Brown Family Datathon Code

Inspiration

We really wanted to focus on the socioeconomic variables of the dataset and how it could correlate to infant mortality.

What it does

We have multiple graphs that show different correlations with certain socioeconomic factors along with factors such as infant mortality and family size.

How we built it

We used the pandas, NumPy, and matplotlib libraries to implement data onto different graphs for us to interpret.

Challenges we ran into

Formatting certain graphs was definitely a pain along the way. Figuring out the syntax of a language we were originally unfamiliar with was a doozy, but we were able to pull off quite a bit. Another challenge was figuring out what data we should use and compare, figuring out the different biases there may be, and properly communicating that.

Accomplishments that we're proud of

Going into this project, we were not well-versed in Python at all. However, coming out of this, we know now how to interact in a python environment and how to learn to learn Python and potentially other languages as well. Creating the various graphs was very satisfying as well.

What we learned

The main overarching thing that we learned as learning how to learn Python. Furthermore, figuring out how to download different libraries and then use the documentation from that library to properly implement what we want was a great thing that we learned.

What's next for Brown Family Datathon Code

Get more gooder ;)

Built With

• python