The dataset ToyotaCorolla.csv contains data on used cars on sale during the late summer of 2004 in the Netherlands. It has 1436 records containing details on 38 attributes, including Price, Age, Kilometers, HP, and other specifications. We plan to analyze the data using various machine learning techniques described in future chapters. 

a. The dataset has two categorical attributes, Fuel_Type and Color. Describe how you would convert these to binary attributes. Confirm this using RapidMiner’s operators to transform categorical data into dummies. What would you do with the attribute Model?

b. Prepare the dataset (as factored into dummies) for machine learning techniques of supervised learning by creating partitions using RapidMiner’s Split Data operator.

Select attributes, and use default values for the random seed and partitioning percentages for training (50%), validation (30%), and holdout (20%) sets. Describe the roles that these partitions will play in modeling.