Analysis, visualization and prediction of COVID-19 pandemic spread using machine learning

Over the years, human beings have faced several health issues related to the spread of viruses. After Spanish flu, Nipah, and Ebola, now COVID-19 has thrown a serious threat to society all over the world. The rate is increasing exponentially, prevention, proper measurement and strategic action are the need of the hour to combat this pandemic. This paper focuses on analyzing COVID-19 dataset using numerous machine learning (ML) algorithms, visualizing the results and evaluating the performance of the best algorithm. The spread of virus outbreak has caused thousands of deaths across the world and is considered to be a pandemic according to WHO reports. There are a number of methods in preventing the risk of infection manually such as predicting the risk of infection, screening the patients, using chatbots to analyze the risk of infection, identifying and speeding up drug development, etc. In this paper, we mainly experimented with KNN, ANN, SVM, linear (LR) and polynomial regression (PR) methods to learn and analyze about pandemic spread. To achieve this, we have considered COVID-19 dataset of Karnataka state. Mostly, district-wise confirmed, active and death cases have been considered for this work. In addition, we have also performed gender-wise infection spread and presented a cumulative dashboard for overall district-wise active, confirmed and recovered cases of Karnataka.