Creating a Dashboard, visualizing ICU occupancy rates in the Austrian federal states. A ubuntu 18.04 server hosts an Apache & WSGI installation, which runs a Plotly Dash via Flask. The data is automatically scraped and served via a GeoServer.
The dataset that is worked with consists of roughly individual 40.000 data points of hurricanes. The aim of my workflow is to predict the future speed and bearing of the individual hurricanes using a Neural Network and a Random Forest Regressor. The data will be cleaned, features created and the prediction will be assessed for it’s accuracy as well as the two methods compared.
Using pgRouting, a navigation functionality is implemented based on a spatial database of a fun fair, where the user can input his or her position and be routed to a nearest Point of Interest. A Python DB connector dynamically creates the SQL queries and visualizes the results.
Within the end of term assignment for the Practice of Software Development course, a KML file is created visualizing both the result of an OGC Web Map Service request as well as the parsed information from a CSV file containing tweets and their locations.
Additionally, the tweets are analysed regarding possible profanity and
visualized accordingly.
