I'm trying to solve an ML problem where the target variable is numeric, let's say the pollution level in a city. But the client is not interested in predicting the actual amount of pollutants, they are just interested in knowing whether the pollution level is high or low based on an agreed upon threshold. (High if the PM2.5 level is above 200, Low otherwise).
Should I treat it as a regression problem and take the numeric PM2.5 levels as target or as a classification problem where I make another feature of high/low pollution level based on the threshold and use that binary variable as a target? What are the advantages and disadvantages of both and What impact it can have on accuracy, if any?