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 ***Applied Question***
 
 This problem involves the `OJ` data set which is part of the `ISLR` package.	
-* Create a training set containing a random sample of 800 observations, and a test set containing the remaining observations.	
-* Fit a support vector classifier to the training data using `cost=0.01`, with `Purchase` as the response and the other variables as predictors. Use the `summary()` function to produce summary statistics, and describe the results obtained.
-* What are the training and test error rates?
-* Use the `tune()` function to select an optimal cost. Consider values in the range 0.01 to 10.
-* Compute the training and test error rates using this new value for `cost`.
-* Repeat parts (ii) through (v) using a support vector machine with a radial kernel. Use the default value for `gamma`.
-* Repeat parts (ii) through (v) using a support vector machine with a polynomial kernel. Set `degree=2`.
-* Overall, which approach seems to give the best results on this data? 
+> i. Create a training set containing a random sample of 800 observations, and a test set containing the remaining observations.	
+> ii. Fit a support vector classifier to the training data using `cost=0.01`, with `Purchase` as the response and the other variables as predictors. Use the `summary()` function to produce summary statistics, and describe the results obtained.
+> iii. What are the training and test error rates?
+> iv. Use the `tune()` function to select an optimal cost. Consider values in the range 0.01 to 10.
+> v. Compute the training and test error rates using this new value for `cost`.
+> vi. Repeat parts (ii) through (v) using a support vector machine with a radial kernel. Use the default value for `gamma`.
+> vii. Repeat parts (ii) through (v) using a support vector machine with a polynomial kernel. Set `degree=2`.
+> viii. Overall, which approach seems to give the best results on this data? 
 
 ***Conceptual Question***