According to a study done by American Psychological Association, Money and Work are the top two sources of stress in an adult's life. In 2007, these two sources comprised of 65 percent of stress, and In 2015 they comprised of 67 percent of stress. We explored the data-set from the National Financial Well-Being survey to learn more about how a wide range of factors relate to consumers' financial well-being.

What it does

We have a short survey for consumers in our application that comprises of 10 most important factors that affect the financial well-being of a person. Depending on the results of the survey, and where they stand relative to the data we gathered, the app offers an exclusive financial plan to the user based on their needs.

How we built it

We collected the data-set from Kaggle, and cleaned it by using the Pandas and Numpy libraries in python. We then built our scatter plot using the libraries hosted by the plotly cdn and the Cloudflare cdn. Plotly is the part of D3.JS framework. We used plotly libraries for data visualization. We also used Google fonts API to load the Raleway font. We then stored the clean data in a data variable. After that we parsed through the whole data using the D3 Javascript library. Now we have a form that is storing the data on a local CherryPy server and looping through the values to calculate the user's financial well-being score. Once it has the average, it's pushing the value to the "y" value data array. this result is then displayed with an annotation above it that was specifically developed to be accessible to color-blind people. X values are auto incrementing from 0 to 5001. This whole process gives user a scatter plot in the end that represents the financial well-being of the user in comparison to all other people in the dataset.

Challenges we ran into

It was incredibly difficult to find a suitable dataset in such a short period of time, and that impacted our application greatly. We ran into various challenges. Since back-end development is new to us we had difficulty setting up a CherryPy server. We were stuck at the configuration process of the server. We also had difficulty with communication & team work.

Accomplishments that we're proud of

In just 24 hours, we built an application using very big data. Our idea solves a serious real world problem. Both of us in the team did not have much experience with the technologies we worked with. We stayed up all night to polish our application!

What we learned

We learned to setup a CherryPy Server. We were new to learning D3.

What's next for FinTopia

We are planning on making a fully customizable financial planning application

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