Inspiration

How to extract the info hidden behind the data. We are the ones make the data speak. We want to utilize the data to help Tailored Brand boost their revenue.

What it does

We built a recommendation system for Tailored Brand company.

How we built it

We make feature selection. Clean data and built a stunning model to predict if a customer's preference. Hence we can advertise on products interesting to customers.

Challenges we ran into

  1. Technical issues we have met. We are restricted to do job on remote virtue machine without internet connection. So we 1.1 can't install tensorflow which prohibits us from building deeper neural networks. 1.2 encountered unstable remote connection to the virtue machine. We are frequently kicked out from VM for no reason. 1.3. are restricted to only have 2 logins so there are at most two machines can do analyzing at one time.
  2. The complicated raw data rendered huge obstacle for our feature selections. ## Accomplishments that we're proud of We thought we have yet made an available model for prediction. ## What we learned Team work spirit. ## What's next for hackSC_Weltmeister March on!

Built With

  • jupyter-notebook
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