Identifying the dataset for final project
#1. Identify the dataset you will be using for the final project
I will be using the data set of hotel bookings from the course dataset .
#Importing the dataset
#Preview of the Dataset
head(hotel_bookings_)
# A tibble: 6 x 32
hotel is_canceled lead_time arrival_date_ye~ arrival_date_mo~
<chr> <dbl> <dbl> <dbl> <chr>
1 Resort Hotel 0 342 2015 July
2 Resort Hotel 0 737 2015 July
3 Resort Hotel 0 7 2015 July
4 Resort Hotel 0 13 2015 July
5 Resort Hotel 0 14 2015 July
6 Resort Hotel 0 14 2015 July
# ... with 27 more variables: arrival_date_week_number <dbl>,
# arrival_date_day_of_month <dbl>, stays_in_weekend_nights <dbl>,
# stays_in_week_nights <dbl>, adults <dbl>, children <dbl>,
# babies <dbl>, meal <chr>, country <chr>, market_segment <chr>,
# distribution_channel <chr>, is_repeated_guest <dbl>,
# previous_cancellations <dbl>,
# previous_bookings_not_canceled <dbl>, ...#USING THE FUNCTION dim() to get the dimensions of the dataset
dim(hotel_bookings_)
[1] 119390 32Selecting the country and is_repeated_guest column from the dataset using select() function to see which country customers repeated
select(hotel_bookings_,country,is_repeated_guest)
# A tibble: 119,390 x 2
country is_repeated_guest
<chr> <dbl>
1 PRT 0
2 PRT 0
3 GBR 0
4 GBR 0
5 GBR 0
6 GBR 0
7 PRT 0
8 PRT 0
9 PRT 0
10 PRT 0
# ... with 119,380 more rows#Applying filter() function to the datset to see how many customers need car parking space.
filter(hotel_bookings_,required_car_parking_spaces==1)
# A tibble: 7,383 x 32
hotel is_canceled lead_time arrival_date_ye~ arrival_date_mo~
<chr> <dbl> <dbl> <dbl> <chr>
1 Resort Hot~ 0 78 2015 July
2 Resort Hot~ 0 99 2015 July
3 Resort Hot~ 0 3 2015 July
4 Resort Hot~ 0 1 2015 July
5 Resort Hot~ 0 1 2015 July
6 Resort Hot~ 0 5 2015 July
7 Resort Hot~ 0 10 2015 July
8 Resort Hot~ 0 3 2015 July
9 Resort Hot~ 0 72 2015 July
10 Resort Hot~ 0 9 2015 July
# ... with 7,373 more rows, and 27 more variables:
# arrival_date_week_number <dbl>, arrival_date_day_of_month <dbl>,
# stays_in_weekend_nights <dbl>, stays_in_week_nights <dbl>,
# adults <dbl>, children <dbl>, babies <dbl>, meal <chr>,
# country <chr>, market_segment <chr>, distribution_channel <chr>,
# is_repeated_guest <dbl>, previous_cancellations <dbl>,
# previous_bookings_not_canceled <dbl>, ...#Arranging the datset based on the reservation using arrange() function
arrange(hotel_bookings_,reservation_status_date)
# A tibble: 119,390 x 32
hotel is_canceled lead_time arrival_date_ye~ arrival_date_mo~
<chr> <dbl> <dbl> <dbl> <chr>
1 Resort Hot~ 1 344 2015 September
2 Resort Hot~ 1 399 2015 December
3 Resort Hot~ 1 202 2015 July
4 City Hotel 1 258 2015 July
5 City Hotel 1 258 2015 July
6 City Hotel 1 258 2015 July
7 City Hotel 1 258 2015 July
8 City Hotel 1 258 2015 July
9 City Hotel 1 258 2015 July
10 City Hotel 1 258 2015 July
# ... with 119,380 more rows, and 27 more variables:
# arrival_date_week_number <dbl>, arrival_date_day_of_month <dbl>,
# stays_in_weekend_nights <dbl>, stays_in_week_nights <dbl>,
# adults <dbl>, children <dbl>, babies <dbl>, meal <chr>,
# country <chr>, market_segment <chr>, distribution_channel <chr>,
# is_repeated_guest <dbl>, previous_cancellations <dbl>,
# previous_bookings_not_canceled <dbl>, ...#Identify potential research questions that your dataset can help answer.
1.Which country customers are mostly repeated customers? 2.How many customers are checking in through the ditsribution market as corporate?