Current global situation and implications it has on our perception of everyday issues

What it does

Shows scare and cases level based on available data

How We built it

Using NLP and ML techniques, with API created in Python and a browser client

Challenges We ran into

Preprocessing and classification of data (medium accuracy), integration of our modules, size of data

Accomplishments that We're proud of

Created a ML model which is able to classify sentiment of an article based on its TF-IDF vectorization, fast processing on backend side with lots of data, nice style of presentation in browser client, ability to see the site on mobile devices without a problem

What We learned

Some tools for integration and cooperation, new Python libraries for NLP and API, integration of React and OpenStreetMap

What's next for Corona Scare Map App

We can try to develop the app for more countries and their administrative territorities, create better predictive model for article sentiments, add more languages to sentiment prediction (e.g. English), add more data sources

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