Exercise

# Predict on test set

Now that you have a randomly split training set and test set, you can use the `lm()`

function as you did in the first exercise to fit a model to your training set, rather than the entire dataset. Recall that you can use the formula interface to the linear regression function to fit a model with a specified target variable using all other variables in the dataset as predictors:

```
mod <- lm(y ~ ., training_data)
```

You can use the `predict()`

function to make predictions from that model on new data. The new dataset must have all of the columns from the training data, but they can be in a different order with different values. Here, rather than re-predicting on the training set, you can predict on the test set, which you did not use for training the model. This will allow you to determine the out-of-sample error for the model in the next exercise:

```
p <- predict(model, new_data)
```

Instructions

**100 XP**

- Fit an
`lm()`

model called`model`

to predict`price`

using all other variables as covariates. Be sure to use the training set,`train`

. - Predict on the test set,
`test`

, using`predict()`

. Store these values in a vector called`p`

.