$5\mathrm{.}5.$ a large number of insurance records are to be examined to develop a model for predicting fraudulent claims. of the claims in the historical database, $1\%$ were judged to be fraudulent. a sample is taken to develop a model, and oversampling is used to provide a balanced sample in light of the very low response rate. when applied to this sample $($n$=800),$ the model ends up correctly classifying $310$ frauds and $270$ non$-$frauds. it missed $90$ frauds and classified $130$ records incorrectly as frauds when they were not.

a$.$ produce the confusion matric for the sample as it stands

b$.$ find the adjusted misclassifcation rate $($adjusting for the oversampling$)$

c$.$ what percentage of new records would you expect to be classified as fraudulent?

Please provide the R code if necessary.