Bootstrapping creates many different versions of the dataset, which allows us to see how different subsets of data can affect the result. Bootstrapping also helps to get a more accurate understanding of the overall given diabetes population and it can also help to calculate more accurate confidence intervals. Bootstrapping will helps us to assess the model performs on new data that its not seen