In this project, we have proposed methods for predicting Covid-19 in patients using machine learning techniques. The machine learning techniques that were used include Time series forecasting. We hope that our forecasts will be a useful tool for governments and individuals towards making decisions and taking the appropriate actions to contain the spreading of the virus to the degree possible. Regardless of what one’s beliefs are, we believe that their associated uncertainty can and should be an integral part of the decision-making process, especially in high risk cases. Apart from the significant public health concerns, the dangers imposed on global supply chains and the economy as a whole are also considerable. Risk-averse people can focus on the worst-case-scenarios and act accordingly. Deciding to discard any formal, statistical forecasts and acting conservatively, still implies an underlying forecasting process, even if this process is not formalised.
The data is available from 22 jan, 2020.
Column Description
• Sno - serial number
• ObservationDate - Date of the observation in MM/DD/YYYY
• Province/State - Province or state of the observation
• Country/Region - Country of observation
• Last Update - Time in UTC at which the row is updated for the given province or country.
• Confirmed - Cumulative number of deaths till that date
• Recovered - Cumulative number of recovered cases till that date