all benefits graph
When we came here, although we were open to ideas, we had couple of ideas in mind, to implement at this hackathon with domains ranging from health to education, but none finance based. We were then team of two, and were looking for teammates and when interacted with people, one of them was willing to join if it's a financial hack. So, we then brainstormed ideas on financial category and came up with an idea for "SSS". There are employer sponsored benefits like 401K retirement plan, Health Savings Account (HSA), Flexible Savings Account (FSA) and Shares, which are usually individually maintained by third party. Often many employees get into confusion and are sceptical if they really should invest into these accounts and if investing, what should be there contribution? Their confusions and questions can be easily answered by SSS.
What it does
We are solving this problem by implementing an integrated solution with all the employer sponsored benefits, how much net value (total money) and face value (money in hand) will the employee be getting, if he is investing into each of these benefits. And how much profit and money would he be getting contributing into these accounts. We show different graphs one consolidated to all the employer sponsored benefits, and one each for the benefit. These graphs can help employee to easily make decision, whether if it's profitable to invest into each of the benefit, if so, how much would mean profitable.
How we built it
We built it using Python as programming language.To integrate front-end, back-end and middle-ware, we needed a framework. We were weighing between Django and Flask. We also wanted to learn new at this hackathon and improve our coding skills. Since we used Django in previous hackathon, we wanted to try out Flask! And as we started coding, and came to the point of data visualization, we were researching on how to represent our data and what python packages needs to be used. Though we juggled with few, we finally stopped at matplotlib - Python Library.. We then had to chose what kind of visualization we need to show, how to represent data and what data to represent. Please look at our screenshots to see what we built.
Challenges we ran into
Our initial challenge was to know if this kind of any application existed, after research, we feel proud to say, that none like this exists. The first night with team of 3, we came up with the design and layout of our application and went to some sleep. When we woke up, we realized one of teammates (the one for whom we came up with financial hack) was gone. We expected him to comeback sometime the 2nd day of hacking. It took afternoon to realize he was never to come back. But as people say, when god takes one, he gives another one. Someone looking at my post from slack channel #teammate and contacted us. When explained, she quickly like the idea and willing to join. Hence we still remained team of 3.
Our major challenge was to find compatible python library. We looked into plotly, Bokah, numpy, pandas etc. We even started coding in plotly, numpy, pandas. We ran into technical issues like plotly needed creation of account, numpy and pandas had dependency issues. And since our team consisted of both Windows and iOS environments, we had to make sure what we were using works on all platforms with out errors. After quiet research and lot of effort, we decided on using matplotlib. Even though we has issues in making it work, we were successful in completing it.
When the time came to data visualization, we didn't know what data to represent, what values should be used on x-axis, y-axis. And after a detailed discussion sessions, when we together agreed on what data to represent, there was an other one, how do we want to represent? After another discussion session, we decided to display it as histogram.
Accomplishments that we're proud of
Ofcourse, we are proud of our innovate app, and the whole journey of this hackathon. We are proud of everything we did for this app. Starting from brainstorming ideas to, banging our head to, completing our project successfully. We used real time tax bracketing, tax related data. So next years tax filling would be a cake walk for us. :p
What we learned
Every brain storming session, every discussion we had, every error we encountered, provided us a chance to learn something new every time. All the research we did before deciding on tools and technologies were totally worth it. Every aspect of this project helped us learn and grow more.
What's next for SSS - Smart Solution for Sceptic
In SSS we just showed visualizations in employee perspective, we want to extend it to employer perspective too. We want to show visualizations on which benefit is most used by employees, which employer benefit is efficiently used by employees, what changes can employer make to encourage employees to contribute better etc.