An application to analyse the delays rate of flights based on airlines and airport information. It deals with three different aspects: airlines, airports, reason for delays. Using the data collected over the period of a year, it creates a visualisation based on the parameter you choose. Now use TimeFlies before booking your next flight and be on top of not choosing a airlines which gets delayed. This application, if used correctly can save thousands of dollars and save a lot of time.
The major challenges which came up in this assignment were developing an appropriate and useful visualisation of the dataset. The visualization should not be a basic bar chart or line graph but should be in accordance to the flight path data. We planned to line the map with flight path and then highlight the ones which get delayed using different colours. But due to limitation of dataset and restriction of time, we were unable to do that. Another challenge was working in the Shiny framework, which is very user friendly but can be restricting at times. But during the process we learnt how to use different packages and integrating them in the Shiny App.
The expansion of this project can be creating a light simulation and understandings which flight would be delayed using the machine learning protocol. This can create a prediction chart and would help travel portals decide the ticket prices along with airlines controlling their flight traffic in order to reduce flight delay and cancellations.
Link to app: https://ayansaraf.shinyapps.io/TimeFlies/
Ayan Saraf
Zhining Wang
Wanxiang Cai
Vibhu Iyer