We built a website that allowed people to view real-time district wise +ve reported cases, help people in need of emergencies and see the trend of COVID-19 spread. We now try to observe the environmental factors that may influence the spread of Covid-19. Our initial suspect is the weather and climate factors and believe that they play some role in this.
What it does
If successful, the features that affect the spread could be used to lower them by machine or deep learning algorithms.
How I built it
We collect data from our website, covindia.com, and weather data from onpoint API. We then use python libraries and dataiku to clean any noise and refine the data. We graph to look for any pattern.
Challenges I ran into
A lot of noise and too many parameters (e.g. lockdown and tests done in different districts) that affect the number of reported cases.
Accomplishments that I'm proud of
We have enough observations, data and resources to publish it on blogs.
What I learned
Data cleaning's a bitch. Noise is a headache.
What's next for The Covindia Project
Try It out
covindiaapi, dataiku, onpointapi, python