Flight Fare Prediction - a Classic Time Series Project

• Flight fare prediction is a classical problem of time series forecasting that finds trends in past observations to outline the future
• Many popular flight booking websites today, including Google Flights, showcase important insights on:
  • Current fair status: high, low or fair
  • Past fare trends, upcoming future trends and
  • Helps decide the right time to book a flight ticket.
• In this project, we are going to build a Python Flight Fare Prediction App, that returns the fare prediction for a given set of travel details, like: departure date, arrival date, departure city, arrival city, stoppages, and the airline carrier.

App Functionality

Steps to run Flight Fare App - on Windows

• Prerequisites: Python 3.9 (ensure Python is added to PATH) + Git Client
• Open GIT CMD >> navigate to working directory >> Clone this Github Repo (or download project files from GitHub directly)
  git clone https://github.com/skillcate/flight-price-prediction.git
• Open Windows Powershell >> navigate to new working directory (cloned repo folder)
• Run Project in Flask (Using PIP + Virtualenv)

    pip install virtualenv                  # install virtual environment        
    virtualenv ENV                          # create virtual environment by the name ENV
    .\ENV\Scripts\activate                  # activate ENV
    pip install -r .\requirements.txt       # install project dependencies
    python app.py                           # run the project
    deactivate                              # close virtual environment once done

Steps to run Flight Fare App - on Mac

• Prerequisites: Python 3.9
• Open Terminal >> navigate to working directory >> Clone this Github Repo (or download project files from GitHub directly)
  git clone https://github.com/skillcate/flight-price-prediction.git
• Navigate to project working directory (cloned repo folder)
• Run Project in Flask (Using PIP + Virtualenv)

    pip install virtualenv                  # install virtual environment
    virtualenv ENV                          # create virtual environment by the name ENV
    source ENV/bin/activate                 # activate ENV
    pip install -r requirements.txt         # install project dependencies
    python app.py                           # run the project
    deactivate                              # close virtual environment once done
    

Bug / Feature Request

Important links:

• Dataset
• Project Folder on Drive