Description as a Tweet:

A platform focused on fintech. The user uploads a budget and the algorithm will make a recommendation of a trip to a desired location that fist within the budget. The platform will also monitor the user's savings and motivate the user to reach the goal.


Airplanes tickets can sometimes be extremely cheap and on other days they can be over priced, this is mainly caused by the change in demand throughout the days.
It is hard to keep track of when the pricing will rise or decrease, which could ruin one’s budget for a trip. Anyone who seeks to travel, usually save up a sum of money for them to spend however they please, yet sometimes an unexpected rise in the pricing of plane tickets may ruin it. As a result, many travelers have fallen into debt after returning from their holiday trips, losing more than $1,100 just from poor budget management. Throughout the years, the poor management of savings has caused Americans to spend around 10% - 15% of their annual income on vacations, and if we take into consideration someone that makes approximately $70,000 a year, that's around $9,000 spent just in one vacation trip.

Since many people are having a hard time dealing with their expenses, we decided to provide a solution to this management problem.

What it does:

NOMAD is a budget manager that provides a general idea of how much a traveler will have to spend and how much they could save on their trips.

How we built it:

NOMAD finds the best planned trips for the user utilizing a type of artificial intelligence known as a recommended system on case-based reasoning (CBR). We used the method retrieve, reuse, revise, retain for the algorithm. With this basis we created an algorithm that can adapt to specific requests from the user.

Technologies we used:

  • Javascript
  • Node.js
  • Express
  • Python
  • AI/Machine Learning

Challenges we ran into:

Due to the difference in time zone, we had a few misunderstandings about the deadlines.

Accomplishments we're proud of:

We were able to create a functional web page that covered all of the parameters we were aiming to include. We were also able to finish our project on time.

What we've learned:

This project allowed us to know how much excessive expense an individual spends in only one vacation and we noticed the consequences of this way of spending money, like serious debt.

What's next:

We hope to get funding for this project and make our algorithm more user-friendly. We also plan on getting involved with companies that specialize in finding an extensive variety of hotels, airlines, touristic activities, etc.

Built with:

Our project was made with HTML/CSS, Node.js, Express, Python, and we used AI/Machine Learning, using the NLPK library.

Prizes we're going for:

  • Best Finance Hack
  • Best Documentation
  • Best Venture Pitch
  • Best Web Hack
  • Funniest Hack
  • Best Mobile Hack
  • Best Machine Learning Hack
  • Best Beginner Software Hack
  • Best Beginner Web Hack

Team Members

Carlos Gomez
Carlos Quihuis
Alexis Hernández Gollas
Rafael Pesqueira
Luis Fernando Lugo Quiroga

Table Number

Table TBD