Predicting hotel bookings

You just got a job at a hospitality research company, and your first task is to build a model that predicts whether or not a hotel stay will include children. To train your model, you will rely on a modified version of the hotel booking demands dataset by Antonio, Almeida, and Nunes (2019). You are restricting your data to the following features:

features <- c('hotel', 'adults', 
              'children', 'meal',
              'reserved_room_type', 
              'customer_type', 
              'arrival_date')

The data has been loaded for you as hotels, along with its corresponding test and train splits, and the model has been declared as lr_model <- logistic_reg().

You will assess model performance by accuracy and area under the ROC curve or AUC.

This exercise is part of the course

Feature Engineering in R

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Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

lr_recipe <- 
  recipe(children ~., data = train) %>%
# Generate "day of the week", "week" and "month" features

  step_date(arrival_date, features = c(___, ___, ___)) %>%

# Create dummy variables for all nominal predictors
  step_dummy(___)

Edit and Run Code

Feature Engineering in R

IntermediateSkill Level

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You’ll wrap up the course by learning about feature engineering and machine learning techniques. You’ll begin by focusing on the problems associated with using all available features in a model and the importance of identifying irrelevant and redundant features and learning to remove these features using embedded methods such as lasso and elastic-net. Next, you’ll explore shrinkage methods such as lasso, ridge, and elastic-net, which can be used to regularize feature weights or select features by setting coefficients to zero. Finally, you’ll finish by focusing on creating an end-to-end feature engineering workflow and reviewing and practicing the previously learned concepts and functions in a small project.

Exercise 1: Reducing the model's features Exercise 2: Sifting through variable importance Exercise 3: Assessing model performance using all available predictors Exercise 4: Building a reduced model Exercise 5: Shrinkage methods Exercise 6: Manual regularization with Lasso Exercise 7: Tuning the penalty Exercise 8: Finalizing the model Exercise 9: Putting it all together Exercise 10: Prep and split Exercise 11: Preprocess Exercise 12: Model Exercise 13: Assess Exercise 14: Congratulations!