predictive\",\n \"accelerate\": \"yes\",\n \"outcome\": \"target\",\n \"remove_dependent\": \"no\",\n \"derive_numeric\": \"yes\",\n \"derive_text\": \"no\",\n \"phrases\": {\"*\": []},\n \"text_types\": {\"*\": [\"sentiments\"]},\n \"expressions\": []\n }\n )\nez_predict(\n test_data ='test_dataframe'\n options={\n \"extra_info\": {\n },\n \"model\": \"Specified model to be used for prediction\",\n \"outcome\": \"target\",\n }\n )\n\n```\nYou can find more information in the [documentation](https://eazyml.readthedocs.io/en/latest/packages/eazyml_model.html).\n\n\n## Useful links and similar projects\n- [Documentation](https://docs.eazyml.com)\n- [Homepage](https://eazyml.com)\n- If you have more questions or want to discuss a specific use case please book an appointment [here](https://eazyml.com/trust-in-ai)\n- Here are some other EazyML's packages :\n\n - [eazyml](https://pypi.org/project/eazyml/): Eazyml provides a suite of APIs for training, testing and optimizing machine learning models with built-in AutoML capabilities, hyperparameter tuning, and cross-validation.\n - [eazyml-dq](https://pypi.org/project/eazyml-dq/): `eazyml-dq` provides APIs for comprehensive data quality assessment, including bias detection, outlier identification, and data drift analysis.\n - [eazyml-cf](https://pypi.org/project/eazyml-cf/): `eazyml-cf` provides APIs for counterfactual explanations, prescriptive analytics, and actionable insights to optimize predictive outcomes.\n - [eazyml-augi](https://pypi.org/project/eazyml-augi/): `eazyml-augi` provides APIs to uncover patterns, generate insights, and discover rules from training datasets.\n - [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.\n - [eazyml-xai-image](https://pypi.org/project/eazyml-xai-image/): eazyml-xai-image provides APIs for image explainable AI (XAI).\n\n## License\nThis project is licensed under the [Proprietary License](https://github.com/EazyML/eazyml-docs/blob/master/LICENSE).\n\n---\n\n*Maintained by [EazyML](https://eazyml.com)* \n*\u00c2\u00a9 2025 EazyML. All rights reserved.*\n\n",
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