As a team of 4 students, slowly transitioning to adulthood and gaining the financial aptitude necessary for success, TDs pitch naturally inspired us and spurred us into a creative frenzy. The TD Student Analyser hack strives to address the problem statement introduced by TD:

How might we best provide financial help needed by those transitioning from student-hood to adulthood?

In order to address this hack, it was first necessary to look at what is was young people were trying to achieve during this transition, as well as to recognize that what is important for one student might not necessarily be important for another student. Therefore, it was of key importance to allow the students a customizable and judgement-free financial literacy program.

What it does

The TD Student Analyser is an interactive data-driven dashboard that visually displays a students' spending habits, relative to others in their cohort and other age cohorts, to allow them the opportunity to personally assess their spending habits against those of other folk in their age range and that of where they are gradually transitioning into.

How we built it

We built the TD Student Analyser by first breaking it down into two distinct parts: (1) The interactive front end, (2) and the data-driven back end,

The front end was written on react in javascript, and leverages a variety of plotting widgets to create an attractive and user-friendly interface that the students will be using. The back-end was written in Python and leverages massive amounts of data from the TD-DaVinci API to analyse distinct clusters within the data (identified to be objectively "good" spenders and "poor" spenders, based on their spending:savings ratios across the different fields they can spend their money on.

Challenges we ran into

One of the biggest challenges we had that had us caught dead in our tracks from the get-go was figuring out a feasible idea to implement. The whole of the first day and the morning of the second day was entirely dedicated to brainstorming and bouncing ideas off of mentors and sponsors in hopes of landing on The Perfect Idea.

Gradually, we began to identify specific functions we can implement that can be of use to a student, as well as different features that would provide value to the overall hack. Hours into hacking on the second day and we were able to bring forth our hack into creation. The rest of that evening was spent polishing the hack, as an elegant implementation is key for systems which are very much geared towards the end-user.

Accomplishments that we're proud of

We're incredibly proud of being able to dissect a real-world problem, and use an API with simulated (but highly realistic) data, to create a unique solution. It was also interesting how the team was able to divide up work and aide one another in each others' tasks in order to arrive at the final solution in a well organized and time-appropriate manner.

What we learned

From this hackathon experience, we've learned the value of appropriately matched team members (both in skill as well as passion) for team morale, compatibility, and optimal hack quality. Prior to HackTheNorth, none of the members of this team knew one another, however through the #looking-for-team Slack channel, and a few weeks to talk to one another to determine skill and personality, the value of seeking appropriate team members really shone through.

What's next for TD Student Analyser

Next steps for TD Student Analyser include firstly enabling the backend to use the entirety of the data set available for a larger variety in data. As well, it is always a goal to further use more complex machine learning models to continue to make accurate and fast predictions in order to optimize the user experience.

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