A data set to predict the average daily rate for a hotel in Lisbon Portugal.
Arguments
- ...
Arguments passed to
pins::pin_read().
Details
Data are originally described in Antonio, de Almeida, and Nunes (2019).
This version of the data is filtered for one hotel (the "Resort Hotel") and
is intended as regression data set for predicting the average daily rate for
a room. The data are post-2016; the 2016 data were used to have a predictor
for the historical daily rates. See the hotel_rates.R file in the
data-raw directory of the package to understand other filters used when
creating this version of the data.
The agent and company fields were changed from random characters to use
a set of random names.
The outcome column is avg_price_per_room.
References
Antonio, N., de Almeida, A., and Nunes, L. (2019). Hotel booking demand datasets. Data in Brief, 22, 41-49.
Examples
# \donttest{
data_hotel_rates()
#> # A tibble: 15,402 × 30
#> avg_price_per_room lead_time arrival_date_day_of_month
#> <dbl> <dbl> <dbl>
#> 1 110 241 2
#> 2 74 273 2
#> 3 81.9 248 2
#> 4 81 236 2
#> 5 112. 243 2
#> 6 90.8 267 2
#> 7 317 94 2
#> 8 159 10 2
#> 9 184 156 2
#> 10 107. 170 2
#> # ℹ 15,392 more rows
#> # ℹ 27 more variables: stays_in_weekend_nights <dbl>,
#> # stays_in_week_nights <dbl>, adults <dbl>, children <dbl>,
#> # babies <dbl>, meal <fct>, country <fct>, market_segment <fct>,
#> # distribution_channel <fct>, is_repeated_guest <dbl>,
#> # previous_cancellations <dbl>, previous_bookings_not_canceled <dbl>,
#> # reserved_room_type <fct>, assigned_room_type <fct>, …
# }