Flight Delay Prediction API

This API predicts flight delays based on real-time weather and airline data. It is built using FastAPI and is deployed on Heroku for easy access.

Features

• FastAPI Framework: Modern and fast web framework for creating APIs.
• CORS Middleware: Allows cross-origin requests from any origin.
• Model Prediction: Uses a pre-trained Random Forest model to predict delays based on flight and weather data.
• Heroku Deployment: Deployed on Heroku for easy cloud access.

Requirements

• Python 3.7+
• FastAPI
• Pandas
• Pickle (for model loading)

Project Files

• Procfile: Specifies how the app is run on Heroku.
• runtime.txt: Specifies the Python version.
• requirements.txt: Lists all dependencies required to run the project.

Setup Instructions

1. Clone the repository:

   git clone https://github.com/sethanimesh/FlightDelayPrediction_API.git

2. Install dependencies:

   You can install the required dependencies using pip:

   pip install -r requirements.txt

3. Run the application locally:

   You can start the FastAPI server using Uvicorn:

   uvicorn flightdelayAPI:app --reload

   The application will now be running at http://127.0.0.1:8000.

4. Model File:

   Ensure that the best_model.pkl file is placed in the root directory of your project. This file should contain the trained model used for making predictions.

Heroku Deployment Instructions

This application is already set up for deployment on Heroku. To deploy your own version, follow these steps:

1. Login to Heroku:

   heroku login

2. Create a new Heroku app:

   heroku create

3. Deploy the app:

   git push heroku main

4. Check logs:

   heroku logs --tail

5. Access the app: Once deployed, you can access the app at the URL provided by Heroku.

Heroku-specific Files

• Procfile: Specifies how to run the FastAPI application on Heroku using Uvicorn.

  web: gunicorn -w 4 -k uvicorn.workers.UvicornWorker flightdelayAPI:app

• runtime.txt: Specifies the Python version to use on Heroku.

  python-3.9.12

• requirements.txt: Lists the Python dependencies needed to run the project.

  fastapi==0.70.0
  uvicorn==0.15.0
  pandas==1.3.3
  gunicorn==20.1.0

API Endpoints

GET /

• Description: Returns a welcome message to verify that the API is running.
• Response:

  {
    "message": "Hello, the Flight Delay Prediction API is running!"
  }

POST /predict

• Description: Takes in flight information and returns whether the flight is predicted to be delayed.
• Request Body:

  {
    "carrier_code": "AA",
    "scheduled_elapsed_time": 180,
    "delay_weather": 1,
    "HourlyPrecipitation_x": 0.5,
    "HourlyWindSpeed_x": 10.5
  }

• Response:

  {
    "prediction": "Delayed" 
  }

Input Parameters

• carrier_code (str): Airline code (e.g., AA, DL, UA)
• scheduled_elapsed_time (int): Scheduled flight time in minutes.
• delay_weather (int): Indicates if there is a weather delay (1 for yes, 0 for no).
• HourlyPrecipitation_x (float): The hourly precipitation value at the time of the flight.
• HourlyWindSpeed_x (float): The hourly wind speed at the time of the flight.

License

This project is licensed under the MIT License.