Flight Finder

Inspiration

Traveling to new places can be intimidating. Spontaneous road trips or even train rides are the common methods to visit new areas. However, to completely experience a new culture and place, one must travel the world. Plane rides often are planned many months in advance, considering the greater costs, commitment, and effort that flying entails.

Our team wanted this to change. We want to make flying more spontaneous and convenient so that traveling to new, foreign places can be done easily. In a world that is increasingly more interconnected, traveling to far areas should be more common.

What it does

Flight Finder is a web app that instantly searches through future flight data so that you can be matched with a trip. All you need is your nearest airport and flight date to find a flight. The web app, sorting through hundreds of thousands of data, matches you to a destination and loads relevant information about it. On top of flight information, the user will find photos of the destination and a concise Wikipedia summary all in one convenient page.

Our main idea is to randomize the travel destination. By utilizing this web app, users will travel to places that are previously overlooked or unknown, to develop a greater understanding of the world and different cultures. Users are no longer confined to common and mainstream travel destinations and no longer have to go through the hassle of scheduling.

How we built it

Utilizing JetBlue's future flight data set, which came in a CSV, we used Python to sort and organize the flights. The data entries were then converted into dictionaries so that they could be used more conveniently within the program. Given user input, the program outputs a round trip that satisfies the user's location and available times. The locations' summaries are instantly generated through the implementation of Wikipedia API in Python. The web app was primarily built with Django in tandem with HTML/CSS.

Challenges We ran into

JetBlue's flight data came with over 100,000 entries which made working with the data difficult due to the sheer size of the CSV file. We came across many errors that could not be searched up due to wifi connectivity issues that at times halted progress. Finding a way to most efficiently store the entries into dictionaries was challenging because the data came with many parameters, such as origin, destination, flight time, and price, and it was not clear at first how they should be organized. The use of the Wikipedia API required learning an entirely new API.

Accomplishments that we're proud of

With Flight Finder, we built a web app that converts multiple steps of planning flights to just one easy click-- making flying a much more convenient and attractive mode of travel. Users will no longer have to go through the hassle of looking through potential destinations and searching up each of them on Google. We were able to work with data at a massive scale and parse it effectively in a way that works instantly from the user's perspective.

What's next for Flight Finder

We want to use more APIs in the future so that the user can obtain more relevant information with a single click. Moreover, we will optimize our sorting and search algorithms in order to make the process on the user's end even faster.

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