Inspiration
We aim to provide truly effective business advice.
What it does
It helped the investor to figure out which industry is most worth investing in.
How we built it
We classify tables into several categories, and then we do an outline with an expected graph of the draft on our iPad. After this, we shared our ideas with the mentors, Sam and Elliot. After this, we pull all things up together and distribute the tasks.
Challenges we ran into
Because the table has too many sublevels within the redundant calculation of data, we are not able to clearly identify each.
Accomplishments that we're proud of
We have an extremely high level of completion. We work through data selection, data cleaning, and data visualization and finally provide analysis and conclusions based on that.
What we learned
I would like to thank you, Harry, for bringing the team new knowledge of the ARIMA model. And Elliot guided us to think about using different ways of modeling, such as randomization. We learned about how to effectively exchange our ideas with groupmates and collaborate more effectively.
What's Next
If we are able to collect more data points in the future, we could feed our ARIMA model with it and perhaps provide more reliable investment suggestions. We plan to dig up more indicators and factors that are connected with the data and the model. This can help us develop a more flexible system to face different clients with various investment preferences and demands.
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