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A team of researchers collects data from the sale of individual residential properties in Ames, Iowa. The researchers want to identify the variables that affect the sale price. Variables include the lot size and various features of the residential property.
After initial exploration with CART® Regression to identify the important predictors, the team uses Random Forests® Regression to create a more intensive model from the same data set. The team compares the model summary table and the R2 plot from the results to evaluate which model provides a better prediction outcome.
These data were adapted based on a public data set containing information on Ames housing data. Original data from DeCock, Truman State University.