This project aims to predict the brand of a car based on certain features using machine learning techniques. It is built using Django, a high-level Python web framework, for the backend and potentially other libraries for machine learning models.

The project is structured as follows:

• CarBrandPrediction/: The main project directory.
  • car_classifier/: The Django app containing the car brand prediction functionality.
    • migrations/: Django database migrations.
    • static/: Static files such as CSS, JavaScript, etc.
    • templates/: HTML templates.
    • admin.py: Configuration for Django admin interface.
    • apps.py: Configuration for the app.
    • models.py: Defines database models.
    • urls.py: URL configuration for the app.
    • views.py: Contains view functions.
  • CarBrandPrediction/: The Django project settings directory.
    • settings.py: Django project settings including database configuration, middleware, installed apps, etc.
    • urls.py: URL configuration for the entire project.
    • wsgi.py: WSGI configuration for deployment.
    • asgi.py: ASGI configuration for deployment.
  • db.sqlite3: SQLite database file (default for Django projects).
  • media/: Directory to store media files uploaded by users.
• requirements.txt: File containing Python dependencies for the project.
• manage.py: Django's command-line utility for administrative tasks.
• Dockerfile: Dockerfile for containerization of the application.
• docker-compose.yml: Docker Compose file for managing the application's services.
• predict.py: Module for predicting car brands.
• test.py: Test file for testing models and functionality.
• README.md: Overview of the project and its structure.

1. Clone this repository to your local machine.
2. Create a virtual environment and activate it.
3. Install dependencies using pip install -r requirements.txt.
4. Run migrations to create necessary database tables: python manage.py migrate.
5. Start the development server: python manage.py runserver.

Once the development server is running, you can access the application by navigating to http://localhost:8000 in your web browser. You should see the home page of the application.

Alternatively, you can use Docker to containerize the application. Run docker-compose up --build to build and start the containers.

Contributions are welcome! Feel free to fork this repository, make changes, and submit pull requests.

This project is licensed under the [MIT] License