From a model building perspective, PCA allows you to create models with fewer features, but still capture most of the information in the original data. However, as you've seen, a disadvantage of PCA is the difficulty of interpreting the model. In this exercise, you will be focusing on building a linear regression model using a subset of the house sales data. The target variable is price.

A model built directly from the data without extracting principal components has a RMSE of $236,461.4. You will apply PCA with tidymodels and compare the new RMSE. Remember, lower RMSEs are better.

The tidyverse and tidymodels packages have been loaded for you.

Foundations of feature extraction - principal components

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