During this Coronavirus pandemic, it's important to stay informed about the news and latest information about Coronavirus. We made a website that has important information about everything related to Coronavirus. When we're all informed about Coronavirus, we're making a world of difference by defeating Coronavirus, one step at a time.
What it does
Our web application performs several tasks. It gives the user a daily digest of the latest news on Coronavirus, as well as a visualization that shows cases of Coronavirus throughout the world. It also shows trends of Coronavirus, and shows thing.
How we built it
We needed data the coronavirus cases, along with financial data. For the coronavirus cases, we pulled data from the Johns Hopkins University Center for Systems Science and Engineering GitHub, as it was easily accessible. For financial data, we used AlphaVantage for its comprehensive portfolio. We stored this data using MongoDB Atlas because of its ability to store and retrieve data quickly. To process the data into our website, we used the numpy and pandas libraries.
We used the gensim library to generate summarizations of relevant news about Coronavirus. Our text summarization performs a word processing algorithm, term frequency–inverse document frequency (TF-IDF), to find the most relevant words and strings them together in a coherent manner.
For the visualizations, we used the matplotlib, seaborn, and plotly libraries. We created line graphs for predicting coronavirus cases and financial data and formed map visualizations for coronavirus cases worldwide.
Finally, to deploy our website, we used Flask, a web micro-framework which generates and serves templates among other features.
This was all done using Python.
Challenges we ran into
We ran into the challenge of modeling the data for Coronavirus confirmed cases, as each country had a different distribution of cases and different timing. We would like to revisit this in the near future.
Accomplishments that we're proud of
We're proud of the various visualizations, as well as the overall quality of the website.
What we learned
We learned about a lot of technologies, including data visualization software, text summarization, and website development. We also learned about trends related to Coronavirus in the process of making our application.
What's next for CoronaDigest
In the future, we would like to future investigate the machine learning models for predicting future cases of Coronavirus. We think a good investigation of this feature could help manufacturers of essential needs understand where to distribute supplies.
Try It out
html, jupyter-notebook, makefile, python