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.

Uniform Manifold Approximation and Projection (UMAP)

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