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.

Explore the data using the data visualization capabilities of R. Which of the pairs among the variables seem to be correlated?

We plan to analyze the data using various data mining techniques described in future chapters. Prepare the data for use as follows:

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.

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 (50%). Describe the roles that these partitions will play in modeling.

# how many total rows are there in the origDummies. df?

# randomly select row numbers for the training partition

# randomly select row numbers for the validation partition: sample from (all rows - training rows)

# Now create the train. data and valid. data dataframes

# 3) Propose three variables that could be used in a linear regression model

# 4) Create a linear regression model on the training dataset using variables
# Age, Kilometer and Manufacturer's Guarantee to predict "Price"
# use "reg" as the name of the model

# See the predicted values and actual values side by side

# plot the residuals

# see the model coefficients and their statistical significance# compute accuracy on a training set

# use the model to make a prediction using new data

# What could be your price estimate for a car 30 months old, 22000 kilometers,
# no manufacturer guarantee?