Make a prediction using R

We can perform the same analysis using R in a notebook. The functions are different for the different language, but the functionality is very close.

We use the same algorithm:

• Load the dataset
• Split the dataset into training and testing partitions
• Develop a model based on the training partition
• Use the model to predict from the testing partition
• Compare predicted versus actual testing

The coding is as follows:

#load in the data set from uci.edu (slightly different from other housing model)housing <- read.table("http://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data")#assign column namescolnames(housing) <- c("CRIM", "ZN", "INDUS", "CHAS", "NOX",                  "RM", "AGE", "DIS", "RAD", "TAX", "PRATIO",                  "B", "LSTAT", "MDEV")#make sure we have the right data being loadedsummary(housing)      CRIM                ZN             INDUS            CHAS         Min.   : 0.00632   Min.   :  0.00   Min.   : 0.46   Min.   :0.00000 ...