The dataset Toyota Corolla.csv contains data on used cars on sale during the late summer of...

 

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The dataset Toyota Corolla.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. a. Explore the data using the data visualization capabilities of R. Which of the pairs among the variables seem to be correlated? b. We plan to analyze the data using various data mining techniques described in future chapters. Prepare the data for use as follows: i. The dataset has two categorical attributes, Fuel Type and Metallic. Describe how you would convert these to binary variables. Confirm this using R's functions to transform categorical data into dummies. ii. Prepare the dataset (as factored into dummies) for data mining techniques of supervised learning by creating partitions in R. Select all the variables and use default values for the random seed and partitioning percentages for training (50%), validation (30%), and test (20%) sets. Describe the roles that these par- titions will play in modeling. The dataset Toyota Corolla.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. a. Explore the data using the data visualization capabilities of R. Which of the pairs among the variables seem to be correlated? b. We plan to analyze the data using various data mining techniques described in future chapters. Prepare the data for use as follows: i. The dataset has two categorical attributes, Fuel Type and Metallic. Describe how you would convert these to binary variables. Confirm this using R's functions to transform categorical data into dummies. ii. Prepare the dataset (as factored into dummies) for data mining techniques of supervised learning by creating partitions in R. Select all the variables and use default values for the random seed and partitioning percentages for training (50%), validation (30%), and test (20%) sets. Describe the roles that these par- titions will play in modeling.

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