Covid Contagion Risk Prediction Using Machine Learning

The COVID-19 pandemic has brought about a crisis in health all around the world and has brought to light the necessity of precise and timely prediction of the danger of infection. In this study, we offer a technique for predicting the risk of COVID contagion based on demographic, health, and environmental characteristics that use machine learning. This method takes into account the relationships between these elements. We demonstrate that the suggested strategy is superior to the baseline models by analyzing the performance of a number of different machine-learning models using a dataset that is freely accessible to the public. Our research shows that machine learning may be useful for forecasting the spread of COVID and developing better safety measures to combat the disease. In this study, we have utilized SVM and random forest to forecast the future case and have fulfilled our primary objectives of the studies, which will enable other researchers to carry out their research in the same area using these algorithms. Some other forms of research may also be carried out in the future.