Memprediksi pemesanan hotel

Anda baru saja bekerja di sebuah perusahaan riset perhotelan, dan tugas pertama Anda adalah membangun model yang memprediksi apakah sebuah masa inap di hotel akan mencakup anak-anak atau tidak. Untuk melatih model Anda, Anda akan menggunakan versi modifikasi dari himpunan data permintaan pemesanan hotel oleh Antonio, Almeida, dan Nunes (2019). Anda membatasi data pada fitur-fitur berikut:

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

Data telah dimuat untuk Anda sebagai hotels, beserta pembagian test dan train yang sesuai, dan model telah dideklarasikan sebagai lr_model <- logistic_reg().

Anda akan mengevaluasi kinerja model menggunakan akurasi dan area di bawah kurva ROC atau AUC.

Latihan ini adalah bagian dari kursus

Rekayasa Fitur di R

Lihat Kursus

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

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 dan Jalankan Kode

Rekayasa Fitur di R

SkillTag.level.intermediateSkillTag.label

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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!