Enter below an R command to create the confusion matrix for your naive bayes model applied to the test set. (What you just did.) You should call this table “tab.stroke.test”. (That is, the command you enter below should assign the table you create to a variable called tab.stroke.test.) Your table should include labels indicating which values are actual and which are predicted.)

Which of the following is the value for smoking_status for the individual in row 800 of the testSroke DF?

Provide below an R command that will output a table of the number of employees by Personality. The default is for the output to be in alphabetic order by Personality. Your command should output this information in increasing order of the number of employees in each Personality category.

Do whatever additional data exploration you wish. Then create a regression of charges vs. all the other variables in the data frame. Call this model lmChargesAll. Enter the R command to do so below.

Enter below the output of the summary(lmChargesAll) command.

Enter below an R command that will output the proportion of individuals in the data frame who had a stroke and the proportion of people who did not have a stroke.

Provide support here for your response to the previous question. Put another way, how did you arrive at your answer?

How many employees in the data set have the Explorer personality type?

How good is your model? Be sure to address how well the model is likely to predict, as well as how well it satisfies the assumptions of regression.

What is the overall accuracy of your model? Provide your answer to four decimal places.