The 2019-nCoV is a highly contagious coronavirus that originated from Wuhan (Hubei province), Mainland China. This new strain of the virus has struck fear in many countries as cities are quarantined and hospitals are overcrowded.
I've used here a Kaggle Dataset Coronavirus 2019-nCoV updated daily, based on John Hopkins data to track the spread of Covid-19 across the globe in order to provide necessary information for governments to tackle it by learning from its trends globally.
The Kernel will be rerun frequently to reflect the daily evolution of the cited dataset.
What it does
It tracks the spread of Covid-19 across the globe in order to provide necessary information for governments to tackle it by learning from its trends globally.
I've started by analyzing the data for Mainland China, where the pandemic originated. I've shown time evolutions and snapshots of Confirmed, Recovered cases as well as Deaths. Then moved to explore the evolution of the pandemics in the rest of the World.
For both Mainland China and the rest of the World I'm also showing the snapshot and time evolution of mortality, calculated in two ways: as Deaths / Confirmed cases (most probably an underestimate) and as Deaths / Recovered cases (most probably an overestimate). .
How I built it
Python & Data Science
Challenges I ran into
Data Analysis & Predictions
Accomplishments that I'm proud of
Generating amazing results in less than 42 hours.
What I learned
Global Covid-19 trends and spread.
What's next for Tracking the spread of Covid-19 around the World
Building solutions to help countries fight this off with the help of Data Analysis.
Try It out
api, artificial-intelligence, data-analysis, data-science-toolkit, deep-learning, machine-learning, python