Prediction Of Covid-19 Infection Based on Lifestyle Habits Employing Random Forest Algorithm
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Abstract
A data of four factors contributing to covid-19 infection and whether the person has been infected or not is taken as input to train a Random Forest Algorithm. Factors like wearing mask, regularity of exercise, consumption of pepper and area population density are taken as input factors and the status of covid-19 infection is taken as output parameter. A random forest model trained with at least 50 data instances will become a powerful predictive model for assessing the risk of covid-19 infection.
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