{"id":14958253,"url":"https://github.com/uznetdev/kabr-prediction","last_synced_at":"2026-02-13T11:52:43.717Z","repository":{"id":253194228,"uuid":"842770858","full_name":"UznetDev/Kabr-prediction","owner":"UznetDev","description":"This model is designed to determine the age of a crab based on its other physical characteristics. 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Using this model, it is possible to determine the age of a crab through its other data. This project demonstrates the use of various machine learning algorithms and data processing techniques to achieve accurate predictions.\n\n## Table of Contents\n\n- [Installation](#installation)\n- [Usage](#usage)\n- [Project Structure](#project-structure)\n- [Libraries Used](#libraries-used)\n- [License](#license)\n- [Contributing](#contributing)\n- [Contact](#contact)\n\n## \u003ci\u003eInstallation\u003c/i\u003e\n\n1. Clone the repository:\n   ```sh\n   git clone https://github.com/UznetDev/Kabr-prediction.git\n   ```\n2. Navigate to the project directory:\n   ```sh\n   cd Kabr-prediction\n   ```\n3. Create a virtual environment:\n   ```sh\n   python -m venv env\n   ```\n4. Activate the virtual environment:\n   - On Windows:\n     ```sh\n     env\\Scripts\\activate\n     ```\n   - On macOS and Linux:\n     ```sh\n     source env/bin/activate\n     ```\n5. Install the necessary libraries:\n   ```sh\n   pip install -r requirements.txt\n   ```\n\n## Usage\n\nTo explore and run the project:\n\n1. Open the `model.ipynb` file in Jupyter Notebook or JupyterLab.\n2. Follow the instructions within the notebook to understand the data processing steps, model training, and evaluation.\n\n## Project Structure\n\n- `README.md`: Provides an overview of the project, installation instructions, and usage guidelines.\n- `model.ipynb`: Jupyter Notebook containing the machine learning workflow.\n- `test_model.ipynb`: Jupyter Notebook for testing model.\n- `requirements.txt`: A list of required dependencies.\n- `.gitignore`: Specifies files and directories to be ignored by git.\n\n## Libraries Used\n- **Pandas**: Data manipulation and analysis.\n- **NumPy**: Numerical operations and array handling.\n- **Scikit-learn**: Machine learning model building and evaluation.\n- **Warnings**: Handling and filtering warning messages.\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## Contributing\n\nContributions are welcome! Please fork the repository and submit a pull request for any changes.\n\n1. **Fork the Repository**:\n    Click on the `Fork` button at the top right corner of this page to create a copy of this repository under your GitHub account.\n\n2. **Clone the Forked Repository**:\n    ```bash\n    git clone https://github.com/UznetDev/Kabr-prediction.git\n    cd Global-Statistics-Dashboard\n    ```\n\n3. **Create a New Branch**:\n    ```bash\n    git checkout -b feature/YourFeatureName\n    ```\n\n4. **Commit Your Changes**:\n    ```bash\n    git add .\n    git commit -m 'Add some feature'\n    ```\n\n5. **Push to the Branch**:\n    ```bash\n    git push origin feature/YourFeatureName\n    ```\n\n6. **Create a Pull Request**:\n    Open a pull request to the original repository.\n\n## \u003ci\u003eContact\u003c/i\u003e\n\nIf you have any questions or suggestions, please contact:\n- Email: uznetdev@example.com\n- GitHub Issues: [Issues section](https://github.com/UznetDev/Kabr-prediction/issues)\n- GitHub Profile: [UznetDev](https://github.com/UznetDev/)\n- Telegram: [UZNet_Dev](https://t.me/UZNet_Dev)\n- Linkedin: [Abdurahmon Niyozaliev](https://www.linkedin.com/in/abdurakhmon-niyozaliyev-%F0%9F%87%B5%F0%9F%87%B8-66545222a/)\n\n\n### \u003ci\u003eThank you for your interest in the project!\u003c/i\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fuznetdev%2Fkabr-prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fuznetdev%2Fkabr-prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fuznetdev%2Fkabr-prediction/lists"}