Exercise

Fitting model to training data

It's time to split your data into a training set to fit a model and a separate test set to evaluate the predictive power of the model. Before making this split however, we first sample 100% of the rows of house_prices without replacement and assign this to house_prices_shuffled. This has the effect of "shuffling" the rows, thereby ensuring that the training and test sets are randomly sampled.

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Use slice() to set train to the first 10,000 rows of house_prices_shuffled and test to the remainder of the 21,613 rows.