This week, we accomplished the trainning model

• We can show the running model with the desired data
• We have deployed our app

This week we will:

• We have filled the database with the current real data from mibici, which are nearly about 276 stations
• We have predicted bike availabilities per station (next day prediction). Here it is a data visualization.

• We have refactored the backend (5 endpoints)
• We have documented in postman

This week we have done the following:

This week so far, we did and delivered the following:

• Backlog with detailed tasks for every sprint planning:
  • https://tree.taiga.io/project/vic3r-bike-rebalancing/backlog
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• As well, we have redesigned the problem:
  • Now we will analyze clusters of respective regions, neighborhoods in every city which must have MiBici.
  • The new model which we are going to implement will be a Bayesian Classification/Markov Chains instead of the Multinomial Logistic Regression.
  • We are analyzing several solutions in Python, one that we have considered so far is the Random Forest algorithm.
• We have defined the team organization

• The models have been defined in the backend
  • https://github.com/vic3r/smart-cities-back
• The mockups have been finished for the web platform
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