We were excited to participate in our first datathon, so we thought we would try our hand at the beginner level challenge just to dip our feet in the water. The city search data caught our attention because data by city yields valuable insights for our understandings of social and political factors around the world. We wanted to create a program that was useful for users who are looking for a place to move to, looking for a place to study abroad in, or even looking for a place to vacation in. If the user is looking for a quick and simple recommendation, this program is perfect for them.

What it does

The program takes user input on importance (on a scale of 1-10) of different categories. These categories include healthcare, pollution, purchasing power, movehub rating, crime, quality of life rankings, and personal, political, and economic freedoms.

How we built it

We wrote a basic level Python script using the Panda and Numpy libraries to iterate through csv files in order to aggregate the data elements into a cumulative score over 8 different factors by city. These cumulative scores serve as the basis for how we evaluate the best fit of each city by user, using direct numerical user input to weight the different categories.

Challenges we ran into

One challenge that we ran into was that some of the data we wanted to use was only available at a national level, while some of the data we wanted to use was only available at a city level. In order to use all the data, we had to manually match up all of the cities with a country using dictionaries. We could then incorporate the generalized national data into our city rankings.

Accomplishments that we're proud of

We're proud that we were able to produce a functional program as first time datathon participants. As a group we broadened our understandings of Python libraries such as Panda and Numpy.

What we learned

We learned to use Panda's functionalities to iterate through data sets, insert and remove columns, merge categories, find valuable data, and weigh different factors.

What's next for Epic City Searcher

If we had more time to work on Epic City Searcher, we would add more measurements. These measurements might include happiness rating, security, public services rating, transportation rating, social cohesion, human rights, economic growth, and economic equality. We would also add a visualization that shows the cities on a map.

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