https://github.com/dipanjans/low_code_machine_learning_pycaret_workshop_2022

This workshop was done as a part of the 1729 conference organized by Fractal Analytics and Analytics Vidhya. Key content covered was hands-on notebooks leveraging PyCaret to compare, build, tune, evaluate and interpret machine learning models
https://github.com/dipanjans/low_code_machine_learning_pycaret_workshop_2022

Last synced: 4 months ago
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This workshop was done as a part of the 1729 conference organized by Fractal Analytics and Analytics Vidhya. Key content covered was hands-on notebooks leveraging PyCaret to compare, build, tune, evaluate and interpret machine learning models

• Host: GitHub
• URL: https://github.com/dipanjans/low_code_machine_learning_pycaret_workshop_2022
• Owner: dipanjanS
• License: gpl-3.0
• Created: 2022-07-07T12:57:48.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-07-07T13:33:36.000Z (over 2 years ago)
• Last Synced: 2024-10-04T13:39:40.970Z (5 months ago)
• Language: Jupyter Notebook
• Size: 3.86 MB
• Stars: 21
• Watchers: 2
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        

# Low-code and Auto-ML with PyCaret

![image](https://user-images.githubusercontent.com/3448263/177779489-e0264d89-ff91-4710-ab9d-e4966d4040aa.png)

This workshop was done as a part of the 1729 conference organized by Fractal Analytics and Analytics Vidhya. Key content covered was hands-on notebooks leveraging PyCaret to compare, build, tune, evaluate and interpret machine learning models.

## Session Outline

Low-code and Auto-ML are essential tools in a data scientist’s toolbox to improve productivity and iterate faster in data science projects. This hands-on workshop will focus on two major notebooks:

- __A Kickstarter notebook__ leveraging the popular open-source library PyCaret and go through learning the essentials:

    - Setup experiments

    - Train ML models

    - Compare ML models

    - Tune ML models

    - Evaluate models 

    - Predictions on new data.

- __An advanced notebook__ where we learn advanced capabilities of PyCaret including the following:

    - Data transformations

    - Imputation

    - Multicollinearity

    - Handling imbalanced data

    - Advanced tuning methods

    - Ensembling (Bagging, Boosting, Stacking, Blending) 

    - Explainable AI to interpret ML Models.

__Recommendation:__ Please open the notebooks in Google Colab

## Author

![image](https://user-images.githubusercontent.com/3448263/177781903-c1873548-9733-49ca-add2-c1c416f1cbed.png)

## Credits

Thanks to [PyCaret](https://pycaret.org), [Moez](https://www.linkedin.com/in/profile-moez) and the awesome documentation.