Prevedere le prenotazioni in hotel

Hai appena iniziato a lavorare in un’azienda di ricerca nel settore dell’ospitalità e il tuo primo compito è costruire un modello che preveda se un soggiorno in hotel includerà bambini oppure no. Per addestrare il tuo modello userai una versione modificata del dataset sulle richieste di prenotazione alberghiera di Antonio, Almeida e Nunes (2019). Limiterai i tuoi dati alle seguenti feature:

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

I dati sono stati caricati per te come hotels, insieme agli split test e train, e il modello è stato dichiarato come lr_model <- logistic_reg().

Valuterai le prestazioni del modello con l’accuracy e con l’area sotto la curva ROC (AUC).

Questo esercizio fa parte del corso

Feature Engineering in R

Visualizza il corso

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

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(___)

Modifica ed esegui il codice

Feature Engineering in R

IntermediárioNível de habilidade

4.8+

120 reviews

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!