Covid-19 Impact Analysis and Death Prediction Using Machine Learning

Abstract

The scale of the COVID-19 epidemic, as well as the global lockdown consequences, are still unknown. However, as events unfold, there has been a rapid fall in social connections, a looming global economic downturn, deaths, and a growing fear of the "unknown," all of which have resulted in a shift in the status quo. Furthermore, the COVID-19 pandemic has had far-reaching consequences around the world, including a significant strain on various countries' healthcare systems, deaths, and other diseases/health difficulties. In this thesis paper I used various Covid19 data to analyze the present situation of the world and also the death of covid19. Then I used various machine learning model to predict the death of covid19 patients. This data is collected from Kaggle. I do analysis with almost 97k data. I used decision tree algorithm, logistic regression, KNN, and random forest classifier model. But I got the highest accuracy of 91.09% from Logistic Regression. I also did survey on the awareness of Covid19 among the students.