i. The dataset has two categorical attributes: Fuel Type and Metallic. a. Describe how you would convert these to binary variables. b. Confirm this using XLMiner's utility to transform categorical data into dummies. c. How would you work with these new variables to avoid including redundant information in models? ii. Prepare the dataset (as factored into dummies) for data mining techniques of Supervised learning by creating partitions using XLMiner's data partitioning utility. Select all the variables and use default variables for the random seed and partitioning percentages for training (50%), and validation (30%), and test (20%) sets. Describe the roles that these partitions will play in modeling