https://github.com/saba-gul/-pycaret-streamline-your-machine-learning-workflow

PyCaret: Simplifying machine learning workflows with a low-code, open-source Python library.
https://github.com/saba-gul/-pycaret-streamline-your-machine-learning-workflow

auc classification-report iris-classification low-code machine-learning-algorithms pycaret pycaret-library

Last synced: about 1 month ago
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PyCaret: Simplifying machine learning workflows with a low-code, open-source Python library.

• Host: GitHub
• URL: https://github.com/saba-gul/-pycaret-streamline-your-machine-learning-workflow
• Owner: Saba-Gul
• Created: 2024-05-27T21:40:26.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-05-27T21:54:39.000Z (7 months ago)
• Last Synced: 2024-05-28T07:51:18.109Z (7 months ago)
• Topics: auc, classification-report, iris-classification, low-code, machine-learning-algorithms, pycaret, pycaret-library
• Language: Jupyter Notebook
• Homepage:
• Size: 113 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# PyCaret: Streamline Your Machine Learning Workflow

PyCaret is an open-source, low-code machine learning library in Python that simplifies the machine learning workflow. With PyCaret, you can perform end-to-end machine learning tasks with just a few lines of code, making it accessible to both beginners and experienced data scientists.

## Features

- **Streamlined Workflow:** PyCaret provides a streamlined workflow for machine learning tasks, including data preparation, model selection, hyperparameter tuning, model evaluation, and deployment.

- **Low-Code Interface:** PyCaret's low-code interface allows users to perform complex machine learning tasks with minimal coding.

- **Automated Preprocessing:** PyCaret automates common data preprocessing tasks such as missing value imputation, feature scaling, and one-hot encoding.

- **Diverse Model Training:** PyCaret supports a wide range of machine learning algorithms, including classification, regression, clustering, and anomaly detection.

- **Model Interpretation:** PyCaret provides tools for model interpretation, including feature importance analysis, SHAP (SHapley Additive exPlanations) values, and confusion matrix visualization.

- **Model Deployment:** PyCaret allows users to deploy machine learning models to various platforms, including cloud services and web applications.

## Usage

1. Clone this repository to your local machine.

2. Install dependencies as mentioned above.

3. Open and run the Jupyter Notebook provided in the repository.

4. Follow along with the Medium article for detailed explanations and instructions.

## Resources

- [Medium Article](https://medium.com/@sabagul/democratizing-machine-learning-with-pycaret-a-low-code-approach-95e9efc496f9) - Link to the Medium article

- [PyCaret Documentation](https://pycaret.org/) - Official PyCaret documentation

- [PyCaret GitHub Repository](https://github.com/pycaret/pycaret) - PyCaret's GitHub repository