Create dataframe X with the feature distance.

Create dataframe y with the feature fare.

Split the data into $80\%$ training, $10\%$ validation and $10\%$ testing sets, with random$\_$state $=42.$

Initialize a linear regression model.

Initialize a k$-$nearest neighbors regression model with k $=3.$

Define a set of $10$ cross$-$validation folds with random$\_$state$=42.$

Fit the models with cross$-$validation to the training data, using the default performance metric.

For each model, print the test score for each fold, as well as the mean and standard deviation for the model.