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Over the past year, the COVID-19 outbreak deeply and thoroughly changed the way the world is. However, the control policy’s efficiency is still in dispute. Through the way of machine learning, now we are able to find and to probe into the data about corona-virus spreading patterns in a short period of time, suiting the remedy to the case, to launch targeted prevention policies, and minimize the economic loss under the premise of control the spread of the virus on a large scale. We directly use the LES algorithm and  K -means clustering to make a comparison about the data feature. Therefore, the results are much more convincing than using any other recursive analyzing method alone. It is precise because of the ID3 algorithm, which we use for further analysis, to find the reason why those policies work.

Using Machine Learning Algorithms to Predict Efficiency of US’s States Mask Policy During COVID-19