Financially Engaged

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  Example of how we analyze the user's habits to give them recommendations to save money

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  Sentiment analysis of social media can be used to indicate the user certain spending habits that may be affecting their online presence

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  I used a combination of API's to figure out where the user can enact necessary purchases at cheaper cost

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  My program identifies which local supermarket or store from which the user can buy a certain item cheaper

Inspiration

Too many times have broke college students looked at their bank statements and lament on how much money they could've saved if they had known about alternative purchases or savings earlier.

What it does

Financially Engaged helps people analyze and improve their personal spending habits. Financially Engaged uses bank statements and online banking information to determine areas in which the user could save money. I identified different patterns in spending that I then provide feedback on to help the user save money and spend less.

How I built it

I used Node.js to create the backend for Financially Engaged, and I used the Viacom DataPoint API to manage multiple other API's. The front end, in the form of a web app, is written in JavaScript.

Challenges I ran into

The Viacom DataPoint API, although extremely useful, is something brand new to me, and there were few online resources I could look at. I have to understand completely how the API simplified and managed all the APIs I was using.

Accomplishments that I'm proud of

My data processing routine is highly streamlined and modular and my statistical model identifies and tags recurring events, or "habits," very accurately. By using the DataPoint API, my app can easily accept new APIs without structurally modifying the back-end.

Built With

• express.js
• javascript
• node.js