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https://github.com/uznetdev/kabr-prediction
This model is designed to determine the age of a crab based on its other physical characteristics. Using this model, it is possible to determine the age of a crab through its other data!
https://github.com/uznetdev/kabr-prediction
linear-regression machine-learning machine-learning-algorithms ml pandas-python python-3 sklearn sklearn-model
Last synced: about 1 month ago
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This model is designed to determine the age of a crab based on its other physical characteristics. Using this model, it is possible to determine the age of a crab through its other data!
- Host: GitHub
- URL: https://github.com/uznetdev/kabr-prediction
- Owner: UznetDev
- License: mit
- Created: 2024-08-15T03:33:53.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-08-15T04:44:22.000Z (5 months ago)
- Last Synced: 2024-12-03T19:54:27.604Z (about 1 month ago)
- Topics: linear-regression, machine-learning, machine-learning-algorithms, ml, pandas-python, python-3, sklearn, sklearn-model
- Language: Jupyter Notebook
- Homepage:
- Size: 690 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Kabr Prediction
This model is designed to determine the age of a crab based on its other physical characteristics. 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.
## Table of Contents
- [Installation](#installation)
- [Usage](#usage)
- [Project Structure](#project-structure)
- [Libraries Used](#libraries-used)
- [License](#license)
- [Contributing](#contributing)
- [Contact](#contact)
## Installation
1. Clone the repository:
```sh
git clone https://github.com/UznetDev/Kabr-prediction.git
```
2. Navigate to the project directory:
```sh
cd Kabr-prediction
```
3. Create a virtual environment:
```sh
python -m venv env
```
4. Activate the virtual environment:
- On Windows:
```sh
env\Scripts\activate
```
- On macOS and Linux:
```sh
source env/bin/activate
```
5. Install the necessary libraries:
```sh
pip install -r requirements.txt
```
## Usage
To explore and run the project:
1. Open the `model.ipynb` file in Jupyter Notebook or JupyterLab.
2. Follow the instructions within the notebook to understand the data processing steps, model training, and evaluation.
## Project Structure
- `README.md`: Provides an overview of the project, installation instructions, and usage guidelines.
- `model.ipynb`: Jupyter Notebook containing the machine learning workflow.
- `test_model.ipynb`: Jupyter Notebook for testing model.
- `requirements.txt`: A list of required dependencies.
- `.gitignore`: Specifies files and directories to be ignored by git.
## Libraries Used
- **Pandas**: Data manipulation and analysis.
- **NumPy**: Numerical operations and array handling.
- **Scikit-learn**: Machine learning model building and evaluation.
- **Warnings**: Handling and filtering warning messages.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Contributing
Contributions are welcome! Please fork the repository and submit a pull request for any changes.
1. **Fork the Repository**:
Click on the `Fork` button at the top right corner of this page to create a copy of this repository under your GitHub account.
2. **Clone the Forked Repository**:
```bash
git clone https://github.com/UznetDev/Kabr-prediction.git
cd Global-Statistics-Dashboard
```
3. **Create a New Branch**:
```bash
git checkout -b feature/YourFeatureName
```
4. **Commit Your Changes**:
```bash
git add .
git commit -m 'Add some feature'
```
5. **Push to the Branch**:
```bash
git push origin feature/YourFeatureName
```
6. **Create a Pull Request**:
Open a pull request to the original repository.
## Contact
If you have any questions or suggestions, please contact:
- Email: [email protected]
- GitHub Issues: [Issues section](https://github.com/UznetDev/Kabr-prediction/issues)
- GitHub Profile: [UznetDev](https://github.com/UznetDev/)
- Telegram: [UZNet_Dev](https://t.me/UZNet_Dev)
- Linkedin: [Abdurahmon Niyozaliev](https://www.linkedin.com/in/abdurakhmon-niyozaliyev-%F0%9F%87%B5%F0%9F%87%B8-66545222a/)
### Thank you for your interest in the project!