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Application predicting class attendance based on weather » Historie » Revize 2

« Předchozí | Revize 2/20 (rozdíl) | Další »
Alex Konig, 2021-03-18 22:57


Application predicting class attendance based on weather

Data

Application uses datasets containing historical information about weather on campus and attendance based on JIS card verifications and historical timetable informations

> rozvrh udává kolik lidí tam mělo bejt, plus vzít JISky kolik tam bylo (> můžu mít procentuelní zastoupení) / ?autentizační systém kolik lidí se přihlásilo na pc?

Weather

Weather data for model trainig contains 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 user
Current options for data sources

Notes:
Values sunny/overcast/partly cloudy will be translated into lux values
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?

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

- prediction model (pluses and minuses ?)
- neuron network
- bayes

Aktualizováno uživatelem Alex Konig před asi 4 roky(ů) · 2 revizí