Inspiration

Invest made is an application made for improving the financial literacy among the teenagers and increasing the number of teenagers investing in the stock market. This idea has tarted after looking at the following alarming statistics

  1. $19,978 the Median education-related debt, according to the 2016 Wells Fargo Millennial Study
  2. 63% of millennials would not regret not buying shares and watching the "value move higher”.
  3. Six in ten millennials have less than $10,000 saved for their post-working years This is alarming because on a macroscopic level, this would effect the market as a whole and make is stable and function properly it he future.

What it does

In order to make investment easy for the youth to understand it starts off by taking a list of companies that the user follows on Facebook and uses this as a base in order to teach a person the basics of the stock market.

How we built it

We built it using Python, Django, Javascript,D3.js, Bootstrap. The back end recommendation system was developed using Python(numpy, Scikit Learn) by collecting data from the FB Graph API and stick ticker data from Alphavantage.

Challenges we ran into

As always, the most challenging part of the project is to integrate different services together. Apart from the integrations we faced issues in the following places.

  1. Developing recommendation system for the users based on users than the collaborative or hybrid filtering.

Accomplishments that we're proud of

Integrating Django with D3 to dynamically visualize the results that we obtained is something that was hard for us initially as we are both new to the same.

What we learned

  1. Django, D3, Dajngo, D3 integration, FB Graph API.

What's next for Investment Made Easy

To help user's create portfolios and compare them with other users in order to improve their profiles.

  1. As this is a prototype, we developed this based on our FB login ID , for a production scenario, we envision creating a system where analytics happen at the user end unlike the traditional bigdata analysis.
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