• A Gmail account with archived mails from the Probo mailing system
• An app password to login to Gmail.

If you don't have above, you can simply play around by selecting to use cached data when prompted.

> python main.py produces the current_predictions.png - and overview of the probability of an event happening the next 14 days after the last event.

• Parsing receipts for laundry bookings from the digital owner's association management tool Probo.
• Parsing my Gmail using imaplib for email confirmations for laundry slots. Only mails NOT deleted (not archived) will be retrieved.
• Currently classifies a laundry-session as a binary event. (Future work would be playing around with the length of the sessions.)
• Fitting a logistic regression model on the data to obtain the probabilities for a laundry session to take place $k$ days after the last session.

There's a lot of competition for the laundry machines in my apartment building. I wanted to know when the best time to do laundry was.

• Find methods to interpolate gap in data from my semester abroad (January 22' to May 22').
• Lookup calendar to find best slot for laundry session.
• Play around with time series models
• Make script update cache with newer data than latest entry