Free money machines have always been fantasy to most. They may not be real, but we can get close.
We've seen how rapidly the finance scene has changed due to the growing role of modern technologies. It only seems natural to use computer power to our advantage. With Money Machine, the average user spends their money wisely with minimal effort.
What it does
Behind the scenes, Money Machine uses machine learning and heaps of stock data to train a model to be used for predicting stocks live-time. Users are able to create accounts and log into their dashboard to deposit and withdraw money. Money Machine provides them choices of good-looking stocks to invest in.
How we built it
The front-end is made of HTML, CSS, and JS. We chose to use SASS with Bulma as our CSS framework to customize our styling of websites. We chose to use Flask as our back-end framework. Firebase is used for user authentication and account management. The machine learning library Keras and data from the Goldman Sachs Marquee API was used to train models for stocks. Graphics were created in Illustrator.
Challenges we ran into
- We took a long time to get settled down with a hack idea.
- There were many first-times for us, forcing us to learn everything as we work.
Accomplishments that we're proud of
- We used Flask for the first time
- We used Machine learning library for the first time
- We used queries with auth tokens
- And we managed to put it all together!
What we learned
- Coming up with a hack idea in advance would save lots of time
- Next time, it would be useful to plan what languages and frameworks we intend on using
What's next for Money Machine
- Incorporation of more technologies (example: blockchain) into our idea
- Optimize the machine learning process
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