Analysis of COVID-19 Data Through Machine Learning Techniques

This pandemic environment of COVID-19 is a global problem that requires deeper research to analyze and predict the impact on humans soon. It is an infectious disease activated by SARS-CoV-2 that can affect the human upper respiratory tract like sinus, nose, throat, and lower respiratory tracks like windpipes, lungs, etc. At present, the non-availability of proper medication insufficient vaccination creates a panic mode across the world, and the disease is spreading exponentially day by day in all countries as well as India. This lay emphasis on humans staying at home as a preventative measure to protect against COVID-19. However, people sealed at home cannot be treated as safe if one person goes out for emergency work. This research paper aims to study the symptoms of a COVID-19 patient and to predict the health condition of a patient using the Fuzzy Logic model. Furthermore, this study aims to analyze the current COVID-19 cases in India and to forecast the number of positive, mortality, and recovered cases for the next few months through various machine learning techniques such as AutoRegression, MLP Regression, Linear Regression, and SVM Regression based on the Kaggle dataset. Our experimental results show that Autoregression produces better accuracy than other regression models.