Application predicting class attendance based on weather¶

Data¶

The application uses datasets containing historical information about the weather on campus and attendance based on JIS card verifications and historical timetable information

• JIS data http://opendata.zcu.cz/Snimace-JIS.html
• Timetable data http://opendata.zcu.cz/Obsazeni-mistnosti.html
• Weather data http://opendata.zcu.cz/Energeticky-dispecink.html
• Authentication system http://opendata.zcu.cz/Autentizacni-system.html

The timetable provides the baseline attendance, JIS card data can be used to find out the real attendance (and therefore fluctuation in attendance). The authentication data could be used to find out how many people have logged in during classes.

Weather¶

Weather data for model training contains the following information: date, temperature, wind, rain, light in k lux

User can input weather values manually, by selecting options from a form

• values: temperature, wind, rain, sunny/overcast/partly cloudy
  There also is an option for data to be automatically downloaded from a server upon request from the user.

Current options for data sources

• RSS from yr.no http://www.kanonbra.com/rss/yr_forecast_rss.php?language=en_US&location=%C4%8Ceahkka/Plze%C5%88sk%C3%BD_Kraj/Plze%C5%88
• JSON from wttr.in http://wttr.in/Plzen,czechia?format=j1
  App provides the ability to ask for data from today or days in the future, and give hourly time specifics (this time will be then approximated into values morning, noon, afternoon, night)
  Again the app will use the following values: temperature, wind, rain, sunny/overcast/partly cloudy

Notes:
Values sunny/overcast/partly cloudy will be translated into lux values
The temperature will be evaluated with certain tolerance (probably determined experimentally)

Datasets¶

Datasets are updated every once in a while with new data. How often update?
-> has to trigger re-training the prediction model, probably do not want to update too often.
Detect when there's new data, check every day? every x days?

New OpenData is added monthly. How complex will be the training? Can it be done on the target device? Will there be a server from which a new model could be downloaded?

Design¶

The output of the Algorithm¶

We need to determine what should be the actual prediction:

• A concrete number of students in a class,
• information about relative occupancy rate.

Motivation¶

TBD

Implementation¶

The application is a mobile app, possibly WebGL app
It is developed in unity which provides more possibilities for export with modifications for given platforms

*Mobile app
Android support 5.0 and higher, use Unity UI tools to provide touch support.
Deliver an apk of the application to download outside of the Playstore or optionally, create a developer account (perhaps at a later date once the project is finished).

*WebGL app
Test options to embed a Unity Canvas app in a simple website. Possible issues with support in various browsers, but unlike the mobile app, the university could provide hosting.

Prediction¶

a) Naive Bayes classifier
b) Neural network

Could classify days into high-medium-low attendance days, or more granularly (very high, very low...).

Risks:
We have very little experience with neural networks. Bayesian classification might provide good results with much less effort.