Forecasting the Future: Predicting COVID-19 Trends with Machine Learning

The outbreak of COVID-19 has caused a global health crisis and has severely impacted the economy and daily life of people. Predicting the spread of COVID-19 is of utmost importance to effectively control the spread of the disease. In this study, we propose a COVID-19 prediction model using Support Vector Machine (SVM) and Linear Regression algorithms. We collected data on the number of confirmed cases, recovered cases, and deaths caused by COVID-19 from January 2020 to March 2023. We divided the data into training and testing datasets and applied feature engineering techniques to extract relevant features. We then trained our model using SVM and Linear Regression algorithms on the training dataset. The results of our experiments show that the SVM model achieved little bit less accuracy than the Linear Regression model in predicting the number of confirmed cases, recovered cases, and deaths. Our model can provide accurate predictions and insights into the future trends of COVID-19 cases.