Polynomial Regression Model to Predict Future Covid Cases

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Abstract
Accurate case predictions are essential for efficient public health management and resource allocation since the COVID-19 pandemic has had a substantial impact on economies and global health. Using polynomial regression, a machine learning technique that fits a polynomial function to the data, this research seeks to create a predictive model for future COVID-19 cases. The model takes into consideration the elements such as population density, healthcare facilities, and governmental initiatives using historical COVID-19 case data from India. In order to forecast the number of upcoming COVID-19 instances, the polynomial regression model is employed. The model's effectiveness is assessed using a number of measures, including mean squared error and R-squared. The outcomes demonstrate that the polynomial regression model can precisely forecast the trend of COVID-19 instances over time. This approach can be useful for forecasting the spread of the virus and informing public health policies. The limitations and future directions of the model are also discussed. Furthermore, the model's adaptability to changing trends and its ability to capture non-linear relationships between variables, make it a promising tool for forecasting future pandemics and other public health crises.

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