Inspiration
The intersection between data science and business fascinate us - we'd like to explore this via helping digital transaction platform forecast its future business and perform better.
What it does
The project forecasts the digital transaction based on Bill.com's historical Data and provides a metrics that quantified relationships between agency and vendors.
How we built it
We applied several machine learning techniques to forecast data. The platform we used are R and Python.
Challenges we ran into
The time differences among teammates and the large categorical dataset turns out to be the primary challenge we faced.
Accomplishments that we're proud of
We managed to collaborate closely as a team and overcame several data analytics challenges.
What we learned
We not only learned to analyze data, but also learned how to teamwork and communicate under tight time constraints.
What's next for Transaction Forecast Challenge - Bill.com
Due to time constraints, we do not build a neural network for prediction - this will be our next step in the future.
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