The members in our team love to travel and we love exploring different trends in data.

What it does

Look at past data on Trip Advisor and other data sources with Airlines, Hotels, sightseeing activities, and more which will allow an individual to make smarter decisions about how they should spend their money on future travels.

How we built it

We used Python using libraries like Matplotlib and Seaborn to create the visualizations based on the travel datasets from Japan, Las Vegas, Europe, and then extracted the dates, prices, airlines to create the plots and then determined the trends that we noticed which will be useful for the user to determine where they should go next. The front end UI interface was created with HTML and CSS. We also used SQL to pre-process and extracted the relevant variables into a new CSV file which we used Python to visualize.

Challenges we ran into

Parsing the data files because they were very large and held lots of information so sometimes it took a while to execute the python code.

Accomplishments that we're proud of

We are proud because it was Sraboni's first time working on a hackathon project with SQL and Python! Additionally, we thought of the idea early on when we arrived at University of Maryland. Our team dynamic collaboration was spectacular because we used our complimentary skillsets to execute a project that we, ourselves, would love to see executed in real life.

What we learned

We learned SQL, more experience with Python, and got an Intro to UX/UI Design from the workshop that we attended.

What's next for Travel Insights

Integrate Trip Advisor's internal data and make partnerships with travel agencies and data collection companies(ex.Nielson, IPSOS) for more reliable and general data. After collection information for a year or more, starting some predictive modeling for future analysis.

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