Skip to content

ML model to predict flight prices based on various features like departure time, arrival time, duration, airline, source, destination, and number of stops.

Notifications You must be signed in to change notification settings

PIYUSH1927/Flightforecast

Folders and files

NameName
Last commit message
Last commit date

Latest commit

ย 

History

3 Commits
ย 
ย 
ย 
ย 

Repository files navigation


๐Ÿš€ Setup and Run the Project

1. Create a Virtual Environment

Run the following command to create a virtual environment:

python -m venv venv

2. Activate the Virtual Environment

  • Windows (Command Prompt):
    venv\Scripts\activate
  • Windows (PowerShell):
    venv\Scripts\Activate.ps1
  • Mac/Linux:
    source venv/bin/activate

3. Install Dependencies

After activating the virtual environment, install the required packages:

pip install -r requirements.txt

4. Run the Django Server

To start the Django development server, run:

python manage.py runserver

5. Deactivate Virtual Environment (Optional)

To exit the virtual environment when you're done:

deactivate

About

ML model to predict flight prices based on various features like departure time, arrival time, duration, airline, source, destination, and number of stops.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published