Flight-Price-Predictions

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/shoaib-1457/Flight-Price-Predictions.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/shoaib-1457/Flight-Price-Predictions.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

If you find a bug (the website couldn’t handle the query and / or gave undesired results), kindly open an issue here by including your search query and the expected result.

Important links:

Dataset