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https://github.com/MindSetLib/Insolver
Low code machine learning library, specified for insurance tasks: prepare data, build model, implement into production.
https://github.com/MindSetLib/Insolver
auto-ml automated-machine-learning automl bayesian-optimization data-science elyra elyra-community feature-engineering hyperparameter-optimization insurance insurance-claims insurance-company insurance-scoring insurance-team low-code machine-learning
Last synced: about 2 months ago
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Low code machine learning library, specified for insurance tasks: prepare data, build model, implement into production.
- Host: GitHub
- URL: https://github.com/MindSetLib/Insolver
- Owner: MindSetLib
- License: mit
- Created: 2020-03-22T21:58:29.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2024-10-03T10:37:21.000Z (3 months ago)
- Last Synced: 2024-10-03T10:49:33.738Z (3 months ago)
- Topics: auto-ml, automated-machine-learning, automl, bayesian-optimization, data-science, elyra, elyra-community, feature-engineering, hyperparameter-optimization, insurance, insurance-claims, insurance-company, insurance-scoring, insurance-team, low-code, machine-learning
- Language: Jupyter Notebook
- Homepage: http://insolver.io
- Size: 43 MB
- Stars: 18
- Watchers: 5
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- jimsghstars - MindSetLib/Insolver - Low code machine learning library, specified for insurance tasks: prepare data, build model, implement into production. (Jupyter Notebook)
README
# Insolver
[](https://pypi.org/project/insolver/)
[](https://insolver.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/MindSetLib/Insolver/actions)
[](https://codecov.io/github/MindSetLib/Insolver)
[](https://github.com/psf/black)
[](https://pepy.tech/project/insolver)
Insolver 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).
## Installation:
Insolver can be installed via pip from PyPI. There are several installation options available:
| Description | Command |
|----------------------------------------------------|---------------------------------------------|
| Regular installation | `pip install insolver` |
| Installation with feature engineering requirements | `pip install insolver[feature_engineering]` |
| Installation with feature monitoring requirements | `pip install insolver[feature_monitoring]` |
| Installation with interpretation requirements | `pip install insolver[interpretation]` |
| Installation with serving requirements | `pip install insolver[serving]` |
| Installation with report requirements | `pip install insolver[report]` |
| Installation with all requirements | `pip install insolver[all]` |
### Insolver is already installed in the easy access cloud via the GitHub login. Try https://mset.space with a familiar notebook-style environment.
## Examples:
- [Binary Classification Example - Rain in Australia Prediction](https://github.com/MindSetLib/Insolver/blob/master/tutorials/Binary%20Classification%20Example%20-%20Rain%20in%20Australia%20Prediction.ipynb)
This 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.
- [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)
This 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.
- [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)
This 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.
- [Gradient Boosting Example - Lending Club](https://github.com/MindSetLib/Insolver/blob/master/tutorials/Gradient%20Boosting%20Example%20-%20Lending%20Club.ipynb)
This 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.
- [Transforms Inference Example](https://github.com/MindSetLib/Insolver/blob/master/tutorials/Transforms%20Inference%20Example.ipynb)
This tutorial demonstrates how to load `InsolverTransform` transforms from a file using the `load_transforms` function.
- [InsolverDataFrame and InsolverTransform Example](https://github.com/MindSetLib/Insolver/blob/master/tutorials/InsolverDataFrame%20and%20InsolverTransform%20Example.ipynb)
This tutorial demonstrates main features of the `InsolverDataFrame` class and the `InsolverTransform` class.
- [Regression Example - FreeMLP](https://github.com/MindSetLib/Insolver/blob/master/tutorials/Regression%20Example%20-%20FreeMLP.ipynb)
This 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.
- [Regression Example - US Accidents](https://github.com/MindSetLib/Insolver/blob/master/tutorials/Regression%20Example%20-%20FreeMLP.ipynb)
This 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.
- [Report Example](https://github.com/MindSetLib/Insolver/blob/master/tutorials/Report%20Example.ipynb)
This tutorial demonstrates how to create a **HTML report** with different models using the `Report` class.
## Documentation:
Available [here](https://insolver.readthedocs.io/)
## Supported libraries:
| GLM | Boosting models | Serving (REST-API) | Model interpretation |
|---------------------|-------------------------------------------|------------------------------------|----------------------|
| - sklearn
- h2o | - XGBoost
- LightGBM
- CatBoost | - Flask
- FastAPI
- Django | - shap plots |
### Run tests:
```shell
python -m pytest
```
tests with coverage:
```shell
python -m pytest . --cov=insolver --cov-report html
```
## Contributing to Insolver:
Please, feel free to open an issue or/and suggest PR, if you find any bugs or any enhancements.
## Demo
### Example of creating models using the Insolver
### Example of a model production service
### Example of an elyra pipeline built with the Insolver inside
## Contacts
[email protected]
+79263790123