Hotel Data Week 4 Data Dive- Five random samples & Scrutinize these subsamples.
Reshu Gupta
2024-03-01
- Initial setup and Configure the data set.
- Load the data set file in variable hotel_data files.
- Data set - Hotels : This data comes from an open hotel booking
demand dataset from Antonio, Almeida and Nunes.
knitr::opts_chunk$set(echo = FALSE)
# Load the 'dplyr' library
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)
# Load the data into hotel_data for further use
hotel_data <- read.csv(file.choose())In this section,
1. Data set ‘hotel_data’ is summarized.
2.
then find the length of dataset - hotel_data by using nrow() and assign
to variable - hotel_data_length.
3. Calculate and print the size of
subsample (50% of hotel_data_lenght).
## hotel is_canceled lead_time arrival_date_year
## Length:119390 Min. :0.0000 Min. : 0 Min. :2015
## Class :character 1st Qu.:0.0000 1st Qu.: 18 1st Qu.:2016
## Mode :character Median :0.0000 Median : 69 Median :2016
## Mean :0.3704 Mean :104 Mean :2016
## 3rd Qu.:1.0000 3rd Qu.:160 3rd Qu.:2017
## Max. :1.0000 Max. :737 Max. :2017
##
## arrival_date_month arrival_date_week_number arrival_date_day_of_month
## Length:119390 Min. : 1.00 Min. : 1.0
## Class :character 1st Qu.:16.00 1st Qu.: 8.0
## Mode :character Median :28.00 Median :16.0
## Mean :27.17 Mean :15.8
## 3rd Qu.:38.00 3rd Qu.:23.0
## Max. :53.00 Max. :31.0
##
## stays_in_weekend_nights stays_in_week_nights adults
## Min. : 0.0000 Min. : 0.0 Min. : 0.000
## 1st Qu.: 0.0000 1st Qu.: 1.0 1st Qu.: 2.000
## Median : 1.0000 Median : 2.0 Median : 2.000
## Mean : 0.9276 Mean : 2.5 Mean : 1.856
## 3rd Qu.: 2.0000 3rd Qu.: 3.0 3rd Qu.: 2.000
## Max. :19.0000 Max. :50.0 Max. :55.000
##
## children babies meal country
## Min. : 0.0000 Min. : 0.000000 Length:119390 Length:119390
## 1st Qu.: 0.0000 1st Qu.: 0.000000 Class :character Class :character
## Median : 0.0000 Median : 0.000000 Mode :character Mode :character
## Mean : 0.1039 Mean : 0.007949
## 3rd Qu.: 0.0000 3rd Qu.: 0.000000
## Max. :10.0000 Max. :10.000000
## NA's :4
## market_segment distribution_channel is_repeated_guest
## Length:119390 Length:119390 Min. :0.00000
## Class :character Class :character 1st Qu.:0.00000
## Mode :character Mode :character Median :0.00000
## Mean :0.03191
## 3rd Qu.:0.00000
## Max. :1.00000
##
## previous_cancellations previous_bookings_not_canceled reserved_room_type
## Min. : 0.00000 Min. : 0.0000 Length:119390
## 1st Qu.: 0.00000 1st Qu.: 0.0000 Class :character
## Median : 0.00000 Median : 0.0000 Mode :character
## Mean : 0.08712 Mean : 0.1371
## 3rd Qu.: 0.00000 3rd Qu.: 0.0000
## Max. :26.00000 Max. :72.0000
##
## assigned_room_type booking_changes deposit_type agent
## Length:119390 Min. : 0.0000 Length:119390 Length:119390
## Class :character 1st Qu.: 0.0000 Class :character Class :character
## Mode :character Median : 0.0000 Mode :character Mode :character
## Mean : 0.2211
## 3rd Qu.: 0.0000
## Max. :21.0000
##
## company days_in_waiting_list customer_type adr
## Length:119390 Min. : 0.000 Length:119390 Min. : -6.38
## Class :character 1st Qu.: 0.000 Class :character 1st Qu.: 69.29
## Mode :character Median : 0.000 Mode :character Median : 94.58
## Mean : 2.321 Mean : 101.83
## 3rd Qu.: 0.000 3rd Qu.: 126.00
## Max. :391.000 Max. :5400.00
##
## required_car_parking_spaces total_of_special_requests reservation_status
## Min. :0.00000 Min. :0.0000 Length:119390
## 1st Qu.:0.00000 1st Qu.:0.0000 Class :character
## Median :0.00000 Median :0.0000 Mode :character
## Mean :0.06252 Mean :0.5714
## 3rd Qu.:0.00000 3rd Qu.:1.0000
## Max. :8.00000 Max. :5.0000
##
## reservation_status_date
## Length:119390
## Class :character
## Mode :character
##
##
##
## ## Data set size: 119390## Subsample size: 59695In this section, 5 sub samples have been created of size -
subsample_size (subsample_size - 50% of hotel_data_length).
Scrutinize #1
In this section, Ist Sub sample - hotel_data_subsample_df_1 has been
scrutinize.
1. Length of Sub sample - hotel_data_subsample_df_1.
2. Print Ist five rows (for few column) of sub sample.
3. Print
Last five rows (for few column) of sub sample.
Note : By seeing 2
& 3, data consistency can be verified.
4. Print the Internal
structure of Sub sample.
5. then summarized - sub sample.
## [1] 59695## hotel lead_time meal market_segment distribution_channel country
## 1 City Hotel 186 SC Online TA TA/TO BRA
## 2 City Hotel 109 BB Online TA TA/TO BEL
## 3 City Hotel 52 SC Online TA TA/TO GBR
## 4 City Hotel 265 BB Groups TA/TO PRT
## 5 City Hotel 14 BB Online TA TA/TO ESP## hotel lead_time meal market_segment distribution_channel country
## 59691 City Hotel 352 SC Online TA TA/TO GBR
## 59692 City Hotel 165 BB Online TA TA/TO ITA
## 59693 City Hotel 45 BB Online TA TA/TO DEU
## 59694 City Hotel 156 BB Online TA TA/TO SWE
## 59695 City Hotel 73 BB Groups TA/TO PRT## 'data.frame': 59695 obs. of 32 variables:
## $ hotel : chr "City Hotel" "City Hotel" "City Hotel" "City Hotel" ...
## $ is_canceled : int 1 0 1 1 1 0 0 1 1 0 ...
## $ lead_time : int 186 109 52 265 14 7 221 89 309 48 ...
## $ arrival_date_year : int 2017 2016 2017 2016 2017 2016 2015 2016 2017 2016 ...
## $ arrival_date_month : chr "April" "September" "June" "April" ...
## $ arrival_date_week_number : int 16 37 26 17 16 45 42 42 19 38 ...
## $ arrival_date_day_of_month : int 22 7 28 20 18 31 17 10 13 14 ...
## $ stays_in_weekend_nights : int 1 0 0 0 0 1 2 1 0 0 ...
## $ stays_in_week_nights : int 1 3 2 4 3 2 1 1 1 2 ...
## $ adults : int 2 2 1 2 3 2 3 2 1 2 ...
## $ children : int 0 0 0 0 0 0 0 0 0 0 ...
## $ babies : int 0 0 0 0 0 0 0 0 0 0 ...
## $ meal : chr "SC" "BB" "SC" "BB" ...
## $ country : chr "BRA" "BEL" "GBR" "PRT" ...
## $ market_segment : chr "Online TA" "Online TA" "Online TA" "Groups" ...
## $ distribution_channel : chr "TA/TO" "TA/TO" "TA/TO" "TA/TO" ...
## $ is_repeated_guest : int 0 0 0 0 0 0 0 0 0 0 ...
## $ previous_cancellations : int 0 0 0 0 0 0 0 0 0 0 ...
## $ previous_bookings_not_canceled: int 0 0 0 0 0 1 0 0 0 0 ...
## $ reserved_room_type : chr "A" "A" "A" "A" ...
## $ assigned_room_type : chr "A" "A" "A" "A" ...
## $ booking_changes : int 0 0 0 0 0 1 1 0 1 0 ...
## $ deposit_type : chr "No Deposit" "No Deposit" "No Deposit" "Non Refund" ...
## $ agent : chr "9" "9" "9" "30" ...
## $ company : chr "NULL" "NULL" "NULL" "NULL" ...
## $ days_in_waiting_list : int 0 0 0 0 0 0 0 0 0 0 ...
## $ customer_type : chr "Transient" "Transient" "Transient" "Transient" ...
## $ adr : num 99 130 120 101 215 ...
## $ required_car_parking_spaces : int 0 0 0 0 0 1 0 0 0 0 ...
## $ total_of_special_requests : int 0 1 1 0 1 1 0 1 0 0 ...
## $ reservation_status : chr "Canceled" "Check-Out" "Canceled" "Canceled" ...
## $ reservation_status_date : chr "2016-10-18" "2016-09-10" "2017-05-09" "2015-07-30" ...## hotel is_canceled lead_time arrival_date_year
## Length:59695 Min. :0.000 Min. : 0.0 Min. :2015
## Class :character 1st Qu.:0.000 1st Qu.: 18.0 1st Qu.:2016
## Mode :character Median :0.000 Median : 69.0 Median :2016
## Mean :0.367 Mean :103.9 Mean :2016
## 3rd Qu.:1.000 3rd Qu.:161.0 3rd Qu.:2017
## Max. :1.000 Max. :629.0 Max. :2017
##
## arrival_date_month arrival_date_week_number arrival_date_day_of_month
## Length:59695 Min. : 1.00 Min. : 1.0
## Class :character 1st Qu.:16.00 1st Qu.: 8.0
## Mode :character Median :28.00 Median :16.0
## Mean :27.15 Mean :15.8
## 3rd Qu.:38.00 3rd Qu.:23.0
## Max. :53.00 Max. :31.0
##
## stays_in_weekend_nights stays_in_week_nights adults children
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. :0.0000
## 1st Qu.: 0.000 1st Qu.: 1.000 1st Qu.: 2.000 1st Qu.:0.0000
## Median : 1.000 Median : 2.000 Median : 2.000 Median :0.0000
## Mean : 0.931 Mean : 2.505 Mean : 1.857 Mean :0.1053
## 3rd Qu.: 2.000 3rd Qu.: 3.000 3rd Qu.: 2.000 3rd Qu.:0.0000
## Max. :19.000 Max. :50.000 Max. :50.000 Max. :3.0000
## NA's :3
## babies meal country market_segment
## Min. :0.000000 Length:59695 Length:59695 Length:59695
## 1st Qu.:0.000000 Class :character Class :character Class :character
## Median :0.000000 Mode :character Mode :character Mode :character
## Mean :0.008158
## 3rd Qu.:0.000000
## Max. :2.000000
##
## distribution_channel is_repeated_guest previous_cancellations
## Length:59695 Min. :0.00000 Min. : 0.0000
## Class :character 1st Qu.:0.00000 1st Qu.: 0.0000
## Mode :character Median :0.00000 Median : 0.0000
## Mean :0.03161 Mean : 0.0856
## 3rd Qu.:0.00000 3rd Qu.: 0.0000
## Max. :1.00000 Max. :26.0000
##
## previous_bookings_not_canceled reserved_room_type assigned_room_type
## Min. : 0.0000 Length:59695 Length:59695
## 1st Qu.: 0.0000 Class :character Class :character
## Median : 0.0000 Mode :character Mode :character
## Mean : 0.1401
## 3rd Qu.: 0.0000
## Max. :72.0000
##
## booking_changes deposit_type agent company
## Min. : 0.0000 Length:59695 Length:59695 Length:59695
## 1st Qu.: 0.0000 Class :character Class :character Class :character
## Median : 0.0000 Mode :character Mode :character Mode :character
## Mean : 0.2212
## 3rd Qu.: 0.0000
## Max. :15.0000
##
## days_in_waiting_list customer_type adr
## Min. : 0.00 Length:59695 Min. : 0.00
## 1st Qu.: 0.00 Class :character 1st Qu.: 69.66
## Median : 0.00 Mode :character Median : 95.00
## Mean : 2.34 Mean :101.83
## 3rd Qu.: 0.00 3rd Qu.:126.00
## Max. :391.00 Max. :510.00
##
## required_car_parking_spaces total_of_special_requests reservation_status
## Min. :0.00000 Min. :0.0000 Length:59695
## 1st Qu.:0.00000 1st Qu.:0.0000 Class :character
## Median :0.00000 Median :0.0000 Mode :character
## Mean :0.06315 Mean :0.5679
## 3rd Qu.:0.00000 3rd Qu.:1.0000
## Max. :3.00000 Max. :5.0000
##
## reservation_status_date
## Length:59695
## Class :character
## Mode :character
##
##
##
## Scrutinize #2
In this section, IInd Sub sample -
sub_sample_hotel_data_2 has been scrutinize.
1. Length of Sub sample
- hotel_data_subsample_df_1.
2. Print Ist five rows (for few column)
of sub sample.
3. Print Last five rows (for few column) of sub
sample.
Note : By seeing 2 & 3, data consistency can be
verified.
4. Print the Internal structure of Sub sample.
5. then
summarized - sub sample
## [1] 59695## hotel lead_time meal market_segment distribution_channel country
## 1 City Hotel 186 SC Online TA TA/TO BRA
## 2 City Hotel 109 BB Online TA TA/TO BEL
## 3 City Hotel 52 SC Online TA TA/TO GBR
## 4 City Hotel 265 BB Groups TA/TO PRT
## 5 City Hotel 14 BB Online TA TA/TO ESP## hotel lead_time meal market_segment distribution_channel country
## 59691 City Hotel 352 SC Online TA TA/TO GBR
## 59692 City Hotel 165 BB Online TA TA/TO ITA
## 59693 City Hotel 45 BB Online TA TA/TO DEU
## 59694 City Hotel 156 BB Online TA TA/TO SWE
## 59695 City Hotel 73 BB Groups TA/TO PRT## 'data.frame': 59695 obs. of 32 variables:
## $ hotel : chr "City Hotel" "City Hotel" "City Hotel" "City Hotel" ...
## $ is_canceled : int 0 1 1 0 1 0 1 1 0 0 ...
## $ lead_time : int 22 35 330 243 228 102 87 105 89 100 ...
## $ arrival_date_year : int 2016 2016 2015 2017 2016 2016 2017 2016 2016 2015 ...
## $ arrival_date_month : chr "September" "March" "September" "May" ...
## $ arrival_date_week_number : int 38 13 37 22 35 22 15 15 37 42 ...
## $ arrival_date_day_of_month : int 15 25 12 29 27 23 15 6 4 15 ...
## $ stays_in_weekend_nights : int 0 1 2 1 2 1 2 0 2 1 ...
## $ stays_in_week_nights : int 1 2 2 0 4 2 2 1 4 3 ...
## $ adults : int 2 2 2 2 2 2 2 2 1 2 ...
## $ children : int 0 1 0 0 0 1 0 0 0 0 ...
## $ babies : int 0 0 0 0 0 1 0 0 0 0 ...
## $ meal : chr "BB" "BB" "BB" "SC" ...
## $ country : chr "GBR" "PRT" "PRT" "GBR" ...
## $ market_segment : chr "Online TA" "Offline TA/TO" "Groups" "Online TA" ...
## $ distribution_channel : chr "TA/TO" "TA/TO" "TA/TO" "TA/TO" ...
## $ is_repeated_guest : int 0 0 0 0 0 0 0 0 0 0 ...
## $ previous_cancellations : int 0 0 1 0 0 0 0 0 0 0 ...
## $ previous_bookings_not_canceled: int 0 0 0 0 0 0 0 0 0 0 ...
## $ reserved_room_type : chr "A" "A" "A" "A" ...
## $ assigned_room_type : chr "B" "A" "A" "A" ...
## $ booking_changes : int 0 0 0 0 0 0 0 0 1 0 ...
## $ deposit_type : chr "No Deposit" "No Deposit" "No Deposit" "No Deposit" ...
## $ agent : chr "9" "3" "1" "9" ...
## $ company : chr "NULL" "NULL" "NULL" "NULL" ...
## $ days_in_waiting_list : int 0 0 0 0 0 0 0 0 0 0 ...
## $ customer_type : chr "Transient" "Transient" "Transient-Party" "Transient" ...
## $ adr : num 159 76 62 107.1 80.8 ...
## $ required_car_parking_spaces : int 0 0 0 0 0 0 0 0 0 0 ...
## $ total_of_special_requests : int 3 1 0 1 0 2 0 0 0 1 ...
## $ reservation_status : chr "Check-Out" "Canceled" "Canceled" "Check-Out" ...
## $ reservation_status_date : chr "2016-09-16" "2016-02-19" "2015-07-06" "2017-05-30" ...## hotel is_canceled lead_time arrival_date_year
## Length:59695 Min. :0.0000 Min. : 0.0 Min. :2015
## Class :character 1st Qu.:0.0000 1st Qu.: 18.0 1st Qu.:2016
## Mode :character Median :0.0000 Median : 69.0 Median :2016
## Mean :0.3729 Mean :103.5 Mean :2016
## 3rd Qu.:1.0000 3rd Qu.:160.0 3rd Qu.:2017
## Max. :1.0000 Max. :709.0 Max. :2017
## arrival_date_month arrival_date_week_number arrival_date_day_of_month
## Length:59695 Min. : 1.00 Min. : 1.0
## Class :character 1st Qu.:16.00 1st Qu.: 8.0
## Mode :character Median :27.00 Median :16.0
## Mean :27.12 Mean :15.7
## 3rd Qu.:38.00 3rd Qu.:23.0
## Max. :53.00 Max. :31.0
## stays_in_weekend_nights stays_in_week_nights adults children
## Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. :0.0000
## 1st Qu.: 0.0000 1st Qu.: 1.000 1st Qu.: 2.000 1st Qu.:0.0000
## Median : 1.0000 Median : 2.000 Median : 2.000 Median :0.0000
## Mean : 0.9266 Mean : 2.492 Mean : 1.859 Mean :0.1002
## 3rd Qu.: 2.0000 3rd Qu.: 3.000 3rd Qu.: 2.000 3rd Qu.:0.0000
## Max. :19.0000 Max. :50.000 Max. :55.000 Max. :3.0000
## babies meal country market_segment
## Min. :0.000000 Length:59695 Length:59695 Length:59695
## 1st Qu.:0.000000 Class :character Class :character Class :character
## Median :0.000000 Mode :character Mode :character Mode :character
## Mean :0.008007
## 3rd Qu.:0.000000
## Max. :2.000000
## distribution_channel is_repeated_guest previous_cancellations
## Length:59695 Min. :0.00000 Min. : 0.00000
## Class :character 1st Qu.:0.00000 1st Qu.: 0.00000
## Mode :character Median :0.00000 Median : 0.00000
## Mean :0.03293 Mean : 0.09644
## 3rd Qu.:0.00000 3rd Qu.: 0.00000
## Max. :1.00000 Max. :26.00000
## previous_bookings_not_canceled reserved_room_type assigned_room_type
## Min. : 0.0000 Length:59695 Length:59695
## 1st Qu.: 0.0000 Class :character Class :character
## Median : 0.0000 Mode :character Mode :character
## Mean : 0.1422
## 3rd Qu.: 0.0000
## Max. :72.0000
## booking_changes deposit_type agent company
## Min. : 0.0000 Length:59695 Length:59695 Length:59695
## 1st Qu.: 0.0000 Class :character Class :character Class :character
## Median : 0.0000 Mode :character Mode :character Mode :character
## Mean : 0.2224
## 3rd Qu.: 0.0000
## Max. :21.0000
## days_in_waiting_list customer_type adr
## Min. : 0.000 Length:59695 Min. : 0.00
## 1st Qu.: 0.000 Class :character 1st Qu.: 69.36
## Median : 0.000 Mode :character Median : 94.50
## Mean : 2.246 Mean : 101.88
## 3rd Qu.: 0.000 3rd Qu.: 126.00
## Max. :391.000 Max. :5400.00
## required_car_parking_spaces total_of_special_requests reservation_status
## Min. :0.00000 Min. :0.0000 Length:59695
## 1st Qu.:0.00000 1st Qu.:0.0000 Class :character
## Median :0.00000 Median :0.0000 Mode :character
## Mean :0.06217 Mean :0.5728
## 3rd Qu.:0.00000 3rd Qu.:1.0000
## Max. :8.00000 Max. :5.0000
## reservation_status_date
## Length:59695
## Class :character
## Mode :character
##
##
## Scrutinize #3
In this section, IIIrd Sub sample -
sub_sample_hotel_data_3 has been scrutinize.
1. Length of Sub sample
- hotel_data_subsample_df_3.
2. Print Ist five rows (for few column)
of sub sample.
3. Print Last five rows (for few column) of sub
sample.
Note : By seeing 2 & 3, data consistency can be
verified.
4. Print the Internal structure of Sub sample.
5. then
summarized - sub sample
## [1] 59695## hotel lead_time meal market_segment distribution_channel country
## 1 City Hotel 186 SC Online TA TA/TO BRA
## 2 City Hotel 109 BB Online TA TA/TO BEL
## 3 City Hotel 52 SC Online TA TA/TO GBR
## 4 City Hotel 265 BB Groups TA/TO PRT
## 5 City Hotel 14 BB Online TA TA/TO ESP## hotel lead_time meal market_segment distribution_channel country
## 59691 City Hotel 352 SC Online TA TA/TO GBR
## 59692 City Hotel 165 BB Online TA TA/TO ITA
## 59693 City Hotel 45 BB Online TA TA/TO DEU
## 59694 City Hotel 156 BB Online TA TA/TO SWE
## 59695 City Hotel 73 BB Groups TA/TO PRT## 'data.frame': 59695 obs. of 32 variables:
## $ hotel : chr "Resort Hotel" "Resort Hotel" "City Hotel" "City Hotel" ...
## $ is_canceled : int 0 0 1 0 1 1 1 1 1 0 ...
## $ lead_time : int 1 211 333 8 115 195 166 92 2 144 ...
## $ arrival_date_year : int 2016 2016 2016 2016 2016 2017 2016 2017 2016 2017 ...
## $ arrival_date_month : chr "November" "May" "September" "November" ...
## $ arrival_date_week_number : int 47 20 39 45 13 30 45 33 2 17 ...
## $ arrival_date_day_of_month : int 17 14 20 5 20 23 1 16 8 27 ...
## $ stays_in_weekend_nights : int 2 1 0 0 2 2 0 0 0 2 ...
## $ stays_in_week_nights : int 7 1 2 1 0 5 3 3 2 4 ...
## $ adults : int 2 2 2 2 1 2 2 2 2 2 ...
## $ children : int 0 0 0 0 0 0 0 0 0 0 ...
## $ babies : int 0 0 0 0 0 0 0 0 0 0 ...
## $ meal : chr "BB" "HB" "BB" "BB" ...
## $ country : chr "PRT" "DEU" "PRT" "CHN" ...
## $ market_segment : chr "Direct" "Groups" "Offline TA/TO" "Offline TA/TO" ...
## $ distribution_channel : chr "Direct" "TA/TO" "TA/TO" "TA/TO" ...
## $ is_repeated_guest : int 1 0 0 0 0 0 0 0 0 0 ...
## $ previous_cancellations : int 0 0 1 0 1 0 0 0 0 0 ...
## $ previous_bookings_not_canceled: int 2 0 0 0 0 0 0 0 0 0 ...
## $ reserved_room_type : chr "A" "A" "A" "A" ...
## $ assigned_room_type : chr "E" "A" "A" "A" ...
## $ booking_changes : int 1 0 0 0 0 0 0 0 0 0 ...
## $ deposit_type : chr "No Deposit" "No Deposit" "Non Refund" "No Deposit" ...
## $ agent : chr "NULL" "298" "58" "359" ...
## $ company : chr "NULL" "NULL" "NULL" "NULL" ...
## $ days_in_waiting_list : int 0 0 19 0 22 0 0 0 0 0 ...
## $ customer_type : chr "Transient" "Transient-Party" "Transient" "Transient-Party" ...
## $ adr : num 59.3 85 90 130 70 ...
## $ required_car_parking_spaces : int 0 0 0 0 0 0 0 0 0 0 ...
## $ total_of_special_requests : int 0 0 0 0 0 0 0 0 0 0 ...
## $ reservation_status : chr "Check-Out" "Check-Out" "Canceled" "Check-Out" ...
## $ reservation_status_date : chr "2016-11-26" "2016-05-16" "2015-11-11" "2016-11-06" ...## hotel is_canceled lead_time arrival_date_year
## Length:59695 Min. :0.0000 Min. : 0.0 Min. :2015
## Class :character 1st Qu.:0.0000 1st Qu.: 18.0 1st Qu.:2016
## Mode :character Median :0.0000 Median : 69.0 Median :2016
## Mean :0.3678 Mean :103.8 Mean :2016
## 3rd Qu.:1.0000 3rd Qu.:160.0 3rd Qu.:2017
## Max. :1.0000 Max. :629.0 Max. :2017
##
## arrival_date_month arrival_date_week_number arrival_date_day_of_month
## Length:59695 Min. : 1.00 Min. : 1.00
## Class :character 1st Qu.:16.00 1st Qu.: 8.00
## Mode :character Median :28.00 Median :16.00
## Mean :27.19 Mean :15.81
## 3rd Qu.:38.00 3rd Qu.:23.00
## Max. :53.00 Max. :31.00
##
## stays_in_weekend_nights stays_in_week_nights adults children
## Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. :0.0000
## 1st Qu.: 0.0000 1st Qu.: 1.000 1st Qu.: 2.000 1st Qu.:0.0000
## Median : 1.0000 Median : 2.000 Median : 2.000 Median :0.0000
## Mean : 0.9255 Mean : 2.498 Mean : 1.856 Mean :0.1029
## 3rd Qu.: 2.0000 3rd Qu.: 3.000 3rd Qu.: 2.000 3rd Qu.:0.0000
## Max. :16.0000 Max. :41.000 Max. :26.000 Max. :3.0000
## NA's :3
## babies meal country market_segment
## Min. :0.000000 Length:59695 Length:59695 Length:59695
## 1st Qu.:0.000000 Class :character Class :character Class :character
## Median :0.000000 Mode :character Mode :character Mode :character
## Mean :0.008376
## 3rd Qu.:0.000000
## Max. :2.000000
##
## distribution_channel is_repeated_guest previous_cancellations
## Length:59695 Min. :0.00000 Min. : 0.00000
## Class :character 1st Qu.:0.00000 1st Qu.: 0.00000
## Mode :character Median :0.00000 Median : 0.00000
## Mean :0.03005 Mean : 0.08279
## 3rd Qu.:0.00000 3rd Qu.: 0.00000
## Max. :1.00000 Max. :26.00000
##
## previous_bookings_not_canceled reserved_room_type assigned_room_type
## Min. : 0.0000 Length:59695 Length:59695
## 1st Qu.: 0.0000 Class :character Class :character
## Median : 0.0000 Mode :character Mode :character
## Mean : 0.1248
## 3rd Qu.: 0.0000
## Max. :71.0000
##
## booking_changes deposit_type agent company
## Min. : 0.0000 Length:59695 Length:59695 Length:59695
## 1st Qu.: 0.0000 Class :character Class :character Class :character
## Median : 0.0000 Mode :character Mode :character Mode :character
## Mean : 0.2242
## 3rd Qu.: 0.0000
## Max. :20.0000
##
## days_in_waiting_list customer_type adr
## Min. : 0.000 Length:59695 Min. : -6.38
## 1st Qu.: 0.000 Class :character 1st Qu.: 69.00
## Median : 0.000 Mode :character Median : 94.70
## Mean : 2.246 Mean :101.85
## 3rd Qu.: 0.000 3rd Qu.:126.00
## Max. :391.000 Max. :508.00
##
## required_car_parking_spaces total_of_special_requests reservation_status
## Min. :0.00000 Min. :0.0000 Length:59695
## 1st Qu.:0.00000 1st Qu.:0.0000 Class :character
## Median :0.00000 Median :0.0000 Mode :character
## Mean :0.06253 Mean :0.5738
## 3rd Qu.:0.00000 3rd Qu.:1.0000
## Max. :3.00000 Max. :5.0000
##
## reservation_status_date
## Length:59695
## Class :character
## Mode :character
##
##
##
## Scrutinize #4
In this section, IVth Sub sample -
sub_sample_hotel_data_4 has been scrutinize.
1. Length of Sub sample
- hotel_data_subsample_df_4.
2. Print Ist five rows (for few column)
of sub sample.
3. Print Last five rows (for few column) of sub
sample.
Note : By seeing 2 & 3, data consistency can be
verified.
4. Print the Internal structure of Sub sample.
5. then
summarized - sub sample
## [1] 59695## hotel lead_time meal market_segment distribution_channel country
## 1 City Hotel 186 SC Online TA TA/TO BRA
## 2 City Hotel 109 BB Online TA TA/TO BEL
## 3 City Hotel 52 SC Online TA TA/TO GBR
## 4 City Hotel 265 BB Groups TA/TO PRT
## 5 City Hotel 14 BB Online TA TA/TO ESP## hotel lead_time meal market_segment distribution_channel country
## 59691 City Hotel 352 SC Online TA TA/TO GBR
## 59692 City Hotel 165 BB Online TA TA/TO ITA
## 59693 City Hotel 45 BB Online TA TA/TO DEU
## 59694 City Hotel 156 BB Online TA TA/TO SWE
## 59695 City Hotel 73 BB Groups TA/TO PRT## 'data.frame': 59695 obs. of 32 variables:
## $ hotel : chr "Resort Hotel" "City Hotel" "City Hotel" "City Hotel" ...
## $ is_canceled : int 0 0 1 0 1 1 1 1 0 0 ...
## $ lead_time : int 70 151 175 152 5 626 40 195 63 34 ...
## $ arrival_date_year : int 2015 2016 2017 2017 2016 2016 2017 2017 2017 2016 ...
## $ arrival_date_month : chr "August" "October" "July" "May" ...
## $ arrival_date_week_number : int 34 44 28 18 7 46 5 33 30 50 ...
## $ arrival_date_day_of_month : int 22 25 15 4 12 7 31 19 26 5 ...
## $ stays_in_weekend_nights : int 2 0 2 0 0 1 0 2 0 2 ...
## $ stays_in_week_nights : int 4 4 1 3 2 2 3 1 3 5 ...
## $ adults : int 2 2 2 2 2 2 2 3 2 2 ...
## $ children : int 2 0 0 0 0 0 0 0 2 0 ...
## $ babies : int 0 0 0 0 0 0 0 0 0 0 ...
## $ meal : chr "HB" "BB" "BB" "BB" ...
## $ country : chr "PRT" "ROU" "CN" "DEU" ...
## $ market_segment : chr "Direct" "Online TA" "Online TA" "Online TA" ...
## $ distribution_channel : chr "Direct" "TA/TO" "TA/TO" "TA/TO" ...
## $ is_repeated_guest : int 0 0 0 0 0 0 0 0 0 0 ...
## $ previous_cancellations : int 0 0 0 0 0 0 0 0 0 0 ...
## $ previous_bookings_not_canceled: int 0 0 0 0 0 0 0 0 0 0 ...
## $ reserved_room_type : chr "C" "A" "A" "D" ...
## $ assigned_room_type : chr "C" "A" "A" "D" ...
## $ booking_changes : int 1 0 0 0 0 0 0 1 4 0 ...
## $ deposit_type : chr "No Deposit" "No Deposit" "No Deposit" "No Deposit" ...
## $ agent : chr "250" "9" "9" "9" ...
## $ company : chr "NULL" "NULL" "NULL" "NULL" ...
## $ days_in_waiting_list : int 0 0 0 0 0 0 0 0 0 0 ...
## $ customer_type : chr "Transient" "Transient" "Transient" "Transient-Party" ...
## $ adr : num 211 90.9 107.1 139.5 91 ...
## $ required_car_parking_spaces : int 1 0 0 0 0 0 0 0 0 0 ...
## $ total_of_special_requests : int 0 1 1 2 1 0 1 0 1 1 ...
## $ reservation_status : chr "Check-Out" "Check-Out" "Canceled" "Check-Out" ...
## $ reservation_status_date : chr "2015-08-28" "2016-10-29" "2017-01-28" "2017-05-07" ...## hotel is_canceled lead_time arrival_date_year
## Length:59695 Min. :0.0000 Min. : 0.0 Min. :2015
## Class :character 1st Qu.:0.0000 1st Qu.: 18.0 1st Qu.:2016
## Mode :character Median :0.0000 Median : 69.0 Median :2016
## Mean :0.3699 Mean :104.1 Mean :2016
## 3rd Qu.:1.0000 3rd Qu.:161.0 3rd Qu.:2017
## Max. :1.0000 Max. :629.0 Max. :2017
##
## arrival_date_month arrival_date_week_number arrival_date_day_of_month
## Length:59695 Min. : 1.00 Min. : 1.00
## Class :character 1st Qu.:16.00 1st Qu.: 8.00
## Mode :character Median :28.00 Median :16.00
## Mean :27.28 Mean :15.79
## 3rd Qu.:38.00 3rd Qu.:23.00
## Max. :53.00 Max. :31.00
##
## stays_in_weekend_nights stays_in_week_nights adults
## Min. : 0.0000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.0000 1st Qu.: 1.000 1st Qu.: 2.000
## Median : 1.0000 Median : 2.000 Median : 2.000
## Mean : 0.9266 Mean : 2.502 Mean : 1.851
## 3rd Qu.: 2.0000 3rd Qu.: 3.000 3rd Qu.: 2.000
## Max. :16.0000 Max. :41.000 Max. :55.000
##
## children babies meal country
## Min. : 0.0000 Min. :0.000000 Length:59695 Length:59695
## 1st Qu.: 0.0000 1st Qu.:0.000000 Class :character Class :character
## Median : 0.0000 Median :0.000000 Mode :character Mode :character
## Mean : 0.1039 Mean :0.008292
## 3rd Qu.: 0.0000 3rd Qu.:0.000000
## Max. :10.0000 Max. :9.000000
## NA's :4
## market_segment distribution_channel is_repeated_guest
## Length:59695 Length:59695 Min. :0.00000
## Class :character Class :character 1st Qu.:0.00000
## Mode :character Mode :character Median :0.00000
## Mean :0.03211
## 3rd Qu.:0.00000
## Max. :1.00000
##
## previous_cancellations previous_bookings_not_canceled reserved_room_type
## Min. : 0.00000 Min. : 0.0000 Length:59695
## 1st Qu.: 0.00000 1st Qu.: 0.0000 Class :character
## Median : 0.00000 Median : 0.0000 Mode :character
## Mean : 0.09446 Mean : 0.1369
## 3rd Qu.: 0.00000 3rd Qu.: 0.0000
## Max. :26.00000 Max. :72.0000
##
## assigned_room_type booking_changes deposit_type agent
## Length:59695 Min. : 0.0000 Length:59695 Length:59695
## Class :character 1st Qu.: 0.0000 Class :character Class :character
## Mode :character Median : 0.0000 Mode :character Mode :character
## Mean : 0.2233
## 3rd Qu.: 0.0000
## Max. :17.0000
##
## company days_in_waiting_list customer_type adr
## Length:59695 Min. : 0.000 Length:59695 Min. : 0.0
## Class :character 1st Qu.: 0.000 Class :character 1st Qu.: 69.0
## Mode :character Median : 0.000 Mode :character Median : 95.0
## Mean : 2.286 Mean :101.8
## 3rd Qu.: 0.000 3rd Qu.:126.0
## Max. :391.000 Max. :451.5
##
## required_car_parking_spaces total_of_special_requests reservation_status
## Min. :0.0000 Min. :0.0000 Length:59695
## 1st Qu.:0.0000 1st Qu.:0.0000 Class :character
## Median :0.0000 Median :0.0000 Mode :character
## Mean :0.0619 Mean :0.5735
## 3rd Qu.:0.0000 3rd Qu.:1.0000
## Max. :3.0000 Max. :5.0000
##
## reservation_status_date
## Length:59695
## Class :character
## Mode :character
##
##
##
## Scrutinize #5
In this section, Vth Sub sample -
sub_sample_hotel_data_5 has been scrutinize.
1. Length of Sub sample
- hotel_data_subsample_df_5.
2. Print Ist five rows(for few column)
of sub sample.
3. Print Last five rows(for few column) of sub
sample.
Note : By seeing 2 & 3, data consistency can be
verified.
4. Print the Internal structure of Sub sample.
5. then
summarized - sub sample
## [1] 59695## hotel meal market_segment distribution_channel country
## 1 City Hotel SC Online TA TA/TO BRA
## 2 City Hotel BB Online TA TA/TO BEL
## 3 City Hotel SC Online TA TA/TO GBR
## 4 City Hotel BB Groups TA/TO PRT
## 5 City Hotel BB Online TA TA/TO ESP## hotel meal market_segment distribution_channel country
## 59691 City Hotel SC Online TA TA/TO GBR
## 59692 City Hotel BB Online TA TA/TO ITA
## 59693 City Hotel BB Online TA TA/TO DEU
## 59694 City Hotel BB Online TA TA/TO SWE
## 59695 City Hotel BB Groups TA/TO PRT## 'data.frame': 59695 obs. of 32 variables:
## $ hotel : chr "City Hotel" "Resort Hotel" "City Hotel" "City Hotel" ...
## $ is_canceled : int 1 0 1 1 0 0 0 0 1 0 ...
## $ lead_time : int 414 24 308 53 212 147 287 37 13 3 ...
## $ arrival_date_year : int 2015 2016 2016 2016 2017 2016 2015 2016 2017 2016 ...
## $ arrival_date_month : chr "December" "July" "November" "June" ...
## $ arrival_date_week_number : int 49 30 48 27 35 38 42 26 4 44 ...
## $ arrival_date_day_of_month : int 5 21 25 27 28 12 15 21 24 23 ...
## $ stays_in_weekend_nights : int 2 0 0 1 2 1 0 0 8 2 ...
## $ stays_in_week_nights : int 1 0 2 2 5 3 3 3 22 1 ...
## $ adults : int 2 2 2 2 2 2 2 2 1 1 ...
## $ children : int 0 0 0 0 0 0 0 0 0 0 ...
## $ babies : int 0 0 0 0 0 0 0 0 0 0 ...
## $ meal : chr "BB" "BB" "BB" "SC" ...
## $ country : chr "PRT" "GBR" "PRT" "ESP" ...
## $ market_segment : chr "Groups" "Online TA" "Offline TA/TO" "Online TA" ...
## $ distribution_channel : chr "TA/TO" "TA/TO" "TA/TO" "TA/TO" ...
## $ is_repeated_guest : int 0 0 0 0 0 0 0 0 0 0 ...
## $ previous_cancellations : int 1 0 0 0 0 0 0 0 0 0 ...
## $ previous_bookings_not_canceled: int 0 0 0 0 0 0 0 0 0 0 ...
## $ reserved_room_type : chr "A" "A" "A" "A" ...
## $ assigned_room_type : chr "A" "A" "A" "A" ...
## $ booking_changes : int 0 0 0 0 0 0 1 0 1 0 ...
## $ deposit_type : chr "Non Refund" "No Deposit" "Non Refund" "No Deposit" ...
## $ agent : chr "1" "NULL" "20" "9" ...
## $ company : chr "NULL" "NULL" "NULL" "NULL" ...
## $ days_in_waiting_list : int 0 0 0 0 0 0 0 0 0 0 ...
## $ customer_type : chr "Transient" "Transient" "Transient" "Transient" ...
## $ adr : num 62 0 52 101.1 92.2 ...
## $ required_car_parking_spaces : int 0 0 0 0 0 0 0 0 0 0 ...
## $ total_of_special_requests : int 0 0 0 0 1 1 0 1 1 0 ...
## $ reservation_status : chr "Canceled" "Check-Out" "Canceled" "Canceled" ...
## $ reservation_status_date : chr "2015-07-23" "2016-07-21" "2016-03-15" "2016-05-09" ...## hotel is_canceled lead_time arrival_date_year
## Length:59695 Min. :0.0000 Min. : 0.0 Min. :2015
## Class :character 1st Qu.:0.0000 1st Qu.: 18.0 1st Qu.:2016
## Mode :character Median :0.0000 Median : 70.0 Median :2016
## Mean :0.3744 Mean :104.8 Mean :2016
## 3rd Qu.:1.0000 3rd Qu.:161.0 3rd Qu.:2017
## Max. :1.0000 Max. :629.0 Max. :2017
##
## arrival_date_month arrival_date_week_number arrival_date_day_of_month
## Length:59695 Min. : 1.00 Min. : 1.00
## Class :character 1st Qu.:16.00 1st Qu.: 8.00
## Mode :character Median :28.00 Median :16.00
## Mean :27.23 Mean :15.73
## 3rd Qu.:38.00 3rd Qu.:23.00
## Max. :53.00 Max. :31.00
##
## stays_in_weekend_nights stays_in_week_nights adults children
## Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. :0.0000
## 1st Qu.: 0.0000 1st Qu.: 1.000 1st Qu.: 2.000 1st Qu.:0.0000
## Median : 1.0000 Median : 2.000 Median : 2.000 Median :0.0000
## Mean : 0.9312 Mean : 2.504 Mean : 1.859 Mean :0.1034
## 3rd Qu.: 2.0000 3rd Qu.: 3.000 3rd Qu.: 2.000 3rd Qu.:0.0000
## Max. :18.0000 Max. :42.000 Max. :50.000 Max. :3.0000
## NA's :1
## babies meal country market_segment
## Min. : 0.000000 Length:59695 Length:59695 Length:59695
## 1st Qu.: 0.000000 Class :character Class :character Class :character
## Median : 0.000000 Mode :character Mode :character Mode :character
## Mean : 0.007639
## 3rd Qu.: 0.000000
## Max. :10.000000
##
## distribution_channel is_repeated_guest previous_cancellations
## Length:59695 Min. :0.00000 Min. : 0.00000
## Class :character 1st Qu.:0.00000 1st Qu.: 0.00000
## Mode :character Median :0.00000 Median : 0.00000
## Mean :0.03176 Mean : 0.09393
## 3rd Qu.:0.00000 3rd Qu.: 0.00000
## Max. :1.00000 Max. :26.00000
##
## previous_bookings_not_canceled reserved_room_type assigned_room_type
## Min. : 0.0000 Length:59695 Length:59695
## 1st Qu.: 0.0000 Class :character Class :character
## Median : 0.0000 Mode :character Mode :character
## Mean : 0.1389
## 3rd Qu.: 0.0000
## Max. :72.0000
##
## booking_changes deposit_type agent company
## Min. : 0.0000 Length:59695 Length:59695 Length:59695
## 1st Qu.: 0.0000 Class :character Class :character Class :character
## Median : 0.0000 Mode :character Mode :character Mode :character
## Mean : 0.2235
## 3rd Qu.: 0.0000
## Max. :20.0000
##
## days_in_waiting_list customer_type adr
## Min. : 0.00 Length:59695 Min. : 0.00
## 1st Qu.: 0.00 Class :character 1st Qu.: 69.29
## Median : 0.00 Mode :character Median : 95.00
## Mean : 2.39 Mean : 102.08
## 3rd Qu.: 0.00 3rd Qu.: 126.00
## Max. :391.00 Max. :5400.00
##
## required_car_parking_spaces total_of_special_requests reservation_status
## Min. :0.00000 Min. :0.0000 Length:59695
## 1st Qu.:0.00000 1st Qu.:0.0000 Class :character
## Median :0.00000 Median :0.0000 Mode :character
## Mean :0.06364 Mean :0.5694
## 3rd Qu.:0.00000 3rd Qu.:1.0000
## Max. :8.00000 Max. :5.0000
##
## reservation_status_date
## Length:59695
## Class :character
## Mode :character
##
##
##
## Box plot for a numeric variable -‘lead_time’ in subsample.
Above graph shown the distribution of lead_time across all sub-samples.
Note: in above plot, 1 - subsample-1, 2 -
subsample-2,3-subsample-3, 4-subsample-4,5-subsample-5
Ask:How you approached each subsample along with
anomaly?
#Anomaly Detection for sub sample data frames.
## # A tibble: 2 × 2
## hotel count
## <chr> <int>
## 1 City Hotel 39534
## 2 Resort Hotel 20161## # A tibble: 2 × 2
## hotel count
## <chr> <int>
## 1 City Hotel 39747
## 2 Resort Hotel 19948## # A tibble: 2 × 2
## hotel count
## <chr> <int>
## 1 City Hotel 39494
## 2 Resort Hotel 20201## # A tibble: 2 × 2
## hotel count
## <chr> <int>
## 1 City Hotel 39618
## 2 Resort Hotel 20077## # A tibble: 2 × 2
## hotel count
## <chr> <int>
## 1 City Hotel 39607
## 2 Resort Hotel 20088
In above section, subsamples have been created with the help of
sample_n() function of dplyr package with 50% size of orignal data
set.
To determine of Anomaly Detection, We have grouped the data by
the “hotel” and then calculate the counts of observation for each hotal
type with in the sub-sample.
Observation is - 1. Each subsample has
different number of hotel types.
2. In sab-sample -
hotel_data_subsample_df_1 and hotel_data_subsample_df_5 , Number of
Resort Hotel is higher then other sub-samples
(hotel_data_subsample_df_2,hotel_data_subsample_df_3,hotel_data_subsample_df_4)
Determing frequency tables for Consistency Analysis
Ask: how you approached each subsample along and consistency analysis?
- Create frequency tables for the “country” variable for each sub-sample(by using table function) for Consistency Analysis and assign in variable.
- For Consistency analysis, comparing the distributions of countries across the subsamples. Consistency would imply that the distribution of countries remains relatively stable across the subsamples.
Performing Monte Carlo Simulation of Chi-Square Test for detemining consistency.
## Observed Chi-Square Test Statistic: 1729186## P-value of sub sample 1: 0.444
## Observed Chi-Square Test Statistic: 1651152## P-value of sun-sample 2: 0.436
## Observed Chi-Square Test Statistic: 1723060## P-value of sub sample 3: 0.452
## Observed Chi-Square Test Statistic: 1748038## P-value of sub sample 4: 0.45
## Observed Chi-Square Test Statistic: 1711324## P-value of sub sample 5: 0.44
- The chi-square test has been run on all sub-sample datasets for determining the Consistencey in sub-sample
- In chi-square test on sub-samples, Since P-value is around .5 which is > 0.1 so we can not conclude a significant difference exists.
#Monte Carlo Simulations of data set hotel_data_subsample_df_1
Monte Carlo Simulations histograms diagram is graphical representation of frequency distribution of data set which were based on randam numbers genetated using Monte Carlo simulations.
In above histograms diagram, The Title of Graph is - “Monte Carlo Simulation of Lead Time which is indicating that the plot represent the distribution of Lead Times(on X Axis) derived from Monte Carlo simulation. The X-axis label - Lead Time specifies the variable being measured.
The hight of each bar represnts the frequency of lead times falling within a specific range. Higher Bar means a higher occurence of lead time with in the range.
The histogram can be compared to expected or historical lead time data. Discrepancies may highlight areas where the simulation differs from real-world observations, prompting further investigation or refinement of the simulation model.
The insights gained from the histogram can be used for decision support, especially in scenarios where lead time variability is a critical factor. Understanding the distribution helps in making informed decisions and developing strategies to manage lead time uncertainties.
Thank You.!!!