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Notebook","readme":"# Insolver\n![PyPI - Python Version](https://img.shields.io/pypi/pyversions/insolver)\n[![PyPI](https://img.shields.io/pypi/v/insolver?style=flat)](https://pypi.org/project/insolver/)\n[![Documentation Status](https://readthedocs.org/projects/insolver/badge/?version=latest)](https://insolver.readthedocs.io/en/latest/?badge=latest)\n[![GitHub Workflow Status](https://img.shields.io/github/actions/workflow/status/MindSetLib/Insolver/insolver-tests.yaml)](https://github.com/MindSetLib/Insolver/actions)\n[![Coverage](https://codecov.io/github/MindSetLib/Insolver/coverage.svg?branch=master)](https://codecov.io/github/MindSetLib/Insolver)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)\n[![Downloads](https://pepy.tech/badge/insolver/week)](https://pepy.tech/project/insolver)\n\nInsolver is a low-code machine learning library, originally created for the insurance industry, but can be used in any other. You can find a more detailed overview [here](https://insolver.readthedocs.io/en/latest/source/overview.html).\n\n## Installation:\n\nInsolver can be installed via pip from PyPI. There are several installation options available:\n\n| Description                                        | Command                                     |\n|----------------------------------------------------|---------------------------------------------|\n| Regular installation                               | `pip install insolver`                      |\n| Installation with feature engineering requirements | `pip install insolver[feature_engineering]` |\n| Installation with feature monitoring requirements  | `pip install insolver[feature_monitoring]`  |\n| Installation with interpretation requirements      | `pip install insolver[interpretation]`      |\n| Installation with serving requirements             | `pip install insolver[serving]`             |\n| Installation with report requirements              | `pip install insolver[report]`              |\n| Installation with all requirements                 | `pip install insolver[all]`                 |\n\n\n\n### Insolver is already installed in the easy access cloud via the GitHub login. Try https://mset.space with a familiar notebook-style environment.\n\n## Examples:\n\n- [Binary Classification Example - Rain in Australia Prediction](https://github.com/MindSetLib/Insolver/blob/master/tutorials/Binary%20Classification%20Example%20-%20Rain%20in%20Australia%20Prediction.ipynb)\nThis tutorial demonstrates how to create **classification models** for the [`weatherAUS`](https://www.kaggle.com/jsphyg/weather-dataset-rattle-package) dataset: getting and preprocessing data, transformations, creating models, plotting SHAP values and comparing models.\n\n- [Data Preprocessing Example I - New York City Airbnb](https://github.com/MindSetLib/Insolver/blob/master/tutorials/Data%20Preprocessing%20Example%20I%20-%20New%20York%20City%20Airbnb.ipynb)\nThis tutorial demonstrates how to use the [`feature_engineering`](https://github.com/MindSetLib/Insolver/tree/master/insolver/feature_engineering) module and all the **main features of each class**. For this, the [`AB_NYC_2019`](https://www.kaggle.com/dgomonov/new-york-city-airbnb-open-data) dataset is used.\n\n- [Data Preprocessing Example II - New York City Airbnb](https://github.com/MindSetLib/Insolver/blob/master/tutorials/Data%20Preprocessing%20Example%20II%20-%20New%20York%20City%20Airbnb.ipynb)\nThis tutorial also demonstrates how to use the [`feature_engineering`](https://github.com/MindSetLib/Insolver/tree/master/insolver/feature_engineering) module, but it covers the **automated data preprossesing** class and all of its features. For this, the [`AB_NYC_2019`](https://www.kaggle.com/dgomonov/new-york-city-airbnb-open-data) dataset is used.\n\n- [Gradient Boosting Example - Lending Club](https://github.com/MindSetLib/Insolver/blob/master/tutorials/Gradient%20Boosting%20Example%20-%20Lending%20Club.ipynb)\nThis tutorial demonstrates how to create **classification models** for the [`Lending Club`](https://www.kaggle.com/wordsforthewise/lending-club) dataset using the **Gradient Boosting libraries** and the `InsolverGBMWrapper` class.\n\n- [Transforms Inference Example](https://github.com/MindSetLib/Insolver/blob/master/tutorials/Transforms%20Inference%20Example.ipynb)\nThis tutorial demonstrates how to load `InsolverTransform` transforms from a file using the `load_transforms` function.\n\n- [InsolverDataFrame and InsolverTransform Example](https://github.com/MindSetLib/Insolver/blob/master/tutorials/InsolverDataFrame%20and%20InsolverTransform%20Example.ipynb)\nThis tutorial demonstrates main features of the `InsolverDataFrame` class and the `InsolverTransform` class.\n\n- [Regression Example - FreeMLP](https://github.com/MindSetLib/Insolver/blob/master/tutorials/Regression%20Example%20-%20FreeMLP.ipynb)\nThis tutorial demonstrates how to create **regression models** for the `freMPL-R` dataset: getting and preprocessing data, transformations, creating models, plotting SHAP values and comparing models.\n\n- [Regression Example - US Accidents](https://github.com/MindSetLib/Insolver/blob/master/tutorials/Regression%20Example%20-%20FreeMLP.ipynb)\nThis tutorial demonstrates how to create **regression models** for the [`US Traffic Accident`](https://smoosavi.org/datasets/us_accidents) dataset: getting and preprocessing data, transformations, creating models, plotting SHAP values and comparing models.\n\n- [Report Example](https://github.com/MindSetLib/Insolver/blob/master/tutorials/Report%20Example.ipynb)\nThis tutorial demonstrates how to create a **HTML report** with different models using the `Report` class.\n\n## Documentation:\n\nAvailable [here](https://insolver.readthedocs.io/)\n\n## Supported libraries:\n\n| GLM                 | Boosting models                           | Serving (REST-API)                 | Model interpretation |\n|---------------------|-------------------------------------------|------------------------------------|----------------------|\n| - sklearn\u003cbr/\u003e- h2o | - XGBoost\u003cbr/\u003e - LightGBM\u003cbr/\u003e - CatBoost | - Flask\u003cbr/\u003e- FastAPI\u003cbr/\u003e- Django | - shap plots         |\n\n### Run tests:\n```shell\npython -m pytest\n```\n\ntests with coverage:\n```shell\npython -m pytest . --cov=insolver --cov-report html\n```\n\n\n## Contributing to Insolver:\n\nPlease, feel free to open an issue or/and suggest PR, if you find any bugs or any enhancements.\n\n## Demo\n### Example of creating models using the Insolver\n![](https://github.com/MindSetLib/Insolver/releases/download/v0.4.6/InsolverDemo.gif)\n\n### Example of a model production service\n![](https://github.com/MindSetLib/Insolver/releases/download/v0.4.6/InsolverImplementation.gif)\n\n### Example of an elyra pipeline built with the Insolver inside\n![](https://github.com/MindSetLib/Insolver/releases/download/v0.4.6/InsolverElyraPipeline.gif)\n\n## Contacts\nfrank@mind-set.ru\n+79263790123\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmindsetlib%2Finsolver","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmindsetlib%2Finsolver","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmindsetlib%2Finsolver/lists"}